arXiv — Machine Learning · · 7 min read

When May an Agent Stop? Evidence-Carrying Termination for Tool-Using LLMs

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Computer Science > Software Engineering

arXiv:2608.23623 (cs)
[Submitted on 22 Aug 2026]

Title:When May an Agent Stop? Evidence-Carrying Termination for Tool-Using LLMs

Authors:Jason Liu
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Abstract:Tool-using agents must decide when to stop. Existing systems already gate terminal success, certify execution traces, or enforce runtime polici es, but do not test this particular receipt-, scope-, and closed-replay design at the COMPLETE boundary across controlled termination faults. W e instantiate and evaluate Evidence-Carrying Termination (ECT): an agent may return COMPLETE only when a typed certificate binds every required answer claim to valid, in-scope trace evidence and a deterministic replay reconstructs the claimed value. A locked static study crosses 48 ful ly synthetic tasks in six tool-use families with clean execution and eight faults. ECT produced 0/288 unsafe completions versus 252/288 for the inspected termination-critic core (difference -87.50 pp, 95% task-cluster interval [-87.50, -87.50] pp). A fresh, prespecified and frozen 576- trajectory study then compares ECT with the critic core, its faithful controller, and a full-trace LLM critic. On 22 primary held-out task clus ters, ECT produced 0/66 premature unsupported terminations versus 40/66 for the controller (difference -60.61 pp, 95% interval [-78.79, -40.91] pp), while supported completion was 97/132 versus 92/132 (difference 3.79 pp, interval [0.00, 9.09] pp), satisfying a -10-point noninferiority margin. ECT executed successful recovery in 18/66 trajectories, of which 17 subsequently completed with support; all three closed-loop gates p assed. ECT certifies support in a recorded trace under declared assumptions, not external truth, safety, or alignment.
Subjects: Software Engineering (cs.SE); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
Cite as: arXiv:2608.23623 [cs.SE]
  (or arXiv:2608.23623v1 [cs.SE] for this version)
  https://doi.org/10.48550/arXiv.2608.23623
arXiv-issued DOI via DataCite

Submission history

From: Jiacheng Liu [view email]
[v1] Sat, 22 Aug 2026 18:56:27 UTC (1,664 KB)
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