aiqbench analysis
This research identifies the "default trap," a phenomenon where LLM agents exhibit strong responsiveness to explicit priority instructions but appear weakly responsive when default-aligned plans are removed, potentially misleading evaluators about true controllability. By analyzing thousands of decision windows across retail and airline tasks, the study demonstrates that presentation-dependent defaults can significantly skew perceived agent behavior compared to actual priority-switching effects. These findings are critical for developers building reliable tool-using agents, as they highlight the necessity of distinguishing between genuine instruction following and artifacts introduced by default plan structures.
The Default Trap: Rethinking Plan Evaluation in Tool-Using LLM Agents
arXiv:2609.36829v1 Announce Type: cross Abstract: 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.
Abstract: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.
Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
arXiv:2609.36829 [cs.AI]
(or arXiv:2609.36829v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2609.36829 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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Full text under the publisher's open licence · 283 words
