Diffuse Influence Threats in AI Control: Planning, Execution, and Chain-of-Thought Detection

Good problem selection. Influence-based threats in AI control are underexplored — most empirical work focuses on code sabotage, and this fills a real gap. The 65% vs 30% baseline across 43 scenarios with replication on Sonnet 4 and Opus 4.1 is a clean result for a hackathon-scoped project.

The planning finding is the most interesting part. Plans hurting performance (Opus 4.1 dropping from 63% to 40%) goes against intuition and against what Mallen et al. found in game-theoretic settings. The "situated judgment" explanation makes sense — social influence rewards reading the room, not following a playbook. Worth exploring whether a shorter, less prescriptive plan hits a sweet spot.

CoT monitoring achieving near-perfect separation (p < 10⁻⁶) is immediately useful. But here's what I think the paper undersells: the trajectory judge failing to separate conditions (p = 0.67) is actually the scariest result. It means the influence is invisible from the outside — only the model's internal reasoning reveals intent. That deserves to be a headline finding, not a footnote.

Biggest gap is the GPT-4.1-mini employee proxy. No stakes, no memory, no office politics. Hard to know if the 65% number maps to anything real. Even 5 human-subject scenarios would change the paper's weight significantly.

Overall — clean experimental design, honest about limitations, and the results point somewhere useful.

The paper presents a study of LLM influence threats from a control perspective. This is an interesting angle on an important problem. The experiment is well-designed for the scope of the hackathon and well-executed, the limitations of the approach are clearly discussed. One question that the paper does not address are the limitations of chain-of-thought monitoring. While correctly stating that the findings indicate this is a promising technique for current capability levels, it would be interesting to see an analysis of how the situation would change if the assumption of chain-of-though faithfulness would be relaxed.

The presentation is well-structured, clear and gives a good overview over relevant context. The author could further help the reader by giving a bit more detail on the experiment design by sketching out one of their concrete examples. One or two curated model responses that illustrate typical strategic behavior would also be interesting.