输出纪律决定下限：面向可审计假设驱动决策的模型无关智能体证伪协议
Output Discipline Sets the Floor: A Falsification-Guided, Model-Agnostic Agent Protocol for Auditable Hypothesis-Driven Decision Making
AI 导读
一项 arXiv 研究提出模型无关的智能体框架，将决策形式化为五阶段闭环，用候选×证据矩阵证伪协议约束推理。
正文 · 原文
Abstract:What binds large language models on auditable, hypothesis-driven decision tasks, where every candidate can be checked against the evidence? On the public TCMEval-SDT benchmark for Traditional Chinese Medicine syndrome differentiation (200 cases), the binding constraint is output discipline and reasoning structure, not model capability alone: two years of model scaling move zero-shot scores by only -0.03 to +0.08, while output-discipline failures (non-terminating reasoning, format non-compliance, large run-to-run spread) persist even in frontier models. We test this with a model-agnostic agent framework formalizing decision making as a five-stage closed loop, enforced by a candidate x evidence matrix falsification protocol: every candidate is judged against every evidence item, at least half of the candidates must be falsified before a conclusion is licensed, and survivors are emitted in a machine-verifiable single-line format. Across five base models, the effect is sign-conditional on zero-shot output discipline: it rescues models whose discipline fails (DeepSeek-V4-Flash: +0.267/+0.271, entirely termination rescue), reduces a high-variance model's run-to-run standard deviation from 0.218 to 0.007 (Kimi K3), and significantly costs already-disciplined models (-0.087 to -0.120 on budget-matched Qwen3-4B/32B). Ablations attribute the cost to the candidate-bounded format layer and show the effect is independent of injected knowledge content (protocol-knowledge decoupling). The protocol also transfers to misinformation-robust mathematical reasoning, detecting injected false premises at 89.8% recall with a 6.8% false-positive rate (MCC 0.54; a flag-everything baseline scores 0) and restoring accuracy to the no-misinformation ceiling. Where zero-shot output discipline is weak, the reasoning protocol sets the floor; base-model capability always sets the ceiling.
| Comments: | 24 pages, 5 figures, 12 tables |
| Subjects: | Artificial Intelligence (cs.AI) |
| ACM classes: | I.2.7; I.2.3 |
| Cite as: | arXiv:2610.18298 [cs.AI] |
| (or arXiv:2610.18298v2 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2610.18298 arXiv-issued DOI via DataCite |
Submission history
From: Jing Liu [view email]
[v1]
Tue, 13 Oct 2026 12:46:44 UTC (111 KB)
[v2]
Mon, 26 Oct 2026 02:40:52 UTC (111 KB)
来源：arXiv cs.AI · arxiv.org