让大模型生成更可靠，实时纠正输出偏差
ATLAS-RTC: Closing the Loop on LLM Agent Output with Token-Level Runtime Control
- 运行时逐标记监控，发现输出偏离即干预
- 首次尝试成功率提升20%~37.8%，失败场景延迟降低88%
- 适合需要高可靠性输出的工具调用和结构化生成任务
我们提出ATLAS-RTC，一种针对自回归语言模型的运行时控制机制，可在解码过程中强制生成结构化输出。该系统在每一步生成时进行监控，利用轻量信号检测输出与预设合约的偏移，并实施针对性干预，如偏置、掩码或回滚。不同于事后验证或静态约束解码，它采用闭环机制，在错误发生前即进行修正。在结构化生成与工具调用任务中，ATLAS-RTC将首次尝试成功率提升20至37.8个百分点，失败主导场景下延迟最高降低88%。结果表明，许多失败源于解码伪影而非任务理解错误，凸显运行时控制作为大模型系统独立层级的重要性。
原文摘要 · Abstract (English)
We present ATLAS-RTC, a runtime control system for autoregressive language models that enforces structured output during decoding. ATLAS-RTC monitors generation at each step, detects drift from output contracts using lightweight signals, and applies targeted interventions such as biasing, masking, and rollback. Unlike post-hoc validation or static constrained decoding, it operates in a closed loop, enabling correction before errors materialize. Across structured generation and tool-calling tasks, ATLAS-RTC improves first-attempt success rates by 20 to 37.8 percentage points, with up to 88% latency reduction in failure-dominated settings. Results show that many failures arise from decoding artifacts rather than task misunderstanding, motivating runtime control as a distinct layer in LLM systems.
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