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GraphACM introduces a graph-based framework to measure fine-grained action consistency in large language model agents. By merging equivalent states into a directed, action-labeled graph across multiple task rollouts, it enables comparisons of actions at shared decision points and defines node-level and global consistency metrics. Experiments on ALFWorld, AppWorld, and WebShop show that the global consistency score predicts task success with an AUROC of 0.811, outperforming prior consistency baselines and verbalized confidence.
UMAS is a parameter‑free, cost‑neutral framework for system‑level uncertainty quantification in multi‑agent large language model systems. It estimates the credibility of inter‑agent influence using each generation’s intrinsic confidence, modulates uncertainty updates at each node, and aggregates these into a system‑level estimate. Evaluations on Debate and DyLAN show UMAS outperforms state‑of‑the‑art baselines by an average of 10.54 AUROC points and improves hallucination detection and answer selection, boosting MAS accuracy by up to 12.6 points on specific tasks.
arXiv:2608. 13598v1 Announce Type: new Abstract: Agent evaluation relies almost entirely on outcome metrics such as success rate, which capture whether an agent succeeds but not how consistently it behaves.
The paper introduces RUPA, a trajectory‑level uncertainty quantification framework for large language model agents. RUPA models an agent’s execution as a directed graph of reasoning states, tool interactions, and environment feedback, then propagates uncertainty across this graph to capture long‑range dependencies. Experiments on benchmarks such as τ‑2, Terminal‑Bench‑2, and GAIA show that RUPA outperforms existing methods, enabling earlier failure detection and more reliable agent execution.
arXiv:2607. 16868v1 Announce Type: new Abstract: Large Language Models (LLMs) often produce confidently stated yet unreliable outputs, posing critical challenges for deployment in safety-sensitive applications.
arXiv:2607. 13884v1 Announce Type: new Abstract: Large Language Model (LLM) agents have shown remarkable capabilities in autonomous decision-making by generating sequential trajectories of states, actions, and observations.