From Trace to Fix: Residual Task State in Long-Horizon Agents
Recovering from failures in long-horizon, stateful language agents is challenging because the action introducing a defect, the symptom exposing it, and the persistent state requiring repair frequently diverge.
ProofPaper ↗
Key points
- We introduce TRACE-FIX, a unified recovery framework centered on versioned residual task state.
- Trace transforms execution logs into a structured dependency-linked ledger, prioritizing unresolved task requirements rather than salient surface errors.
- To rigorously decouple localization accuracy from downstream repair capability, we present LongH-Debug, evaluating root-cause attribution, verified-fault recovery, and end-to-end closed-loop execution independently.
- Our empirical analysis demonstrates that structured residual tracking substantially improves causal localization over raw-trace baselines, while budget-aware sequential planning converts verified fault signals into more reliable task recovery without destructive trial-and-error.
Sources (1)
- [1]From Trace to Fix: Residual Task State in Long-Horizon AgentsarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 19, 04:41 AM
Recovering from failures in long-horizon, stateful language agents is challenging because the action introducing a defect, the symptom exposing it, and the persistent state requiring repair frequently diverge.
We introduce TRACE-FIX, a unified recovery framework centered on versioned residual task state.
Extractive summary: sentences quoted from the sources.
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