Thirteen models were tested on three synthetic tasks: inserting semantically empty tokens significantly improves performance, without leaving any interpretable trace.
Vatsal Baherwani, Tom Goldstein, and Ashwinee Panda ask a simple question: does a language model actually express all of its reasoning in the tokens it produces? Their answer, published on July 24, 2026, is no—and the demonstration is embarrassing for chain-reading security approaches.
Across three synthetic reasoning tasks and thirteen models, inserting filler tokens without semantic content improves accuracy by up to 13 percentage points. The magnitude of the gain depends on the type of token and varies from model to model. More troublingly, Claude Opus 4.5 manages to satisfy hidden constraints of modular arithmetic while maintaining its accuracy on the main task, i.e., to pursue a goal that is completely invisible in its chain of thought.
The authors also observe that reinforcement learning induces in Qwen3-235B marked preferences for certain fill token content, without the benefit persisting at the time of the test. The conclusion is that real calculations take place without leaving a legible trace in the output — a blind spot for any supervision device that assumes that monitoring the text produced is equivalent to monitoring reasoning.
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