arXiv Machine Learning
How Strongly Should Task State Influence an LLM Agent?
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Constitutional Gating and Deterministic Recovery for Multi-Agent LLM Negotiation: Ablations Against a Stateful Adversarial Gatekeeper
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Reason Less, Verify More: Deterministic Gates Recover a Silent Policy-Violation Failure Mode in Tool-Using LLM Agents
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Long-Horizon State Tracking in LLMs: Executing MD5 through a Deep Sequence of Dependent Tool Calls
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arXiv AI
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Oversight Has a Capacity: Calibrating Agent Guards to a Subjective, Fatiguing Human
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arXiv AI
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NOMOS: Compiling Written Policies into Statically Verified Tool-Call Gates for LLM Agents
NOMOS is a four‑pass compiler that transforms natural‑language policies into deterministic, statically verified tool‑call gates for large‑language‑model agents. By performing tool‑schema‑level checks without relying on provers, solvers, or additional LLM calls, it repairs or rejects 37% of airline and 13% of retail policy candidates, enabling most shipped rules to become operable. In benchmark tests, the compiled gates reduce policy violations from 66.3% to 2.6% in airline scenarios and from 30.8% to 6.9% in retail, while achieving a zero attack success rate on banking and low rates on other suites, all with microsecond decision times.
arXiv AI
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The Agent Knew: Internal States Forecast Unauthorized Actions and Stop Them, and the Lies That Follow
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