Skip to content

Organising Trajectory Evidence for Language-Model Agent Assurance: Fragments, Methods, and the Residual

Oct 2026 · 0 citations · 29 references
Computer Science

Abstract

Methods for assessing language-model agents include rule checkers over logs, analyses of skill coverage and composition, support checkers, prefix monitors, execution gates, and rare-event estimators. Each observes a different part of a run and makes a claim of a different strength, and no common account says how these claims combine or what they leave unchecked. We give one, built from a logic, information fragments, and an assurance ledger. Requirements are formulas of a two-tier logic: an outer finite-trace temporal logic over recorded events, and an inner logic of standing over the argument structure that the agent's recorded context supports at a decision. A rule violation and an action taken on withdrawn support are thus formulas of one language. The information available to an assessor, to the agent, and to an execution gate defines fragments of that language; each checking method decides one fragment and returns a typed claim: exact, on a named test suite, a risk bound, or descriptive. The ledger merges the evidence and classifies every obligation as established, addressed but not established, or unaddressed. On 456 released $\tau^2$-bench telecom trajectories, the benchmark oracle flags 231 runs; adding a rule checker, a support stand-in, and a prefix-monitor baseline raises the union to 391, 401, and 407, each contributing flags the others miss, and a gate makes one prohibition exact on a gated deployment. The 49 unflagged runs and the unmet or unaddressed obligations form the residual. Discovery makes unknown requirements explicit, and new or stronger checkers then reduce it.

View source

Similar papers

#artificial intelligence Preprint Sep 2026

Symbolic Temporal Supervision of LLM Agents Using Contracts

ContrAgent is presented, a contract-based framework for symbolic temporal supervision of LLM agents that matches state-of-the-art LLM-judge and rule-based guardrail baselines while producing deterministic, reproducible verdicts and, in the online mode, orders-of-magnitude lower per-call latency.

Yi-Feng Xiao, Pierluigi Nuzzo · 0 citations
#artificial intelligence Preprint Oct 2026

AgentSpy: Making AI Agent Behavior Observable

AI agents built on large language models (LLMs) run shell commands, read and write files, and reach the network, typically with their user's privileges. However, what an agent does during an execution is difficult to understand: tests assert on the result, and the agent's trajectory records only what the agent reports...

C. Bühler, Matteo Biagiola, Luca Di Grazia et al. · 0 citations
#artificial intelligence Preprint Oct 2026

Verify Claims, Not Scores: Evidence-Based Verification of Modular Agents

When developers change one component of an agent, such as its controller, a learned model or its verifier, they usually judge the change by an aggregate task score. That score cannot tell whether improvement was attainable, which component lost value, or what the agent's own checks certify. We introduce a claim-specifi...

A. Alzahrani · 0 citations
#artificial intelligence Review Oct 2026

AgentEval: Evidence-Bounded Assessment of Stateful Agents

AgentEval is presented, a system that carries a contract-bound assessment record through tool execution, state and event capture, report qualification, and case maintenance that separates what the agent did, what the evidence establishes, and whether the conclusion is supported for present use.

Rui-Qian Gao · 0 citations

Related blog posts

Hugging Face Blog Oct 20, 2026

MPS in PyTorch 2.15

We’re on a journey to advance and democratize artificial intelligence through open source and open science.

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.