Set-shifting Behavioral Test for Harnessed Agents
- URL: http://arxiv.org/abs/2607.13396v1
- Date: Wed, 15 Jul 2026 02:49:05 GMT
- Title: Set-shifting Behavioral Test for Harnessed Agents
- Abstract summary: We borrow set-shifting from cognitive psychology to study how well agents adapt to hidden reliability shifts.<n>We find that agents, by default, settle on a small recurring routine within a few turns of each boundary.<n>We score the set-shifting accuracy for each agent trajectory: the joint probability of routing to the target tool group in every post-shift window.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: What happens to an LLM agent's tool choice when the reliable tool silently changes within an ongoing session? We borrow set-shifting from cognitive psychology to study how well agents adapt to hidden reliability shifts. Our benchmark mounts tool-skill libraries with redundancies, where many tools solve the same task but differ in hidden reliability. In our evaluation framework, a branched schedule shifts the reliable tool group at hidden boundaries and pairs every shift with a no-shift control. We find that agents, by default, settle on a small recurring routine within a few turns of each boundary, with call shares concentrating on a few discrete values after each reliability shift. We score the set-shifting accuracy for each agent trajectory: the joint probability of routing to the target tool group in every post-shift window. We test open-weight LLMs in an open-source agentic harness and find qualitatively distinct failure modes across the same set of routines. We also find that set framing, how the toolset presents the alternatives as competing or complementary, shifts the routing dynamics.
Related papers
- Disagree to Explore, Agree to Commit: Routing-Guided Test-Time Scaling for Software Agents [60.650808962786364]
We study whether native MoE router traces can guide steering and selection without an external judge or selection-time test execution.<n>Our analysis shows that routing provides a robust behavioral role signal; token-granular readouts and decision-matched comparison sets turn it into effective control.<n>We evaluate on SWE-bench Verified using open-weight sparse MoE agents across scales and reasoning-effort settings.
arXiv Detail & Related papers (2026-08-23T03:07:03Z) - AgentPatch: Coarse-to-Fine Weak-Task Repair for Merging Agentic Multimodal Large Language Models [22.004098225093177]
AgentPatch is a training-free coarse-to-fine repair framework for agentic large language models.<n>It selects a stable merged backbone, restores diluted weak-task-specific signals through Weak-Task Unique Residual Recovery, and applies an Agent-Guided Behavior-Critical Patch.<n>AgentPatch produces a single static checkpoint without routing or ensembles.
arXiv Detail & Related papers (2026-08-07T01:52:45Z) - ScrambleToolBench: Agents Search Exhaustively Even When Their Own Map Points to the Next Step [74.83535434786145]
We introduce ScrambleToolBench, an interactive terminal benchmark designed to isolate behavioral reasoning.<n>The benchmark requires agents to uncover hidden tool behaviors entirely through trial-and-error interaction.<n>Our evaluation of state-of-the-art language models reveals that successful initial discovery does not translate into robust adaptation.
arXiv Detail & Related papers (2026-08-03T15:07:46Z) - Autonomous Topology Mutation: Safe Runtime Restructuring for Multi-Agent LLM Systems with Capability, State, and Shadow Invariants [0.11470070927586014]
We introduce Autonomous Topology Mutation (ATM), a runtime team-mutation mechanism for multi-agent LLM frameworks.<n>ATM combines telemetry-driven overload detection with three safety invariants that gate each structural change.<n>On 720 DeepSeek-V3-driven task runs with deterministic tool stubs across four ablation conditions and three workloads, the ATM factoriser split lifts code-task success from 3.3% to 61.7%.
arXiv Detail & Related papers (2026-06-04T11:38:06Z) - TeamTR: Trust-Region Fine-Tuning for Multi-Agent LLM Coordination [11.65571332626047]
TeamTR is a trust-region framework that resamples trajectories after each component update and enforces per-agent divergence control.<n>We show that TeamTR outperforms single-agent and sequential baselines with 7.1% on average, mitigates coordination regressions, and supports plug-and-play component replacement.
arXiv Detail & Related papers (2026-05-01T23:42:57Z) - Helpful to a Fault: Measuring Illicit Assistance in Multi-Turn, Multilingual LLM Agents [35.76774274440008]
STING (Sequential Testing of Illicit N-step Goal execution) is an automated red-teaming framework.<n>It constructs a step-by-step illicit plan grounded in a benign persona and iteratively probes a target agent with adaptive follow-ups.<n>We introduce an analysis framework that models multi-turn red-teaming as a time-to-first-jailbreak random variable.
arXiv Detail & Related papers (2026-02-18T10:31:19Z) - Unsafer in Many Turns: Benchmarking and Defending Multi-Turn Safety Risks in Tool-Using Agents [68.20752678837377]
We propose a principled taxonomy that transforms single-turn harmful tasks into multi-turn attack sequences.<n>Using this taxonomy, we construct MT-AgentRisk, the first benchmark to evaluate multi-turn tool-using agent safety.<n>We propose ToolShield, a training-free, tool-agnostic, self-exploration defense.
arXiv Detail & Related papers (2026-02-13T18:38:18Z) - Tool-to-Agent Retrieval: Bridging Tools and Agents for Scalable LLM Multi-Agent Systems [1.2092584191043323]
We introduce Tool-to-Agent Retrieval, a unified framework that embeds both tools and their parent agents in a shared vector space.<n>By explicitly representing tool capabilities and traversing metadata to the agent level, Tool-to-Agent Retrieval enables granular tool-level or agent-level retrieval.
arXiv Detail & Related papers (2025-11-03T18:58:28Z) - Multi-Agent Tool-Integrated Policy Optimization [67.12841355267678]
Large language models (LLMs) increasingly rely on multi-turn tool-integrated planning for knowledge-intensive and complex reasoning tasks.<n>Existing implementations typically rely on a single agent, but they suffer from limited context length and noisy tool responses.<n>No existing methods support effective reinforcement learning post-training of tool-integrated multi-agent frameworks.
arXiv Detail & Related papers (2025-10-06T10:44:04Z) - T^2Agent A Tool-augmented Multimodal Misinformation Detection Agent with Monte Carlo Tree Search [51.91311158085973]
multimodal misinformation often arises from mixed forgery sources, requiring dynamic reasoning and adaptive verification.<n>We propose T2Agent, a novel misinformation detection agent that incorporates a toolkit with Monte Carlo Tree Search.<n>Extensive experiments show that T2Agent consistently outperforms existing baselines on challenging mixed-source multimodal misinformation benchmarks.
arXiv Detail & Related papers (2025-05-26T09:50:55Z) - Learning to Use Tools via Cooperative and Interactive Agents [58.77710337157665]
Tool learning empowers large language models (LLMs) as agents to use external tools and extend their utility.
We propose ConAgents, a Cooperative and interactive Agents framework, which coordinates three specialized agents for tool selection, tool execution, and action calibration separately.
Our experiments on three datasets show that the LLMs, when equipped with ConAgents, outperform baselines with substantial improvement.
arXiv Detail & Related papers (2024-03-05T15:08:16Z) - Randomized Entity-wise Factorization for Multi-Agent Reinforcement
Learning [59.62721526353915]
Multi-agent settings in the real world often involve tasks with varying types and quantities of agents and non-agent entities.
Our method aims to leverage these commonalities by asking the question: What is the expected utility of each agent when only considering a randomly selected sub-group of its observed entities?''
arXiv Detail & Related papers (2020-06-07T18:28:41Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.