mlsanityData to 14 Oct 2026

Agentic Self-Awareness: Aligning Knowing, Acting, and Holding

arXiv 2610.19226 ↗First posted 13 Oct 2026cs.AIcited by 0

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

Abstract (arXiv)

Frontier AI agents increasingly act in environments where seemingly solvable tasks may lack prerequisites, contain conflicting requirements, or reveal blockers only during execution. We formulate agentic self-awareness (ASA) as the alignment of task-grounded feasibility judgments (know), warranted behavior (act), and responses to reconsideration (hold), distinguishing what a task permits from what an agent can solve. We build paired feasible and blocked tasks in Terminal-Bench 2.1 and retail τ2-bench and introduce an authorship-aware M0-M3 rubric that separates recognizing a blocker from taking the certified deferral action for the correct reason. Separate probes assess stated feasibility beliefs and responses to a single challenge. In a Terminal-Bench protocol baseline, blocker recognition reaches 84.4%, but strict self-awareness (SA) reaches only 40.6%. Harness search without model-weight updates increases strict SA from 45.3% to 93.8% on reused Terminal-Bench search tasks; selection for abstention alone, however, can reduce success on feasible tasks. Independently initialized joint-objective searches improve both blocked-task SA and feasible-task solve on their own search sets. Retail held-out gains remain limited, and Terminal-Bench blocked-task transfer is not measured. This evaluation pipeline makes the recognition-action gap measurable and sets a two-sided target for improving agent decisions under changing task evidence.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in General LLM agent frameworks and benchmarks, one of 16 directions in LLM agents and tool use. Builds on 35 of its 43 references that are in the map. First posted on arXiv 13 Oct 2026.

Builds on

35 of 43 references.

Closest ancestors · 14

Same direction, newest first

  1. 2026AgentAbstain: Do LLM Agents Know When Not to Act?11 Jul
  2. 2026Agentic Abstention: Do Agents Know When to Stop Instead of Act?27 Jun
  3. 2026From Knowing to Acting: Benchmarking Self-Awareness Capability of LLM Agents9 Jun
  4. 2026HiL-Bench (Human-in-Loop Benchmark): Do Agents Know When to Ask for Help?10 Apr
  5. 2026Agentic Uncertainty Reveals Agentic Overconfidence6 Feb
  6. 2026Uncertainty Quantification in LLM Agents: Foundations, Emerging Challenges, and Opportunities4 Feb
  7. 2026Agentic Confidence Calibration22 Jan
  8. 2026Agentic Uncertainty Quantification22 Jan
  9. 2025UProp: Investigating the Uncertainty Propagation of LLMs in Multi-Step Agentic Decision-Making20 Jun
  10. 2025Position: Uncertainty Quantification Needs Reassessment for Large-language Model Agents28 May
  11. 2024Automated Design of Agentic Systems15 Aug
  12. 2024Towards Uncertainty-Aware Language Agent25 Jan
2 more
  1. 2023The Rise and Potential of Large Language Model Based Agents: A Survey14 Sep
  2. 2023A Survey on Large Language Model based Autonomous Agents22 Aug

Other references · 21

  1. 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,695
  2. 2017On Calibration of Modern Neural Networkscited by 4,960
  3. 2023Reflexion: Language Agents with Verbal Reinforcement Learningcited by 4,276
  4. 2022Language Models (Mostly) Know What They Knowcited by 1,856
  5. 2023Towards Understanding Sycophancy in Language Modelscited by 1,040
  6. 2023Can LLMs Express Their Uncertainty? An Empirical Evaluation of Confidence Elicitation in LLMscited by 974
  7. 2017Selective Classification for Deep Neural Networkscited by 852
  8. 2023DSPy: Compiling Declarative Language Model Calls into Self-Improving Pipelinescited by 832
  9. 2022Teaching Models to Express Their Uncertainty in Wordscited by 751
  10. 2025τ2-Bench: Evaluating Conversational Agents in a Dual-Control Environmentcited by 627
  11. 2026Terminal-Bench: Benchmarking Agents on Hard, Realistic Tasks in Command Line Interfacescited by 585
  12. 2023Robots That Ask For Help: Uncertainty Alignment for Large Language Model Plannerscited by 308
9 more
  1. 2025Why Language Models Hallucinatecited by 220
  2. 2025AbstentionBench: Reasoning LLMs Fail on Unanswerable Questionscited by 145
  3. 2023Are You Sure? Challenging LLMs Leads to Performance Drops in The FlipFlop Experimentcited by 77
  4. 2024Overconfidence is Key: Verbalized Uncertainty Evaluation in Large Language and Vision-Language Modelscited by 54
  5. 2026HarnessX: A Composable, Adaptive, and Evolvable Agent Harness Foundrycited by 47
  6. 2026The Confidence Dichotomy: Analyzing and Mitigating Miscalibration in Tool-Use Agentscited by 16
  7. 2026On Calibration of Large Language Models: From Response To Capabilitycited by 7
  8. 2026MIRROR: A Hierarchical Benchmark for Metacognitive Calibration in Large Language Modelscited by 6
  9. 2026Do Agents Know What They Can't Do? Evaluating Feasibility Awareness in Tool-Using Agentscited by 2

Built on by

Not cited yet.

Similar, not cited

Close in wording, no citation either way

  1. 2026TRACE: A Self-Evolving Skill Bank for Consistent, Limit-Aware LLM Agents
  2. 2025Agentic Knowledgeable Self-awareness
  3. 2026Self-Designed Evaluators and Warm Memory for Long-Horizon Agents
  4. 2026Self-Aware Recursively Self-Improving Agents for Personal Singularity: A Goal-, Scope-, Tool-, and Benchmark-Driven Multi-Agent Architecture
  5. 2026Artificial Id: Drive and Persistent Alignment in Agentic AI
  6. 2026Evaluating Bounded Autonomy in Regulated Agentic AI: A Diagnostic Harness with Constitutional Rewards, Escalation Labels, and Runtime Governance
  7. 2026When Should an AI Act? A Human-Centered Model of Scene, Context, and Behavior for Agentic AI Design
  8. 2026SAAS: Self-Aware Reinforcement Learning for Over-Search Mitigation in Agentic Search