Test-time reasoning effort and unauthorized tool use in language-model agents: a prespecified equivalence study
- URL: http://arxiv.org/abs/2608.03169v1
- Date: Tue, 04 Aug 2026 06:01:14 GMT
- Title: Test-time reasoning effort and unauthorized tool use in language-model agents: a prespecified equivalence study
- Abstract summary: Language-model agents that execute through tool calls operate under access-control policies that restrict which operations each role may perform.<n>We vary reasoning effort (low, max) inside GPT-5.6 across 14 confirmatory scenarios of TRIO-20.<n>Across 840 trajectories and two model tiers, no unauthorized tool call occurred.
- Score: 1.6787220460106658
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Language-model agents that execute multi-step workflows through tool calls operate under access-control policies that restrict which operations each role may perform. The APIs serving these agents expose a reasoning-effort parameter that operators adjust for cost and latency. Whether this parameter also changes the rate of unauthorized tool use has not been tested by direct manipulation within a single model. We vary reasoning effort (low, max) inside GPT-5.6 across the 14 confirmatory scenarios of TRIO-20, a suite of 20 matched workplace triads in which a policy-prohibited tool call is effective and its effect on the target metric is stated in the environment, effective but discoverable only through rule inspection, or ineffective. The three conditions derive from one code base and differ in two configuration fields, with identical prompts and tool sets. All analyses were prespecified in a frozen plan before confirmatory collection. Across 840 trajectories and two model tiers, no unauthorized tool call occurred. Exact one-sided 95% limits place each arm's violation rate below 3.50% (Terra, n = 84) and 5.21% (Sol, n = 56). The interaction estimand, with a simultaneous exact 95% interval of $\pm 4.34$ percentage points on Terra, lies inside the $\pm 7.01$-point equivalence margin. Raising effort did change behaviour, but only in inspection: rule-probe rates rose in all conditions, most where probing carried no instrumental payoff, a pattern inconsistent with the hypothesis of targeted search ($-14.3$ points, 95% CI $-27.4$ to $+1.2$). Raw trajectories are released at https://github.com/WenJing95/trio-20.
Related papers
- Tunable Tool-Call Rates in LLM Agents via Representation Steering [45.332672398938136]
We show that a tool can be controlled by a single linear direction in its residual stream, extracted without any training from the model's own tool-use preference signal.<n>We also show that the direction generalizes to unseen tools with strength comparable to each tool's own direction without favoring any specific tool choice.
arXiv Detail & Related papers (2026-08-25T22:35:18Z) - TRACE: A Self-Evolving Skill Bank for Consistent, Limit-Aware LLM Agents [41.82249291285459]
TRACE iteratively improves a skill-based agent's behavioral knowledge without modifying model weights.<n>On GPT-5.5, TRACE improves consistency (Pass3) by 34.6 points, from 59.9% to 94.5%, while shrinking the gap between potential and reliable performance to just 4.0 points.<n>These results show that TRACE converts high model potential into stable, consistent performance gain.
arXiv Detail & Related papers (2026-08-24T04:36:03Z) - Beyond Pass@k: Measuring Reliability and Security of Agentic Code Generation [2.1734195143282693]
We propose reliability@k, the same estimator applied correctly, with n = independent rollouts and c = fully-passing rollouts per (task, agent) pair.<n>Motivated by evidence that functional correctness does not imply security safety, we additionally propose security-adjusted reliability@k.
arXiv Detail & Related papers (2026-08-11T16:01:15Z) - Business Truth, not SQL Accuracy: A Rule-Gated 7B Analytics Agent Outperforms a Direct-Prompted 32B Baseline [0.0]
This paper introduces WarehouseBench, 400 frozen tasks over two synthetic warehouses in which roughly half the correct responses are a clarification, an abstention or a refusal.<n>QueryProof, a 7B agent, uses rules derived from a semantic layer and physical catalog to determine its behaviour.
arXiv Detail & Related papers (2026-08-10T08:14:24Z) - Teach it to stop, not just to click [0.0]
We show a repaired policy's success rate is dominated by upstream footing.<n>We release a library (cua_reliability) for routine k-seed reporting.
arXiv Detail & Related papers (2026-07-19T08:46:06Z) - CFAgentBench: A Reproducible Environment and Benchmark for Autonomous Construction-Finance Agents [0.0]
CFAgentBench is a benchmark for autonomous construction-finance agents.<n>It contains 1,014 machine-gradeable task specifications across 8 domains and 77 families.<n>The strongest agent reaches pass1 = 0.67 but only pass5 = 0.38.
arXiv Detail & Related papers (2026-06-20T11:34:52Z) - Scaling Laws for Agent Harnesses via Effective Feedback Compute [53.68149869349268]
emphEffective Feedback Compute (EFC) is a trace-level scaling coordinate that credits feedback only when it is informative, valid, non-redundant, and retained for subsequent decisions.<n>EFC-based coordinates consistently predict failure rates better than raw-compute baselines.
arXiv Detail & Related papers (2026-05-28T09:45:47Z) - AgentAtlas: Beyond Outcome Leaderboards for LLM Agents [0.025718125188898048]
AgentAtlas reframes agent evaluation as a diagnostic vocabulary and audit protocol.<n>The paper contributes: (i) a six-state control-decision taxonomy (Act / Ask / Refuse / Stop / Confirm / Recover); (ii) a trajectory-failure vocabulary with primary error source and downstream impact; and (iv) an illustrative protocol study on a synthetic 1,342-item set evaluated with eight models.
arXiv Detail & Related papers (2026-05-19T22:05:12Z) - ComplexMCP: Evaluation of LLM Agents in Dynamic, Interdependent, and Large-Scale Tool Sandbox [61.862814740220806]
$textbfComplexMCP$ is a benchmark designed to evaluate agents in rigorous conditions.<n>Built on the Model Context Protocol (MCP), $textbfComplexMCP$ provides over 300 meticulously tested tools.
arXiv Detail & Related papers (2026-05-11T16:20:51Z) - Measuring the Permission Gate: A Stress-Test Evaluation of Claude Code's Auto Mode [9.116800340266066]
Claude Code's auto mode is the first deployed permission system for AI coding agents.<n>Anthropic reports a 0.4% false positive rate and 17% false negative rate on production traffic.<n>We evaluate 253 state-changing actions at the individual action level against oracle ground truth.
arXiv Detail & Related papers (2026-04-04T17:56:30Z) - When Agents Disagree With Themselves: Measuring Behavioral Consistency in LLM-Based Agents [0.0]
ReAct-style agents produce 2.0--4.2 distinct action sequences per 10 runs on average, even with identical inputs.<n>Tasks with consistent behavior achieve 80--92% accuracy, while highly inconsistent tasks achieve only 25--60%.<n>Our results suggest that monitoring behavioral consistency during execution could enable early error detection and improve agent reliability.
arXiv Detail & Related papers (2026-02-12T06:15:14Z) - SABER: Small Actions, Big Errors -- Safeguarding Mutating Steps in LLM Agents [52.20768003832476]
We analyze execution traces on $$-Bench (Airline/Retail) and SWE-Bench Verified.<n>We formalize emphdecisive deviations, earliest action, level divergences that flip success to failure.<n>We introduce cm, a model-agnostic, gradient-free, test-time safeguard.
arXiv Detail & Related papers (2025-11-26T01:28:22Z) - SOPBench: Evaluating Language Agents at Following Standard Operating Procedures and Constraints [59.645885492637845]
SOPBench is an evaluation pipeline that transforms each service-specific SOP code program into a directed graph of executable functions.<n>Our approach transforms each service-specific SOP code program into a directed graph of executable functions and requires agents to call these functions based on natural language SOP descriptions.<n>We evaluate 18 leading models, and results show the task is challenging even for top-tier models.
arXiv Detail & Related papers (2025-03-11T17:53:02Z) - Provably Efficient Action-Manipulation Attack Against Continuous Reinforcement Learning [49.48615590763914]
We propose a black-box attack algorithm named LCBT, which uses the Monte Carlo tree search method for efficient action searching and manipulation.
We conduct our proposed attack methods on three aggressive algorithms: DDPG, PPO, and TD3 in continuous settings, which show a promising attack performance.
arXiv Detail & Related papers (2024-11-20T08:20:29Z) - $τ$-bench: A Benchmark for Tool-Agent-User Interaction in Real-World Domains [43.43344028212623]
$tau$-bench is a benchmark emulating dynamic conversations between a user and a language agent.
We employ an efficient and faithful evaluation process that compares the database state at the end of a conversation with the annotated goal state.
arXiv Detail & Related papers (2024-06-17T19:33:08Z)
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.