mlsanityData to 7 Oct 2026

Set-shifting Behavioral Test for Harnessed Agents

arXiv 2607.13396 ↗First posted 15 Jul 2026cs.AI cs.CL cs.SEcited by 0

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

Abstract (arXiv)

What happens to an LLM agent's tool choice when the reliable tool silently changes within an ongoing session? We borrow the notion of set-shifting from cognitive psychology to study how well agents adapt to hidden reliability shifts. Our cognitive test for LLM agents mounts libraries of redundant tools and skills, in which many tools solve the same task but differ in hidden reliability. Using a branching schedule, we shift the reliable tool group in the environment and compare it with a stable control, allowing us to isolate the effect of each shift on the agent's behavior. We conduct our study on a panel of LLMs equipped with harnesses and show that the same set of shifts results in distinct behaviors across models: some latch onto a fixed routine within a few turns, whereas others continue to vary. Less capable models often omit the reliable tool group, while frontier models keep calling it alongside the other groups. We introduce a suite of measures to quantify agent behavior after reliability shifts. While policy prompting substantially alters behavior in some tested models, our findings highlight agents' brittleness when changes occur in indirectly observable context.

Key facts. No citing papers in the map yet (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 27 of its 51 references that are in the map. First posted on arXiv 15 Jul 2026.

Builds on

27 of 51 references.

Closest ancestors · 4

Same direction, newest first

  1. 2026Capable but Unreliable: Canonical Path Deviation as a Causal Mechanism of Agent Failure in Long-Horizon Tasks22 Feb
  2. 2025CostBench: Evaluating Multi-Turn Cost-Optimal Planning and Adaptation in Dynamic Environments for LLM Tool-Use Agents4 Nov
  3. 2024τ-bench: A Benchmark for Tool-Agent-User Interaction in Real-World Domains17 Jun
  4. 2023AgentBench: Evaluating LLMs as Agents7 Aug

Other references · 23

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  6. 2023ToolLLM: Facilitating Large Language Models to Master 16000+ Real-world APIscited by 1,880
  7. 2023Gorilla: Large Language Model Connected with Massive APIscited by 1,302
  8. 2026DeepSeek-V4: Towards Highly Efficient Million-Token Context Intelligencecited by 877
  9. 2025τ2-Bench: Evaluating Conversational Agents in a Dual-Control Environmentcited by 558
  10. 2022The Primacy Bias in Deep Reinforcement Learningcited by 242
  11. 2023MetaTool Benchmark for Large Language Models: Deciding Whether to Use Tools and Which to Usecited by 209
  12. 2025Agentic Reasoning and Tool Integration for LLMs via Reinforcement Learningcited by 107
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  3. 2024Unveiling Selection Biases: Exploring Order and Token Sensitivity in Large Language Modelscited by 20
  4. 2025ToolScope: Enhancing LLM Agent Tool Use through Tool Merging and Context-Aware Filteringcited by 19
  5. 2025Tool Preferences in Agentic LLMs are Unreliablecited by 13
  6. 2025BiasBusters: Uncovering and Mitigating Tool Selection Bias in Large Language Modelscited by 8
  7. 2024Reflection-Bench: Evaluating Epistemic Agency in Large Language Modelscited by 7
  8. 2026Breaking Contextual Inertia: Reinforcement Learning with Single-Turn Anchors for Stable Multi-Turn Interactioncited by 7
  9. 2025Visual Large Language Models Exhibit Human-Level Cognitive Flexibility in the Wisconsin Card Sorting Testcited by 5
  10. 2025Large Language Models are Near-Optimal Decision-Makers with a Non-Human Learning Behaviorcited by 3
  11. 2026From Biased Chatbots to Biased Agents: Examining Role Assignment Effects on LLM Agent Robustnesscited by 3

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