arXiv cs.AI· Daijin Hu, Xi Wang·· 3 天前AI 评分22
RASSG：面向流式工具执行的修复感知语义存活门控
RASSG: Repair-Aware Semantic Survival Gating for Streaming Tool Execution
AI 导读
针对流式语音助手在用户自我纠正后仍执行过期工具调用的问题，研究者提出 Repair-Aware Semantic Survival Gating（RASSG），在连续三次语音识别结果参数一致时启动工具，并结合参数失效风险与执行成本在不可逆前决定取消。
正文 · 原文
Abstract:Streaming speech assistants can launch tools before users finish speaking, overlapping execution with incoming speech to reduce user waiting time. However, subsequent self-corrections can invalidate apparently stable arguments, causing stale execution whose irreversible effects persist even after the final call is corrected. We propose Repair-Aware Semantic Survival Gating (RASSG) to jointly control tool launch and cancellation during streaming speech. RASSG launches a tool when three consecutive speech recognition results arrive with identical nonempty arguments, then combines calibrated argument invalidity risk with execution costs to decide cancellation at the last feasible opportunity before effects become irreversible. Evaluation on ReCAST, FDB-v3, and Audio2Tool uses a controlled execution clock with fixed tool runtimes and cancellation parameters. On ReCAST's prospective-repair subset, RASSG reduces the stale execution rate from LocalAgreement-2's 50.00% to 3.15%, with a mean delay of 3.676s across successful trajectories. The stale reduction also holds against every early-execution baseline on both public speech datasets. Ablations with identical initial launches support active cancellation, showing the value of controlling ongoing calls before their effects become irreversible. Code and data are available at this https URL.
| Subjects: | Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.14659 [cs.AI] |
| (or arXiv:2610.14659v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2610.14659 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Daijin Hu [view email]
[v1]
Sat, 10 Oct 2026 07:17:54 UTC (388 KB)
来源：arXiv cs.AI · arxiv.org