Skip to content

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

Sep 2026 · 0 citations · 21 references
Computer Science

TL;DR

This work presents \textsc{Revise}, a validity-guided runtime for fine-grained recovery in structured agent workflows, a validity-guided runtime for fine-grained recovery in structured agent workflows that matches a latest-version oracle with no stale outputs or effects.

Abstract

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 \textsc{Revise}, a validity-guided runtime for fine-grained recovery in structured agent workflows. When a revision arrives, \textsc{Revise} 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, \textsc{Revise} 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\%.

View source

Similar papers

#artificial intelligence Preprint Sep 2026

Beyond Approved Actions: Runtime Validation of Persistent Outcomes in Agent Workflows

EffectMatch is presented, a runtime that collects persistent changes within a controlled execution boundary and compares them with what the application approved for the current state and execution and blocks the silent acceptance and downstream propagation of persistent outcomes inconsistent with application approval.

Hao-Ran Zhang, Heng-Tong Zhang, Zhi-Yu Liang et al. · 0 citations
Preprint Aug 2026

BEGIN AI TRANSACTION: Semantic Isolation for Durable AI Workflows

The prototype, SemIso, propagates semantic context and blocks incompatible resources and branch merges with microsecond-scale checks and shows that these guarantees can be checked and enforced efficiently in middleware.

Barzan Mozafari · 1 citation
#artificial intelligence Preprint Sep 2026

TROVE: Adaptive Agent Skill Orchestration via Trace-Grounded Route Validation and Editing

Agents tend to optimize, select, or constrain execution structures before decisive runtime outcomes are observed. However, such pre-execution commitment creates an orchestration bottleneck: when intermediate evidence invalidates the pending continuation, agents must either execute stale steps or replan broadly, compoun...

Tian-Xing Wang, Ming-Ming Zhao, Shuai Huang et al. · 1 citation
#artificial intelligence Preprint Sep 2026

Fresh Memory, Stale Plans: Dependency-Scoped Validation for Distributed LLM-Agent Memory

P LAN F ENCE is introduced, a dependency-scoped action-validation protocol for distributed LLM-agent teams that avoids repeated update-path coordination as churn grows and avoids validating unrelated state as the shared keyspace grows.

Evan Chen, Shi-Qiang Wang, Christopher G. Brinton · 1 citation
Preprint Aug 2026

Correct Is Not Governed: Provenance Integrity in Agentic Workflows

Agentic workflows are commonly evaluated by whether they reach the correct outcome. That is insufficient in institutional settings, where a correct action may rely on the wrong authority, an unsupported completion claim, or work made stale by a later change. We define governed execution as work whose decisions, complet...

Jesus Salas · 4 citations

Related blog posts

Hugging Face Blog Oct 20, 2026

MPS in PyTorch 2.15

We’re on a journey to advance and democratize artificial intelligence through open source and open science.

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.