ChronoMem: Version Control and Semantic Rollback for Large Language Model Agent Memory

2026-07-30

AI Summary

This work addresses the limitations of existing large language model agents, whose memory systems lack version control and semantic rollback capabilities, rendering them vulnerable to issues such as memory corruption, concept drift, and erroneous updates. To overcome these challenges, we propose a semantic-level memory versioning mechanism that captures full memory snapshots upon each write operation, constructs a structured version history, and introduces the first natural language–driven global memory rollback system. Our approach integrates hybrid lexical-semantic retrieval, rank fusion, and reranking techniques to accurately map rollback intents to relevant historical versions. Experimental results demonstrate that our system significantly outperforms prompt-only and retrieval-only baselines in long-horizon dialogues and rollback tasks, achieving superior performance on rollback-consistent question answering and historical summarization. We also release the first benchmark for evaluating memory rollback capabilities.

Abstract

LLM agents increasingly rely on long-term memory to support multi-session interaction and personalization. However, existing agent memory systems are designed around forward-only evolution, continuously accumulating, consolidating, and overwriting knowledge, with no principled mechanism to inspect, version, or revert prior states. This makes agents brittle under corrections, concept drift, and memory corruption, particularly after they have already been exposed to subsequent information. We present ChronoMem, a semantic version-control layer for agentic memory integrated into the production-ready, open-source Agent Development Kit by Google. ChronoMem commits whole-memory snapshots at each memory write, maintains structured version histories, and supports natural-language rollback requests by mapping undo intents to concrete historical versions through hybrid lexical and semantic retrieval, rank fusion, and reranking. We further introduce a post-exposure evaluation protocol that tests whether an agent can behave counterfactually after rollback by answering queries and summarizing history as if future updates had never occurred. On long-horizon conversational benchmarks augmented with evolving memory states and rollback tasks, ChronoMem substantially improves rollback-consistent question answering and history summarization relative to prompt-only and retrieval-only baselines, while achieving strong performance in semantic version selection. To our knowledge, ChronoMem is the first open-source system and benchmark for systematic semantic global memory rollback in LLM agents.

Problem

• version control
• semantic rollback
• LLM agent memory
• memory corruption
• concept drift

Innovation

• semantic rollback
• version control
• LLM agent memory
• memory snapshot
• counterfactual evaluation