Two Paths to Spot Truth in AI Answers
Two Paths to Spot Truth in AI Answers
Large language models can sound confident while being wrong. This study shows their internal activity holds two distinct "truth signals."
- Question-anchored: The model checks the answer against information it drew from the question.
- Answer-anchored: The model looks for self-contained evidence inside the answer it just produced.
By selectively "muting" attention routes and swapping token traces, the authors tease these pathways apart and uncover a few surprises: the two signals line up with the model’s knowledge boundaries, and the network’s hidden states can tell which path they’re using.
Why it matters: understanding these routes enables better detectors for hallucinations, and points toward models that know when to trust themselves - and when to ask for help.
Paper: https://arxiv.org/abs/2601.07422
Paper: https://arxiv.org/abs/2601.07422v1
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