Uncertainty Quantification for LLM Agents: A Taxonomy, an Evaluation Protocol, and an Empirical Study

• URL: http://arxiv.org/abs/2609.07395v1
• Date: Mon, 07 Sep 2026 12:06:49 GMT
• Title: Uncertainty Quantification for LLM Agents: A Taxonomy, an Evaluation Protocol, and an Empirical Study
• Abstract summary: Large language models act as agents that plan, call tools, retrieve evidence, maintain memory, and interact over long horizons.<n>Existing work on quantifying uncertainty for LLMs was built almost entirely for single-turn question answering.<n>This paper argues that errors and uncertainty arise from multi-turn conversations, environments, and tools rather than from a single-turn question answering setting.
• Score: 5.818268354990671
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large language models (LLMs) are no longer deployed only for single-turn conversation but increasingly act as agents that plan, call tools, retrieve evidence, maintain memory, and interact over long horizons, often together with other agents through multi-turn conversations. Therefore, knowing when to trust the agentic system is a prerequisite for safe deployment. However, existing work on quantifying uncertainty for LLMs was built almost entirely for single-turn question answering. This paper argues that errors and uncertainty arise from multi-turn conversations, environments, and tools rather than from a single-turn question answering setting. It comes late, however, and is compounded in a single score that is too coarse to represent the unreliability. We organize the literature with a three-axis taxonomy, (1) what the uncertainty is, (2) how it is estimated, and (3) where uncertainty arises during an agent pipeline. We investigate step-level and trajectory-level calibration and show with a simple counterexample that the first does not imply the second. Experiments on real agent traces across four models and up to a 50-step budget show that the proposed metric and reporting protocol (Trajectory-Checkpoint Expected Calibration Error, TC-ECE) can be computed and that step errors are coupled along a trajectory. We find that confidence estimates from the agent's own responses do not consistently outperform a simple baseline. The experiments also show that averaging all trajectories together can hide overconfidence at later stages, which becomes visible when results are analyzed across different horizons. In simpler terms, this paper identifies where the uncertainty comes from in the agentic system pipeline, how to teach agents to know when they are wrong, and why one confidence number is not enough.

Related papers

• MNIST-PRO: MNIST is Back as a Partially Observable World for AI Agents [74.83535434786145]
  MNIST-PRO is a benchmark that isolates agentic perception by converting MNIST digit recognition into a sequential, glimpse-based search task with lookback constraints.<n>We evaluate ten multimodal models across four memory representations, including raw visual history, textual states, structured metric grid maps, and a consolidated visual canvas.
  arXiv  Detail & Related papers  (2026-08-31T16:05:39Z)
• From Sequence to Structure: Relational Uncertainty Propagation for LLM Agents [75.69180947909148]
  We propose a trajectory-level UQ framework for large language model (LLM) agents.<n>RuPA represents an execution history as a directed trajectory graph in which reasoning states, tool interactions, and environment feedback are nodes connected by temporal and semantic dependency edges.<n>We evaluate RUPA on representative agent benchmarks, including $$-2, Terminal-Bench-2, and GAIA, using 6 open-source LLMs spanning multiple model families.
  arXiv  Detail & Related papers  (2026-08-17T01:40:14Z)
• CoMet: Context and Multiplicity Decomposition for Multimodal Uncertainty Estimation [45.629365817272536]
  Uncertainty estimation has been a long-standing challenge in AI models.<n>We propose CoMet, an MLLM uncertainty estimation method by decomposing uncertainty into a context-specific term and a multiplicity-specific term.<n>We train a lightweight post-hoc uncertainty module to estimate these quantities, which enables efficient uncertainty estimation without autoregressive answer generation or repeated sampling.
  arXiv  Detail & Related papers  (2026-06-30T17:46:09Z)
• Ask the World Before Acting: Environment Probing for Calibrated Agent World Models [2.571318992830046]
  Language agents acting over long horizons must maintain beliefs about tool states, object locations, graph edges, and subgoal dependencies.<n>We formulate environment probing as a budgeted decision problem for structured agent world models.<n>We introduce EnvProbe, a simple scoring policy that combines task criticality, staleness, verbalized uncertainty, and dependency role.
  arXiv  Detail & Related papers  (2026-06-30T09:45:49Z)
• Multiagent Protocols with Aggregated Confidence Signals [7.128020779227392]
  Confidence is used for reliability, oversight, and a range of downstream decision tasks in Natural Language Processing (NLP)<n>Prior work uses confidence within multiagent debate (MAD) to weight messages, trigger debate, or calibrate individual agents, but it never aggregates these into a single confidence for the system itself.<n>We introduce three protocols that produce a final answer along with a single aggregated confidence by first transforming raw confidence signals to make them comparable across models, then combining them via soft voting or a probability fusion.
  arXiv  Detail & Related papers  (2026-06-11T17:12:11Z)
• Causal Agent Replay: Counterfactual Attribution for LLM-Agent Failures [0.0]
  Causal Agent Replay (CAR) models an agent run as a structural causal model.<n>It applies a do-operation to a step, and re-executes a trajectory forward under the same policy.<n>CAR is open source and runs on hosted or free local models.
  arXiv  Detail & Related papers  (2026-06-06T17:44:23Z)
• The Alpha Illusion: Reported Alpha from LLM Trading Agents Should Not Be Treated as Deployment Evidence [23.772113719172896]
  Report headline Sharpe ratios from end-to-end LLM trading agents should not be treated as deployment evidence.<n>Report robustness is not tradable probability, narrative reasoning is not numerical execution, and model priors may become undisclosed implicit factor exposures.
  arXiv  Detail & Related papers  (2026-05-16T09:14:35Z)
• Agent-BRACE: Decoupling Beliefs from Actions in Long-Horizon Tasks via Verbalized State Uncertainty [70.43119366710778]
  We introduce Agent-BRACE: Agent Belief state Representation via Abstraction and Confidence Estimation.<n>It is a method that decouples an LLM agent into a belief state model and a policy model, jointly optimized via reinforcement learning.<n>It achieves an average absolute improvement of +14.5% across long-horizon, partially observable language environments.
  arXiv  Detail & Related papers  (2026-05-12T02:37:04Z)
• LiveFMBench: Unveiling the Power and Limits of Agentic Workflows in Specification Generation [75.05397479715576]
  Large language models (LLMs) and agents have shown promising progress, but their true capabilities and failure modes remain unclear.<n>We present the first systematic and contamination-aware study of LLM- and agent-based formal specification generation for C programs.
  arXiv  Detail & Related papers  (2026-05-02T11:31:33Z)
• Correction and Corruption: A Two-Rate View of Error Flow in LLM Protocols [51.56484100374058]
  We propose a paired-outcome measurement interface for auditing a single protocol step on exact-match tasks.<n>For each instance, the interface records a baseline correctness bit and a post-step correctness bit.<n>These rates predict accuracy changes and define a reusable empirical interface testable across seeds, mixtures, and pipelines.
  arXiv  Detail & Related papers  (2026-04-20T13:25:40Z)
• AI Knows What's Wrong But Cannot Fix It: Helicoid Dynamics in Frontier LLMs Under High-Stakes Decisions [51.56484100374058]
  Helicoid dynamics is the name given to a specific failure regime in that second domain.<n>System engages competently, drifts into error, accurately names what went wrong, then reproduces the same pattern at a higher level of sophistication.<n>This prospective case series documents that regime across seven leading systems.
  arXiv  Detail & Related papers  (2026-03-12T05:25:49Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.