{ "metadata": { "version": "0.9.4", "updated_at": "2026-10-18", "search_refreshed_at": "2026-10-18", "topic_id": "continuous", "name": "Continuous-state reasoning", "definition": "Use vectors—lists of numbers—to carry intermediate reasoning from one step to the next, instead of selecting a text token at every step. Some methods use the model's hidden states; others combine several candidate token representations.", "record_count": 71, "topic_url": "https://latent-implicit-thinking.github.io/topics/continuous-states/", "source_url": "https://latent-implicit-thinking.github.io/data/papers.json" }, "comparisons": [ { "title": "Implicit CoT: proposal and implementation", "text": "Deng et al. (2023) proposed implicit chain-of-thought reasoning: language models carry out intermediate reasoning in continuous hidden states rather than generate it as text. Section 2.3 defines this setting; section 3 develops a distillation method to implement it. That method transfers a teacher's reasoning into hidden states across layers. Reasoning through continuous hidden states is the broader proposal; it does not require the particular distillation procedure used in this paper. Its across-layer computation differs from the repeated hidden-state feedback used by later methods such as Coconut.", "source_ids": [ "2311.01460", "2412.06769" ], "source_urls": [ "https://arxiv.org/html/2311.01460v1#S2.SS3", "https://arxiv.org/html/2311.01460v1#S3", "https://arxiv.org/html/2412.06769v3#S3" ], "source_labels": [ "Implicit CoT proposal, §2.3", "Distillation implementation, §3", "Coconut, §3" ] }, { "title": "From stepwise internalization to Coconut", "text": "Coconut explicitly credits Deng et al. (2024) as the inspiration for its multistage training curriculum. Stepwise internalization gradually removes written CoT tokens until the model answers directly. Coconut instead replaces removed steps with continuous hidden-state inputs, retaining a sequence of internal reasoning steps before the answer. It also follows the earlier work in resetting the optimizer between training stages.", "source_ids": [ "2405.14838", "2412.06769" ], "source_urls": [ "https://arxiv.org/html/2405.14838v1#S3", "https://arxiv.org/html/2412.06769v3#S3" ], "source_labels": [ "Deng et al. (2024), §3", "Coconut, §3" ] }, { "title": "Coconut and CODI: similar computation, different training", "text": "Coconut and CODI both pass continuous states between reasoning steps, but supervise them differently. Coconut gradually replaces written steps with hidden-state inputs and trains on the remaining text. CODI jointly trains explicit and implicit reasoning tasks, matching their hidden states at a selected position before the answer. These models avoid decoding a word at every intermediate step; they still perform successive model passes, so fewer written tokens does not mean no reasoning-time computation.", "source_ids": [ "2412.06769", "2502.21074" ], "source_urls": [ "https://arxiv.org/html/2412.06769v3#S3", "https://arxiv.org/html/2502.21074v3#S3" ], "source_labels": [ "Coconut, §3", "CODI, §3" ] } ], "demos": [ ], "papers": [ { "id": "2409.08561", "title": "Expediting and Elevating Large Language Model Reasoning via Hidden Chain-of-Thought Decoding", "authors": "Tianqiao Liu, Zui Chen, Zitao Liu, Mi Tian, Weiqi Luo", "first_public": "2024-09-13", "venue": "arXiv", "arxiv": "2409.08561", "paper_url": "https://arxiv.org/abs/2409.08561", "lens": [ "continuous" ], "representation": [ "continuous-state" ], "computation": [ "token-wise" ], "training": [ "supervised" ], "questions": [ "capability", "efficiency" ], "evidence": [ "empirical" ], "modality": [ "language" ], "summary": "Trains one model to compress written reasoning into special-token representations, then trains another to answer using those representations.", "provenance": "Awesome-Latent-CoT", "author_names": [ "Tianqiao Liu", "Zui Chen", "Zitao Liu", "Mi Tian", "Weiqi Luo" ], "record_url": "https://latent-implicit-thinking.github.io/papers/2409.08561/", "legacy_record_url": "https://latent-implicit-thinking.github.io/papers/#paper-2409-08561", "citation_date": "2024-09-13", "json_url": "https://latent-implicit-thinking.github.io/papers/2409.08561/record.json", "markdown_url": "https://latent-implicit-thinking.github.io/papers/2409.08561/index.md", "bibtex_url": "https://latent-implicit-thinking.github.io/papers/2409.08561/citation.bib", "csl_url": "https://latent-implicit-thinking.github.io/papers/2409.08561/citation.csl.json" }, { "id": "2410.13640", "title": "Latent Space Chain-of-Embedding Enables Output-free LLM Self-Evaluation", "authors": "Yiming Wang, Pei Zhang, Baosong Yang, Derek F. Wong, Rui Wang", "first_public": "2024-10-17", "venue": "arXiv", "arxiv": "2410.13640", "paper_url": "https://arxiv.org/abs/2410.13640", "lens": [ "continuous", "analysis" ], "representation": [ "continuous-state" ], "computation": [ "token-wise" ], "training": [ "inference-only" ], "questions": [ "evaluation", "efficiency" ], "evidence": [ "empirical" ], "modality": [ "language" ], "summary": "Uses the sequence of hidden states produced during inference to estimate whether an answer is correct, without extra training or labeled correctness examples.", "provenance": "Primary-source literature search, 2026-09-04", "author_names": [ "Yiming Wang", "Pei Zhang", "Baosong Yang", "Derek F. Wong", "Rui Wang" ], "record_url": "https://latent-implicit-thinking.github.io/papers/2410.13640/", "legacy_record_url": "https://latent-implicit-thinking.github.io/papers/#paper-2410-13640", "citation_date": "2024-10-17", "json_url": "https://latent-implicit-thinking.github.io/papers/2410.13640/record.json", "markdown_url": "https://latent-implicit-thinking.github.io/papers/2410.13640/index.md", "bibtex_url": "https://latent-implicit-thinking.github.io/papers/2410.13640/citation.bib", "csl_url": "https://latent-implicit-thinking.github.io/papers/2410.13640/citation.csl.json" }, { "id": "2412.06769", "title": "Training Large Language Models to Reason in a Continuous Latent Space", "authors": "Shibo Hao, Sainbayar Sukhbaatar, DiJia Su, Xian Li, Zhiting Hu, Jason Weston, Yuandong Tian", "first_public": "2024-12-09", "venue": "COLM 2025", "arxiv": "2412.06769", "paper_url": "https://arxiv.org/abs/2412.06769", "code_url": "https://github.com/facebookresearch/coconut", "lens": [ "continuous" ], "representation": [ "continuous-state" ], "computation": [ "recurrent", "token-wise" ], "training": [ "curriculum" ], "questions": [ "capability", "efficiency" ], "evidence": [ "empirical" ], "modality": [ "language", "symbolic" ], "summary": "Feeds a model's last hidden state back as its next input, enabling recurrent reasoning in continuous space before returning to language.", "provenance": "Awesome-Latent-CoT", "author_names": [ "Shibo Hao", "Sainbayar Sukhbaatar", "DiJia Su", "Xian Li", "Zhiting Hu", "Jason Weston", "Yuandong Tian" ], "record_url": "https://latent-implicit-thinking.github.io/papers/2412.06769/", "legacy_record_url": "https://latent-implicit-thinking.github.io/papers/#paper-2412-06769", "citation_date": "2024-12-09", "json_url": "https://latent-implicit-thinking.github.io/papers/2412.06769/record.json", "markdown_url": "https://latent-implicit-thinking.github.io/papers/2412.06769/index.md", "bibtex_url": "https://latent-implicit-thinking.github.io/papers/2412.06769/citation.bib", "csl_url": "https://latent-implicit-thinking.github.io/papers/2412.06769/citation.csl.json", "publication": { "title": "Training Large Language Models to Reason in a Continuous Latent Space", "author_names": [ "Shibo Hao", "Sainbayar Sukhbaatar", "DiJia Su", "Xian Li", "Zhiting Hu", "Jason E Weston", "Yuandong Tian" ], "year": 2025, "type": "paper-conference", "container_title": "Conference on Language Modeling (COLM 2025)", "url": "https://openreview.net/forum?id=Itxz7S4Ip3", "source_url": "https://colmweb.org/2025/AcceptedPapers.html", "verified_at": "2026-10-18" }, "publication_bibtex_url": "https://latent-implicit-thinking.github.io/papers/2412.06769/publication.bib", "publication_csl_url": "https://latent-implicit-thinking.github.io/papers/2412.06769/publication.csl.json", "method_comparisons": [ { "title": "Implicit CoT: proposal and implementation", "text": "Deng et al. (2023) proposed implicit chain-of-thought reasoning: language models carry out intermediate reasoning in continuous hidden states rather than generate it as text. Section 2.3 defines this setting; section 3 develops a distillation method to implement it. That method transfers a teacher's reasoning into hidden states across layers. Reasoning through continuous hidden states is the broader proposal; it does not require the particular distillation procedure used in this paper. Its across-layer computation differs from the repeated hidden-state feedback used by later methods such as Coconut.", "source_ids": [ "2311.01460", "2412.06769" ], "source_urls": [ "https://arxiv.org/html/2311.01460v1#S2.SS3", "https://arxiv.org/html/2311.01460v1#S3", "https://arxiv.org/html/2412.06769v3#S3" ], "source_labels": [ "Implicit CoT proposal, §2.3", "Distillation implementation, §3", "Coconut, §3" ], "topic_id": "continuous", "topic_label": "Continuous-state reasoning", "topic_url": "https://latent-implicit-thinking.github.io/topics/continuous-states/" }, { "title": "From stepwise internalization to Coconut", "text": "Coconut explicitly credits Deng et al. (2024) as the inspiration for its multistage training curriculum. Stepwise internalization gradually removes written CoT tokens until the model answers directly. Coconut instead replaces removed steps with continuous hidden-state inputs, retaining a sequence of internal reasoning steps before the answer. It also follows the earlier work in resetting the optimizer between training stages.", "source_ids": [ "2405.14838", "2412.06769" ], "source_urls": [ "https://arxiv.org/html/2405.14838v1#S3", "https://arxiv.org/html/2412.06769v3#S3" ], "source_labels": [ "Deng et al. (2024), §3", "Coconut, §3" ], "topic_id": "continuous", "topic_label": "Continuous-state reasoning", "topic_url": "https://latent-implicit-thinking.github.io/topics/continuous-states/" }, { "title": "Coconut and CODI: similar computation, different training", "text": "Coconut and CODI both pass continuous states between reasoning steps, but supervise them differently. Coconut gradually replaces written steps with hidden-state inputs and trains on the remaining text. CODI jointly trains explicit and implicit reasoning tasks, matching their hidden states at a selected position before the answer. These models avoid decoding a word at every intermediate step; they still perform successive model passes, so fewer written tokens does not mean no reasoning-time computation.", "source_ids": [ "2412.06769", "2502.21074" ], "source_urls": [ "https://arxiv.org/html/2412.06769v3#S3", "https://arxiv.org/html/2502.21074v3#S3" ], "source_labels": [ "Coconut, §3", "CODI, §3" ], "topic_id": "continuous", "topic_label": "Continuous-state reasoning", "topic_url": "https://latent-implicit-thinking.github.io/topics/continuous-states/" } ] }, { "id": "2412.13171", "title": "Compressed Chain of Thought: Efficient Reasoning Through Dense Representations", "authors": "Jeffrey Cheng, Benjamin Van Durme", "first_public": "2024-12-17", "venue": "arXiv", "arxiv": "2412.13171", "paper_url": "https://arxiv.org/abs/2412.13171", "lens": [ "continuous" ], "representation": [ "continuous-state" ], "computation": [ "token-wise" ], "training": [ "distillation" ], "questions": [ "efficiency", "capability" ], "evidence": [ "empirical" ], "modality": [ "language" ], "summary": "Compresses verbose reasoning traces into shorter dense representations and evaluates the trade-off between reasoning quality and generated length.", "provenance": "Awesome-Latent-CoT", "author_names": [ "Jeffrey Cheng", "Benjamin Van Durme" ], "record_url": "https://latent-implicit-thinking.github.io/papers/2412.13171/", "legacy_record_url": "https://latent-implicit-thinking.github.io/papers/#paper-2412-13171", "citation_date": "2024-12-17", "json_url": "https://latent-implicit-thinking.github.io/papers/2412.13171/record.json", "markdown_url": "https://latent-implicit-thinking.github.io/papers/2412.13171/index.md", "bibtex_url": "https://latent-implicit-thinking.github.io/papers/2412.13171/citation.bib", "csl_url": "https://latent-implicit-thinking.github.io/papers/2412.13171/citation.csl.json" }, { "id": "2501.19201", "title": "Efficient Reasoning with Hidden Thinking", "authors": "Xuan Shen, Yizhou Wang, Yufa Zhou, Xiangxi Shi, Pu Zhao, Yanzhi Wang, Jiuxiang Gu", "first_public": "2025-01-31", "venue": "arXiv", "arxiv": "2501.19201", "paper_url": "https://arxiv.org/abs/2501.19201", "lens": [ "internalized", "continuous" ], "representation": [ "continuous-state" ], "computation": [ "token-wise" ], "training": [ "distillation" ], "questions": [ "capability", "efficiency" ], "evidence": [ "empirical" ], "modality": [ "language" ], "summary": "Compresses visible reasoning into hidden thinking states to reduce generated reasoning length while preserving task performance.", "provenance": "Primary-source literature search, 2026-09-04", "author_names": [ "Xuan Shen", "Yizhou Wang", "Yufa Zhou", "Xiangxi Shi", "Pu Zhao", "Yanzhi Wang", "Jiuxiang Gu" ], "record_url": "https://latent-implicit-thinking.github.io/papers/2501.19201/", "legacy_record_url": "https://latent-implicit-thinking.github.io/papers/#paper-2501-19201", "citation_date": "2025-01-31", "json_url": "https://latent-implicit-thinking.github.io/papers/2501.19201/record.json", "markdown_url": "https://latent-implicit-thinking.github.io/papers/2501.19201/index.md", "bibtex_url": "https://latent-implicit-thinking.github.io/papers/2501.19201/citation.bib", "csl_url": "https://latent-implicit-thinking.github.io/papers/2501.19201/citation.csl.json" }, { "id": "2502.03275", "title": "Token Assorted: Mixing Latent and Text Tokens for Improved Language Model Reasoning", "authors": "DiJia Su, Hanlin Zhu, Yingchen Xu, Jiantao Jiao, Yuandong Tian, Qinqing Zheng", "first_public": "2025-02-05", "venue": "arXiv", "arxiv": "2502.03275", "paper_url": "https://arxiv.org/abs/2502.03275", "lens": [ "learned-tokens", "continuous" ], "representation": [ "discrete-token", "text-latent-hybrid" ], "computation": [ "token-wise" ], "training": [ "supervised" ], "questions": [ "capability", "efficiency" ], "evidence": [ "empirical" ], "modality": [ "language", "symbolic" ], "summary": "Interleaves latent and textual tokens so a model can combine compressed internal computation with readable reasoning steps.", "provenance": "Primary-source literature search, 2026-09-04", "author_names": [ "DiJia Su", "Hanlin Zhu", "Yingchen Xu", "Jiantao Jiao", "Yuandong Tian", "Qinqing Zheng" ], "record_url": "https://latent-implicit-thinking.github.io/papers/2502.03275/", "legacy_record_url": "https://latent-implicit-thinking.github.io/papers/#paper-2502-03275", "citation_date": "2025-02-05", "json_url": "https://latent-implicit-thinking.github.io/papers/2502.03275/record.json", "markdown_url": "https://latent-implicit-thinking.github.io/papers/2502.03275/index.md", "bibtex_url": "https://latent-implicit-thinking.github.io/papers/2502.03275/citation.bib", "csl_url": "https://latent-implicit-thinking.github.io/papers/2502.03275/citation.csl.json" }, { "id": "2502.08524", "title": "LLM Pretraining with Continuous Concepts", "authors": "Jihoon Tack, Jack Lanchantin, Jane Yu, Andrew Cohen, Ilia Kulikov, Janice Lan, Shibo Hao, Yuandong Tian, Jason Weston, Xian Li", "first_public": "2025-02-12", "venue": "arXiv", "arxiv": "2502.08524", "paper_url": "https://arxiv.org/abs/2502.08524", "code_url": "https://github.com/facebookresearch/RAM/tree/main/projects/cocomix", "lens": [ "continuous" ], "representation": [ "continuous-state" ], "computation": [ "token-wise" ], "training": [ "pretraining" ], "questions": [ "capability", "efficiency" ], "evidence": [ "empirical" ], "modality": [ "language" ], "summary": "Interleaves continuous concept representations with text during pretraining so language models learn to compute over concepts as well as tokens.", "provenance": "Awesome-Latent-CoT", "author_names": [ "Jihoon Tack", "Jack Lanchantin", "Jane Yu", "Andrew Cohen", "Ilia Kulikov", "Janice Lan", "Shibo Hao", "Yuandong Tian", "Jason Weston", "Xian Li" ], "record_url": "https://latent-implicit-thinking.github.io/papers/2502.08524/", "legacy_record_url": "https://latent-implicit-thinking.github.io/papers/#paper-2502-08524", "citation_date": "2025-02-12", "json_url": "https://latent-implicit-thinking.github.io/papers/2502.08524/record.json", "markdown_url": "https://latent-implicit-thinking.github.io/papers/2502.08524/index.md", "bibtex_url": "https://latent-implicit-thinking.github.io/papers/2502.08524/citation.bib", "csl_url": "https://latent-implicit-thinking.github.io/papers/2502.08524/citation.csl.json" }, { "id": "2502.15589", "title": "LightThinker: Thinking Step-by-Step Compression", "authors": "Jintian Zhang, Yuqi Zhu, Mengshu Sun, Yujie Luo, Shuofei Qiao, Lun Du, Da Zheng, Huajun Chen, Ningyu Zhang", "first_public": "2025-02-21", "venue": "arXiv", "arxiv": "2502.15589", "paper_url": "https://arxiv.org/abs/2502.15589", "lens": [ "internalized", "continuous" ], "representation": [ "continuous-state" ], "computation": [ "token-wise" ], "training": [ "distillation" ], "questions": [ "efficiency", "capability" ], "evidence": [ "empirical" ], "modality": [ "language" ], "summary": "Compresses segments of a generated reasoning chain into dense states while retaining a stepwise inference process.", "provenance": "Primary-source literature search, 2026-09-04", "author_names": [ "Jintian Zhang", "Yuqi Zhu", "Mengshu Sun", "Yujie Luo", "Shuofei Qiao", "Lun Du", "Da Zheng", "Huajun Chen", "Ningyu Zhang" ], "record_url": "https://latent-implicit-thinking.github.io/papers/2502.15589/", "legacy_record_url": "https://latent-implicit-thinking.github.io/papers/#paper-2502-15589", "citation_date": "2025-02-21", "json_url": "https://latent-implicit-thinking.github.io/papers/2502.15589/record.json", "markdown_url": "https://latent-implicit-thinking.github.io/papers/2502.15589/index.md", "bibtex_url": "https://latent-implicit-thinking.github.io/papers/2502.15589/citation.bib", "csl_url": "https://latent-implicit-thinking.github.io/papers/2502.15589/citation.csl.json" }, { "id": "2502.21074", "title": "CODI: Compressing Chain-of-Thought into Continuous Space via Self-Distillation", "authors": "Zhenyi Shen, Hanqi Yan, Linhai Zhang, Zhanghao Hu, Yali Du, Yulan He", "first_public": "2025-02-28", "venue": "EMNLP 2025", "arxiv": "2502.21074", "paper_url": "https://arxiv.org/abs/2502.21074", "lens": [ "continuous", "internalized" ], "representation": [ "continuous-state" ], "computation": [ "token-wise" ], "training": [ "self-distillation" ], "questions": [ "capability", "efficiency" ], "evidence": [ "empirical" ], "modality": [ "language", "symbolic" ], "summary": "Uses self-distillation to align continuous internal reasoning states with explicit chain-of-thought behavior while reducing visible reasoning tokens.", "provenance": "Awesome-Latent-CoT", "author_names": [ "Zhenyi Shen", "Hanqi Yan", "Linhai Zhang", "Zhanghao Hu", "Yali Du", "Yulan He" ], "record_url": "https://latent-implicit-thinking.github.io/papers/2502.21074/", "legacy_record_url": "https://latent-implicit-thinking.github.io/papers/#paper-2502-21074", "citation_date": "2025-02-28", "json_url": "https://latent-implicit-thinking.github.io/papers/2502.21074/record.json", "markdown_url": "https://latent-implicit-thinking.github.io/papers/2502.21074/index.md", "bibtex_url": "https://latent-implicit-thinking.github.io/papers/2502.21074/citation.bib", "csl_url": "https://latent-implicit-thinking.github.io/papers/2502.21074/citation.csl.json", "publication": { "title": "CODI: Compressing Chain-of-Thought into Continuous Space via Self-Distillation", "author_names": [ "Zhenyi Shen", "Hanqi Yan", "Linhai Zhang", "Zhanghao Hu", "Yali Du", "Yulan He" ], "year": 2025, "type": "paper-conference", "container_title": "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing", "publisher": "Association for Computational Linguistics", "doi": "10.18653/v1/2025.emnlp-main.36", "pages": "677-693", "url": "https://aclanthology.org/2025.emnlp-main.36/", "source_url": "https://aclanthology.org/2025.emnlp-main.36/", "verified_at": "2026-10-18" }, "publication_bibtex_url": "https://latent-implicit-thinking.github.io/papers/2502.21074/publication.bib", "publication_csl_url": "https://latent-implicit-thinking.github.io/papers/2502.21074/publication.csl.json", "method_comparisons": [ { "title": "Hidden-state distillation: the 2023 method and CODI", "text": "The 2023 implicit CoT paper proposes reasoning through hidden states and implements it with a teacher, a thought emulator and an answer-generating student. CODI also transfers explicit reasoning through hidden-state supervision, but uses one model for both teacher and student tasks. It aligns their states just before the answer, while the student takes successive continuous reasoning steps.", "source_ids": [ "2311.01460", "2502.21074" ], "source_urls": [ "https://arxiv.org/html/2311.01460v1#S2.SS3", "https://arxiv.org/html/2311.01460v1#S3", "https://arxiv.org/html/2502.21074v3#S3" ], "source_labels": [ "Deng et al. (2023), §2.3", "Deng et al. (2023), §3", "CODI, §3" ], "topic_id": "internalized", "topic_label": "Internalized reasoning", "topic_url": "https://latent-implicit-thinking.github.io/topics/internalized-reasoning/" }, { "title": "Coconut and CODI: similar computation, different training", "text": "Coconut and CODI both pass continuous states between reasoning steps, but supervise them differently. Coconut gradually replaces written steps with hidden-state inputs and trains on the remaining text. CODI jointly trains explicit and implicit reasoning tasks, matching their hidden states at a selected position before the answer. These models avoid decoding a word at every intermediate step; they still perform successive model passes, so fewer written tokens does not mean no reasoning-time computation.", "source_ids": [ "2412.06769", "2502.21074" ], "source_urls": [ "https://arxiv.org/html/2412.06769v3#S3", "https://arxiv.org/html/2502.21074v3#S3" ], "source_labels": [ "Coconut, §3", "CODI, §3" ], "topic_id": "continuous", "topic_label": "Continuous-state reasoning", "topic_url": "https://latent-implicit-thinking.github.io/topics/continuous-states/" } ] }, { "id": "2505.13308", "title": "Seek in the Dark: Reasoning via Test-Time Instance-Level Policy Gradient in Latent Space", "authors": "Hengli Li, Chenxi Li, Tong Wu, Xuekai Zhu, Yuxuan Wang, Zhaoxin Yu, Eric Hanchen Jiang, Song-Chun Zhu, Zixia Jia, Ying Nian Wu, Zilong Zheng", "first_public": "2025-05-19", "venue": "arXiv", "arxiv": "2505.13308", "paper_url": "https://arxiv.org/abs/2505.13308", "lens": [ "continuous" ], "representation": [ "continuous-state" ], "computation": [ "search" ], "training": [ "test-time-optimization" ], "questions": [ "capability", "controllability" ], "evidence": [ "empirical" ], "modality": [ "language", "symbolic" ], "summary": "Optimizes latent states for individual problems at test time using policy gradients rather than searching over written reasoning traces.", "provenance": "Primary-source literature search, 2026-09-04", "author_names": [ "Hengli Li", "Chenxi Li", "Tong Wu", "Xuekai Zhu", "Yuxuan Wang", "Zhaoxin Yu", "Eric Hanchen Jiang", "Song-Chun Zhu", "Zixia Jia", "Ying Nian Wu", "Zilong Zheng" ], "record_url": "https://latent-implicit-thinking.github.io/papers/2505.13308/", "legacy_record_url": "https://latent-implicit-thinking.github.io/papers/#paper-2505-13308", "citation_date": "2025-05-19", "json_url": "https://latent-implicit-thinking.github.io/papers/2505.13308/record.json", "markdown_url": "https://latent-implicit-thinking.github.io/papers/2505.13308/index.md", "bibtex_url": "https://latent-implicit-thinking.github.io/papers/2505.13308/citation.bib", "csl_url": "https://latent-implicit-thinking.github.io/papers/2505.13308/citation.csl.json" }, { "id": "2505.14827", "title": "Text Generation Beyond Discrete Token Sampling", "authors": "Yufan Zhuang, Liyuan Liu, Chandan Singh, Jingbo Shang, Jianfeng Gao", "first_public": "2025-05-20", "venue": "arXiv", "arxiv": "2505.14827", "paper_url": "https://arxiv.org/abs/2505.14827", "lens": [ "continuous", "beyond-sequential" ], "representation": [ "continuous-state" ], "computation": [ "token-wise" ], "training": [ "inference-only" ], "questions": [ "capability", "efficiency" ], "evidence": [ "empirical" ], "modality": [ "language" ], "summary": "Feeds posterior expectations back into a language model so intermediate generation can remain continuous instead of sampling discrete tokens.", "provenance": "Primary-source literature search, 2026-09-04", "author_names": [ "Yufan Zhuang", "Liyuan Liu", "Chandan Singh", "Jingbo Shang", "Jianfeng Gao" ], "record_url": "https://latent-implicit-thinking.github.io/papers/2505.14827/", "legacy_record_url": "https://latent-implicit-thinking.github.io/papers/#paper-2505-14827", "citation_date": "2025-05-20", "json_url": "https://latent-implicit-thinking.github.io/papers/2505.14827/record.json", "markdown_url": "https://latent-implicit-thinking.github.io/papers/2505.14827/index.md", "bibtex_url": "https://latent-implicit-thinking.github.io/papers/2505.14827/citation.bib", "csl_url": "https://latent-implicit-thinking.github.io/papers/2505.14827/citation.csl.json" }, { "id": "2505.16552", "title": "Think Silently, Think Fast: Dynamic Latent Compression of LLM Reasoning Chains", "authors": "Wenhui Tan, Jiaze Li, Jianzhong Ju, Zhenbo Luo, Ruihua Song, Jian Luan", "first_public": "2025-05-22", "venue": "arXiv", "arxiv": "2505.16552", "paper_url": "https://arxiv.org/abs/2505.16552", "lens": [ "internalized", "continuous" ], "representation": [ "continuous-state" ], "computation": [ "token-wise" ], "training": [ "distillation", "supervised" ], "questions": [ "efficiency", "capability", "controllability" ], "evidence": [ "empirical" ], "modality": [ "language" ], "summary": "Dynamically compresses portions of a reasoning chain into latent states according to the problem and intermediate context.", "provenance": "Primary-source literature search, 2026-09-04", "author_names": [ "Wenhui Tan", "Jiaze Li", "Jianzhong Ju", "Zhenbo Luo", "Ruihua Song", "Jian Luan" ], "record_url": "https://latent-implicit-thinking.github.io/papers/2505.16552/", "legacy_record_url": "https://latent-implicit-thinking.github.io/papers/#paper-2505-16552", "citation_date": "2025-05-22", "json_url": "https://latent-implicit-thinking.github.io/papers/2505.16552/record.json", "markdown_url": "https://latent-implicit-thinking.github.io/papers/2505.16552/index.md", "bibtex_url": "https://latent-implicit-thinking.github.io/papers/2505.16552/citation.bib", "csl_url": "https://latent-implicit-thinking.github.io/papers/2505.16552/citation.csl.json" }, { "id": "2505.18454", "title": "Hybrid Latent Reasoning via Reinforcement Learning", "authors": "Zhenrui Yue, Bowen Jin, Huimin Zeng, Honglei Zhuang, Zhen Qin, Jinsung Yoon, Lanyu Shang, Jiawei Han, Dong Wang", "first_public": "2025-05-24", "venue": "arXiv", "arxiv": "2505.18454", "paper_url": "https://arxiv.org/abs/2505.18454", "lens": [ "continuous", "internalized" ], "representation": [ "text-latent-hybrid", "continuous-state" ], "computation": [ "token-wise" ], "training": [ "reinforcement-learning" ], "questions": [ "capability", "efficiency", "controllability" ], "evidence": [ "empirical" ], "modality": [ "language", "symbolic" ], "summary": "Uses reinforcement learning to choose and train a mixture of latent and written reasoning steps.", "provenance": "Primary-source literature search, 2026-09-04", "author_names": [ "Zhenrui Yue", "Bowen Jin", "Huimin Zeng", "Honglei Zhuang", "Zhen Qin", "Jinsung Yoon", "Lanyu Shang", "Jiawei Han", "Dong Wang" ], "record_url": "https://latent-implicit-thinking.github.io/papers/2505.18454/", "legacy_record_url": "https://latent-implicit-thinking.github.io/papers/#paper-2505-18454", "citation_date": "2025-05-24", "json_url": "https://latent-implicit-thinking.github.io/papers/2505.18454/record.json", "markdown_url": "https://latent-implicit-thinking.github.io/papers/2505.18454/index.md", "bibtex_url": "https://latent-implicit-thinking.github.io/papers/2505.18454/citation.bib", "csl_url": "https://latent-implicit-thinking.github.io/papers/2505.18454/citation.csl.json" }, { "id": "2505.18962", "title": "System-1.5 Reasoning: Traversal in Language and Latent Spaces with Dynamic Shortcuts", "authors": "Xiaoqiang Wang, Suyuchen Wang, Yun Zhu, Bang Liu", "first_public": "2025-05-25", "venue": "arXiv", "arxiv": "2505.18962", "paper_url": "https://arxiv.org/abs/2505.18962", "lens": [ "continuous", "internalized" ], "representation": [ "text-latent-hybrid", "continuous-state" ], "computation": [ "token-wise" ], "training": [ "supervised" ], "questions": [ "capability", "efficiency", "controllability" ], "evidence": [ "empirical" ], "modality": [ "language" ], "summary": "Learns dynamic shortcuts between language and latent states to skip portions of an otherwise explicit reasoning trajectory.", "provenance": "Primary-source literature search, 2026-09-04", "author_names": [ "Xiaoqiang Wang", "Suyuchen Wang", "Yun Zhu", "Bang Liu" ], "record_url": "https://latent-implicit-thinking.github.io/papers/2505.18962/", "legacy_record_url": "https://latent-implicit-thinking.github.io/papers/#paper-2505-18962", "citation_date": "2025-05-25", "json_url": "https://latent-implicit-thinking.github.io/papers/2505.18962/record.json", "markdown_url": "https://latent-implicit-thinking.github.io/papers/2505.18962/index.md", "bibtex_url": "https://latent-implicit-thinking.github.io/papers/2505.18962/citation.bib", "csl_url": "https://latent-implicit-thinking.github.io/papers/2505.18962/citation.csl.json" }, { "id": "2505.20674", "title": "PonderLM: Pretraining Language Models to Ponder in Continuous Space", "authors": "Boyi Zeng, Shixiang Song, Siyuan Huang, Yixuan Wang, He Li, Ziwei He, Xinbing Wang, Zhiyu Li, Zhouhan Lin", "first_public": "2025-05-27", "venue": "arXiv", "arxiv": "2505.20674", "paper_url": "https://arxiv.org/abs/2505.20674", "lens": [ "continuous", "recurrent" ], "representation": [ "continuous-state" ], "computation": [ "recurrent", "token-wise" ], "training": [ "pretraining" ], "questions": [ "capability", "efficiency", "scaling" ], "evidence": [ "empirical", "scaling" ], "modality": [ "language" ], "summary": "Pretrains language models with repeated continuous-space pondering steps before token prediction.", "provenance": "Primary-source literature search, 2026-09-04", "author_names": [ "Boyi Zeng", "Shixiang Song", "Siyuan Huang", "Yixuan Wang", "He Li", "Ziwei He", "Xinbing Wang", "Zhiyu Li", "Zhouhan Lin" ], "record_url": "https://latent-implicit-thinking.github.io/papers/2505.20674/", "legacy_record_url": "https://latent-implicit-thinking.github.io/papers/#paper-2505-20674", "citation_date": "2025-05-27", "json_url": "https://latent-implicit-thinking.github.io/papers/2505.20674/record.json", "markdown_url": "https://latent-implicit-thinking.github.io/papers/2505.20674/index.md", "bibtex_url": "https://latent-implicit-thinking.github.io/papers/2505.20674/citation.bib", "csl_url": "https://latent-implicit-thinking.github.io/papers/2505.20674/citation.csl.json" }, { "id": "2505.23648", "title": "Continuous Chain of Thought Enables Parallel Exploration and Reasoning", "authors": "Halil Alperen Gozeten, M. Emrullah Ildiz, Xuechen Zhang, Hrayr Harutyunyan, Ankit Singh Rawat, Samet Oymak", "first_public": "2025-05-29", "venue": "arXiv", "arxiv": "2505.23648", "paper_url": "https://arxiv.org/abs/2505.23648", "lens": [ "continuous", "analysis" ], "representation": [ "continuous-state" ], "computation": [ "parallel" ], "training": [ "analysis-only" ], "questions": [ "theory", "capability" ], "evidence": [ "formal", "empirical" ], "modality": [ "symbolic" ], "summary": "Analyzes and demonstrates how continuous intermediate states can explore several candidate reasoning paths in parallel.", "provenance": "Primary-source literature search, 2026-09-04", "author_names": [ "Halil Alperen Gozeten", "M. Emrullah Ildiz", "Xuechen Zhang", "Hrayr Harutyunyan", "Ankit Singh Rawat", "Samet Oymak" ], "record_url": "https://latent-implicit-thinking.github.io/papers/2505.23648/", "legacy_record_url": "https://latent-implicit-thinking.github.io/papers/#paper-2505-23648", "citation_date": "2025-05-29", "json_url": "https://latent-implicit-thinking.github.io/papers/2505.23648/record.json", "markdown_url": "https://latent-implicit-thinking.github.io/papers/2505.23648/index.md", "bibtex_url": "https://latent-implicit-thinking.github.io/papers/2505.23648/citation.bib", "csl_url": "https://latent-implicit-thinking.github.io/papers/2505.23648/citation.csl.json" }, { "id": "2506.11752", "title": "DART: Distilling Autoregressive Reasoning to Silent Thought", "authors": "Nan Jiang, Ziming Wu, De-Chuan Zhan, Fuming Lai, Shaobing Lian", "first_public": "2025-06-13", "venue": "arXiv", "arxiv": "2506.11752", "paper_url": "https://arxiv.org/abs/2506.11752", "lens": [ "internalized", "continuous" ], "representation": [ "continuous-state" ], "computation": [ "token-wise" ], "training": [ "distillation" ], "questions": [ "capability", "efficiency" ], "evidence": [ "empirical" ], "modality": [ "language", "symbolic" ], "summary": "Distills an autoregressive textual reasoner into a model that carries the intermediate process through silent internal states.", "provenance": "Primary-source literature search, 2026-09-04", "author_names": [ "Nan Jiang", "Ziming Wu", "De-Chuan Zhan", "Fuming Lai", "Shaobing Lian" ], "record_url": "https://latent-implicit-thinking.github.io/papers/2506.11752/", "legacy_record_url": "https://latent-implicit-thinking.github.io/papers/#paper-2506-11752", "citation_date": "2025-06-13", "json_url": "https://latent-implicit-thinking.github.io/papers/2506.11752/record.json", "markdown_url": "https://latent-implicit-thinking.github.io/papers/2506.11752/index.md", "bibtex_url": "https://latent-implicit-thinking.github.io/papers/2506.11752/citation.bib", "csl_url": "https://latent-implicit-thinking.github.io/papers/2506.11752/citation.csl.json" }, { "id": "2506.17218", "title": "Machine Mental Imagery: Empower Multimodal Reasoning with Latent Visual Tokens", "authors": "Zeyuan Yang, Xueyang Yu, Delin Chen, Maohao Shen, Chuang Gan", "first_public": "2025-06-20", "venue": "arXiv", "arxiv": "2506.17218", "paper_url": "https://arxiv.org/abs/2506.17218", "lens": [ "learned-tokens", "continuous" ], "representation": [ "multimodal-latent" ], "computation": [ "token-wise" ], "training": [ "supervised" ], "questions": [ "capability", "interpretability" ], "evidence": [ "empirical" ], "modality": [ "multimodal", "vision" ], "summary": "Uses latent visual tokens as an internal imagery channel for multimodal reasoning without rendering every intermediate state.", "provenance": "Primary-source literature search, 2026-09-04", "author_names": [ "Zeyuan Yang", "Xueyang Yu", "Delin Chen", "Maohao Shen", "Chuang Gan" ], "record_url": "https://latent-implicit-thinking.github.io/papers/2506.17218/", "legacy_record_url": "https://latent-implicit-thinking.github.io/papers/#paper-2506-17218", "citation_date": "2025-06-20", "json_url": "https://latent-implicit-thinking.github.io/papers/2506.17218/record.json", "markdown_url": "https://latent-implicit-thinking.github.io/papers/2506.17218/index.md", "bibtex_url": "https://latent-implicit-thinking.github.io/papers/2506.17218/citation.bib", "csl_url": "https://latent-implicit-thinking.github.io/papers/2506.17218/citation.csl.json" }, { "id": "2506.18582", "title": "Parallel Continuous Chain-of-Thought with Jacobi Iteration", "authors": "Haoyi Wu, Zhihao Teng, Kewei Tu", "first_public": "2025-06-23", "venue": "arXiv", "arxiv": "2506.18582", "paper_url": "https://arxiv.org/abs/2506.18582", "lens": [ "continuous", "beyond-sequential" ], "representation": [ "continuous-state" ], "computation": [ "parallel" ], "training": [ "supervised" ], "questions": [ "capability", "efficiency" ], "evidence": [ "empirical" ], "modality": [ "language", "symbolic" ], "summary": "Applies Jacobi-style iteration to update continuous reasoning states in parallel rather than strictly one step at a time.", "provenance": "Primary-source literature search, 2026-09-04", "author_names": [ "Haoyi Wu", "Zhihao Teng", "Kewei Tu" ], "record_url": "https://latent-implicit-thinking.github.io/papers/2506.18582/", "legacy_record_url": "https://latent-implicit-thinking.github.io/papers/#paper-2506-18582", "citation_date": "2025-06-23", "json_url": "https://latent-implicit-thinking.github.io/papers/2506.18582/record.json", "markdown_url": "https://latent-implicit-thinking.github.io/papers/2506.18582/index.md", "bibtex_url": "https://latent-implicit-thinking.github.io/papers/2506.18582/citation.bib", "csl_url": "https://latent-implicit-thinking.github.io/papers/2506.18582/citation.csl.json" }, { "id": "2508.00574", "title": "SynAdapt: Learning Adaptive Reasoning in Large Language Models via Synthetic Continuous Chain-of-Thought", "authors": "Jianwei Wang, Ziming Wu, Fuming Lai, Shaobing Lian, Ziqian Zeng", "first_public": "2025-08-01", "venue": "arXiv", "arxiv": "2508.00574", "paper_url": "https://arxiv.org/abs/2508.00574", "lens": [ "continuous" ], "representation": [ "continuous-state" ], "computation": [ "token-wise" ], "training": [ "supervised" ], "questions": [ "capability", "efficiency" ], "evidence": [ "empirical" ], "modality": [ "language" ], "summary": "Uses synthetic continuous reasoning trajectories to train language models to allocate internal reasoning adaptively.", "provenance": "Primary-source literature search, 2026-09-04", "author_names": [ "Jianwei Wang", "Ziming Wu", "Fuming Lai", "Shaobing Lian", "Ziqian Zeng" ], "record_url": "https://latent-implicit-thinking.github.io/papers/2508.00574/", "legacy_record_url": "https://latent-implicit-thinking.github.io/papers/#paper-2508-00574", "citation_date": "2025-08-01", "json_url": "https://latent-implicit-thinking.github.io/papers/2508.00574/record.json", "markdown_url": "https://latent-implicit-thinking.github.io/papers/2508.00574/index.md", "bibtex_url": "https://latent-implicit-thinking.github.io/papers/2508.00574/citation.bib", "csl_url": "https://latent-implicit-thinking.github.io/papers/2508.00574/citation.csl.json" }, { "id": "2508.03440", "title": "LLMs are Single-threaded Reasoners: Demystifying the Working Mechanism of Soft Thinking", "authors": "Junhong Wu, Jinliang Lu, Zixuan Ren, Gangqiang Hu, Zhi Wu, Dai Dai, Hua Wu", "first_public": "2025-08-05", "added_at": "2026-10-18", "venue": "arXiv", "arxiv": "2508.03440", "paper_url": "https://arxiv.org/abs/2508.03440", "lens": [ "analysis", "continuous" ], "representation": [ "continuous-state" ], "computation": [ "token-wise" ], "training": [ "analysis-only", "inference-only" ], "questions": [ "interpretability", "limitations", "capability" ], "evidence": [ "probing", "empirical", "boundary" ], "modality": [ "language", "symbolic" ], "summary": "Probes soft-token reasoning and finds that tested models often follow the highest-probability component rather than exploring several paths at once. Introduces stochastic soft thinking, including Gumbel-Softmax sampling, to reduce this greedy behavior.", "provenance": "Awesome-Latent-CoT; primary sources checked 2026-10-18", "author_names": [ "Junhong Wu", "Jinliang Lu", "Zixuan Ren", "Gangqiang Hu", "Zhi Wu", "Dai Dai", "Hua Wu" ], "record_url": "https://latent-implicit-thinking.github.io/papers/2508.03440/", "legacy_record_url": "https://latent-implicit-thinking.github.io/papers/#paper-2508-03440", "citation_date": "2025-08-05", "json_url": "https://latent-implicit-thinking.github.io/papers/2508.03440/record.json", "markdown_url": "https://latent-implicit-thinking.github.io/papers/2508.03440/index.md", "bibtex_url": "https://latent-implicit-thinking.github.io/papers/2508.03440/citation.bib", "csl_url": "https://latent-implicit-thinking.github.io/papers/2508.03440/citation.csl.json" }, { "id": "2509.20317", "title": "SIM-CoT: Supervised Implicit Chain-of-Thought", "authors": "Xilin Wei, Xiaoran Liu, Yuhang Zang, Xiaoyi Dong, Yuhang Cao, Jiaqi Wang, Xipeng Qiu, Dahua Lin", "first_public": "2025-09-24", "venue": "arXiv", "arxiv": "2509.20317", "paper_url": "https://arxiv.org/abs/2509.20317", "code_url": "https://github.com/InternLM/SIM-CoT", "lens": [ "continuous", "internalized" ], "representation": [ "continuous-state" ], "computation": [ "token-wise" ], "training": [ "supervised", "distillation" ], "questions": [ "capability", "efficiency", "interpretability" ], "evidence": [ "empirical" ], "modality": [ "language", "symbolic" ], "summary": "Trains continuous intermediate states with explicit supervision so reasoning can proceed without emitting a full textual trace.", "provenance": "Primary-source literature search, 2026-09-04", "author_names": [ "Xilin Wei", "Xiaoran Liu", "Yuhang Zang", "Xiaoyi Dong", "Yuhang Cao", "Jiaqi Wang", "Xipeng Qiu", "Dahua Lin" ], "record_url": "https://latent-implicit-thinking.github.io/papers/2509.20317/", "legacy_record_url": "https://latent-implicit-thinking.github.io/papers/#paper-2509-20317", "citation_date": "2025-09-24", "json_url": "https://latent-implicit-thinking.github.io/papers/2509.20317/record.json", "markdown_url": "https://latent-implicit-thinking.github.io/papers/2509.20317/index.md", "bibtex_url": "https://latent-implicit-thinking.github.io/papers/2509.20317/citation.bib", "csl_url": "https://latent-implicit-thinking.github.io/papers/2509.20317/citation.csl.json" }, { "id": "2510.04573", "title": "LaDiR: Latent Diffusion Enhances LLMs for Text Reasoning", "authors": "Haoqiang Kang, Yizhe Zhang, Nikki Lijing Kuang, Nicklas Majamaki, Navdeep Jaitly, Yi-An Ma, Lianhui Qin", "first_public": "2025-10-06", "added_at": "2026-10-18", "venue": "arXiv", "arxiv": "2510.04573", "paper_url": "https://arxiv.org/abs/2510.04573", "lens": [ "continuous", "beyond-sequential" ], "representation": [ "continuous-state" ], "computation": [ "diffusion" ], "training": [ "supervised" ], "questions": [ "architecture", "capability", "scaling" ], "evidence": [ "empirical" ], "modality": [ "language", "symbolic" ], "summary": "Uses a variational autoencoder to compress textual reasoning steps into blocks of continuous states. A diffusion model repeatedly revises each block before generating the next, then produces a textual answer; diversity guidance encourages alternative reasoning trajectories.", "provenance": "Awesome-Latent-CoT; primary sources checked 2026-10-18", "author_names": [ "Haoqiang Kang", "Yizhe Zhang", "Nikki Lijing Kuang", "Nicklas Majamaki", "Navdeep Jaitly", "Yi-An Ma", "Lianhui Qin" ], "record_url": "https://latent-implicit-thinking.github.io/papers/2510.04573/", "legacy_record_url": "https://latent-implicit-thinking.github.io/papers/#paper-2510-04573", "citation_date": "2025-10-06", "json_url": "https://latent-implicit-thinking.github.io/papers/2510.04573/record.json", "markdown_url": "https://latent-implicit-thinking.github.io/papers/2510.04573/index.md", "bibtex_url": "https://latent-implicit-thinking.github.io/papers/2510.04573/citation.bib", "csl_url": "https://latent-implicit-thinking.github.io/papers/2510.04573/citation.csl.json" }, { "id": "2510.15522", "title": "LLM Latent Reasoning as Chain of Superposition", "authors": "Jingcheng Deng, Liang Pang, Zihao Wei, Shicheng Xu, Zenghao Duan, Kun Xu, Yang Song, Huawei Shen, Xueqi Cheng", "first_public": "2025-10-17", "venue": "arXiv", "arxiv": "2510.15522", "paper_url": "https://arxiv.org/abs/2510.15522", "code_url": "https://github.com/DJC-GO-SOLO/Latent-SFT", "lens": [ "continuous" ], "representation": [ "text-latent-hybrid", "continuous-state" ], "computation": [ "token-wise", "parallel" ], "training": [ "supervised" ], "questions": [ "capability", "efficiency" ], "evidence": [ "empirical" ], "modality": [ "language" ], "summary": "Models latent reasoning as a sequence of superposed token distributions and trains the process with supervised fine-tuning.", "provenance": "Primary-source literature search, 2026-09-04", "author_names": [ "Jingcheng Deng", "Liang Pang", "Zihao Wei", "Shicheng Xu", "Zenghao Duan", "Kun Xu", "Yang Song", "Huawei Shen", "Xueqi Cheng" ], "record_url": "https://latent-implicit-thinking.github.io/papers/2510.15522/", "legacy_record_url": "https://latent-implicit-thinking.github.io/papers/#paper-2510-15522", "citation_date": "2025-10-17", "json_url": "https://latent-implicit-thinking.github.io/papers/2510.15522/record.json", "markdown_url": "https://latent-implicit-thinking.github.io/papers/2510.15522/index.md", "bibtex_url": "https://latent-implicit-thinking.github.io/papers/2510.15522/citation.bib", "csl_url": "https://latent-implicit-thinking.github.io/papers/2510.15522/citation.csl.json" }, { "id": "2510.24940", "title": "SemCoT: Accelerating Chain-of-Thought Reasoning through Semantically-Aligned Implicit Tokens", "authors": "Yinhan He, Wendy Zheng, Yaochen Zhu, Zaiyi Zheng, Lin Su, Sriram Vasudevan, Qi Guo, Liangjie Hong, Jundong Li", "first_public": "2025-10-28", "venue": "arXiv", "arxiv": "2510.24940", "paper_url": "https://arxiv.org/abs/2510.24940", "code_url": "https://github.com/YinhanHe123/SemCoT", "lens": [ "internalized", "continuous" ], "representation": [ "continuous-state" ], "computation": [ "token-wise" ], "training": [ "distillation", "supervised" ], "questions": [ "efficiency", "capability", "interpretability" ], "evidence": [ "empirical" ], "modality": [ "language" ], "summary": "Compresses textual reasoning into semantically aligned implicit tokens intended to preserve useful intermediate computation with fewer generated steps.", "provenance": "Primary-source literature search, 2026-09-04", "author_names": [ "Yinhan He", "Wendy Zheng", "Yaochen Zhu", "Zaiyi Zheng", "Lin Su", "Sriram Vasudevan", "Qi Guo", "Liangjie Hong", "Jundong Li" ], "record_url": "https://latent-implicit-thinking.github.io/papers/2510.24940/", "legacy_record_url": "https://latent-implicit-thinking.github.io/papers/#paper-2510-24940", "citation_date": "2025-10-28", "json_url": "https://latent-implicit-thinking.github.io/papers/2510.24940/record.json", "markdown_url": "https://latent-implicit-thinking.github.io/papers/2510.24940/index.md", "bibtex_url": "https://latent-implicit-thinking.github.io/papers/2510.24940/citation.bib", "csl_url": "https://latent-implicit-thinking.github.io/papers/2510.24940/citation.csl.json" }, { "id": "2511.21581", "title": "Learning When to Stop: Adaptive Latent Reasoning via Reinforcement Learning", "authors": "Alex Ning, Yen-Ling Kuo, Gabe Gomes", "first_public": "2025-11-26", "venue": "arXiv", "arxiv": "2511.21581", "paper_url": "https://arxiv.org/abs/2511.21581", "code_url": "https://github.com/apning/adaptive-latent-reasoning", "lens": [ "continuous" ], "representation": [ "continuous-state" ], "computation": [ "recurrent", "token-wise" ], "training": [ "reinforcement-learning" ], "questions": [ "efficiency", "controllability", "capability" ], "evidence": [ "empirical" ], "modality": [ "language", "symbolic" ], "summary": "Learns a stopping policy for continuous latent reasoning so the model can allocate a variable number of internal steps.", "provenance": "Primary-source literature search, 2026-09-04", "author_names": [ "Alex Ning", "Yen-Ling Kuo", "Gabe Gomes" ], "record_url": "https://latent-implicit-thinking.github.io/papers/2511.21581/", "legacy_record_url": "https://latent-implicit-thinking.github.io/papers/#paper-2511-21581", "citation_date": "2025-11-26", "json_url": "https://latent-implicit-thinking.github.io/papers/2511.21581/record.json", "markdown_url": "https://latent-implicit-thinking.github.io/papers/2511.21581/index.md", "bibtex_url": "https://latent-implicit-thinking.github.io/papers/2511.21581/citation.bib", "csl_url": "https://latent-implicit-thinking.github.io/papers/2511.21581/citation.csl.json" }, { "id": "2512.21711", "title": "Do Latent Tokens Think? A Causal and Adversarial Analysis of Chain-of-Continuous-Thought", "authors": "Yuyi Zhang, Boyu Tang, Tianjie Ju, Sufeng Duan, Gongshen Liu", "first_public": "2025-12-25", "added_at": "2026-10-18", "venue": "arXiv", "arxiv": "2512.21711", "paper_url": "https://arxiv.org/abs/2512.21711", "lens": [ "analysis", "continuous" ], "representation": [ "continuous-state" ], "computation": [ "token-wise" ], "training": [ "analysis-only" ], "questions": [ "interpretability", "evaluation", "limitations" ], "evidence": [ "empirical", "mechanistic", "boundary" ], "modality": [ "language" ], "summary": "Tests COCONUT with latent-state interventions and deliberately biased examples on MMLU and HotpotQA. Finds weak sensitivity to steering and dependence on dataset shortcuts in the evaluated models, challenging whether their intermediate states faithfully carry the reasoning.", "provenance": "Awesome-Latent-CoT; primary sources checked 2026-10-18", "author_names": [ "Yuyi Zhang", "Boyu Tang", "Tianjie Ju", "Sufeng Duan", "Gongshen Liu" ], "record_url": "https://latent-implicit-thinking.github.io/papers/2512.21711/", "legacy_record_url": "https://latent-implicit-thinking.github.io/papers/#paper-2512-21711", "citation_date": "2025-12-25", "json_url": "https://latent-implicit-thinking.github.io/papers/2512.21711/record.json", "markdown_url": "https://latent-implicit-thinking.github.io/papers/2512.21711/index.md", "bibtex_url": "https://latent-implicit-thinking.github.io/papers/2512.21711/citation.bib", "csl_url": "https://latent-implicit-thinking.github.io/papers/2512.21711/citation.csl.json" }, { "id": "2601.21358", "title": "Latent Chain-of-Thought as Planning: Decoupling Reasoning from Verbalization", "authors": "Jiecong Wang, Hao Peng, Zhanyi Wang, Chunyang Liu, Guanlin Wu", "first_public": "2026-01-29", "venue": "arXiv", "arxiv": "2601.21358", "paper_url": "https://arxiv.org/abs/2601.21358", "code_url": "https://github.com/yunsaijc/PLaT", "lens": [ "continuous" ], "representation": [ "continuous-state", "text-latent-hybrid" ], "computation": [ "recurrent", "token-wise" ], "training": [ "supervised" ], "questions": [ "capability", "controllability", "interpretability" ], "evidence": [ "empirical" ], "modality": [ "language" ], "summary": "Separates an internal planning process from verbalization and studies whether the latent plan supports effective downstream reasoning.", "provenance": "Primary-source literature search, 2026-09-04", "author_names": [ "Jiecong Wang", "Hao Peng", "Zhanyi Wang", "Chunyang Liu", "Guanlin Wu" ], "record_url": "https://latent-implicit-thinking.github.io/papers/2601.21358/", "legacy_record_url": "https://latent-implicit-thinking.github.io/papers/#paper-2601-21358", "citation_date": "2026-01-29", "json_url": "https://latent-implicit-thinking.github.io/papers/2601.21358/record.json", "markdown_url": "https://latent-implicit-thinking.github.io/papers/2601.21358/index.md", "bibtex_url": "https://latent-implicit-thinking.github.io/papers/2601.21358/citation.bib", "csl_url": "https://latent-implicit-thinking.github.io/papers/2601.21358/citation.csl.json" }, { "id": "2601.21598", "title": "Beyond Imitation: Reinforcement Learning for Active Latent Planning", "authors": "Zhi Zheng, Wee Sun Lee", "first_public": "2026-01-29", "venue": "arXiv", "arxiv": "2601.21598", "paper_url": "https://arxiv.org/abs/2601.21598", "code_url": "https://github.com/zz1358m/ATP-Latent-master", "lens": [ "continuous" ], "representation": [ "continuous-state" ], "computation": [ "recurrent", "token-wise" ], "training": [ "reinforcement-learning" ], "questions": [ "capability", "controllability" ], "evidence": [ "empirical" ], "modality": [ "language" ], "summary": "Uses reinforcement learning to train an active latent-planning process rather than only imitating supervised reasoning traces.", "provenance": "Primary-source literature search, 2026-09-04", "author_names": [ "Zhi Zheng", "Wee Sun Lee" ], "record_url": "https://latent-implicit-thinking.github.io/papers/2601.21598/", "legacy_record_url": "https://latent-implicit-thinking.github.io/papers/#paper-2601-21598", "citation_date": "2026-01-29", "json_url": "https://latent-implicit-thinking.github.io/papers/2601.21598/record.json", "markdown_url": "https://latent-implicit-thinking.github.io/papers/2601.21598/index.md", "bibtex_url": "https://latent-implicit-thinking.github.io/papers/2601.21598/citation.bib", "csl_url": "https://latent-implicit-thinking.github.io/papers/2601.21598/citation.csl.json" }, { "id": "2602.00449", "title": "Do Latent-CoT Models Think Step-by-Step? A Mechanistic Study on Sequential Reasoning Tasks", "authors": "Jia Liang, Liangming Pan", "first_public": "2026-01-31", "venue": "arXiv", "arxiv": "2602.00449", "paper_url": "https://arxiv.org/abs/2602.00449", "code_url": "https://github.com/jialiang19/latent-cot-thinking", "lens": [ "analysis", "continuous" ], "representation": [ "continuous-state" ], "computation": [ "token-wise" ], "training": [ "analysis-only" ], "questions": [ "interpretability", "limitations", "evaluation" ], "evidence": [ "empirical", "mechanistic", "probing" ], "modality": [ "symbolic" ], "summary": "Uses mechanistic interventions on sequential tasks to test whether latent chain-of-thought models actually execute stepwise internal procedures.", "provenance": "Primary-source literature search, 2026-09-04", "author_names": [ "Jia Liang", "Liangming Pan" ], "record_url": "https://latent-implicit-thinking.github.io/papers/2602.00449/", "legacy_record_url": "https://latent-implicit-thinking.github.io/papers/#paper-2602-00449", "citation_date": "2026-01-31", "json_url": "https://latent-implicit-thinking.github.io/papers/2602.00449/record.json", "markdown_url": "https://latent-implicit-thinking.github.io/papers/2602.00449/index.md", "bibtex_url": "https://latent-implicit-thinking.github.io/papers/2602.00449/citation.bib", "csl_url": "https://latent-implicit-thinking.github.io/papers/2602.00449/citation.csl.json" }, { "id": "2602.01148", "title": "Capabilities and Fundamental Limits of Latent Chain-of-Thought", "authors": "Jiaxuan Zou, Yaozhong Xiong, Yong Liu", "first_public": "2026-02-01", "venue": "arXiv", "arxiv": "2602.01148", "paper_url": "https://arxiv.org/abs/2602.01148", "lens": [ "analysis", "continuous" ], "representation": [ "continuous-state" ], "computation": [ "recurrent", "parallel" ], "training": [ "analysis-only" ], "questions": [ "theory", "capability", "limitations" ], "evidence": [ "formal", "empirical", "boundary" ], "modality": [ "symbolic" ], "summary": "Characterizes tasks that latent chain-of-thought can express and identifies theoretical and empirical limits of the approach.", "provenance": "Primary-source literature search, 2026-09-04", "author_names": [ "Jiaxuan Zou", "Yaozhong Xiong", "Yong Liu" ], "record_url": "https://latent-implicit-thinking.github.io/papers/2602.01148/", "legacy_record_url": "https://latent-implicit-thinking.github.io/papers/#paper-2602-01148", "citation_date": "2026-02-01", "json_url": "https://latent-implicit-thinking.github.io/papers/2602.01148/record.json", "markdown_url": "https://latent-implicit-thinking.github.io/papers/2602.01148/index.md", "bibtex_url": "https://latent-implicit-thinking.github.io/papers/2602.01148/citation.bib", "csl_url": "https://latent-implicit-thinking.github.io/papers/2602.01148/citation.csl.json" }, { "id": "2602.08332", "title": "Latent Reasoning with Supervised Thinking States", "authors": "Ido Amos, Avi Caciularu, Mor Geva, Amir Globerson, Jonathan Herzig, Lior Shani, Idan Szpektor", "first_public": "2026-02-09", "venue": "arXiv", "arxiv": "2602.08332", "paper_url": "https://arxiv.org/abs/2602.08332", "lens": [ "learned-tokens", "continuous" ], "representation": [ "text-latent-hybrid", "continuous-state" ], "computation": [ "token-wise", "recurrent" ], "training": [ "supervised" ], "questions": [ "capability", "efficiency" ], "evidence": [ "empirical" ], "modality": [ "language" ], "summary": "Supervises intermediate thinking states that bridge textual reasoning traces and latent computation.", "provenance": "Primary-source literature search, 2026-09-04", "author_names": [ "Ido Amos", "Avi Caciularu", "Mor Geva", "Amir Globerson", "Jonathan Herzig", "Lior Shani", "Idan Szpektor" ], "record_url": "https://latent-implicit-thinking.github.io/papers/2602.08332/", "legacy_record_url": "https://latent-implicit-thinking.github.io/papers/#paper-2602-08332", "citation_date": "2026-02-09", "json_url": "https://latent-implicit-thinking.github.io/papers/2602.08332/record.json", "markdown_url": "https://latent-implicit-thinking.github.io/papers/2602.08332/index.md", "bibtex_url": "https://latent-implicit-thinking.github.io/papers/2602.08332/citation.bib", "csl_url": "https://latent-implicit-thinking.github.io/papers/2602.08332/citation.csl.json" }, { "id": "2602.11683", "title": "ThinkRouter: Efficient Reasoning via Routing Thinking between Latent and Discrete Spaces", "authors": "Xin Xu, Tong Yu, Xiang Chen, Haoliang Wang, Julian McAuley, Saayan Mitra", "first_public": "2026-02-12", "venue": "arXiv, work in progress", "arxiv": "2602.11683", "paper_url": "https://arxiv.org/abs/2602.11683", "lens": [ "continuous", "learned-tokens" ], "representation": [ "text-latent-hybrid", "continuous-state" ], "computation": [ "token-wise" ], "training": [ "inference-only" ], "questions": [ "capability", "efficiency", "controllability" ], "evidence": [ "empirical" ], "modality": [ "language" ], "summary": "Routes intermediate computation between latent and discrete representations to trade off reasoning quality, readability, and token cost.", "provenance": "Primary-source literature search, 2026-09-04", "author_names": [ "Xin Xu", "Tong Yu", "Xiang Chen", "Haoliang Wang", "Julian McAuley", "Saayan Mitra" ], "record_url": "https://latent-implicit-thinking.github.io/papers/2602.11683/", "legacy_record_url": "https://latent-implicit-thinking.github.io/papers/#paper-2602-11683", "citation_date": "2026-02-12", "json_url": "https://latent-implicit-thinking.github.io/papers/2602.11683/record.json", "markdown_url": "https://latent-implicit-thinking.github.io/papers/2602.11683/index.md", "bibtex_url": "https://latent-implicit-thinking.github.io/papers/2602.11683/citation.bib", "csl_url": "https://latent-implicit-thinking.github.io/papers/2602.11683/citation.csl.json" }, { "id": "2606.01243", "title": "Unlocking the Black Box of Latent Reasoning: An Interpretability-Guided Approach to Intervention", "authors": "Shuochen Chang, Tong Bai, Xiaofeng Zhang, Qianli Ma, Qingyang Liu, Zhaohe Liao, Yibo Miao, Li Niu", "first_public": "2026-05-31", "added_at": "2026-10-18", "venue": "ACL 2026", "arxiv": "2606.01243", "paper_url": "https://arxiv.org/abs/2606.01243", "lens": [ "analysis", "continuous" ], "representation": [ "continuous-state" ], "computation": [ "token-wise" ], "training": [ "supervised", "test-time-optimization" ], "questions": [ "interpretability", "controllability", "capability" ], "evidence": [ "probing", "mechanistic", "empirical" ], "modality": [ "language", "symbolic" ], "summary": "Uses probes, ablations, and targeted edits to study how continuous reasoning states affect answers. Builds interventions from learned semantic mappings and geometric constraints, improving the evaluated models by editing their latent states without updating base-model weights.", "provenance": "Awesome-Latent-CoT; primary sources checked 2026-10-18", "author_names": [ "Shuochen Chang", "Tong Bai", "Xiaofeng Zhang", "Qianli Ma", "Qingyang Liu", "Zhaohe Liao", "Yibo Miao", "Li Niu" ], "record_url": "https://latent-implicit-thinking.github.io/papers/2606.01243/", "legacy_record_url": "https://latent-implicit-thinking.github.io/papers/#paper-2606-01243", "citation_date": "2026-05-31", "json_url": "https://latent-implicit-thinking.github.io/papers/2606.01243/record.json", "markdown_url": "https://latent-implicit-thinking.github.io/papers/2606.01243/index.md", "bibtex_url": "https://latent-implicit-thinking.github.io/papers/2606.01243/citation.bib", "csl_url": "https://latent-implicit-thinking.github.io/papers/2606.01243/citation.csl.json" }, { "id": "2606.12689", "title": "Observable Patterns Are Not Explanations: A Causal-Geometric Analysis of Latent Reasoning Models", "authors": "Darpan Aswal, Thomas Palmeira Ferraz, Yongxin Zhou, Maxime Peyrard", "first_public": "2026-06-10", "added_at": "2026-10-18", "venue": "arXiv", "arxiv": "2606.12689", "paper_url": "https://arxiv.org/abs/2606.12689", "lens": [ "analysis", "continuous" ], "representation": [ "continuous-state" ], "computation": [ "token-wise" ], "training": [ "analysis-only" ], "questions": [ "interpretability", "evaluation", "limitations" ], "evidence": [ "empirical", "mechanistic", "probing", "boundary" ], "modality": [ "language", "symbolic" ], "summary": "Compares Coconut and CODI with controls lacking their proposed reasoning recurrence or curriculum. Finds that decodable patterns can also appear in controls, while interventions reveal varying causal use of latent states concentrated in low-dimensional directions.", "provenance": "Awesome-Latent-CoT; primary sources checked 2026-10-18", "author_names": [ "Darpan Aswal", "Thomas Palmeira Ferraz", "Yongxin Zhou", "Maxime Peyrard" ], "record_url": "https://latent-implicit-thinking.github.io/papers/2606.12689/", "legacy_record_url": "https://latent-implicit-thinking.github.io/papers/#paper-2606-12689", "citation_date": "2026-06-10", "json_url": "https://latent-implicit-thinking.github.io/papers/2606.12689/record.json", "markdown_url": "https://latent-implicit-thinking.github.io/papers/2606.12689/index.md", "bibtex_url": "https://latent-implicit-thinking.github.io/papers/2606.12689/citation.bib", "csl_url": "https://latent-implicit-thinking.github.io/papers/2606.12689/citation.csl.json" }, { "id": "2606.13106", "title": "Demystifying Hidden-State Recurrence: Switchable Latent Reasoning with On-Policy Reinforcement Learning", "authors": "Jiayu Yang, Chao Chen, Shengen Wu, Yinhong Liu, Yuxuan Fan, Lujundong Li, Songning Lai, Chengwei Qin, Zhijiang Guo", "first_public": "2026-06-11", "venue": "arXiv", "arxiv": "2606.13106", "paper_url": "https://arxiv.org/abs/2606.13106", "lens": [ "continuous", "recurrent", "analysis" ], "representation": [ "continuous-state", "text-latent-hybrid" ], "computation": [ "recurrent", "token-wise" ], "training": [ "reinforcement-learning", "curriculum" ], "questions": [ "capability", "interpretability", "controllability" ], "evidence": [ "empirical", "mechanistic" ], "modality": [ "language" ], "summary": "Trains switchable hidden-state recurrence with on-policy reinforcement learning and analyzes how the internal loop contributes to reasoning.", "provenance": "Primary-source literature search, 2026-09-04", "author_names": [ "Jiayu Yang", "Chao Chen", "Shengen Wu", "Yinhong Liu", "Yuxuan Fan", "Lujundong Li", "Songning Lai", "Chengwei Qin", "Zhijiang Guo" ], "record_url": "https://latent-implicit-thinking.github.io/papers/2606.13106/", "legacy_record_url": "https://latent-implicit-thinking.github.io/papers/#paper-2606-13106", "citation_date": "2026-06-11", "json_url": "https://latent-implicit-thinking.github.io/papers/2606.13106/record.json", "markdown_url": "https://latent-implicit-thinking.github.io/papers/2606.13106/index.md", "bibtex_url": "https://latent-implicit-thinking.github.io/papers/2606.13106/citation.bib", "csl_url": "https://latent-implicit-thinking.github.io/papers/2606.13106/citation.csl.json" }, { "id": "2606.20075", "title": "What Makes Effective Supervision in Latent Chain-of-Thought: An Information-Theoretic Analysis", "authors": "Xinghao Chen, Chak Tou Leong, Wenjin Guo, Jian Wang, Wenjie Li, Xiaoyu Shen", "first_public": "2026-06-18", "venue": "arXiv", "arxiv": "2606.20075", "paper_url": "https://arxiv.org/abs/2606.20075", "code_url": "https://github.com/EIT-NLP/Supervision-in-Latent-CoT", "lens": [ "analysis", "continuous" ], "representation": [ "continuous-state" ], "computation": [ "token-wise" ], "training": [ "analysis-only" ], "questions": [ "theory", "interpretability", "limitations" ], "evidence": [ "formal", "empirical" ], "modality": [ "language", "symbolic" ], "summary": "Uses information-theoretic analysis to study which supervision signals make latent chain-of-thought training effective.", "provenance": "Primary-source literature search, 2026-09-04", "author_names": [ "Xinghao Chen", "Chak Tou Leong", "Wenjin Guo", "Jian Wang", "Wenjie Li", "Xiaoyu Shen" ], "record_url": "https://latent-implicit-thinking.github.io/papers/2606.20075/", "legacy_record_url": "https://latent-implicit-thinking.github.io/papers/#paper-2606-20075", "citation_date": "2026-06-18", "json_url": "https://latent-implicit-thinking.github.io/papers/2606.20075/record.json", "markdown_url": "https://latent-implicit-thinking.github.io/papers/2606.20075/index.md", "bibtex_url": "https://latent-implicit-thinking.github.io/papers/2606.20075/citation.bib", "csl_url": "https://latent-implicit-thinking.github.io/papers/2606.20075/citation.csl.json" }, { "id": "2607.00341", "title": "DiscoLoop: Looping Discrete Embeddings and Continuous Hidden States for Multi-hop Reasoning", "authors": "Hengyu Fu, Tianyu Guo, Zixuan Wang, Hanlin Zhu, Jason D. Lee, Jiantao Jiao, Stuart Russell, Song Mei", "first_public": "2026-07-01", "venue": "arXiv", "arxiv": "2607.00341", "paper_url": "https://arxiv.org/abs/2607.00341", "lens": [ "recurrent", "continuous" ], "representation": [ "discrete-token", "continuous-state" ], "computation": [ "recurrent", "depth-wise" ], "training": [ "pretraining", "supervised" ], "questions": [ "architecture", "capability", "generalization" ], "evidence": [ "empirical", "mechanistic", "probing" ], "modality": [ "language", "symbolic" ], "summary": "Feeds both token embeddings and hidden states back through repeated transformer layers to connect successive steps of multi-hop reasoning.", "provenance": "Primary-source literature search, 2026-09-05", "author_names": [ "Hengyu Fu", "Tianyu Guo", "Zixuan Wang", "Hanlin Zhu", "Jason D. Lee", "Jiantao Jiao", "Stuart Russell", "Song Mei" ], "record_url": "https://latent-implicit-thinking.github.io/papers/2607.00341/", "legacy_record_url": "https://latent-implicit-thinking.github.io/papers/#paper-2607-00341", "citation_date": "2026-07-01", "json_url": "https://latent-implicit-thinking.github.io/papers/2607.00341/record.json", "markdown_url": "https://latent-implicit-thinking.github.io/papers/2607.00341/index.md", "bibtex_url": "https://latent-implicit-thinking.github.io/papers/2607.00341/citation.bib", "csl_url": "https://latent-implicit-thinking.github.io/papers/2607.00341/citation.csl.json" }, { "id": "2608.13570", "title": "Think in Latent, Explain in Language: Self-Explainable Latent Reasoning", "authors": "Dayuan Zhao, Shengcao Cao, Yu-Xiong Wang, Liang-Yan Gui", "first_public": "2026-07-01", "venue": "arXiv", "arxiv": "2608.13570", "paper_url": "https://arxiv.org/abs/2608.13570", "code_url": "https://jasondayuan.github.io/SELR/", "lens": [ "continuous", "analysis" ], "representation": [ "continuous-state", "text-latent-hybrid" ], "computation": [ "token-wise" ], "training": [ "supervised" ], "questions": [ "capability", "efficiency", "interpretability" ], "evidence": [ "empirical" ], "modality": [ "language", "multimodal", "vision" ], "summary": "Jointly trains latent states for answer prediction and for decoding back into readable reasoning, using one model rather than a separate post-hoc explanation decoder.", "provenance": "Primary-source literature search, 2026-09-05", "author_names": [ "Dayuan Zhao", "Shengcao Cao", "Yu-Xiong Wang", "Liang-Yan Gui" ], "record_url": "https://latent-implicit-thinking.github.io/papers/2608.13570/", "legacy_record_url": "https://latent-implicit-thinking.github.io/papers/#paper-2608-13570", "citation_date": "2026-07-01", "json_url": "https://latent-implicit-thinking.github.io/papers/2608.13570/record.json", "markdown_url": "https://latent-implicit-thinking.github.io/papers/2608.13570/index.md", "bibtex_url": "https://latent-implicit-thinking.github.io/papers/2608.13570/citation.bib", "csl_url": "https://latent-implicit-thinking.github.io/papers/2608.13570/citation.csl.json" }, { "id": "2607.06648", "title": "Final Checkpoints Are Not Enough: Analyzing Latent Reasoning Faithfulness Along Training Trajectories", "authors": "Hengyu Jin, Shu Yang, Di Wang", "first_public": "2026-07-07", "venue": "arXiv", "arxiv": "2607.06648", "paper_url": "https://arxiv.org/abs/2607.06648", "lens": [ "analysis", "continuous" ], "representation": [ "continuous-state" ], "computation": [ "token-wise" ], "training": [ "analysis-only" ], "questions": [ "interpretability", "evaluation", "limitations" ], "evidence": [ "empirical", "mechanistic", "boundary" ], "modality": [ "language", "symbolic" ], "summary": "Tests how answers respond to changed inputs and altered hidden reasoning states throughout training. These responses can change even while accuracy remains high.", "provenance": "Primary-source literature search, 2026-09-05", "author_names": [ "Hengyu Jin", "Shu Yang", "Di Wang" ], "record_url": "https://latent-implicit-thinking.github.io/papers/2607.06648/", "legacy_record_url": "https://latent-implicit-thinking.github.io/papers/#paper-2607-06648", "citation_date": "2026-07-07", "json_url": "https://latent-implicit-thinking.github.io/papers/2607.06648/record.json", "markdown_url": "https://latent-implicit-thinking.github.io/papers/2607.06648/index.md", "bibtex_url": "https://latent-implicit-thinking.github.io/papers/2607.06648/citation.bib", "csl_url": "https://latent-implicit-thinking.github.io/papers/2607.06648/citation.csl.json" }, { "id": "2607.16972", "title": "Training Continuous Chain of Thought Models: A Tale of Two Regimes", "authors": "Varun Yerram, He He, Eunsol Choi", "first_public": "2026-07-18", "venue": "arXiv", "arxiv": "2607.16972", "paper_url": "https://arxiv.org/abs/2607.16972", "code_url": "https://github.com/Varun221/cmtp_research", "lens": [ "continuous", "analysis" ], "representation": [ "continuous-state" ], "computation": [ "token-wise" ], "training": [ "supervised" ], "questions": [ "capability", "efficiency", "evaluation", "limitations" ], "evidence": [ "empirical", "boundary" ], "modality": [ "language", "symbolic" ], "summary": "Introduces direct latent supervision through averaged trace embeddings and finds that both direct and indirect continuous-CoT training degrade sharply on tasks with substantially longer reasoning traces.", "provenance": "Primary-source literature search, 2026-09-05", "author_names": [ "Varun Yerram", "He He", "Eunsol Choi" ], "record_url": "https://latent-implicit-thinking.github.io/papers/2607.16972/", "legacy_record_url": "https://latent-implicit-thinking.github.io/papers/#paper-2607-16972", "citation_date": "2026-07-18", "json_url": "https://latent-implicit-thinking.github.io/papers/2607.16972/record.json", "markdown_url": "https://latent-implicit-thinking.github.io/papers/2607.16972/index.md", "bibtex_url": "https://latent-implicit-thinking.github.io/papers/2607.16972/citation.bib", "csl_url": "https://latent-implicit-thinking.github.io/papers/2607.16972/citation.csl.json" }, { "id": "2608.02585", "title": "GradCuit: Credit-Assigned Gradient Flow Enables Robust and Interpretable Test-Time Latent Reasoning", "authors": "Zhaoxin Yu, Qi Shen, Hengli Li, Zhaowei Zhang, Song-Chun Zhu, Chi Zhang, Zilong Zheng", "first_public": "2026-08-03", "venue": "arXiv", "arxiv": "2608.02585", "paper_url": "https://arxiv.org/abs/2608.02585", "lens": [ "continuous" ], "representation": [ "continuous-state" ], "computation": [ "token-wise" ], "training": [ "test-time-optimization" ], "questions": [ "capability", "scaling", "efficiency", "interpretability" ], "evidence": [ "empirical", "probing" ], "modality": [ "language", "symbolic" ], "summary": "At test time, adjusts extra hidden states inside the transformer using feedback on generated answers, while keeping the model’s weights fixed.", "provenance": "Primary-source literature search, 2026-09-05", "author_names": [ "Zhaoxin Yu", "Qi Shen", "Hengli Li", "Zhaowei Zhang", "Song-Chun Zhu", "Chi Zhang", "Zilong Zheng" ], "record_url": "https://latent-implicit-thinking.github.io/papers/2608.02585/", "legacy_record_url": "https://latent-implicit-thinking.github.io/papers/#paper-2608-02585", "citation_date": "2026-08-03", "json_url": "https://latent-implicit-thinking.github.io/papers/2608.02585/record.json", "markdown_url": "https://latent-implicit-thinking.github.io/papers/2608.02585/index.md", "bibtex_url": "https://latent-implicit-thinking.github.io/papers/2608.02585/citation.bib", "csl_url": "https://latent-implicit-thinking.github.io/papers/2608.02585/citation.csl.json" }, { "id": "2608.04928", "title": "Does Out-of-Sight Equal Out-of-Mind in CoT Monitorability?", "authors": "Pedro Ferreira, Wilker Aziz, Ivan Titov", "first_public": "2026-08-05", "venue": "arXiv", "arxiv": "2608.04928", "paper_url": "https://arxiv.org/abs/2608.04928", "lens": [ "analysis", "continuous" ], "representation": [ "continuous-state", "text-latent-hybrid" ], "computation": [ "token-wise" ], "training": [ "analysis-only" ], "questions": [ "evaluation", "interpretability", "limitations" ], "evidence": [ "empirical", "probing" ], "modality": [ "language", "symbolic" ], "summary": "Compares monitoring from explicit traces, latent-state probes, and latent verbalizations, finding that monitorability depends strongly on task structure and level of internal access.", "provenance": "Primary-source literature search, 2026-09-05", "author_names": [ "Pedro Ferreira", "Wilker Aziz", "Ivan Titov" ], "record_url": "https://latent-implicit-thinking.github.io/papers/2608.04928/", "legacy_record_url": "https://latent-implicit-thinking.github.io/papers/#paper-2608-04928", "citation_date": "2026-08-05", "json_url": "https://latent-implicit-thinking.github.io/papers/2608.04928/record.json", "markdown_url": "https://latent-implicit-thinking.github.io/papers/2608.04928/index.md", "bibtex_url": "https://latent-implicit-thinking.github.io/papers/2608.04928/citation.bib", "csl_url": "https://latent-implicit-thinking.github.io/papers/2608.04928/citation.csl.json" }, { "id": "2609.01117", "title": "Latent Recurrent Thoughts: Recurrent Refinement of Proposed Latents for Reasoning with Frozen LLMs", "authors": "Zhaoliang Chen, Jie Fu", "first_public": "2026-09-01", "venue": "arXiv", "arxiv": "2609.01117", "paper_url": "https://arxiv.org/abs/2609.01117", "lens": [ "continuous", "recurrent" ], "representation": [ "continuous-state" ], "computation": [ "recurrent" ], "training": [ "supervised" ], "questions": [ "architecture", "capability", "efficiency" ], "evidence": [ "empirical" ], "modality": [ "language", "symbolic" ], "summary": "Uses a task-specific proposer and small recurrent auxiliary network to iteratively refine continuous thoughts that a frozen language model then decodes.", "provenance": "Primary-source literature search, 2026-09-05", "author_names": [ "Zhaoliang Chen", "Jie Fu" ], "record_url": "https://latent-implicit-thinking.github.io/papers/2609.01117/", "legacy_record_url": "https://latent-implicit-thinking.github.io/papers/#paper-2609-01117", "citation_date": "2026-09-01", "json_url": "https://latent-implicit-thinking.github.io/papers/2609.01117/record.json", "markdown_url": "https://latent-implicit-thinking.github.io/papers/2609.01117/index.md", "bibtex_url": "https://latent-implicit-thinking.github.io/papers/2609.01117/citation.bib", "csl_url": "https://latent-implicit-thinking.github.io/papers/2609.01117/citation.csl.json" }, { "id": "2609.09928", "title": "Structural Process Supervision for Latent Chain-of-Thought Reasoning", "authors": "Yiqi Li, Xu Chen, Chen Ju, Jiangchao Yao, Zhaoyang Li, Jinsong Lan, Xiaoyong Zhu, Yu Wang", "first_public": "2026-09-09", "added_at": "2026-10-18", "venue": "arXiv", "arxiv": "2609.09928", "paper_url": "https://arxiv.org/abs/2609.09928", "lens": [ "continuous" ], "representation": [ "continuous-state" ], "computation": [ "token-wise" ], "training": [ "supervised", "distillation", "curriculum" ], "questions": [ "capability", "efficiency" ], "evidence": [ "empirical" ], "modality": [ "language", "symbolic" ], "summary": "Aligns continuous thoughts with written reasoning through shared, learnable prototypes, allowing several text steps to supervise several latent states. A positional alignment constraint is gradually relaxed during training.", "provenance": "Primary-source literature search, 2026-10-18", "author_names": [ "Yiqi Li", "Xu Chen", "Chen Ju", "Jiangchao Yao", "Zhaoyang Li", "Jinsong Lan", "Xiaoyong Zhu", "Yu Wang" ], "record_url": "https://latent-implicit-thinking.github.io/papers/2609.09928/", "legacy_record_url": "https://latent-implicit-thinking.github.io/papers/#paper-2609-09928", "citation_date": "2026-09-09", "json_url": "https://latent-implicit-thinking.github.io/papers/2609.09928/record.json", "markdown_url": "https://latent-implicit-thinking.github.io/papers/2609.09928/index.md", "bibtex_url": "https://latent-implicit-thinking.github.io/papers/2609.09928/citation.bib", "csl_url": "https://latent-implicit-thinking.github.io/papers/2609.09928/citation.csl.json" }, { "id": "2609.16372", "title": "Register Tokens for Bounded-State Reasoning in Diffusion Language Models", "authors": "Albert Ge, Chandan Singh, Yufan Zhuang, Xiaodong Liu, Jianfeng Gao, Frederic Sala", "first_public": "2026-09-14", "venue": "arXiv", "arxiv": "2609.16372", "paper_url": "https://arxiv.org/abs/2609.16372", "lens": [ "continuous", "beyond-sequential" ], "representation": [ "continuous-state", "discrete-token" ], "computation": [ "diffusion", "stateful" ], "training": [ "supervised", "reinforcement-learning" ], "questions": [ "architecture", "capability", "efficiency" ], "evidence": [ "empirical" ], "modality": [ "language", "symbolic" ], "summary": "Trains a diffusion language model to carry a fixed number of continuous register states between generated chunks. The model still writes text within each chunk, but clears earlier text and continues from the prompt and retained registers.", "provenance": "Primary-source literature search, 2026-10-18", "added_at": "2026-10-18", "author_names": [ "Albert Ge", "Chandan Singh", "Yufan Zhuang", "Xiaodong Liu", "Jianfeng Gao", "Frederic Sala" ], "record_url": "https://latent-implicit-thinking.github.io/papers/2609.16372/", "legacy_record_url": "https://latent-implicit-thinking.github.io/papers/#paper-2609-16372", "citation_date": "2026-09-14", "json_url": "https://latent-implicit-thinking.github.io/papers/2609.16372/record.json", "markdown_url": "https://latent-implicit-thinking.github.io/papers/2609.16372/index.md", "bibtex_url": "https://latent-implicit-thinking.github.io/papers/2609.16372/citation.bib", "csl_url": "https://latent-implicit-thinking.github.io/papers/2609.16372/citation.csl.json" }, { "id": "2609.19717", "title": "Learn Your Own Thoughts: Abstract Token Curriculum", "authors": "Khashayar Gatmiry, Avrajit Ghosh, Parsa Mirtaheri, Jason D. Lee, Nika Haghtalab, Emmanuel Abbe, Peter Bartlett", "first_public": "2026-09-17", "added_at": "2026-10-18", "venue": "arXiv", "arxiv": "2609.19717", "paper_url": "https://arxiv.org/abs/2609.19717", "lens": [ "continuous" ], "representation": [ "continuous-state" ], "computation": [ "token-wise" ], "training": [ "curriculum" ], "questions": [ "capability", "theory", "generalization" ], "evidence": [ "empirical", "formal" ], "modality": [ "symbolic" ], "summary": "Trains on progressively harder problems so that a model learns continuous intermediate thoughts without explicit reasoning-step supervision. Analyzes parity learning theoretically and tests graph reachability and arithmetic.", "provenance": "Primary-source literature search, 2026-10-18", "author_names": [ "Khashayar Gatmiry", "Avrajit Ghosh", "Parsa Mirtaheri", "Jason D. Lee", "Nika Haghtalab", "Emmanuel Abbe", "Peter Bartlett" ], "record_url": "https://latent-implicit-thinking.github.io/papers/2609.19717/", "legacy_record_url": "https://latent-implicit-thinking.github.io/papers/#paper-2609-19717", "citation_date": "2026-09-17", "json_url": "https://latent-implicit-thinking.github.io/papers/2609.19717/record.json", "markdown_url": "https://latent-implicit-thinking.github.io/papers/2609.19717/index.md", "bibtex_url": "https://latent-implicit-thinking.github.io/papers/2609.19717/citation.bib", "csl_url": "https://latent-implicit-thinking.github.io/papers/2609.19717/citation.csl.json" }, { "id": "2609.19878", "title": "Uni-LaDiR: Latent Diffusion Unifies Multimodal Reasoning", "authors": "Haoqiang Kang, Yizhe Zhang, Nikki Lijing Kuang, Jiatao Gu, Yian Ma, Lianhui Qin", "first_public": "2026-09-17", "venue": "arXiv", "arxiv": "2609.19878", "paper_url": "https://arxiv.org/abs/2609.19878", "lens": [ "continuous", "beyond-sequential" ], "representation": [ "multimodal-latent", "continuous-state" ], "computation": [ "diffusion" ], "training": [ "supervised", "distillation" ], "questions": [ "architecture", "capability", "generalization" ], "evidence": [ "empirical" ], "modality": [ "multimodal", "vision", "language" ], "summary": "Encodes teacher reasoning steps from text, images, point clouds, and robot states into a shared continuous representation. A jointly trained diffusion reasoner generates these states at inference, evaluated on visual question answering and robotic manipulation.", "provenance": "Primary-source literature search, 2026-10-18", "added_at": "2026-10-18", "author_names": [ "Haoqiang Kang", "Yizhe Zhang", "Nikki Lijing Kuang", "Jiatao Gu", "Yian Ma", "Lianhui Qin" ], "record_url": "https://latent-implicit-thinking.github.io/papers/2609.19878/", "legacy_record_url": "https://latent-implicit-thinking.github.io/papers/#paper-2609-19878", "citation_date": "2026-09-17", "json_url": "https://latent-implicit-thinking.github.io/papers/2609.19878/record.json", "markdown_url": "https://latent-implicit-thinking.github.io/papers/2609.19878/index.md", "bibtex_url": "https://latent-implicit-thinking.github.io/papers/2609.19878/citation.bib", "csl_url": "https://latent-implicit-thinking.github.io/papers/2609.19878/citation.csl.json" }, { "id": "2609.20398", "title": "Schema-Anchored Latent Reasoning for Semantic Parsing-Based Knowledge Base Question Answering", "authors": "Guangze Gao, Zixuan Li, Sikui Zhang, Chunfeng Yuan, Wenjuan Li, Bing Li, Xiaolong Jin, Weiming Hu", "first_public": "2026-09-17", "added_at": "2026-10-18", "venue": "arXiv", "arxiv": "2609.20398", "paper_url": "https://arxiv.org/abs/2609.20398", "lens": [ "continuous" ], "representation": [ "continuous-state" ], "computation": [ "token-wise" ], "training": [ "supervised" ], "questions": [ "capability", "interpretability" ], "evidence": [ "empirical", "probing" ], "modality": [ "language", "symbolic" ], "summary": "Aligns continuous thoughts with a codebook of knowledge-base relations and classes, using schema traces derived from correct logical forms. Feeds the aligned codes into later reasoning steps before generating an executable query.", "provenance": "Primary-source literature search, 2026-10-18", "author_names": [ "Guangze Gao", "Zixuan Li", "Sikui Zhang", "Chunfeng Yuan", "Wenjuan Li", "Bing Li", "Xiaolong Jin", "Weiming Hu" ], "record_url": "https://latent-implicit-thinking.github.io/papers/2609.20398/", "legacy_record_url": "https://latent-implicit-thinking.github.io/papers/#paper-2609-20398", "citation_date": "2026-09-17", "json_url": "https://latent-implicit-thinking.github.io/papers/2609.20398/record.json", "markdown_url": "https://latent-implicit-thinking.github.io/papers/2609.20398/index.md", "bibtex_url": "https://latent-implicit-thinking.github.io/papers/2609.20398/citation.bib", "csl_url": "https://latent-implicit-thinking.github.io/papers/2609.20398/citation.csl.json" }, { "id": "2609.20981", "title": "CaLR: Causal Latent Revision for Robust Diffusion Reasoning", "authors": "Wei Cai, Jian Zhao, Yuchen Yuan, Xuelong Li", "first_public": "2026-09-17", "lens": [ "continuous", "beyond-sequential" ], "representation": [ "continuous-state" ], "training": [ "test-time-optimization" ], "questions": [ "capability", "controllability" ], "evidence": [ "empirical" ], "modality": [ "language", "symbolic" ], "summary": "Uses an expert-derived causal dependency matrix to guide gradient-based revision of diffusion reasoning states. The aim is to preserve logical dependencies while revising several intermediate positions during parallel generation.", "computation": [ "diffusion", "parallel" ], "venue": "arXiv", "arxiv": "2609.20981", "paper_url": "https://arxiv.org/abs/2609.20981", "provenance": "Primary-source literature search, 2026-10-18", "added_at": "2026-10-18", "author_names": [ "Wei Cai", "Jian Zhao", "Yuchen Yuan", "Xuelong Li" ], "record_url": "https://latent-implicit-thinking.github.io/papers/2609.20981/", "legacy_record_url": "https://latent-implicit-thinking.github.io/papers/#paper-2609-20981", "citation_date": "2026-09-17", "json_url": "https://latent-implicit-thinking.github.io/papers/2609.20981/record.json", "markdown_url": "https://latent-implicit-thinking.github.io/papers/2609.20981/index.md", "bibtex_url": "https://latent-implicit-thinking.github.io/papers/2609.20981/citation.bib", "csl_url": "https://latent-implicit-thinking.github.io/papers/2609.20981/citation.csl.json" }, { "id": "2609.21662", "title": "When Steering Fails in Latent Reasoning: A Latent-to-Language Transition Gap", "authors": "Gaoxiang Huang, Lei Qi", "first_public": "2026-09-18", "added_at": "2026-10-18", "venue": "arXiv", "arxiv": "2609.21662", "paper_url": "https://arxiv.org/abs/2609.21662", "summary": "Tests whether changing continuous reasoning states changes the model's later text output. Finds weaker steering effects than in explicit chain-of-thought, despite comparable changes to the hidden representations.", "lens": [ "analysis", "continuous" ], "representation": [ "continuous-state", "text-latent-hybrid" ], "computation": [ "token-wise" ], "training": [ "analysis-only" ], "questions": [ "controllability", "interpretability", "limitations" ], "evidence": [ "empirical", "probing", "boundary" ], "modality": [ "language" ], "provenance": "Primary-source literature search, 2026-10-18", "author_names": [ "Gaoxiang Huang", "Lei Qi" ], "record_url": "https://latent-implicit-thinking.github.io/papers/2609.21662/", "legacy_record_url": "https://latent-implicit-thinking.github.io/papers/#paper-2609-21662", "citation_date": "2026-09-18", "json_url": "https://latent-implicit-thinking.github.io/papers/2609.21662/record.json", "markdown_url": "https://latent-implicit-thinking.github.io/papers/2609.21662/index.md", "bibtex_url": "https://latent-implicit-thinking.github.io/papers/2609.21662/citation.bib", "csl_url": "https://latent-implicit-thinking.github.io/papers/2609.21662/citation.csl.json" }, { "id": "2609.29102", "title": "ELF-REG: Scaling Continuous Diffusion Language Models to Reasoning Tasks", "authors": "Zeyu Michael Li, William Xingxu Chen, Bingshuo Qian, Jiayin Liu, Xiang Cheng", "first_public": "2026-09-24", "lens": [ "continuous", "beyond-sequential" ], "representation": [ "continuous-state" ], "training": [ "supervised", "distillation" ], "questions": [ "capability", "efficiency" ], "evidence": [ "empirical" ], "modality": [ "language", "symbolic" ], "summary": "Adapts continuous diffusion to mathematics and code using intermediate-feature supervision and a global representation from a frozen autoregressive teacher. It denoises continuous response representations and decodes all response tokens only at the end.", "computation": [ "diffusion", "parallel" ], "venue": "arXiv", "arxiv": "2609.29102", "paper_url": "https://arxiv.org/abs/2609.29102", "provenance": "Primary-source literature search, 2026-10-18", "added_at": "2026-10-18", "author_names": [ "Zeyu Michael Li", "William Xingxu Chen", "Bingshuo Qian", "Jiayin Liu", "Xiang Cheng" ], "record_url": "https://latent-implicit-thinking.github.io/papers/2609.29102/", "legacy_record_url": "https://latent-implicit-thinking.github.io/papers/#paper-2609-29102", "citation_date": "2026-09-24", "json_url": "https://latent-implicit-thinking.github.io/papers/2609.29102/record.json", "markdown_url": "https://latent-implicit-thinking.github.io/papers/2609.29102/index.md", "bibtex_url": "https://latent-implicit-thinking.github.io/papers/2609.29102/citation.bib", "csl_url": "https://latent-implicit-thinking.github.io/papers/2609.29102/citation.csl.json" }, { "id": "2610.08820", "title": "Task-Oriented Key-Layer KV Communication for Efficient Latent Multi-Agent Collaboration", "authors": "Dongsen Zhang, Peipei Li, Zekun Li, Wenjun Xu", "first_public": "2026-09-24", "added_at": "2026-10-18", "venue": "arXiv", "arxiv": "2610.08820", "paper_url": "https://arxiv.org/abs/2610.08820", "lens": [ "continuous" ], "representation": [ "cache-memory", "hidden-state" ], "computation": [ "token-wise", "stateful" ], "training": [ "inference-only" ], "questions": [ "capability", "efficiency", "architecture" ], "evidence": [ "empirical" ], "modality": [ "language", "symbolic" ], "summary": "Lets agents perform hidden-state reasoning and pass working memory through the key-value cache of one selected transformer layer. Reuses that layer as the entry point for subsequent reasoning updates, avoiding earlier layers and decoding only the final agent's response into text.", "provenance": "Primary-source literature search, 2026-10-18", "author_names": [ "Dongsen Zhang", "Peipei Li", "Zekun Li", "Wenjun Xu" ], "record_url": "https://latent-implicit-thinking.github.io/papers/2610.08820/", "legacy_record_url": "https://latent-implicit-thinking.github.io/papers/#paper-2610-08820", "citation_date": "2026-09-24", "json_url": "https://latent-implicit-thinking.github.io/papers/2610.08820/record.json", "markdown_url": "https://latent-implicit-thinking.github.io/papers/2610.08820/index.md", "bibtex_url": "https://latent-implicit-thinking.github.io/papers/2610.08820/citation.bib", "csl_url": "https://latent-implicit-thinking.github.io/papers/2610.08820/citation.csl.json" }, { "id": "2609.33271", "title": "Next Thoughts Are Distributions: Generative Autoregressive Reasoning in the Latent Space", "authors": "Yang Li, Yi Wang, Shiyuan Huang, Yang Liu, Hao Wang, Chengzhi Mao", "first_public": "2026-09-27", "added_at": "2026-10-18", "venue": "arXiv", "arxiv": "2609.33271", "paper_url": "https://arxiv.org/abs/2609.33271", "lens": [ "continuous", "beyond-sequential" ], "representation": [ "continuous-state" ], "computation": [ "token-wise", "diffusion" ], "training": [ "supervised", "reinforcement-learning" ], "questions": [ "capability", "efficiency", "scaling" ], "evidence": [ "empirical" ], "modality": [ "language", "symbolic" ], "summary": "Adds a diffusion head that samples the next continuous thought from a distribution rather than predicting one fixed vector. Feeds each sampled thought back into an autoregressive reasoner, supporting variable-length latent traces and multiple reasoning paths.", "provenance": "Primary-source literature search, 2026-10-18", "author_names": [ "Yang Li", "Yi Wang", "Shiyuan Huang", "Yang Liu", "Hao Wang", "Chengzhi Mao" ], "record_url": "https://latent-implicit-thinking.github.io/papers/2609.33271/", "legacy_record_url": "https://latent-implicit-thinking.github.io/papers/#paper-2609-33271", "citation_date": "2026-09-27", "json_url": "https://latent-implicit-thinking.github.io/papers/2609.33271/record.json", "markdown_url": "https://latent-implicit-thinking.github.io/papers/2609.33271/index.md", "bibtex_url": "https://latent-implicit-thinking.github.io/papers/2609.33271/citation.bib", "csl_url": "https://latent-implicit-thinking.github.io/papers/2609.33271/citation.csl.json" }, { "id": "2609.33402", "title": "VaME: Exploring Variational Latent Reasoning for Multimodal Embeddings", "authors": "Peixi Wu, Mingzhou Jiang, Feipeng Ma, Biao Yang, Yunhao Zhou, Wei Yuan, Bosong Chai, Huizu Lin, Jie Chen, Zhangchi Hu, Fan Yang, Wenwu Ou, Hebei Li, Xiaoyan Sun", "first_public": "2026-09-27", "venue": "arXiv", "arxiv": "2609.33402", "paper_url": "https://arxiv.org/abs/2609.33402", "lens": [ "continuous" ], "representation": [ "multimodal-latent", "continuous-state" ], "computation": [ "token-wise", "search" ], "training": [ "supervised", "reinforcement-learning" ], "questions": [ "capability", "efficiency" ], "evidence": [ "empirical" ], "modality": [ "multimodal", "vision" ], "summary": "Learns a distribution over continuous reasoning trajectories before producing a retrieval embedding. A lightweight decoder provides reconstruction supervision and a semantic reward for reinforcement learning, evaluated on image, video, and visual-document retrieval.", "provenance": "Primary-source literature search, 2026-10-18", "added_at": "2026-10-18", "author_names": [ "Peixi Wu", "Mingzhou Jiang", "Feipeng Ma", "Biao Yang", "Yunhao Zhou", "Wei Yuan", "Bosong Chai", "Huizu Lin", "Jie Chen", "Zhangchi Hu", "Fan Yang", "Wenwu Ou", "Hebei Li", "Xiaoyan Sun" ], "record_url": "https://latent-implicit-thinking.github.io/papers/2609.33402/", "legacy_record_url": "https://latent-implicit-thinking.github.io/papers/#paper-2609-33402", "citation_date": "2026-09-27", "json_url": "https://latent-implicit-thinking.github.io/papers/2609.33402/record.json", "markdown_url": "https://latent-implicit-thinking.github.io/papers/2609.33402/index.md", "bibtex_url": "https://latent-implicit-thinking.github.io/papers/2609.33402/citation.bib", "csl_url": "https://latent-implicit-thinking.github.io/papers/2609.33402/citation.csl.json" }, { "id": "2609.35694", "title": "Reasoning with Continuous Latent Diffusion", "authors": "Xiang Cheng", "first_public": "2026-09-28", "lens": [ "continuous", "beyond-sequential" ], "representation": [ "continuous-state" ], "training": [ "supervised", "distillation", "reinforcement-learning", "curriculum" ], "questions": [ "capability", "efficiency" ], "evidence": [ "empirical" ], "modality": [ "language", "symbolic" ], "summary": "CEDR learns compact representations from several teacher layers, denoises them at different rates, and trains a smaller prompt encoder to replace the teacher at inference. It generates complete mathematical and coding solutions through continuous refinement before text decoding.", "computation": [ "diffusion", "parallel" ], "venue": "arXiv", "arxiv": "2609.35694", "paper_url": "https://arxiv.org/abs/2609.35694", "provenance": "Primary-source literature search, 2026-10-18", "added_at": "2026-10-18", "author_names": [ "Xiang Cheng" ], "record_url": "https://latent-implicit-thinking.github.io/papers/2609.35694/", "legacy_record_url": "https://latent-implicit-thinking.github.io/papers/#paper-2609-35694", "citation_date": "2026-09-28", "json_url": "https://latent-implicit-thinking.github.io/papers/2609.35694/record.json", "markdown_url": "https://latent-implicit-thinking.github.io/papers/2609.35694/index.md", "bibtex_url": "https://latent-implicit-thinking.github.io/papers/2609.35694/citation.bib", "csl_url": "https://latent-implicit-thinking.github.io/papers/2609.35694/citation.csl.json" }, { "id": "2609.37351", "title": "Port-Hamiltonian Latent Deliberation: Mitigating the Deliberation Drift Cliff in Test-Time Compute Scaling", "authors": "Zeyu Jia", "first_public": "2026-09-29", "added_at": "2026-10-18", "venue": "arXiv", "arxiv": "2609.37351", "paper_url": "https://arxiv.org/abs/2609.37351", "summary": "Separates hidden-state updates into attracting and rotating components to reduce errors from running a recurrent reasoner for more steps. Tests a tensor-based implementation on logical and multi-hop reasoning tasks.", "lens": [ "recurrent", "continuous" ], "representation": [ "continuous-state" ], "computation": [ "recurrent" ], "training": [ "supervised" ], "questions": [ "theory", "generalization", "limitations", "efficiency" ], "evidence": [ "formal", "empirical" ], "modality": [ "language", "symbolic" ], "provenance": "Primary-source literature search, 2026-10-18", "author_names": [ "Zeyu Jia" ], "record_url": "https://latent-implicit-thinking.github.io/papers/2609.37351/", "legacy_record_url": "https://latent-implicit-thinking.github.io/papers/#paper-2609-37351", "citation_date": "2026-09-29", "json_url": "https://latent-implicit-thinking.github.io/papers/2609.37351/record.json", "markdown_url": "https://latent-implicit-thinking.github.io/papers/2609.37351/index.md", "bibtex_url": "https://latent-implicit-thinking.github.io/papers/2609.37351/citation.bib", "csl_url": "https://latent-implicit-thinking.github.io/papers/2609.37351/citation.csl.json" }, { "id": "2609.39740", "title": "LatentHarness: Learning Latent Actions for Memory and Reasoning via Counterfactual Policy Distillation", "authors": "Xiaoqiang Wang, Suyuchen Wang, Bang Liu", "first_public": "2026-09-30", "added_at": "2026-10-18", "venue": "arXiv", "arxiv": "2609.39740", "paper_url": "https://arxiv.org/abs/2609.39740", "lens": [ "continuous", "recurrent" ], "representation": [ "continuous-state", "cache-memory" ], "computation": [ "stateful", "recurrent" ], "training": [ "distillation" ], "questions": [ "capability", "efficiency", "architecture" ], "evidence": [ "empirical" ], "modality": [ "language" ], "summary": "Learns when to perform another internal reasoning step, recall stored evidence or intermediate states, or emit a token. Trains these choices by branching over alternative actions and measuring their effects on the next emitted token.", "provenance": "Primary-source literature search, 2026-10-18", "author_names": [ "Xiaoqiang Wang", "Suyuchen Wang", "Bang Liu" ], "record_url": "https://latent-implicit-thinking.github.io/papers/2609.39740/", "legacy_record_url": "https://latent-implicit-thinking.github.io/papers/#paper-2609-39740", "citation_date": "2026-09-30", "json_url": "https://latent-implicit-thinking.github.io/papers/2609.39740/record.json", "markdown_url": "https://latent-implicit-thinking.github.io/papers/2609.39740/index.md", "bibtex_url": "https://latent-implicit-thinking.github.io/papers/2609.39740/citation.bib", "csl_url": "https://latent-implicit-thinking.github.io/papers/2609.39740/citation.csl.json" }, { "id": "2610.02193", "title": "Hierarchical Continuous Diffusion Language Models", "authors": "Hui Ren, Zihan Li, Chang Liu, Huidong Liu, Alexander Schwing", "first_public": "2026-10-01", "lens": [ "continuous", "beyond-sequential" ], "representation": [ "continuous-state", "discrete-token" ], "training": [ "supervised" ], "questions": [ "architecture", "capability" ], "evidence": [ "formal", "empirical" ], "modality": [ "language", "symbolic" ], "summary": "Keeps a continuous state throughout denoising while reading out tokens at each step and feeding them back into the next state update. Tests whether this coupling preserves dependencies during parallel generation on Sudoku, Countdown, and language modeling.", "computation": [ "diffusion", "parallel" ], "venue": "arXiv", "arxiv": "2610.02193", "paper_url": "https://arxiv.org/abs/2610.02193", "provenance": "Primary-source literature search, 2026-10-18", "added_at": "2026-10-18", "author_names": [ "Hui Ren", "Zihan Li", "Chang Liu", "Huidong Liu", "Alexander Schwing" ], "record_url": "https://latent-implicit-thinking.github.io/papers/2610.02193/", "legacy_record_url": "https://latent-implicit-thinking.github.io/papers/#paper-2610-02193", "citation_date": "2026-10-01", "json_url": "https://latent-implicit-thinking.github.io/papers/2610.02193/record.json", "markdown_url": "https://latent-implicit-thinking.github.io/papers/2610.02193/index.md", "bibtex_url": "https://latent-implicit-thinking.github.io/papers/2610.02193/citation.bib", "csl_url": "https://latent-implicit-thinking.github.io/papers/2610.02193/citation.csl.json" }, { "id": "2610.02665", "title": "Large Language Continuous Diffusion Models", "authors": "Zhihan Yang, Wei Guo, Jean-Marie Lemercier, Simon Welker, Yonggan Fu, Mohammad Mahdi Kamani, Sajad Norouzi, Julius Berner, Tomas Geffner, Karsten Kreis, Yongxin Chen, Molei Tao, John Thickstun, Pavlo Molchanov, Ante Jukić, Arash Vahdat, Morteza Mardani", "first_public": "2026-10-02", "lens": [ "continuous", "beyond-sequential" ], "representation": [ "continuous-state" ], "training": [ "pretraining", "supervised" ], "questions": [ "architecture", "capability", "efficiency", "controllability" ], "evidence": [ "empirical" ], "modality": [ "language", "symbolic" ], "summary": "Sigma denoises learned continuous token embeddings in blocks, starting from autoregressive pretrained weights. Studies how guidance and temperature affect mathematical reasoning, coding, output diversity, and performance when fewer refinement steps are used.", "computation": [ "diffusion", "parallel" ], "venue": "arXiv", "arxiv": "2610.02665", "paper_url": "https://arxiv.org/abs/2610.02665", "provenance": "Primary-source literature search, 2026-10-18", "added_at": "2026-10-18", "author_names": [ "Zhihan Yang", "Wei Guo", "Jean-Marie Lemercier", "Simon Welker", "Yonggan Fu", "Mohammad Mahdi Kamani", "Sajad Norouzi", "Julius Berner", "Tomas Geffner", "Karsten Kreis", "Yongxin Chen", "Molei Tao", "John Thickstun", "Pavlo Molchanov", "Ante Jukić", "Arash Vahdat", "Morteza Mardani" ], "record_url": "https://latent-implicit-thinking.github.io/papers/2610.02665/", "legacy_record_url": "https://latent-implicit-thinking.github.io/papers/#paper-2610-02665", "citation_date": "2026-10-02", "json_url": "https://latent-implicit-thinking.github.io/papers/2610.02665/record.json", "markdown_url": "https://latent-implicit-thinking.github.io/papers/2610.02665/index.md", "bibtex_url": "https://latent-implicit-thinking.github.io/papers/2610.02665/citation.bib", "csl_url": "https://latent-implicit-thinking.github.io/papers/2610.02665/citation.csl.json" }, { "id": "2610.04864", "title": "Enhancing Long-Video VLM Embeddings with Query-Aware Streaming Latent Reasoning", "authors": "Haozhe Chi, Song Jin, Yang Jin, Yadong Mu", "first_public": "2026-10-04", "venue": "arXiv", "arxiv": "2610.04864", "paper_url": "https://arxiv.org/abs/2610.04864", "lens": [ "continuous", "recurrent" ], "representation": [ "multimodal-latent" ], "computation": [ "recurrent", "stateful" ], "training": [ "supervised", "self-distillation" ], "questions": [ "capability", "efficiency" ], "evidence": [ "empirical" ], "modality": [ "multimodal", "vision" ], "summary": "Processes selected video clips sequentially while persistent think tokens accumulate evidence for a fixed-size retrieval embedding. Intermediate retrieval supervision and self-distillation train partial-video readouts; the output is an embedding rather than a generated reasoning trace.", "provenance": "Primary-source literature search, 2026-10-18", "added_at": "2026-10-18", "author_names": [ "Haozhe Chi", "Song Jin", "Yang Jin", "Yadong Mu" ], "record_url": "https://latent-implicit-thinking.github.io/papers/2610.04864/", "legacy_record_url": "https://latent-implicit-thinking.github.io/papers/#paper-2610-04864", "citation_date": "2026-10-04", "json_url": "https://latent-implicit-thinking.github.io/papers/2610.04864/record.json", "markdown_url": "https://latent-implicit-thinking.github.io/papers/2610.04864/index.md", "bibtex_url": "https://latent-implicit-thinking.github.io/papers/2610.04864/citation.bib", "csl_url": "https://latent-implicit-thinking.github.io/papers/2610.04864/citation.csl.json" }, { "id": "2610.06666", "title": "What Matters for Latent Reasoning with Flow Matching", "authors": "Yassine Ouali, Adrian Bulat, Georgios Tzimiropoulos", "first_public": "2026-10-05", "added_at": "2026-10-18", "venue": "arXiv", "arxiv": "2610.06666", "paper_url": "https://arxiv.org/abs/2610.06666", "lens": [ "continuous", "beyond-sequential" ], "representation": [ "continuous-state" ], "computation": [ "parallel", "diffusion" ], "training": [ "supervised", "self-training" ], "questions": [ "capability", "efficiency", "interpretability", "evaluation" ], "evidence": [ "empirical", "probing" ], "modality": [ "language", "symbolic" ], "summary": "Compresses symbolic reasoning into latent codes and trains a flow model to refine a block of noise into a thought before decoding the answer. Studies training choices, self-training on verified thoughts, and tests of whether the answer actually uses those thoughts.", "provenance": "Primary-source literature search, 2026-10-18", "author_names": [ "Yassine Ouali", "Adrian Bulat", "Georgios Tzimiropoulos" ], "record_url": "https://latent-implicit-thinking.github.io/papers/2610.06666/", "legacy_record_url": "https://latent-implicit-thinking.github.io/papers/#paper-2610-06666", "citation_date": "2026-10-05", "json_url": "https://latent-implicit-thinking.github.io/papers/2610.06666/record.json", "markdown_url": "https://latent-implicit-thinking.github.io/papers/2610.06666/index.md", "bibtex_url": "https://latent-implicit-thinking.github.io/papers/2610.06666/citation.bib", "csl_url": "https://latent-implicit-thinking.github.io/papers/2610.06666/citation.csl.json" }, { "id": "2610.07175", "title": "CALR: Continuous Anchored Latent Reasoning via Render-of-Thought Compression", "authors": "Zhaoyang Wei, Bowen Jiang, Yanchao Hao, Wenchao Ding, Zheng Wei, Shaocheng Wu, Zhenjun Han, Jianbin Jiao", "first_public": "2026-10-05", "added_at": "2026-10-18", "venue": "arXiv", "arxiv": "2610.07175", "paper_url": "https://arxiv.org/abs/2610.07175", "lens": [ "continuous" ], "representation": [ "continuous-state", "multimodal-latent" ], "computation": [ "token-wise" ], "training": [ "supervised", "curriculum" ], "questions": [ "capability", "efficiency", "interpretability" ], "evidence": [ "empirical", "probing" ], "modality": [ "language", "symbolic", "vision" ], "summary": "Compresses images of written derivations into continuous states, training both the answers they support and the reasoning that can be recovered from them. Gradually shifts from generating latent blocks in parallel to generating them in sequence.", "provenance": "Primary-source literature search, 2026-10-18", "author_names": [ "Zhaoyang Wei", "Bowen Jiang", "Yanchao Hao", "Wenchao Ding", "Zheng Wei", "Shaocheng Wu", "Zhenjun Han", "Jianbin Jiao" ], "record_url": "https://latent-implicit-thinking.github.io/papers/2610.07175/", "legacy_record_url": "https://latent-implicit-thinking.github.io/papers/#paper-2610-07175", "citation_date": "2026-10-05", "json_url": "https://latent-implicit-thinking.github.io/papers/2610.07175/record.json", "markdown_url": "https://latent-implicit-thinking.github.io/papers/2610.07175/index.md", "bibtex_url": "https://latent-implicit-thinking.github.io/papers/2610.07175/citation.bib", "csl_url": "https://latent-implicit-thinking.github.io/papers/2610.07175/citation.csl.json" }, { "id": "2610.09145", "title": "Noise Your Prompt: Noising Conditioning Tokens in Continuous Diffusion Language Models", "authors": "Justin Jung", "first_public": "2026-10-06", "lens": [ "continuous", "beyond-sequential" ], "representation": [ "continuous-state" ], "training": [ "supervised" ], "questions": [ "generalization", "capability", "limitations" ], "evidence": [ "empirical", "boundary" ], "modality": [ "language", "symbolic" ], "summary": "Adds noise to conditioning tokens during training instead of keeping the prompt clean. Tests improvements in Sudoku and N-Queens generalization and solution diversity, while reporting that the gains do not transfer to every language-generation task.", "computation": [ "diffusion" ], "venue": "arXiv", "arxiv": "2610.09145", "paper_url": "https://arxiv.org/abs/2610.09145", "provenance": "Primary-source literature search, 2026-10-18", "added_at": "2026-10-18", "author_names": [ "Justin Jung" ], "record_url": "https://latent-implicit-thinking.github.io/papers/2610.09145/", "legacy_record_url": "https://latent-implicit-thinking.github.io/papers/#paper-2610-09145", "citation_date": "2026-10-06", "json_url": "https://latent-implicit-thinking.github.io/papers/2610.09145/record.json", "markdown_url": "https://latent-implicit-thinking.github.io/papers/2610.09145/index.md", "bibtex_url": "https://latent-implicit-thinking.github.io/papers/2610.09145/citation.bib", "csl_url": "https://latent-implicit-thinking.github.io/papers/2610.09145/citation.csl.json" }, { "id": "2610.09416", "title": "Efficient Reasoning with Flow Language Models", "authors": "Hanru Bai, Faissal Izermine, Oscar Davis, T. Konstantin Rusch", "first_public": "2026-10-07", "lens": [ "analysis", "continuous", "beyond-sequential" ], "representation": [ "continuous-state" ], "training": [ "analysis-only", "supervised" ], "questions": [ "theory", "interpretability", "efficiency" ], "evidence": [ "formal", "empirical", "mechanistic" ], "modality": [ "symbolic" ], "summary": "Compares continuous flow and discrete diffusion models at matched sizes and denoising steps. A theoretical account and intermediate-state interventions examine whether retaining information about several candidates helps solve mazes and Sudoku before final discretization.", "computation": [ "diffusion", "parallel" ], "venue": "arXiv", "arxiv": "2610.09416", "paper_url": "https://arxiv.org/abs/2610.09416", "provenance": "Primary-source literature search, 2026-10-18", "added_at": "2026-10-18", "author_names": [ "Hanru Bai", "Faissal Izermine", "Oscar Davis", "T. Konstantin Rusch" ], "record_url": "https://latent-implicit-thinking.github.io/papers/2610.09416/", "legacy_record_url": "https://latent-implicit-thinking.github.io/papers/#paper-2610-09416", "citation_date": "2026-10-07", "json_url": "https://latent-implicit-thinking.github.io/papers/2610.09416/record.json", "markdown_url": "https://latent-implicit-thinking.github.io/papers/2610.09416/index.md", "bibtex_url": "https://latent-implicit-thinking.github.io/papers/2610.09416/citation.bib", "csl_url": "https://latent-implicit-thinking.github.io/papers/2610.09416/citation.csl.json" }, { "id": "2610.09876", "title": "Think Before You Paint: Recursive Latent Reasoning for Diffusion Models", "authors": "Paweł Skierś, Małgorzata Grzanka, Wojciech Masarczyk, Jan-Willem van de Meent, Kamil Deja", "first_public": "2026-10-07", "venue": "arXiv", "arxiv": "2610.09876", "paper_url": "https://arxiv.org/abs/2610.09876", "lens": [ "recurrent", "continuous", "beyond-sequential" ], "representation": [ "multimodal-latent", "continuous-state" ], "computation": [ "recurrent", "diffusion" ], "training": [ "supervised" ], "questions": [ "architecture", "capability", "generalization" ], "evidence": [ "empirical" ], "modality": [ "vision", "symbolic" ], "summary": "PaTh uses a small recursive network to refine learned image-state tokens inside each denoising step and steer a frozen diffusion model through adapters. It learns visual puzzles from reconstruction targets without symbolic solutions or an external verifier.", "provenance": "Primary-source literature search, 2026-10-18", "added_at": "2026-10-18", "author_names": [ "Paweł Skierś", "Małgorzata Grzanka", "Wojciech Masarczyk", "Jan-Willem van de Meent", "Kamil Deja" ], "record_url": "https://latent-implicit-thinking.github.io/papers/2610.09876/", "legacy_record_url": "https://latent-implicit-thinking.github.io/papers/#paper-2610-09876", "citation_date": "2026-10-07", "json_url": "https://latent-implicit-thinking.github.io/papers/2610.09876/record.json", "markdown_url": "https://latent-implicit-thinking.github.io/papers/2610.09876/index.md", "bibtex_url": "https://latent-implicit-thinking.github.io/papers/2610.09876/citation.bib", "csl_url": "https://latent-implicit-thinking.github.io/papers/2610.09876/citation.csl.json" }, { "id": "2610.17679", "title": "Dynamic K-Head: Difficulty-Supervised Budget Selection for Continuous Latent Reasoning", "authors": "Ying Li, Yuqi Chen, Sixuan Li, Yunfeng Cai", "first_public": "2026-10-13", "added_at": "2026-10-18", "venue": "arXiv", "arxiv": "2610.17679", "paper_url": "https://arxiv.org/abs/2610.17679", "lens": [ "continuous" ], "representation": [ "continuous-state" ], "computation": [ "token-wise" ], "training": [ "supervised" ], "questions": [ "capability", "efficiency", "controllability", "evaluation" ], "evidence": [ "empirical" ], "modality": [ "language", "symbolic" ], "summary": "Predicts from the question how many continuous reasoning updates to perform. Evaluates total cost including latent updates, special tokens, and any written continuation rather than counting latent steps alone.", "provenance": "Primary-source literature search, 2026-10-18", "author_names": [ "Ying Li", "Yuqi Chen", "Sixuan Li", "Yunfeng Cai" ], "record_url": "https://latent-implicit-thinking.github.io/papers/2610.17679/", "legacy_record_url": "https://latent-implicit-thinking.github.io/papers/#paper-2610-17679", "citation_date": "2026-10-13", "json_url": "https://latent-implicit-thinking.github.io/papers/2610.17679/record.json", "markdown_url": "https://latent-implicit-thinking.github.io/papers/2610.17679/index.md", "bibtex_url": "https://latent-implicit-thinking.github.io/papers/2610.17679/citation.bib", "csl_url": "https://latent-implicit-thinking.github.io/papers/2610.17679/citation.csl.json" }, { "id": "2610.19080", "title": "Backpropagating Verifier Rewards Through Continuous Flow Language Models", "authors": "Xu Han, Jiajing Hu, Li-Ping Liu", "first_public": "2026-10-13", "lens": [ "continuous", "beyond-sequential" ], "representation": [ "continuous-state" ], "training": [ "reinforcement-learning" ], "questions": [ "capability", "efficiency" ], "evidence": [ "empirical" ], "modality": [ "language", "symbolic" ], "summary": "Trains a critic to score intermediate continuous token representations, enabling reward gradients through the denoising trajectory before final discrete decoding. Evaluates this post-training approach on four reasoning tasks.", "computation": [ "diffusion" ], "venue": "arXiv", "arxiv": "2610.19080", "paper_url": "https://arxiv.org/abs/2610.19080", "provenance": "Primary-source literature search, 2026-10-18", "added_at": "2026-10-18", "author_names": [ "Xu Han", "Jiajing Hu", "Li-Ping Liu" ], "record_url": "https://latent-implicit-thinking.github.io/papers/2610.19080/", "legacy_record_url": "https://latent-implicit-thinking.github.io/papers/#paper-2610-19080", "citation_date": "2026-10-13", "json_url": "https://latent-implicit-thinking.github.io/papers/2610.19080/record.json", "markdown_url": "https://latent-implicit-thinking.github.io/papers/2610.19080/index.md", "bibtex_url": "https://latent-implicit-thinking.github.io/papers/2610.19080/citation.bib", "csl_url": "https://latent-implicit-thinking.github.io/papers/2610.19080/citation.csl.json" }, { "id": "2610.19568", "title": "INTACT: Inducing Latent Reasoning on a Frozen Backbone", "authors": "Kutay Tire, Atula Tejaswi, Haris Vikalo, Sujay Sanghavi", "first_public": "2026-10-14", "added_at": "2026-10-18", "venue": "arXiv", "arxiv": "2610.19568", "paper_url": "https://arxiv.org/abs/2610.19568", "lens": [ "continuous" ], "representation": [ "text-latent-hybrid" ], "computation": [ "token-wise" ], "training": [ "distillation", "reinforcement-learning" ], "questions": [ "capability", "efficiency", "generalization" ], "evidence": [ "empirical" ], "modality": [ "language", "symbolic" ], "summary": "Trains a small encoder to turn a frozen model's hidden states into successive blocks of continuous thoughts. After this latent prefix, the frozen model writes its remaining reasoning and answer; the encoder learns through distillation and answer-correctness rewards.", "provenance": "Primary-source literature search, 2026-10-18", "author_names": [ "Kutay Tire", "Atula Tejaswi", "Haris Vikalo", "Sujay Sanghavi" ], "record_url": "https://latent-implicit-thinking.github.io/papers/2610.19568/", "legacy_record_url": "https://latent-implicit-thinking.github.io/papers/#paper-2610-19568", "citation_date": "2026-10-14", "json_url": "https://latent-implicit-thinking.github.io/papers/2610.19568/record.json", "markdown_url": "https://latent-implicit-thinking.github.io/papers/2610.19568/index.md", "bibtex_url": "https://latent-implicit-thinking.github.io/papers/2610.19568/citation.bib", "csl_url": "https://latent-implicit-thinking.github.io/papers/2610.19568/citation.csl.json" }, { "id": "2610.19871", "title": "Rationale Distribution Distillation via Flow Matching for Distributional Continuous Latent Reasoning", "authors": "Mingyeong Kim, Gyusang Cho, Juho Lee, Giung Nam", "first_public": "2026-10-14", "added_at": "2026-10-18", "venue": "arXiv", "arxiv": "2610.19871", "paper_url": "https://arxiv.org/abs/2610.19871", "lens": [ "continuous", "beyond-sequential" ], "representation": [ "continuous-state" ], "computation": [ "parallel", "diffusion" ], "training": [ "distillation" ], "questions": [ "capability", "efficiency", "scaling" ], "evidence": [ "empirical" ], "modality": [ "language", "symbolic" ], "summary": "Distills multiple written reasoning paths for each question into a flow model over fixed-length latent sequences, including mixtures of complete rationales. Generates all latent slots together, then uses a frozen language model to decode answers from sampled rationale contexts.", "provenance": "Primary-source literature search, 2026-10-18", "author_names": [ "Mingyeong Kim", "Gyusang Cho", "Juho Lee", "Giung Nam" ], "record_url": "https://latent-implicit-thinking.github.io/papers/2610.19871/", "legacy_record_url": "https://latent-implicit-thinking.github.io/papers/#paper-2610-19871", "citation_date": "2026-10-14", "json_url": "https://latent-implicit-thinking.github.io/papers/2610.19871/record.json", "markdown_url": "https://latent-implicit-thinking.github.io/papers/2610.19871/index.md", "bibtex_url": "https://latent-implicit-thinking.github.io/papers/2610.19871/citation.bib", "csl_url": "https://latent-implicit-thinking.github.io/papers/2610.19871/citation.csl.json" }, { "id": "2610.21182", "title": "Does Latent Chain-of-Thought Actually Reason? A Single, Unifying Taxonomy for Latent Reasoning", "authors": "Jingjie Qiu, Sheldon Yu, Xunyi Jiang, Jiajin Liu, Zihan Huang, Zhendong Chu, Tong Yu, Julian McAuley, Junda Wu", "first_public": "2026-10-15", "venue": "arXiv", "arxiv": "2610.21182", "paper_url": "https://arxiv.org/abs/2610.21182", "lens": [ "analysis", "continuous" ], "representation": [ "continuous-state" ], "computation": [ "token-wise" ], "training": [ "analysis-only" ], "questions": [ "taxonomy", "interpretability", "evaluation", "limitations" ], "evidence": [ "empirical", "mechanistic", "probing" ], "modality": [ "language", "symbolic" ], "summary": "Uses chain deletion, content replacement, probes, and trajectory analysis across 21 model-task configurations. Separates what continuous states encode from whether the model uses their content, finding that dependence on the chain varies with task and training recipe.", "provenance": "Primary-source literature search, 2026-10-18", "added_at": "2026-10-18", "author_names": [ "Jingjie Qiu", "Sheldon Yu", "Xunyi Jiang", "Jiajin Liu", "Zihan Huang", "Zhendong Chu", "Tong Yu", "Julian McAuley", "Junda Wu" ], "record_url": "https://latent-implicit-thinking.github.io/papers/2610.21182/", "legacy_record_url": "https://latent-implicit-thinking.github.io/papers/#paper-2610-21182", "citation_date": "2026-10-15", "json_url": "https://latent-implicit-thinking.github.io/papers/2610.21182/record.json", "markdown_url": "https://latent-implicit-thinking.github.io/papers/2610.21182/index.md", "bibtex_url": "https://latent-implicit-thinking.github.io/papers/2610.21182/citation.bib", "csl_url": "https://latent-implicit-thinking.github.io/papers/2610.21182/citation.csl.json" } ] }