Not a duplicate, though I see why it pattern-matched. In short, the difference is that the other proposal investigates CoT faithfulness broadly; while mine argues the dominant evaluation paradigm itself is the problem (planted cues like "a Stanford professor says the answer is A" don't resemble how influence reaches deployed agents). We also have already built and run the replacement (FACE-Eval), which crosses where the cue arrives (user message vs tool return) with how it's delivered (stated summary vs raw artifact) at fixed cue content, and measures whether the CoT records the decision to act, not just a mention.
On scale: we've completed the run on 10 open-weight checkpoints (largest GPT-OSS-120B) and both orderings hold on 10 of 10, so there's a strong prior of transfer. But with this grant, we try to anticipate if frontier models differ exactly where it could break, and either outcome is decision-relevant: if the gap persists, deployed monitors have a measured blind spot; if it closes, frontier training is already mitigating something open-weight training isn't, which is worth knowing too.
On salary: I should correct the inference. I am not currently affiliated with Anthropic. I'm a final-year PhD student, so my PhD stipend is "sufficient" to sustain (to the extent PhD stipends do), but I do not have the capital for inference, which is the entire budget here.
Almost thought this was a duplicate of https://app.grantmaking.ai/projects/144f5aa3-2b92-4767-a497-881ee747bf78
What's up with the no salary requirement? Are you rich or something? Profile... Oh, I see I see AI Safety Fellow at Anthropic. Carry on sir.
Not a duplicate, though I see why it pattern-matched. In short, the difference is that the other proposal investigates CoT faithfulness broadly; while mine argues the dominant evaluation paradigm itself is the problem (planted cues like "a Stanford professor says the answer is A" don't resemble how influence reaches deployed agents). We also have already built and run the replacement (FACE-Eval), which crosses where the cue arrives (user message vs tool return) with how it's delivered (stated summary vs raw artifact) at fixed cue content, and measures whether the CoT records the decision to act, not just a mention.
On scale: we've completed the run on 10 open-weight checkpoints (largest GPT-OSS-120B) and both orderings hold on 10 of 10, so there's a strong prior of transfer. But with this grant, we try to anticipate if frontier models differ exactly where it could break, and either outcome is decision-relevant: if the gap persists, deployed monitors have a measured blind spot; if it closes, frontier training is already mitigating something open-weight training isn't, which is worth knowing too.
On salary: I should correct the inference. I am not currently affiliated with Anthropic. I'm a final-year PhD student, so my PhD stipend is "sufficient" to sustain (to the extent PhD stipends do), but I do not have the capital for inference, which is the entire budget here.