Confidence Laundering in Agent Systems: Why Uncertainty Needs a Latent Carrier
Not on the map. Why?
Abstract (arXiv)
Modern agent systems can turn uncertainty into overconfidence. Fragile upstream decisions are often exposed to downstream components as clean intermediate artifacts, while the uncertainty behind those decisions is lost at the interface. As a result, local ambiguity can become system-level error amplification. We argue that this reveals an interface bottleneck in agent uncertainty propagation: uncertainty does not propagate simply because a trajectory contains uncertain steps; it propagates only when it survives the handoff between components. We define uncertain decision handoff as the transfer of an intermediate decision made under uncertainty, and identify confidence laundering as a failure mode in which fragile upstream states are repackaged as procedurally valid artifacts that downstream agents over-trust. To address this bottleneck, we propose latent uncertainty as an uncertainty-bearing carrier attached to decision handoffs. Rather than replacing text with hidden states, latent uncertainty aims to preserve pre-commitment fragility in a form that downstream components can use. This position shifts agent uncertainty propagation from step-wise uncertainty estimation toward uncertainty-preserving interface design for more recoverable agent systems.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 56 of its 66 references that are in the map. First posted on arXiv 9 Jun 2026.
Builds on
56 of 66 references.
References · 56
- 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,297
- 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,695
- 2022Self-Consistency Improves Chain of Thought Reasoning in Language Modelscited by 5,708
- 2023Toolformer: Language Models Can Teach Themselves to Use Toolscited by 4,182
- 2023Tree of Thoughts: Deliberate Problem Solving with Large Language Modelscited by 3,211
- 2022Language Models (Mostly) Know What They Knowcited by 1,856
- 2023Can LLMs Express Their Uncertainty? An Empirical Evaluation of Confidence Elicitation in LLMscited by 974
- 2023Semantic Uncertainty: Linguistic Invariances for Uncertainty Estimation in Natural Language Generationcited by 819
- 2023Just Ask for Calibration: Strategies for Eliciting Calibrated Confidence Scores from Language Models Fine-Tuned with Human Feedbackcited by 812
- 2023SelfCheckGPT: Zero-Resource Black-Box Hallucination Detection for Generative Large Language Modelscited by 810
- 2024Training Large Language Models to Reason in a Continuous Latent Spacecited by 797
- 2022Teaching Models to Express Their Uncertainty in Wordscited by 751
38 more
- 2023The Internal State of an LLM Knows When It's Lyingcited by 659
- 2023Active Retrieval Augmented Generationcited by 516
- 2023How Language Model Hallucinations Can Snowballcited by 311
- 2023Robots That Ask For Help: Uncertainty Alignment for Large Language Model Plannerscited by 308
- 2023Generating with Confidence: Uncertainty Quantification for Black-box Large Language Modelscited by 293
- 2024Semantic Entropy Probes: Robust and Cheap Hallucination Detection in LLMscited by 259
- 2025Learning to Reason without External Rewardscited by 210
- 2025Agentic Reinforced Policy Optimizationcited by 172
- 2024Kernel Language Entropy: Fine-grained Uncertainty Quantification for LLMs from Semantic Similaritiescited by 159
- 2025Where LLM Agents Fail and How They can Learn From Failurescited by 133
- 2023LM-Polygraph: Uncertainty Estimation for Language Modelscited by 131
- 2024A Survey on Uncertainty Quantification of Large Language Models: Taxonomy, Open Research Challenges, and Future Directionscited by 121
- 2025Uncertainty Quantification and Confidence Calibration in Large Language Models: A Surveycited by 95
- 2025The Obvious Invisible Threat: LLM-Powered GUI Agents' Vulnerability to Fine-Print Injectionscited by 59
- 2023Let Models Speak Ciphers: Multiagent Debate through Embeddingscited by 51
- 2025First Return, Entropy-Eliciting Explorecited by 45
- 2024Detecting Hallucinations in Large Language Model Generation: A Token Probability Approachcited by 42
- 2025UProp: Investigating the Uncertainty Propagation of LLMs in Multi-Step Agentic Decision-Makingcited by 42
- 2025VCRL: Variance-based Curriculum Reinforcement Learning for Large Language Modelscited by 36
- 2025Unlocking Exploration in RLVR: Uncertainty-aware Advantage Shaping for Deeper Reasoningcited by 34
- 2025Enabling Agents to Communicate Entirely in Latent Spacecited by 34
- 2024CLEAR: Towards Contextual LLM-Empowered Privacy Policy Analysis and Risk Generation for Large Language Model Applicationscited by 33
- 2024Towards Uncertainty-Aware Language Agentcited by 32
- 2025AgentRouter: A Knowledge-Graph-Guided LLM Router for Collaborative Multi-Agent Question Answeringcited by 32
- 2025AutoData: A Multi-Agent System for Open Web Data Collectioncited by 25
- 2025GraphTracer: Graph-Guided Failure Tracing in LLM Agents for Robust Multi-Turn Deep Searchcited by 23
- 2026Uncertainty Quantification in LLM Agents: Foundations, Emerging Challenges, and Opportunitiescited by 20
- 2025KVComm: Enabling Efficient LLM Communication through Selective KV Sharingcited by 18
- 2025Search Wisely: Mitigating Sub-optimal Agentic Searches By Reducing Uncertaintycited by 17
- 2025MAPRO: Recasting Multi-Agent Prompt Optimization as Maximum a Posteriori Inferencecited by 17
- 2026Agentic Confidence Calibrationcited by 15
- 2026EvolveRouter: Co-Evolving Routing and Prompt for Multi-Agent Question Answeringcited by 15
- 2024SAUP: Situation Awareness Uncertainty Propagation on LLM Agentcited by 14
- 2025LLMs4All: A Review of Large Language Models Across Academic Disciplinescited by 14
- 2024Rethinking LLM Uncertainty: A Multi-Agent Approach to Estimating Black-Box Model Uncertaintycited by 11
- 2025Do not Abstain! Identify and Solve the Uncertaintycited by 11
- 2026TRACER: Trajectory Risk Aggregation for Critical Episodes in Agentic Reasoningcited by 11
- 2025Towards Agents That Know When They Don't Know: Uncertainty as a Control Signal for Structured Reasoningcited by 10
50 most cited of 56 All 56 (JSON)
Built on by
Citing papers per year: 2026: 1.
1 paper.