Not All LLM Reasoning is Visible in the Chain-of-Thought
Tests whether fixed filler tokens improve reasoning without exposing intermediate steps in text. Finds task- and model-dependent effects, studies their internal mechanisms, and reports limits of training models to exploit them.
First public: · Added to this list: October 22, 2026
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Thinking tokens — Give a model extra tokens for computation, such as pauses, filler symbols, or learned planning tokens. These tokens need not spell out the solution.
Analysis and evaluation — Measure how well these methods work, investigate what their internal computation does, and identify where they fail. This includes experiments, studies of model internals, and mathematical results.