mlsanityData to 7 Oct 2026

SAUP: Situation Awareness Uncertainty Propagation on LLM Agent

arXiv 2412.01033 ↗First posted 2 Dec 2024cs.CL cs.LGcited by 14

On the map: LLM agents and tool use → General LLM agent frameworks and benchmarks

Abstract (arXiv)

Large language models (LLMs) integrated into multistep agent systems enable complex decision-making processes across various applications. However, their outputs often lack reliability, making uncertainty estimation crucial. Existing uncertainty estimation methods primarily focus on final-step outputs, which fail to account for cumulative uncertainty over the multistep decision-making process and the dynamic interactions between agents and their environments. To address these limitations, we propose SAUP (Situation Awareness Uncertainty Propagation), a novel framework that propagates uncertainty through each step of an LLM-based agent's reasoning process. SAUP incorporates situational awareness by assigning situational weights to each step's uncertainty during the propagation. Our method, compatible with various one-step uncertainty estimation techniques, provides a comprehensive and accurate uncertainty measure. Extensive experiments on benchmark datasets demonstrate that SAUP significantly outperforms existing state-of-the-art methods, achieving up to 20% improvement in AUROC.

Key facts. Cited by 14 papers in the map (data through 7 Oct 2026). Placed in General LLM agent frameworks and benchmarks, one of 16 directions in LLM agents and tool use. Builds on 24 of its 40 references that are in the map. First posted on arXiv 2 Dec 2024.

Builds on

24 of 40 references.

Closest ancestors · 3

Same direction, newest first

  1. 2024Self-Reflection in LLM Agents: Effects on Problem-Solving Performance5 May
  2. 2024Towards Uncertainty-Aware Language Agent25 Jan
  3. 2023The Rise and Potential of Large Language Model Based Agents: A Survey14 Sep

Other references · 21

  1. 2023GPT-4 Technical Reportcited by 18,132
  2. 2024The Llama 3 Herd of Modelscited by 14,192
  3. 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,266
  4. 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,355
  5. 2021Evaluating Large Language Models Trained on Codecited by 8,053
  6. 2020Measuring Massive Multitask Language Understandingcited by 7,768
  7. 2023Reflexion: Language Agents with Verbal Reinforcement Learningcited by 4,064
  8. 2018HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answeringcited by 3,686
  9. 2023A Survey of Large Language Modelscited by 2,537
  10. 2023AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversationcited by 1,906
  11. 2022Language Models (Mostly) Know What They Knowcited by 1,783
  12. 2021WebGPT: Browser-assisted question-answering with human feedbackcited by 1,722
9 more
  1. 2018Know What You Don't Know: Unanswerable Questions for SQuADcited by 1,538
  2. 2021CodeT5: Identifier-aware Unified Pre-trained Encoder-Decoder Models for Code Understanding and Generationcited by 890
  3. 2023Semantic Uncertainty: Linguistic Invariances for Uncertainty Estimation in Natural Language Generationcited by 795
  4. 2024Large Language Models: A Surveycited by 506
  5. 2023Do Large Language Models Know What They Don't Know?cited by 238
  6. 2021On Hallucination and Predictive Uncertainty in Conditional Language Generationcited by 176
  7. 2023Making Language Models Better Tool Learners with Execution Feedbackcited by 69
  8. 2020Uncertainty Estimation in Autoregressive Structured Predictioncited by 21
  9. 2022Rainproof: An Umbrella To Shield Text Generators From Out-Of-Distribution Datacited by 21

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 4; 2026: 9.

14 papers.

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  1. 2024A Survey on Uncertainty Quantification of Large Language Models: Taxonomy, Open Research Challenges, and Future Directionscited by 115
  2. 2025UProp: Investigating the Uncertainty Propagation of LLMs in Multi-Step Agentic Decision-Makingcited by 36
  3. 2025Towards Agents That Know When They Don't Know: Uncertainty as a Control Signal for Structured Reasoningcited by 10
  4. 2026SELAUR: Self Evolving LLM Agent via Uncertainty-aware Rewardscited by 9
  5. 2025Agentic Knowledgeable Self-awarenesscited by 8
  6. 2025BrowseConf: Confidence-Guided Test-Time Scaling for Web Agentscited by 5
  7. 2026Every Response Counts: Quantifying Uncertainty of LLM-based Multi-Agent Systems through Tensor Decompositioncited by 5
  8. 2026From Passive Metric to Active Signal: The Evolving Role of Uncertainty Quantification in Large Language Modelscited by 1
  9. 2026Helicase: Uncertainty-Guided Supply Chain Knowledge Graph Construction with Autonomous Multi-Agent LLMscited by 1
  10. 2026Confidence Laundering in Agent Systems: Why Uncertainty Needs a Latent Carriercited by 1
  11. 2026How to Speculate about Uncertainty in Agentic Coding? A Draft-Model Gate Methodcited by 1
  12. 2026Proper Scoring Rules for Agentic Uncertainty Quantificationcited by 0
2 more
  1. 2026Semantic Uncertainty-Guided Orchestration in Hierarchical Multi-Agent Systemscited by 0
  2. 2026Uncertainty Quantification for LLM Agents: A Taxonomy, an Evaluation Protocol, and an Empirical Studycited by 0

Similar, not cited

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  1. 2026PropUQ-MAS: Propagation-Aware Uncertainty Quantification for LLM Multi-Agent Systems
  2. 2026From Sequence to Structure: Relational Uncertainty Propagation for LLM Agents
  3. 2026Uncertainty Quantification in LLM Agents: Foundations, Emerging Challenges, and Opportunities
  4. 2025Position: Uncertainty Quantification Needs Reassessment for Large-language Model Agents
  5. 2026Uncertainty Decomposition for Clarification Seeking in LLM Agents
  6. 2026Runtime Uncertainty Monitoring for LLM-Based Multi-Agent Systems Using Bayesian Networks
  7. 2026Sequential Probabilistic Uncertainty Estimation for Parallel Multi-Agent Reasoning Systems
  8. 2024Rethinking the Uncertainty: A Critical Review and Analysis in the Era of Large Language Models