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Source note3 min read

MemOps: Benchmarking Lifecycle Memory Operations in Long-Horizon Conversations

Source

arXiv

arxiv.org/abs/2607.12893v1

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Source

Plain-Language Brief

The paper introduces MemOps, a new benchmark for evaluating the memory capabilities of large language models in extended, multi-session interactions. Unlike existing benchmarks that focus on downstream question answering, MemOps focuses on specific memory operations such as remembering, forgetting, updating, and reflects on their compositions. It provides a structured trace for each memory event and evaluates long-term memory performance through six categories of operation-level probes in both adjacent-evidence and long-context settings.

What the Document Says

  • The paper discusses the need for a new benchmark to evaluate the memory capabilities of large language models (LLMs) in extended, multi-session interactions.
  • Existing benchmarks focus on downstream question answering, but this approach conflates multiple causes of memory failure.
  • The proposed benchmark, MemOps, reformulates conversational memory as a sequence of lifecycle operations and evaluates each event with a structured trace.
  • MemOps includes six categories of operation-level probes for evaluation in both adjacent-evidence and long-context settings.
  • Across different types of systems (retrieval-based, parametric, and managed-memory), MemOps reveals that current systems struggle with maintaining consistent and reliable memory performance.

Key Concepts Mentioned

  • long-term memory
  • large language models
  • benchmark
  • downstream question answering
  • lifecycle operations
  • MemOps

Claims Made by the Document

  • Existing benchmarks conflate heterogeneous causes of memory failure.
  • Current systems remain far from uniformly reliable in long-term memory performance.

Evidence Used

  • The authors argue that existing benchmarks conflate the causes of memory failure because they only score the correctness of a final answer, which can credit correct answers despite reliance on inconsistent or unsafe memory states.
  • Results from MemOps reveal that current systems struggle with maintaining consistent and reliable memory performance.

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.