Set-shifting Behavioral Test for Harnessed Agents
Quick Answer
This study explores how LLM agents adapt to hidden reliability shifts in tool choices using a set-shifting behavioral test.
Quick Take
The evaluation reveals that agents quickly settle into routines, with distinct failure modes observed across open-weight . Additionally, the presentation of tool alternatives significantly influences routing dynamics.
Key Points
- Agents default to a small recurring routine after reliability shifts.
- Distinct failure modes are observed across open-weight LLMs.
- Set framing influences how agents route to tool alternatives.
- The benchmark involves tool-skill libraries with hidden reliability.
- Accuracy is scored based on routing to target tool groups post-shift.
Paper Resources
📖 Reader Mode
~2 min readAbstract: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.
| Subjects: | Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Software Engineering (cs.SE) |
| Cite as: | arXiv:2607.13396 [cs.AI] |
| (or arXiv:2607.13396v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2607.13396 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Ziwei Ye [view email]
[v1]
Wed, 15 Jul 2026 02:49:05 UTC (117 KB)
— Originally published at arxiv.org
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