Skip to content

Invalidation Contracts for Cross-Episode Agent Memory

Aug 2026 · 1 citation · 37 references
Computer Science

Abstract

LLM agents that cache recovery suggestions from API errors can skip re-derivation in later episodes, spending fewer tokens and fewer model calls on constraints they have already learned. Server-side data drift turns those cached fixes into silent failures, and the usual remedy, re-deriving on every episode, gives the savings back. We introduce invalidation contracts, a protocol layer that attaches version stamps and cacheability hints to every recovery suggestion so the client can evict stale entries without trial and error, and keep the rest. The contract decomposes realized savings into two independent factors: validity, the fraction of cached suggestions that remain correct after a drift event, and compliance, the fraction the planner applies on the first attempt. Validity depends only on the protocol and is vendor-independent. Compliance depends on the planner model: identical wire bytes yield 100% first-try compliance on Claude Haiku 4.5 and 11% or below on Claude Sonnet 5, which exhibits input-schema conservatism, refusing fixes that add fields the original request did not contain. We evaluate across seven models, three serving paths, two domains, and approximately 9,400 episodes. Row-level invalidation raises compliance by 0 to 66.7 percentage points across the seven models, 55.6 to 66.7 on three, and recovers 29-33% of baseline token cost on four of seven models, while table-level invalidation destroys co-located entries and drops post-drift first-try rates to 0% on five of seven. Eviction precision is 1.00 at row granularity on every model under the row-level oracle of Section 4.1. The contract adds 15% to response payload. Version-stamp validity is deterministic by construction and produced identical results across every model and serving path, with zero contract failures in the entire evaluation.

View source

Similar papers

#artificial intelligence Preprint Sep 2026

ERRAND: Budgeted Maintenance of Agent Memory

This work introduces ERRAND, which treats revalidation as a priced errand, which clears every non-oracle policy on the preregistered calibers, primary in every setting and conditional at every binding budget, leading eager revalidation by 10.0pp at the base cap.

Bei-Ning Wu, Zi-Hao Ding, Jun Huang · 2 citations
#artificial intelligence Preprint Sep 2026

Dude, Where's My State? Execution Information Requirements for Stateful Agents

The EIR is introduced, a lower bound on the information that must remain accessible for correct completion under specified task and access conditions and LACUNA is developed, a framework that generates tasks with known dependencies and varies information demand, retention, and recovery separately from the difficulty of...

N. Mehrotra, Ashish Tiwari, Priyanshu Gupta et al. · 0 citations
#artificial intelligence Preprint Oct 2026

Auditing Action Settlement in LLM Agent Environments: Order, Progress, and Replay

Concurrent actions in large language model (LLM) agent environments require arbitration even when each proposal is individually valid. We implement a typed snapshot-settlement contract and audit three distinct properties: order sensitivity, useful progress, and replay consistency. Five settlement policies are tested in...

Hao-Tian Chen, Bo-Wen Ye, Yu-Ning Zhang et al. · 0 citations
Preprint Sep 2026

TRACE: Governing Memory Validity in Evolving Multi-Agent Systems

TRACE is presented, a training-free layer that treats re-entry as an eligibility decision rather than a storage or retrieval operation, reconciling a departure checkpoint against absence-period updates, resolving explicit and implicit invalidation, and releasing a bounded Return View only when it covers the returning r...

Wen-Jun Xiong, Shengtao Zhang, Shangding Gu et al. · 0 citations
#artificial intelligence Preprint Sep 2026

Recoverability as a System Primitive for Long-Horizon AI Agents

This work introduces recoverability as a system primitive that makes reuse an explicit decision: select a supported starting point and a permitted recovery action, or withhold automatic continuation, and establishes why recovery decisions need their own evaluation.

Zhi-Hui Zhang, Wei Liu · 0 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.