mlsanityData to 7 Oct 2026

The Default Trap: Rethinking Plan Evaluation in Tool-Using LLM Agents

arXiv 2609.36829 ↗First posted 29 Sep 2026cs.AI cs.LGcited by 0

On the map: LLM agents and tool use → Financial trading and forecasting agents

Abstract (arXiv)

An executor can respond strongly to a change in a supplied plan's priority while showing a small change in the same information-selection probability when a default-aligned whole plan is removed. We call the risk of interpreting the latter as weak responsiveness to alternative priorities the default trap. We compare paired plans that prioritize different information targets with a shared no-plan reference. An accounting identity relates these distinct behavioral contrasts. Across 3,200 decision windows on 160 selected Retail, Airline, and AgentDojo tasks, switching priorities strongly redirects two models' choices, while the two plan-versus-default contrasts differ. In 2,160 additional windows, reversing account-list order shifts default target selection by 63.3-98.3 percentage points; priority-switching effects remain 96.7-100.0 points in either order. A separate 3,240-window component study finds strong control under single priority sentences, with effects of additional text varying by group and direction. Finally, 1,080 full-task episodes yield observed success differences of -19.4 to +8.3 points relative to no plan. All Retail and Airline success intervals include zero; AgentDojo results describe four fixed application worlds. These findings support joint reporting of priority responsiveness, presentation-dependent defaults, and task success and cost.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Financial trading and forecasting agents, one of 16 directions in LLM agents and tool use. Builds on 25 of its 27 references that are in the map. First posted on arXiv 29 Sep 2026.

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25 of 27 references.

References · 25

  1. 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,355
  2. 2023Lost in the Middle: How Language Models Use Long Contextscited by 3,383
  3. 2023WebArena: A Realistic Web Environment for Building Autonomous Agentscited by 1,908
  4. 2024τ-bench: A Benchmark for Tool-Agent-User Interaction in Real-World Domainscited by 1,280
  5. 2021Fantastically Ordered Prompts and Where to Find Them: Overcoming Few-Shot Prompt Order Sensitivitycited by 1,122
  6. 2023Language Models Don't Always Say What They Think: Unfaithful Explanations in Chain-of-Thought Promptingcited by 1,081
  7. 2023Measuring Faithfulness in Chain-of-Thought Reasoningcited by 668
  8. 2025Why Do Multi-Agent LLM Systems Fail?cited by 559
  9. 2025τ2-Bench: Evaluating Conversational Agents in a Dual-Control Environmentcited by 558
  10. 2023Plan-and-Solve Prompting: Improving Zero-Shot Chain-of-Thought Reasoning by Large Language Modelscited by 550
  11. 2022PlanBench: An Extensible Benchmark for Evaluating Large Language Models on Planning and Reasoning about Changecited by 372
  12. 2024AgentBoard: An Analytical Evaluation Board of Multi-turn LLM Agentscited by 319
13 more
  1. 2025Plan-and-Act: Improving Planning of Agents for Long-Horizon Taskscited by 190
  2. 2023ReWOO: Decoupling Reasoning from Observations for Efficient Augmented Language Modelscited by 174
  3. 2023ADaPT: As-Needed Decomposition and Planning with Language Modelscited by 172
  4. 2023An LLM Compiler for Parallel Function Callingcited by 170
  5. 2025Towards a Science of Scaling Agent Systemscited by 125
  6. 2026Towards a Science of AI Agent Reliabilitycited by 58
  7. 2025BiasBusters: Uncovering and Mitigating Tool Selection Bias in Large Language Modelscited by 8
  8. 2026Revision or Re-Solving? Decomposing Second-Pass Gains in Multi-LLM Pipelinescited by 7
  9. 2026From Plan to Action: How Well Do Agents Follow the Plan?cited by 4
  10. 2026Plans Don't Persist: Why Context Management Is Load Bearing for LLM Agentscited by 4
  11. 2026Looking Is Not Picking: An Attention-Segment Account of Tool-Selection Failures in LLM Agentscited by 3
  12. 2026Does Rank Still Matter? Position Bias When AI Agents Shop on Our Behalfcited by 3
  13. 2026Strategic Evaluation of Planning Strategies for LLM Agents in Cyber-Physical Systemscited by 1

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