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:2607. 07405v1 Announce Type: new Abstract: Tool-using LLM agents can violate the very policies they are deployed to enforce while appearing to complete the task successfully.
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.
arXiv:2609.25686v1 Announce Type: cross Abstract: Long-horizon assigned work requires an LLM agent to track the state of a task: which steps are done, blocked, cancelled, or open to repetition. Agent...
The paper investigates how tool‑using language‑model agents can safely commit changes to infrastructure when external state may change between read and commit. By distinguishing invalidating races from predicate‑preserving and irrelevant ones, the authors evaluate three commit‑time guard granularities—global epoch, read‑set version, and semantic commit predicate—using a deterministic simulator and three quantized model families. The study finds that only the complete predicate guard consistently eliminates unsafe commits, while freshness‑based guards block a large proportion of benign races and model‑side signals fail to replace precise semantic enforcement.
arXiv:2608. 02680v1 Announce Type: cross Abstract: Tool-using language-model agents repeatedly rediscover procedures they have already executed, producing traces that mix reusable structure with retries, exploration, accidental ordering, and repeated lookups.
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.
arXiv:2602. 20064v2 Announce Type: replace-cross Abstract: Large language models are increasingly deployed as agents: they plan, call tools, read untrusted data, and act on the results.
The paper introduces PACE (Policy‑Attested Contract Execution), a framework that sits between large‑language‑model (LLM) based autonomous AI agents and on‑chain DeFi operations. PACE defines typed transaction intents, a deterministic policy verifier, and signed Policy Decision Records (PDRs) that cryptographically bind an approved intent, policy, and simulation report to the exact on‑chain execution bytes, providing replay and expiration protection. In evaluations across 40 tasks and six baselines, PACE achieves zero unsafe executions and zero false positives, outperforming unguarded agents by a large margin.
The paper examines how large‑language‑model (LLM) decompilers, which produce clean, idiomatic C code, are currently evaluated mainly on recompilability and passing shipped tests. It shows that these metrics can mask significant behavioral differences: a decompiled function may recompile and pass all tests yet diverge on other inputs or lose disclosed vulnerabilities. To address this, the authors propose Decompile‑Diverge, a behavioral oracle that synthesizes drivers, fuzzes inputs, and compares the decompiled code’s behavior to the original, revealing divergences in up to 13% of cases and exposing gaps in current evaluation suites.
The paper investigates typed decision models used as guardrails in agent systems, evaluating seven open‑weight models on tasks such as prompt‑injection, jailbreak, and toxic‑content screening. Accuracy for allow‑or‑block decisions ranges from 36% to 72% above chance, with models either over‑allowing or over‑blocking. The authors demonstrate that simple textual manipulations—like adding misleading names or server log lines—can dramatically increase fail‑open rates, and that all tested defenses can be bypassed, suggesting these models are unsuitable as the sole decision component.
The paper investigates whether large language models (LLMs) can automatically verify no-code bug fixes—solutions that instruct users to change settings, upgrade versions, or alter workflows—by executing them in a real browser environment. Using 322 fixes from a benchmark, the study tests three executor agents (OpenCUA‑72B, Claude Sonnet 5, and Meta’s Muse Glimmer) and finds that only 17.6% of issues could be set up and passed sanity checks, with resolution rates ranging from 14.6% to 49.7% depending on the executor. Even the best configuration resolved fewer than half of the fixes, and executor choice significantly affected outcomes, highlighting the need for execution-based verification before such fixes reach users.
arXiv:2607. 23386v1 Announce Type: new Abstract: We document a failure class in frontier large language models -- exception chain collapse -- observed in eligibility evaluation under nested conditional rules of the form "A is required UNLESS B applies, UNLESS C overrides B".