# REVISE: Validity-Guided Recovery for Online Revisions in Agent Workflows

arXiv: https://arxiv.org/abs/2609.00643 · HTML: https://mlsanity.com/paper/2609.00643/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 20 of its 21 references that are in the map.
- First posted on arXiv 1 Sep 2026.

## Builds on (most cited first, 20 of 20)

- [SWE-bench: Can Language Models Resolve Real-World GitHub Issues?](https://mlsanity.com/paper/2310.06770/) (2023)
- [SGLang: Efficient Execution of Structured Language Model Programs](https://mlsanity.com/paper/2312.07104/) (2023)
- [An LLM Compiler for Parallel Function Calling](https://mlsanity.com/paper/2312.04511/) (2023)
- [AIOS: LLM Agent Operating System](https://mlsanity.com/paper/2403.16971/) (2024)
- [SWE-chat: Coding Agent Interactions From Real Users in the Wild](https://mlsanity.com/paper/2604.20779/) (2026)
- [Atomix: Timely, Transactional Tool Use for Reliable Agentic Workflows](https://mlsanity.com/paper/2602.14849/) (2026)
- [Cordon: Semantic Transactions for Tool-Using LLM Agents](https://mlsanity.com/paper/2606.17573/) (2026)
- [Parallelizing Tool Execution and LLM Generation for Low-Latency Agent Serving](https://mlsanity.com/paper/2603.18897/) (2026)
- [TraceLab: Characterizing Coding Agent Workloads for LLM Serving](https://mlsanity.com/paper/2606.30560/) (2026)
- [WebRollback: Enhancing Web Agents with Explicit Rollback Mechanisms](https://mlsanity.com/paper/2504.11788/) (2025)
- [Generator-Assistant Stepwise Rollback Framework for Large Language Model Agent](https://mlsanity.com/paper/2503.02519/) (2025)
- [Crab: A Semantics-Aware Checkpoint/Restore Runtime for Agent Sandboxes](https://mlsanity.com/paper/2604.28138/) (2026)
- [DeltaBox: Scaling Stateful AI Agents with Millisecond-Level Sandbox Checkpoint/Rollback](https://mlsanity.com/paper/2605.22781/) (2026)
- [ACRFence: Preventing Semantic Rollback Attacks in Agent Checkpoint-Restore](https://mlsanity.com/paper/2603.20625/) (2026)
- [DART: Semantic Recoverability for Structured Tool Agents](https://mlsanity.com/paper/2605.23311/) (2026)
- [AgentRewind: Recoverable Execution for Long-Horizon LLM Agents](https://mlsanity.com/paper/2608.14380/) (2026)
- [Revisable by Design: A Theory of Streaming LLM Agent Execution](https://mlsanity.com/paper/2604.23283/) (2026)
- [Leyline: KV Cache Directives for Agentic Inference](https://mlsanity.com/paper/2606.01065/) (2026)
- [SWE-Review: Closing the Loop on Issue Resolution with Agentic Code Review](https://mlsanity.com/paper/2607.06065/) (2026)
- [Efficient Parallel Self-Adjusting Computation](https://mlsanity.com/paper/2105.06712/) (2021)

## Abstract (arXiv)

Agent revisions expose a fundamental correctness--efficiency trade-off during concurrent execution. Discarding ongoing work preserves latest-version correctness but wastes progress that may remain valid, whereas reusing prior work preserves efficiency but risks propagating stale state into outputs and tool effects. Existing recovery strategies resolve this trade-off in an imbalanced way with coarse-grained policies: they either favor efficiency by allowing potentially stale work to continue, or favor correctness by restarting the workflow or recomputing a linear suffix from the earliest conflict, thereby discarding unaffected progress. We present textscRevise, a validity-guided runtime for fine-grained recovery in structured agent workflows. When a revision arrives, textscRevise first intersects its delta with recorded data and control dependencies and propagates the resulting impact through the partially executed DAG to identify affected work. It then stops invalid work, preserves validity-established progress beyond the earliest conflict, and recomputes only the affected region. Incomplete provenance conservatively expands recovery, while reused results are revalidated before commit. Analysis of real coding-agent traces show online recovery opportunities: 118 sessions retain observable work before a queued later message is delivered; across 167 overlapping assistant responses, enqueue-to-completion overlap reaches 56.55~s at p95. Across 300 challenging revision/commit executions, textscRevise matches a latest-version oracle with no stale outputs or effects. On unmodified LangGraph and LLMCompiler applications using Qwen3-14B, it reduces model calls by 40.6--56.0% relative to full restart and by 31.3--43.6% relative to suffix recomputation. Under serving pressure, it further reduces revision-to-correct-completion tokens by 13.26% and improves SLO goodput by 3.07--5.43%.