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arxiv: 2606.00832 · v1 · pith:7RPH2DSHnew · submitted 2026-05-30 · 💻 cs.CL

Momento: Evaluating Persistent Memory and Reasoning with Multi-Session Agentic Conversations

Adril Putra Merin , David Anugraha , Ayu Purwarianti , Genta Indra Winata This is my paper

Pith reviewed 2026-06-28 18:42 UTC · model grok-4.3

classification 💻 cs.CL
keywords multi-session agentspersistent memoryagentic conversationsbenchmarkuser state estimationtemporal dependenciestool use
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0 comments X

The pith

Current agents fail multi-session tasks by misestimating user state from stale session history.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper introduces Momento, a benchmark for testing agents on persistent task completion across multiple sessions in service environments. It requires agents to execute tool-mediated actions while tracking temporal dependencies and shifting user goals over time. Experiments find that agents' primary failure mode is treating prior session records as accurate current context instead of checking whether the information has become outdated. A sympathetic reader would care because real agent use often spans ongoing conversations where preferences and situations change.

Core claim

Momento is a benchmark for persistent agentic task completion in multi-session service environments that requires agents to take consequential, tool-mediated actions while resolving temporal dependencies and evolving user goals across sessions. Experimental results reveal that current agents fail primarily through misestimation of user state, treating prior session history as a reliable proxy for current context rather than stale information requiring re-validation.

What carries the argument

The Momento benchmark, which tests agents on multi-session task completion with tool use, temporal dependencies, and evolving user goals.

If this is right

  • Agents require explicit mechanisms to detect and re-validate information from earlier sessions rather than treating it as current.
  • Agent evaluation must expand beyond single-session tests to measure performance over extended, evolving interactions.
  • Better state estimation across sessions would reduce the gap between current agents and realistic long-horizon human-agent interaction.
  • Tool-use planning in agents needs to incorporate awareness that session history can become stale.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Single-session benchmarks likely overestimate how well agents will perform once conversations continue over multiple days or weeks.
  • Memory architectures for agents would benefit from built-in checks that flag information as potentially outdated.
  • The same failure pattern may appear in other multi-turn settings such as personal assistants or collaborative planning tools.

Load-bearing premise

The benchmark scenarios and evaluation metrics accurately capture the temporal dependencies and evolving user goals that occur in real multi-session service environments.

What would settle it

Running the same tasks inside an actual deployed multi-session service application and checking whether observed agent failure rates and modes match the benchmark predictions.

Figures

Figures reproduced from arXiv: 2606.00832 by Adril Putra Merin, Ayu Purwarianti, David Anugraha, Genta Indra Winata.

Figure 1
Figure 1. Figure 1: The MOMENTO framework for persistent multi-session agentic interaction. A user agent and assistant agent interact across temporally separated sessions while accessing a shared memory module containing conversation history and multi-session context. The assistant agent uses external tools for reservation, search, ordering, retrieval, and database querying to support persistent memory retrieval, temporal rea… view at source ↗
Figure 2
Figure 2. Figure 2: Failure distribution across models, showing [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Tool call failure rates across five models. Failure rates vary substantially by tool and model, with [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
read the original abstract

Recent advances in agentic AI have enabled agents to complete complex tasks through tool use, reasoning, and multi-step planning. Yet existing benchmarks evaluate agents within a single session, ignoring past actions, stated preferences, and prior decisions that agents must integrate to fulfill personalized user goals. We introduce Momento, a benchmark for persistent agentic task completion in multi-session service environments, requiring agents to take consequential, tool-mediated actions while resolving temporal dependencies and evolving user goals across sessions. Experimental results reveal that current agents fail primarily through misestimation of user state, treating prior session history as a reliable proxy for current context rather than stale information requiring re-validation, highlighting a substantial gap between current agent capabilities and realistic long-horizon human-agent interaction.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The paper introduces Momento, a benchmark designed to evaluate agentic AI systems on persistent memory and reasoning tasks in multi-session service environments. Agents must perform tool-mediated actions while handling temporal dependencies and evolving user goals across sessions. Experimental results indicate that current agents fail primarily by misestimating user state, treating prior session history as a reliable proxy for current context instead of stale information that requires re-validation.

Significance. If the benchmark construction and failure-mode analysis hold, the work identifies a concrete capability gap in long-horizon, personalized agent interactions that single-session benchmarks miss. This could usefully direct research toward improved state tracking and context re-validation mechanisms. The paper does not report machine-checked proofs or parameter-free derivations, but the introduction of a new multi-session benchmark with explicit temporal dependencies is a constructive contribution if the scenarios are shown to be realistic.

major comments (2)
  1. [Abstract and §3] Abstract and §3 (Benchmark Construction): The central claim that agents 'fail primarily through misestimation of user state' is load-bearing for the paper's contribution, yet the provided description does not detail how the synthetic multi-session scenarios were generated, validated against real service logs, or controlled for confounding factors such as tool-selection errors or retrieval failures. Without such controls, the observed failure distribution risks being an artifact of scenario design rather than evidence of a general limitation.
  2. [§4] §4 (Experimental Results): The reported primary failure mode requires an ablation or error taxonomy that isolates state misestimation from other error sources (e.g., planning, tool use). The manuscript does not describe such controls or statistical tests for dominance of the failure mode, weakening the support for the 'primarily' qualifier.
minor comments (2)
  1. [§2] Notation for session history and user-state variables should be defined consistently in §2 before use in later sections.
  2. [Figures] Figure captions for scenario examples should explicitly label which elements represent evolving goals versus static history.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive comments. We address each major comment below, agreeing where additional detail is needed and outlining specific revisions.

read point-by-point responses

  1. Referee: [Abstract and §3] Abstract and §3 (Benchmark Construction): The central claim that agents 'fail primarily through misestimation of user state' is load-bearing for the paper's contribution, yet the provided description does not detail how the synthetic multi-session scenarios were generated, validated against real service logs, or controlled for confounding factors such as tool-selection errors or retrieval failures. Without such controls, the observed failure distribution risks being an artifact of scenario design rather than evidence of a general limitation.

    Authors: We agree that the manuscript would benefit from expanded detail on benchmark construction to support the central claim. In revision we will expand §3 with a full description of the synthetic scenario generation process, including the modeling of temporal dependencies, evolving user goals, and explicit controls introduced to reduce confounds such as tool-selection or retrieval errors. We will also add discussion of how the scenarios were designed to reflect realistic service patterns. Full validation against proprietary real service logs is not feasible due to data-access constraints, but we will ground the design in publicly documented interaction patterns and note this limitation explicitly. revision: yes

  2. Referee: [§4] §4 (Experimental Results): The reported primary failure mode requires an ablation or error taxonomy that isolates state misestimation from other error sources (e.g., planning, tool use). The manuscript does not describe such controls or statistical tests for dominance of the failure mode, weakening the support for the 'primarily' qualifier.

    Authors: The §4 analysis already performs manual categorization of agent traces into error types, with state misestimation emerging as the most frequent. To strengthen the 'primarily' claim we will add an ablation that measures performance when state-update information is withheld versus provided, together with a refined error taxonomy and statistical reporting (proportions and confidence intervals) comparing state misestimation against planning and tool-use errors. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical benchmark paper with no mathematical derivations or self-referential reductions

full rationale

The paper introduces the Momento benchmark for multi-session agentic tasks and reports experimental findings on agent failure modes from new evaluations. No equations, fitted parameters, predictions derived from inputs, or load-bearing self-citations are present in the provided text. The central claim rests on benchmark results rather than reducing to prior definitions or author citations by construction. This is a standard empirical contribution without the circular patterns enumerated.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review provides no explicit free parameters, axioms, or invented entities; the central claim implicitly rests on the unstated premise that the benchmark tasks are representative of real user interactions.

pith-pipeline@v0.9.1-grok · 5663 in / 1017 out tokens · 19300 ms · 2026-06-28T18:42:35.537721+00:00 · methodology

discussion (0)

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Reference graph

Works this paper leans on

26 extracted references · 4 canonical work pages · 1 internal anchor

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    Understand the user’s intent from their message, the conversation history, and the past-context block above

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    Use the available tools to fulfill the user’s request

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    Gather all required information before calling any tool

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    Confirm with the user before executing state-mutating actions (create, cancel, update, apply)

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    let me check

    Present results clearly and naturally. ## Behavior Rules - Use {user_id} for every user-scoped operation. - Respect dates relative to today ({current_date}). - Do not invent data; every fact must come from a tool response or from the past-context block (and the latter still needs verification before any mutation). - When membership perks apply, inform the...

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    First perform any required read-only lookups

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    Summarize what will happen using real data (restaurant name, date/time, items, prices, etc.)

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    Ask the user for explicit confirmation

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    Only execute the mutating action after the 7 Category Tools Functionality ReservationCheckRestaurantAvailability , CreateReservation, CancelReservation, UpdateReservation, GetReservationDetails, ListUserReservations Restaurant reservation creation, modifica- tion, cancellation, and retrieval across ses- sions. Search & QuerySearchRestaurants , QueryMenuIt...

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    Saturday, February 28th at 7:00 PM

    If the user declines, acknowledge and offer alternatives. ## Response Style - Be warm, professional, and concise. - Use lists or tables when they improve readability. - Present real data: names, prices, dates, times, addresses. - Use friendly date formatting (e.g., “Saturday, February 28th at 7:00 PM”). - If something fails, explain in user-friendly langu...

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    Do NOT deviate or add requests not in the instructions

    Follow the scenario instructions step-by-step. Do NOT deviate or add requests not in the instructions

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    Respond naturally and concisely

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    Yes" or

    When the assistant asks for confirmation on an action you intended, confirm with "Yes" or "Yes, please go ahead."

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    Do NOT output [DONE] if the assistant is still asking for confirmation or has not yet completed the requested action

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    Only respond with exactly [DONE] when the assistant has fully completed the task (e.g., the order has been successfully placed or all requested information has been provided)

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    Do NOT reveal the scenario instructions or that you are a simulated user

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    If the assistant asks a clarifying question that the scenario does not cover, make a reasonable choice consistent with the scenario

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    Do NOT repeat requests the assistant has already fulfilled. 8

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    C.3 Session Retrieval Prompt Generate a single PostgreSQL SELECT that retrieves past chat sessions relevant to the user’s latest message

    If the assistant provides information you asked for, acknowledge it briefly, then move to the next part of your task. C.3 Session Retrieval Prompt Generate a single PostgreSQL SELECT that retrieves past chat sessions relevant to the user’s latest message. ## Hard Rules - Output ONLY the SQL. No prose. No markdown fences. No semicolon inside or at the end....

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