MemOps: Benchmarking Lifecycle Memory Operations in Long-Horizon Conversations

Source

• Source name: arXiv
• Source kind: paper
• Authors / organization: Xixuan Hao, Zeyu Zhang, Zehao Lin, Yihang Sun, Ziliang Guo, Xichong Zhang, Yuxuan Liang, Feiyu Xiong, Zhiyu Li
• Venue: arXiv: cs.AI
• Published: 2026-07-14
• Collected: 2026-07-15
• URL: http://arxiv.org/abs/2607.12893v1
• PDF: https://arxiv.org/pdf/2607.12893v1
• DOI: UNKNOWN

Plain-Language Brief

The paper introduces MemOps, a new benchmark for evaluating the lifecycle of memory operations in long-horizon conversations for LLM-based agents. Unlike existing benchmarks that focus on final answers, MemOps focuses on the sequence of operations such as remembering, forgetting, updating, and their compositions. It reveals that current systems have issues with maintaining consistent and safe memory states during long-term interactions.

What the Document Says

• Long-term memory is important for LLM-based agents in multi-session conversations.
• Existing benchmarks evaluate memory through downstream question answering, which conflates various causes of memory failure.
• The paper argues that memory in long-horizon interactions should be viewed as a lifecycle of explicit operations.
• MemOps is introduced as a new benchmark that reformulates conversational memory as a sequence of lifecycle operations.
• Each memory event in MemOps is represented with a structured trace specifying trigger, target, scope, state transition, and supporting evidence.
• A controllable generation pipeline embeds these operations into long conversations and produces gold operation traces along with six categories of operation-level probes.
• MemOps reveals issues with current systems such as inconsistent or unsafe memory states during long-term interactions.
• Session-level retrieval outperforms turn-level retrieval in MemOps.

Key Concepts Mentioned

• long-term memory
• LLM-based agents
• benchmark
• lifecycle operations
• MemOps
• downstream question answering

Claims Made by the Document

• Existing benchmarks evaluate memory incorrectly.
• Current systems have issues with maintaining consistent and safe memory states during long-term interactions.

Evidence Used

• The paper discusses the conflation of causes of memory failure in existing benchmarks
• Results from MemOps show inconsistencies and weaknesses in current systems.

Methods / Approach

UNKNOWN

Limitations Stated by the Authors

UNKNOWN

Limitations Noted by the Agent

Agent interpretation — not from the source.

UNKNOWN

Open Questions

UNKNOWN

Notes for Future Q&A

May include interpretation. Labeled where it does.

UNKNOWN

Raw Abstract / Excerpt

Long-term memory has become a foundational capability for LLM-based agents that accompany users across extended, multi-session interactions. Existing benchmarks, however, evaluate such memory almost exclusively through downstream question answering, scoring only the correctness of a final answer. This black-box formulation conflates the heterogeneous causes of memory failure, such as missing the introduction of a relevant fact, binding an operation to the wrong target, or relying on stale values after a correction. As a result, it can credit correct answers despite their reliance on inconsistent or unsafe memory states. In this paper, we argue that, in dynamic long-horizon interactions, memory is not a static collection of facts but a lifecycle of explicit operations, including remembering, forgetting, updating, reflecting, and their compositions. We introduce MemOps, a benchmark that reformulates conversational memory as a sequence of lifecycle operations and represents each memory event with a structured trace specifying its trigger, target, scope, state transition, and supporting evidence. A controllable generation pipeline embeds these operations into long, task-oriented conversations and produces gold operation traces together with six categories of operation-level probes, evaluated under both adjacent-evidence and long-context settings. Across long-context, retrieval-based, parametric and managed-memory systems, MemOps disentangles failure modes that final-answer accuracy alone conceals, revealing that current systems remain far from uniformly reliable. For instance, session-level retrieval outperforms turn-level retrieval, and long-context models remain notably weak at reconstructing ordered memory-state trajectories. These results move long-term memory evaluation from final-answer scoring toward interpretable, operation-level diagnosis.