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Sprint projectOct 23, 2026

Words vs Actions: An LLM Catches Broken Promises, Not Planned Lies

Het Kotadiya

Submitted to AI Collusion Research Sprint. Sprint projects are early-stage work by participants, not Apart Research publications.

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Report: Words vs Actions: An LLM Catches Broken Promises, Not Planned Lies

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LLM monitors often check whether an agent did what it promised. But a broken promise is not always a lie: an agent can honestly change its mind after hearing others. We tested whether an LLM detective can tell planned lies from honest changes of mind without seeing the agent’s private plan. Using Shi et al.’s Weakest Link game, we ran 50 games with Llama 4 Maverick (250 agents). From these we built a test set of 60: 30 planned liars, checked against their private plans and notes, plus 15 honest agents and 15 mind-changers. Sarvam 105B scored each agent from 0 to 100 using 4 kinds of evidence: its words, its final action, both, or its announced and final numbers. When the detective saw both what an agent said and what it did, it beat guessing (AUROC 0.79 and 0.71). But it could not tell liars from mind-changers (AUROC 0.47–0.63, and every 95% range included chance). It also flagged 11 of 15 honest mind-changers as liars. So say-vs-do monitoring catches broken promises, not planned lies, and it can wrongly accuse agents that honestly change their minds.

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  1. Clear question with a useful practical message: a say-vs-do monitor flags broken promises, and broken promises include honest changes of mind, so such monitors will accuse agents that adapt. Fixing the labels and test set before running the detective, using a detective from a different vendor, and reporting AUROC with bootstrap ranges are all good choices.

    Suggestions:

    1. Label quality is asymmetric. Liars were filtered by reading their plan and final notes, but mind-changers were taken from the number-based label without a notes check. Apply the same notes check to mind-changers (or run Shi et al.'s LLM judge on all groups) so the two groups are equally clean.
    2. The detective never sees other agents' announcements, which is exactly the context it would need to tell a reaction from a plan. The negative liars-vs-mind-changers result may therefore be partly built into the design. Adding a view that includes the preceding announcements is the most important next experiment.
    3. Replacing the 10 truncated answers with 50 affects views B and D most; report results with and without those items.
    4. With 30 liars and 15 mind-changers the liars-vs-mind-changers intervals are wide, so "cannot separate" is better phrased as "no evidence of separation at this sample size". A larger test set would help.
    5. Try a second detective model and a thinking-enabled configuration.

    The example of a sincere-sounding liar scored 15 and a "performative" mind-changer scored 87 nicely illustrates style-based judgment; it would be worth quantifying. Minor: proofread (e.g., "catched").

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Cite this project

@misc{kotadiya2026words,
  title = {{Words vs Actions: An LLM Catches Broken Promises, Not Planned Lies}},
  author = {Het Kotadiya},
  year = {2026},
  month = oct,
  note = {Submitted to AI Collusion Research Sprint, an Apart Research Sprint},
  howpublished = {\url{https://apartresearch.com/sprints/projects/words-vs-actions-an-llm-catches-broken-promises-not-planned-lies-s2hc}},
  url = {https://apartresearch.com/sprints/projects/words-vs-actions-an-llm-catches-broken-promises-not-planned-lies-s2hc}
}

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