MEMOREPAIR: Barrier-First Cascade Repair in Agentic Memory
- URL: http://arxiv.org/abs/2605.07242v1
- Date: Fri, 08 May 2026 04:57:29 GMT
- Title: MEMOREPAIR: Barrier-First Cascade Repair in Agentic Memory
- Abstract summary: We present a barrier-first cascade-repair contract for agentic memory.<n>A repair event induces a controlled transition from invalidated descendant state to validated successor state.<n>We show that the induced publication problem reduces to maximum-weight predecessor closure and can be solved exactly by a single s-t min-cut.
- Score: 6.860488391361694
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Agentic memory evolves across tasks into durable derived artifacts: summaries, cached outputs, embeddings, learned skills, and executable tool procedures. When a source artifact is deleted, corrected, or invalidated by tool or API migration, descendants derived from that source can remain visible and steer future actions with stale support. We formalize this failure mode as the cascade update problem, where repair targets the visible derived state of the memory store. We present MemoRepair, a barrier-first cascade-repair contract for agentic memory. A repair event induces a controlled transition from invalidated descendant state to validated successor state: affected descendants are withdrawn before repair, successors are constructed from retained support and staged repaired predecessors under the current interface, and republication is restricted to validated predecessor-closed successors. This contract induces a scalarized repair-selection problem for a fixed repair-cost tradeoff. We show that the induced publication problem reduces to maximum-weight predecessor closure and can be solved exactly by a single s-t min-cut. Experiments on ToolBench and MemoryArena show that, with complete influence provenance, MemoRepair reduces invalidated-memory exposure from 69.8-94.3% under systems without cascade repair to 0%. Compared with exhaustive Repair all, it recovers 91.1-94.3% of validated successors while reducing normalized repair-operator cost from 1.00 to 0.57-0.76.
Related papers
- When Not to Imitate: Boundary-Aware Skill Memory for Reliable Tool-Use LLM Agents [62.10623192614306]
Extracting skills from past successes is critical for the efficient evolution of Large Language Model (LLM) agents.<n>We propose textbfBoundary-Aware Skill Memory (BASM), which augments each skill with explicit boundary fields.<n>BASM consistently outperforms success-distilled skill-memory baselines.
arXiv Detail & Related papers (2026-08-23T10:16:06Z) - From Faulty Memories to Corrected Actions: Dependency-Guided Rollback Repair for Memory-Augmented Agents [19.125716338799545]
textbfdependency-guided rollback repair builds a typed memory-to-action graph from provenance, traces explicit downstream dependencies, and selectively replays only answer-relevant affected computation.<n>We evaluate this approach on a 150-case controlled benchmark spanning three tool-use domains and four memory failure types.
arXiv Detail & Related papers (2026-08-11T05:19:55Z) - RepairFormer: Automated Repair of Structured Inputs Using Transformers [0.669087470775851]
This paper presents RepairFormer, a transformer-based framework for structured input repair.<n>The approach formulates repair as a supervised sequence generation task and uses format tags, oracle validation, and boundary-localized repair to generate valid outputs.<n>In evaluation, RepairFormer achieves a 88% in repair and 94% in recovery, showing strongest content preservation when repairs are successful.
arXiv Detail & Related papers (2026-08-05T17:08:31Z) - AuditCoder: Responsibility-Preserving Task Graphs for Auditable Code Generation and Bounded Repair [2.2743778085578437]
AuditCoder treats the program and an auditable construction trace as joint outputs.<n>For tasks with stable, locally testable boundaries, the graph functions not only as a decomposition structure but also as a persistent index for validation and repair.
arXiv Detail & Related papers (2026-07-31T15:29:14Z) - Beyond Fail-to-Pass: Iterative Hardening of Co-Generated Bug Reproduction Tests and Fixes [55.114939648705395]
Large language models (LLMs) have made automated program repair increasingly practical for real-world bugs.<n>Bug reproduction tests (BRTs) help close this gap by turning a bug report into an executable, bug-specific signal.<n>We propose CoHarden, a co-generation framework that uses the Lax signal as an in-loop convergence criterion.
arXiv Detail & Related papers (2026-07-22T07:30:07Z) - Conditional Co-Ablation: Recovering Self-Repair Backups in Transformer Circuits [47.668752416295185]
Conditional Co-Ablation asks how much each remaining unit's ablation effect grows once a primary set has been removed.<n>On the GPT-2-small IOI circuit, CoAx raises backup-head recovery from 0.33 to 0.91 ROC-AUC.
arXiv Detail & Related papers (2026-07-02T09:32:57Z) - Enhancing Software Engineering Through Closed-Loop Memory Optimization [50.418699819003486]
We introduce ours, a closed-loop framework for memory augmentation in software engineering (SE) agents.<n>ours grounds memory utility in textitvalidated downstream impact, establishing utility as a task-agnostic textbf benchmark and an annotation-free textbfoptimization signal.<n>Results demonstrate that ours consistently improves SE agents across settings, achieving absolute gains of up to $uparrow5.25%$ in success rate and $uparrow4.63%$ in resolve efficiency.
arXiv Detail & Related papers (2026-06-04T03:17:21Z) - Guarded Repair for Harm-Aware Post-hoc Replacement of LLM Mathematical Reasoning [0.0]
GuardedRepair is a best-of-N repair framework that diagnoses cached reasoning traces, selectively triggers repair, and accepts answer-changing candidates.<n>We show that GuardedRepair substantially improves the fixed/broken tradeoff, while also revealing that replacement risk is reduced rather than eliminated.
arXiv Detail & Related papers (2026-05-23T14:51:13Z) - MemRepair: Hierarchical Memory for Agentic Repository-Level Vulnerability Repair [10.709112246848058]
Modern software ecosystems face a rapidly growing number of disclosed vulnerabilities.<n>Most existing systems treat vulnerability repair as a single generation step over the currently visible code context.<n>We present MemRepair, a memoryaugmented agentic framework that formulates vulnerability repair as an iterative, experience-driven process.
arXiv Detail & Related papers (2026-05-17T13:29:46Z) - A Parametric Memory Head for Continual Generative Retrieval [52.66674234249913]
Generative information retrieval (GenIR) consolidates retrieval into a single neural model that decodes document identifiers (docids) directly from queries.<n>We show that sequential adaptation improves retrieval on newly added documents but substantially degrades performance on earlier slices.<n>We propose post-adaptation memory tuning (PAMT), a memory-only stabilization stage that augments an adapted model with a modular parametric memory head.
arXiv Detail & Related papers (2026-04-25T17:38:51Z) - QiMeng-PRepair: Precise Code Repair via Edit-Aware Reward Optimization [50.722636027998]
Large Language Models (LLMs) achieve strong program repair performance but often suffer from over-editing.<n>We propose PRepair, a framework that mitigates over-editing and improves repair accuracy.
arXiv Detail & Related papers (2026-04-07T14:56:38Z) - Beyond Crash-to-Patch: Patch Evolution for Linux Kernel Repair [2.267311896805228]
Linux kernel fixes undergo iterative revision on mailing lists before acceptance, with reviewer feedback shaping correctness, handling, and API compliance.<n>We present a large-scale study of kernel patch evolution, reconstructing 6946 syzbot-linked bug-fix lifecycles.<n>We develop PatchAdvisor, a repair framework that integrates retrieval-based memory with a fine-tuned diagnostic advisor to guide a coding agent toward reviewer-aligned patches.
arXiv Detail & Related papers (2026-04-04T20:23:32Z) - Are Benchmark Tests Strong Enough? Mutation-Guided Diagnosis and Augmentation of Regression Suites [49.16055123488827]
Insufficiently strong test suites can admit plausible yet semantically incorrect patches, inflating reported success rates.<n>We introduce STING, a framework for targeted test augmentation that uses semantically altered program variants as diagnostic stressors.<n>STING produces 1,014 validated tests spanning 211 instances and increases patch-region line and branch coverage by 10.8% and 9.5%.
arXiv Detail & Related papers (2026-04-02T01:13:40Z) - WARP: Guaranteed Inner-Layer Repair of NLP Transformers [4.191577542171072]
We present WARP, a constraint-based repair framework that extends repair beyond the last layer of Transformer models.<n> WARP formulates repair as a convex quadratic program derived from a first-order linearization of the logit gap.<n>We show that WARP converges to solutions satisfying all repair constraints under mild assumptions.
arXiv Detail & Related papers (2026-04-01T14:12:49Z) - NextMem: Towards Latent Factual Memory for LLM-based Agents [58.35585202907478]
NextMem is a latent factual memory framework that utilizes an autoregressive autoencoder to efficiently construct latent memory.<n>Extensive experiments demonstrate that NextMem achieves superior performance.
arXiv Detail & Related papers (2026-02-26T14:35:27Z) - Specification Vibing for Automated Program Repair [8.68148153927532]
VibeRepair is a specification-centric APR technique that treats repair as behavior-specification repair rather than ad-hoc code editing.<n>On Defects4J v1.2, VibeRepair correctly repairs 174 bugs, exceeding the strongest state-of-the-art baseline by 28 bugs.<n>On Defects4J v2.0, it repairs 178 bugs, outperforming prior approaches by 33 bugs, representing a 23% improvement.
arXiv Detail & Related papers (2026-02-09T04:44:58Z) - Outcome-Conditioned Reasoning Distillation for Resolving Software Issues [49.16055123488827]
We present an Outcome-Conditioned Reasoning Distillation(O-CRD) framework that uses resolved in-repository issues with verified patches as supervision.<n>Starting from a historical fix, the method reconstructs a stage-wise repair trace backward from the verified outcome.<n>On SWE-Bench Lite, this approach increases Pass@1 by 10.4% with GPT-4o, 8.6% with DeepSeek-V3, and 10.3% with GPT-5.
arXiv Detail & Related papers (2026-01-30T18:25:39Z) - Remember Me, Refine Me: A Dynamic Procedural Memory Framework for Experience-Driven Agent Evolution [52.76038908826961]
We propose $textbfReMe$ ($textitRemember Me, Refine Me$) to bridge the gap between static storage and dynamic reasoning.<n>ReMe innovates across the memory lifecycle via three mechanisms: $textitmulti-faceted distillation$, which extracts fine-grained experiences.<n>Experiments on BFCL-V3 and AppWorld demonstrate that ReMe establishes a new state-of-the-art in agent memory system.
arXiv Detail & Related papers (2025-12-11T14:40:01Z) - Inconsistency Handling in Prioritized Databases with Universal Constraints: Complexity Analysis and Links with Active Integrity Constraints [2.4622211579286133]
This paper revisits the problem of repairing and querying inconsistent databases equipped with universal constraints.<n>We adopt symmetric difference repairs, in which both deletions and additions of facts can be used to restore consistency.<n>We show how existing notions of optimal repairs, defined for simpler denial constraints and repairs solely based on fact deletion, can be suitably extended to our richer setting.
arXiv Detail & Related papers (2023-06-06T09:17:56Z)
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.