← 研踪 PaperTrailarXiv:2611.01585 · v1
StateGate: A Behavioral-Fidelity Prompt Optimization Method for Long-Chain LLM Agents
首次提交 2026-11-01 13:13
版本更新 2026-11-01 13:13
雷达观测 2026-11-04 23:43
摘要
Currently, long-chain LLM agents commonly suffer from behavioral degradation in complex tasks, where prompts are written in full but steps are not executed in full. To address this issue, existing approaches face three major challenges. First, constraints are strongly bound to their textual carriers; once natural-language constraint sentences are removed, their binding force vanishes. Second, the execution process lacks independent verification, as the evidence for judgment is often supplied by the executing party itself, leading to the failure of self-referential validation. Third, existing remediation schemes are difficult to generalize, and hard-coded mechanisms cannot be transferred across tasks. In this paper, we propose StateGate, a novel prompt-optimization scheme that transforms semantic constraints into symbolic gates enforced by an external deterministic runtime. By decoupling the content of a constraint from its carrier of efficacy, StateGate achieves behavioral fidelity in long-chain tasks without losing the binding force of the prompt. Specifically, we first propose four independent compression strategies (deduplication, generalization, externalization, and text-to-gating) together with their corresponding decision chains, enabling principled prompt symbolization. Second, we design a three-component architecture consisting of a state file, a validator, and an orchestrator, which thoroughly separates judgment authority from execution authority. Third, we formally prove that the compliance of symbolic constraints is independent of context size. Finally, we validate the framework on a vulnerability-discovery task in the security domain; the results show that StateGate achieves 100% execution closure and accurate vulnerability detection.
作者备注：13 pages, 3 figures, 7 tables. Preprint
观测到的版本
相邻记录可能跳过中间版本；首次发现 v2 及以上也会标为版本更新。
v1 · 2026-11-01 13:13 · 最新观测版本
StateGate: A Behavioral-Fidelity Prompt Optimization Method for Long-Chain LLM Agents
Currently, long-chain LLM agents commonly suffer from behavioral degradation in complex tasks, where prompts are written in full but steps are not executed in full. To address this issue, existing approaches face three major challenges. First, constraints are strongly bound to their textual carriers; once natural-language constraint sentences are removed, their binding force vanishes. Second, the execution process lacks independent verification, as the evidence for judgment is often supplied by the executing party itself, leading to the failure of self-referential validation. Third, existing remediation schemes are difficult to generalize, and hard-coded mechanisms cannot be transferred across tasks. In this paper, we propose StateGate, a novel prompt-optimization scheme that transforms semantic constraints into symbolic gates enforced by an external deterministic runtime. By decoupling the content of a constraint from its carrier of efficacy, StateGate achieves behavioral fidelity in long-chain tasks without losing the binding force of the prompt. Specifically, we first propose four independent compression strategies (deduplication, generalization, externalization, and text-to-gating) together with their corresponding decision chains, enabling principled prompt symbolization. Second, we design a three-component architecture consisting of a state file, a validator, and an orchestrator, which thoroughly separates judgment authority from execution authority. Third, we formally prove that the compliance of symbolic constraints is independent of context size. Finally, we validate the framework on a vulnerability-discovery task in the security domain; the results show that StateGate achieves 100% execution closure and accurate vulnerability detection.
查看此版本解读 →查看此版本原文 ↗