JANUS: Foreseeing Latent Risk for Long-Horizon Agent Safety
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Computer Science > Artificial Intelligence
Title:JANUS: Foreseeing Latent Risk for Long-Horizon Agent Safety
Abstract:Agent safety is moving from content moderation toward preventing operational failures before tool-using agents act. We propose Janus, a foresight-oriented framework for long-horizon agent safety that trains guards to anticipate delayed risks from partial trajectories. Janus synthesizes diverse agent trajectories via multi-agent simulation and learns a shared policy with two coupled tasks: an anticipation task that forecasts safety-relevant futures and an adjudication task that decides safety from both the observed prefix and anticipated future. The two tasks are jointly optimized with CoAA-RL, which rewards forecasts by their utility for downstream safety judgment. The resulting guard model, Vanguard, blocks unsafe actions before execution. Across four agent-safety benchmarks, Vanguard improves average protection by 15.9 percentage points over baseline guards while increasing benign task completion by 5.1 percentage points.
| Subjects: | Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Cryptography and Security (cs.CR) |
| Cite as: | arXiv:2607.19913 [cs.AI] |
| (or arXiv:2607.19913v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2607.19913
arXiv-issued DOI via DataCite (pending registration)
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