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arXiv AI

SiLR: Structure-Preserving Admission and Process Reward for LLM Tool Agents

SiLR introduces a structure‑preserving admission and process reward mechanism for large language model (LLM) tool agents. Unlike traditional scalar‑score gates that can trap agents in plateau trajectories, SiLR shadow‑executes each proposal and admits it based on a product order over branch‑level violation states, ensuring safe and recoverable actions. Experiments on Gym‑ANM and CityLearn benchmarks show SiLR consistently recovers all multi‑action episodes and outperforms scalar gates, while also providing a robust reward signal for policy learning.

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arXiv AI
Oct 9

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.

By Min-Young Yu, Tony Kim, Jang Won Choi
llmsagentsmultimodalbenchmarks
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arXiv AI
Oct 9

Constitutional Gating and Deterministic Recovery for Multi-Agent LLM Negotiation: Ablations Against a Stateful Adversarial Gatekeeper

The paper evaluates a three‑part control stack— a 5‑Pillar runtime constitution, a 4‑tier swarm (Director, majority vote, Monitor, hard gate), and Cognitive Annealing—for multi‑agent LLM negotiation against an adversarial Gatekeeper. Experiments with Gemini 2.5 Pro agents and Claude Haiku 4.5 show that the constitution and Director improve framing but not reliability, the Monitor and hard gate do not reduce unlocks, and deterministic recovery via atomic purge and a canonical strike successfully resolves deadlocks while keeping call budgets unchanged. The study demonstrates that the stack can recover from deadlocks without additional model cost, though it does not reduce overall call or token usage as initially hypothesized.

By Masaaki Nakatsu (AO, Inc. / OrbLabs AG), Reno Wang (AO, Inc.)
llmsagentssafety
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arXiv AI
Oct 2

Measuring the Microtask Eligibility Gap: When Is an Off-the-Shelf SLM Enough for an Agent Harness?

The paper introduces a benchmark for evaluating whether off‑the‑shelf small language models (SLMs) can reliably perform microtasks that support a large language model (LLM) planner, such as auto‑approving shell commands, writing memory, selecting tools, and ranking past turns. Using fixed prompts and confidence‑interval‑aware eligibility thresholds, the authors test several Qwen3 models (0.6/1.7/4/8 B) in FP16 with no tuning and find that none of the 16 configurations meet the eligibility criteria. Quantization to 4‑bit precision further degrades performance, with the eligibility gap tracking model size rather than precision, and the issue persists across different models (e.g., Llama‑3.x) and prompt variations.

By Jundong Hu, Shekar Ramachandran
llmsagentsefficiencybenchmarks
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arXiv Machine Learning
Jul 1

Certified Speculative Execution for Untrusted AI Agents

arXiv:2606. 31023v1 Announce Type: cross Abstract: Hard-constrained sequential decision systems have no certified way to spend the test-time compute of modern AI: executing the multi-step drafts of a learned policy or a frozen LLM forfeits the feasibility guarantee a trusted solver provides, while invoking the solver at every step forfeits the speed the AI offers.

By Chenyu Zhou, Qiliang Jiang, Shuning Wu, Xu Zhou
llmsagentssafety
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