mlsanityData to 14 Oct 2026

Polished but Unresolved: Identifying Late-Stage Pressure States in Long-Horizon Tool-Use Agents

arXiv 2609.00823 ↗First posted 1 Sep 2026cs.AI cs.CLcited by 1

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Abstract (arXiv)

Long-horizon tool-use agents need not only to search and plan, but also to decide when to finalize. We study late-stage pressure states, in which an agent is biased toward submitting a final answer that appears complete and polished while key constraints remain unresolved. We first train a linear probe to show that this pressure state is identifiable from the agent's hidden states. Then, we use activation interventions along this pressure direction and find that shifting the hidden states changes both the pressure score and whether the agent continues tool use or submits early. Through controlled context manipulations, we further see that the pressure is mitigated by constraint clarity and action mapping. Based on these findings, we propose Probe-Sensed Pressure Relief (PSPR), a plugin that applies lightweight pressure relief direction under moderate pressure and moves to structured organization under high pressure risk. Experiments on multiple long-horizon benchmarks show that our method consistently strengthens existing agent methods.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 19 of its 27 references that are in the map. First posted on arXiv 1 Sep 2026.

Builds on

19 of 27 references.

References · 19

  1. 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,695
  2. 2023Steering Llama 2 via Contrastive Activation Additioncited by 1,085
  3. 2025BrowseComp: A Simple Yet Challenging Benchmark for Browsing Agentscited by 647
  4. 2024TravelPlanner: A Benchmark for Real-World Planning with Language Agentscited by 392
  5. 2024AgentBoard: An Analytical Evaluation Board of Multi-turn LLM Agentscited by 339
  6. 2025Layer by Layer: Uncovering Hidden Representations in Language Modelscited by 311
  7. 2023LEACE: Perfect linear concept erasure in closed formcited by 267
  8. 2024Semantic Entropy Probes: Robust and Cheap Hallucination Detection in LLMscited by 259
  9. 2024Improving Instruction-Following in Language Models through Activation Steeringcited by 160
  10. 2024Adaptive Activation Steering: A Tuning-Free LLM Truthfulness Improvement Method for Diverse Hallucinations Categoriescited by 77
  11. 2025Angular Steering: Behavior Control via Rotation in Activation Spacecited by 53
  12. 2025TRAJECT-Bench:A Trajectory-Aware Benchmark for Evaluating Agentic Tool Usecited by 51
7 more
  1. 2026DeepPlanning: Benchmarking Long-Horizon Agentic Planning with Verifiable Constraintscited by 46
  2. 2026The Long-Horizon Task Mirage? Diagnosing Where and Why Agentic Systems Breakcited by 25
  3. 2025Improving Reasoning Performance in Large Language Models via Representation Engineeringcited by 23
  4. 2026Why Reasoning Fails to Plan: A Planning-Centric Analysis of Long-Horizon Decision Making in LLM Agentscited by 23
  5. 2026ResearchGym: Evaluating Language Model Agents on Real-World AI Researchcited by 22
  6. 2026Inference-Time Budget Control for LLM Search Agentscited by 13
  7. 2026When Is Enough Not Enough? Illusory Completion in Search Agentscited by 5

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

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  1. 2026Beyond Token Savings: A Systematic Study of Context Compression in LLM Agents
  2. 2026Control Under Compression: Reliability Frontiers for Tool-Using Agents
  3. 2026AgentEscapeBench: Evaluating Out-of-Domain Tool-Grounded Reasoning in LLM Agents
  4. 2026AgentProcessBench: Diagnosing Step-Level Process Quality in Tool-Using Agents
  5. 2026Rethinking the Role of Entropy in Optimizing Tool-Use Behaviors for Large Language Model Agents
  6. 2026Ask the Tool, Don't Guess: Agent Tool Calls Hold Their Progress, and the Serving System Should Read It
  7. 2026Interlock: From Local Correctness to Task Completion in Long-Horizon Agents
  8. 2026ToolPRMBench: Evaluating and Advancing Process Reward Models for Tool-using Agents