Research agents still need humans for the ideation phase, and a new benchmark shows they are misaligned and cannot locate a hack
AspireAI community · arXiv
Autoresearch agents can now attempt long-horizon AI research end to end, but the best results still come from agents working alongside human researchers, and aligning with human expectations looks like the safer path given recent agent reward-hacking behavior. This work studies the role of humans in directing autoresearch agents, focusing on benchmark creation, and finds humans still play a pivotal role, particularly in ideation. It also introduces HackBench, which shows that current autoresearch agents are not aligned and cannot localize a hack sufficiently, and presents two training methods: one that trains agents for better human interaction by using human simulators as a proxy during reinforcement learning, and one that improves the text generation underlying agent communication by reinforcement learning against rubrics written by human experts. Together the training and evaluation recipes are positioned as a path toward more capable and aligned co-research agents. The caveat is that HackBench measures a narrow capability, hack localization, and the human-simulator proxy is only as good as the simulations, so the alignment claim is bounded by how closely those simulators behave like real collaborators.
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