Fail-Fast, Restart-Smart: Early Failure Prediction and Restart for SWE Agentic Tasks

• URL: http://arxiv.org/abs/2608.03222v1
• Date: Tue, 04 Aug 2026 06:55:46 GMT
• Title: Fail-Fast, Restart-Smart: Early Failure Prediction and Restart for SWE Agentic Tasks
• Abstract summary: FailFast-RestartSmart is a two-stage controller for a single active trajectory.<n>It predicts failure from observable prefixes without policy logits or hidden states.<n>Upon an alarm, RestartSmart launches a fresh same-policy rollout without prior prompt history.
• Score: 11.325524635118846
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Software engineering (SWE) agents resolve repository-level issues through long trajectories that grow increasingly expensive as context accumulates. Failed runs tend to be longer and exhibit redundant exploration or looping, suggesting that some failures may be detectable before completion. Early termination, however, risks interrupting trajectories that would otherwise succeed; conversely, an unsuccessful trajectory may still contain useful repository edits. We present FailFast-RestartSmart, a two-stage controller for a single active trajectory. FailFast is a lightweight 0.6B monitor trained with terminal and dense fail-to-pass supervision to predict failure from observable prefixes without policy logits or hidden states. Upon an alarm, RestartSmart launches a fresh same-policy rollout without prior prompt history and offers the interrupted repository diff as an optional overlay that the agent may inspect, apply, or discard. On SWE-bench Verified, a monitor trained solely on Qwen3.6-27B trajectories transfers to three other policies, including a closed-API model, and saves 14.6%-20.4% of execution tokens at a target 5% false-positive rate; on Qwen3.6-27B, its 20.4% saving exceeds the 12.5% achieved by our per-step AgentStop adaptation. At a target 25% false-positive rate, RestartSmart raises Qwen3.6-27B resolution from 66.6% to 71.8%, whereas cold restart reaches only 66.8%. Together, these results support early termination with sequential same-policy recovery.

Related papers

• ReDraft, Don't Just Distill: Reference-Driven Revision for Continual VLLM Post-Training [66.72708893922605]
  Post-training of large multimodal models should add new capabilities while preserving those from pre-training.<n>SFT gives explicit target supervision that learns a task from near-zero accuracy, but its off-policy targets move the model far enough to cause forgetting.<n>We introduce ReDraft, which obtains both from the model's own failures.
  arXiv  Detail & Related papers  (2026-09-15T04:59:03Z)
• RobustSGPO: Search-Space Control for Agent Harness Evolution [56.722805974785125]
  We introduce RobustSGPO, which specifies the requested edit, constructs and checks the patch, and continues search from either the incumbent or retained snapshots.<n>We evaluate permission scheduling, cumulative controls, and task-family transfer in the AgentX brainstorming workflow using 120 tasks, 95 runs, and 7,350 candidate attempts.
  arXiv  Detail & Related papers  (2026-09-09T03:00:03Z)
• FailForge: Distilling Procedural Competence from Persistent Failures into Code Agents [49.519184792774304]
  FailForge is an agentic framework that converts failed rollouts into training signal.<n>FailForge recovers over 26% of previously failed instances at marginal additional cost.
  arXiv  Detail & Related papers  (2026-08-09T08:22:57Z)
• AgentDebugX: An Open-Source Toolkit for Failure Observability, Attribution, and Recovery in LLM Agents [83.37969790806069]
  LLM agent failures are difficult to debug because the step where an error surfaces is often not the one that caused it.<n>We present Agent DebuggerX, an open-source framework that organizes debug as a closed loop of Detect, Attribute, Recover, and Rerun.<n>At its core, Deep Debugger performs multi-turn root-cause diagnosis through global trajectory understanding.
  arXiv  Detail & Related papers  (2026-07-21T06:21:13Z)
• Doomed from the Start: Early Abort of LLM Agent Episodes via a Recall-Controlled Probe Cascade [14.63102636181275]
  Large language model (LLM) agents often waste inference compute by continuing multi-step trajectories that are already doomed to fail.<n>We study early failure prediction and inference-time early stopping for LLM agents using hidden-state probes.<n>We turn this signal into a recall-controlled abort cascade for reducing LLM agent inference costs.
  arXiv  Detail & Related papers  (2026-07-07T17:03:55Z)
• Mastermind: Strategy-grounded Learning for Repository-Scale Vulnerability Reproduction [62.42945715963878]
  Repository-level vulnerability reproduction is a demanding software engineering (SE) task.<n>Recent LLM agents can often execute these steps when the approach is correct, yet they still fail by choosing the wrong strategy.<n>This paper argues that strategy, rather than the full action trajectory, is the right learning unit for such SE agents.
  arXiv  Detail & Related papers  (2026-07-02T06:27:27Z)
• AEGIS: A Backup Reflex for Physical AI [0.0]
  We introduce AEGIS, a selective escalation method that uses a lightweight probe on a weak policy's frozen activations to detect high-risk steps.<n>When the probe flags a step, control switches to a stronger separate policy, but only for the steps that need it.<n>On LIBERO-Spatial, AEGIS recovers 10.1% of the trajectories the weak policy alone loses, versus 4.6% for budget-matched blind escalation and 5.1% for a random-trigger placebo.
  arXiv  Detail & Related papers  (2026-06-04T19:09:22Z)
• Handoff Debt: The Rediscovery Cost When Coding Agents Take Over Interrupted Tasks [0.0]
  Real software work is messier because tasks are interrupted, reassigned, reviewed, and resumed from partial states left by another agent or engineer.<n>We study this missing dimension through emphhandoff debt: the rediscovery cost imposed when a predecessor's work is opaque or incomplete.<n>Our takeover protocol interrupts a coding agent at deterministic handoff points, freezes the repository, and evaluates successor agents under four handoff views.
  arXiv  Detail & Related papers  (2026-06-01T20:40:38Z)
• ESPO: Early-Stopping Proximal Policy Optimization [78.79610718910628]
  ESPO (Early-Stopping Proximal Policy Optimization) detects trajectory failure on-the-fly and terminates rollouts early.<n>On DeepSeek-R1-Distill-Qwen-7B trained for mathematical reasoning, ESPO surpasses PPO on AIME2024 (46.28% vs. 45.25%), AMC2023 (85.83% vs. 82.94%), and MATH-500 (87.42% vs. 85.43%)
  arXiv  Detail & Related papers  (2026-05-28T12:40:22Z)
• VLAA-GUI: Knowing When to Stop, Recover, and Search, A Modular Framework for GUI Automation [98.38575149237442]
  VLAA-GUI is a modular GUI framework built around three integrated components.<n>A mandatory Completeness Verifier enforces UI-observable success criteria and verification at every finish step.<n>A mandatory Loop Breaker provides multi-tier switching interaction mode after repeated failures.
  arXiv  Detail & Related papers  (2026-04-23T07:42:37Z)
• SafetyDrift: Predicting When AI Agents Cross the Line Before They Actually Do [0.0]
  SafetyDrift models agent safety trajectories as absorbing Markov chains.<n>In communication tasks, agents that reach even a mild risk state have an 85% chance of violating safety within five steps.<n>A monitor built on these models detects 94.7% of violations with 3.7 steps of advance warning at negligible computational cost.
  arXiv  Detail & Related papers  (2026-03-28T05:52:04Z)
• Safety Recovery in Reasoning Models Is Only a Few Early Steering Steps Away [97.11976870616273]
  We propose a lightweight inference-time defense that treats safety recovery as a satising constraint rather than an objective.<n>In our evaluations across six open-source MLRMs and four jailbreak benchmarks, SafeThink reduces attack success rates by 30-60%.
  arXiv  Detail & Related papers  (2026-02-11T18:09:17Z)
• Traversal-as-Policy: Log-Distilled Gated Behavior Trees as Externalized, Verifiable Policies for Safe, Robust, and Efficient Agents [18.383906296536185]
  Traversal-as-Policy: distill sandboxed OpenHands execution logs into a single executable Gated Behavior Tree (GBT)<n>Each node encodes a state-conditioned action macro mined and merge-checked from successful trajectories.<n>At runtime, a lightweight traverser matches the base model's intent to child macros.
  arXiv  Detail & Related papers  (2026-01-30T16:25:08Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.