Papers
Topics
Authors
Recent
Search
2000 character limit reached

MINTEval: Evaluating Memory under Multi-Target Interference in Long-Horizon Agent Systems

Published 18 May 2026 in cs.CL and cs.AI | (2605.18565v2)

Abstract: Real-world agents operate over long and evolving horizons, where information is repeatedly updated and may interfere across memories, requiring accurate recall and aggregated reasoning over multiple pieces of information. However, existing benchmarks focus on static, independent recall and fail to capture these dynamic interactions between evolving memories. In this paper, we study how current memory-augmented agents perform in realistic, interference-heavy, long-horizon settings across diverse domains and question types. We introduce MINTEval (Long-Horizon Memory under INTerference Evaluation), a benchmark featuring (1) long, highly interconnected contexts with frequently updated information that induces substantial interference, (2) diverse domains (state tracking, multi-turn dialogue, Wikipedia revisions, and GitHub commits), enabling evaluation of domain generalization, and (3) diverse question types that assess robustness to interference, including (i) single-target recall tasks requiring retrieval of a specific target from long contexts, and (ii) multi-target aggregation tasks requiring reasoning over multiple relevant pieces of information. Overall, MINTEval has 15.6k question-answering pairs over long-horizon contexts averaging 138.8k tokens and extending up to 1.8M tokens per instance. We evaluate 7 representative systems, including vanilla long-context LLMs, RAG, and memory-augmented agent frameworks. Across all systems, we observe consistently low performance (avg. 27.9% accuracy), especially on questions requiring aggregated reasoning over multiple pieces of evidence. Our analysis shows that performance is primarily limited by retrieval and memory construction. Furthermore, current memory systems struggle to recall and reason over earlier facts that are revised or interfered with by subsequent context, with accuracy degrading as the number of intervening updates increases.

Summary

  • The paper introduces MINTEval, a benchmark that rigorously evaluates memory management under multi-target interference in long-horizon agent systems across four domains.
  • It demonstrates that retrieval and memory construction bottlenecks lead to low performance, with overall accuracy around 27.9% and marked deficits in lookback and aggregation tasks.
  • The study highlights the need for advanced memory designs incorporating temporal cues, robust revision tracking, and balanced update operations to improve recall in complex streams.

MINTEval: Evaluating Memory under Multi-Target Interference in Long-Horizon Agent Systems

Motivation and Benchmark Design

MINTEval presents a rigorous evaluation framework targeting memory management in agent systems exposed to dense, evolving context streams where interference is endemic. The benchmark addresses three core limitations in prior memory-agent evaluation: lack of high-density, interdependent updates; omission of multi-domain generalization; and insufficient probing of robust recall for earlier, revised, or contradictory states. Real-world agent deployments—spanning state tracking, dialogue systems, knowledge revision (Wikipedia), and source code evolution—require effective handling of both proactive and retroactive interference, with memory systems that can aggregate, retrieve, and reason over temporally distributed evidence amid conflicting revisions.

MINTEval is architected with four domains (state tracking/bAbI, dialogue/HorizonBench, Wiki revisions, Git commits) and five probing question types: single-target recall (Simple, History/lookback) and multi-target aggregation (Ordering, Counting, Multihop). Inputs are characterized by extensive horizon (up to 1.8M tokens), high-density revision streams (avg. 86 updates per context), and temporal conflicts. This structure exposes persistent retrieval, provenance-tracking, and aggregation bottlenecks with current memory and retrieval-augmented architectures.

Evaluation of System Architectures

Seven paradigms are benchmarked: vanilla long-context LLMs, standard RAG, graph-structured retrieval (HippoRAG), and multiple memory-augmented systems (MemAgent, AtomMem, Mem-&/MemAlpha, SimpleMem). Models employ Qwen3.6-35B-A3B and Gemini-3.1-Flash-Lite answering agents, with context input limits (65k–1M tokens). Retrieval embedding models include Qwen3-Embedding-4B and Gemini-Embedding-001.

Empirical results indicate universally low performance across MINTEval (avg. 27.9%). The best-performing architecture, MemAgent, achieves only 33.4%. There is substantial variance across domains and question types: memory systems demonstrate strong performance on synthetically simple tasks (bAbI) but degrade on revision-centric domains (Wiki, Git), especially for lookback and aggregation questions. Single-target Simple recall achieves 47.5% accuracy, History lookback only 21.0%, and multi-target aggregation 26.5%. These outcomes empirically confirm that current memory management architectures are not saturated and are fundamentally challenged by interference-heavy streams.

Error Analysis and System Bottlenecks

Detailed error decomposition reveals that retrieval/memory construction is the dominant bottleneck, responsible for a 41.7% performance drop; answering-stage errors add a further 25.2%. Only 58.3% of cases contain required evidence in constructed memory or retrieved documents. Memory systems struggle with revision provenance: compression and deduplication procedures (in systems such as SimpleMem) are hazardous in revision-centric contexts, discarding crucial historical details and hindering accurate aggregation or ordering.

As lookback distance increases, performance degrades sharply for Full Context and RAG, while memory-augmented systems retain higher robustness by encoding temporal structure. Explicit temporal cues (timestamps) substantially mitigate interference-induced accuracy loss. The addition of domain- and out-of-domain distractors further degrades performance, especially in RAG architectures, underlining sensitivity to irrelevant context.

Memory systems exhibit a pronounced operational bias towards insertion/appending, with insufficient modification and deletion functionality (AtomMem: 87.6% insertion; Mem-&: 65.9%), resulting in context accretion and reduced coherence over long horizons. Chunk size and update iterations in memory processing affect error rates: larger chunks, with fewer updates, improve performance by reducing overwrite-induced loss.

Implications and Theoretical Insights

MINTEval establishes that retrieval and memory construction—not answering agent capacity—are the main limiting factors in robust long-horizon memory reasoning. The results demand new research directions focusing on explicit provenance preservation, fine-grained revision identification, balanced CRUD operation design, and temporal marker integration. The cross-domain generalization gap suggests architectures must move beyond domain-specific heuristics to universally robust memory strategies.

Current compression and retention policies in memory systems may be effective for short, loosely connected conversational histories but are demonstrably inadequate for revision-centric domains with fine-grained changes. Future solutions should prioritize actionable memory update tracking, deletion/modification operation parity, and multi-target aggregation reasoning capabilities.

Future Directions

The data provided by MINTEval offers a precise analytical basis for the development of agent memory architectures capable of scaling to real-world deployment: supporting persistent recall under interference, aggregation across distributed evidence, and fine-grained differentiation of historical versus current states. Mechanisms for explicit revision tracking and provenance maintenance will likely become central. The benchmark clearly delineates the need for research on advanced retrieval strategies integrating temporal and semantic cues, persistent memory representation models, and hierarchical memory management methods with dynamic operation balancing.

MINTEval paves the way for exploring architectures that combine LLMs with controllable, interpretable memory modules capable of operating across extensively revised, conflicting, and noisy streams with strong aggregation and lookback fidelity.

Conclusion

MINTEval constitutes a unified, interference-heavy benchmark for robustly evaluating memory systems in agentic contexts with dynamic revisions, temporal conflicts, and multi-target reasoning. Existing systems demonstrate consistently low accuracy, with pronounced deficiencies in lookback and aggregation tasks, and retrieval/memory construction as the primary bottleneck. The findings highlight the unsolved challenges in scalable, precise, and interference-resilient agent memory, mandating new designs in memory construction, update operation balance, provenance tracking, and cross-domain generalization. The implications are immediate for both practical agent deployment and theoretical advancement in long-horizon reasoning, underscoring MINTEval's priority as a reference benchmark for future research (2605.18565).

Paper to Video (Beta)

No one has generated a video about this paper yet.

Sign Up to Generate All Videos Subscribe on YouTube

Whiteboard

No one has generated a whiteboard explanation for this paper yet.

Sign Up to Generate

Explain it Like I'm 14

What this paper is about (big picture)

This paper introduces MINTEVAL, a new way to test how well AI “agents” remember things over long periods when information keeps changing and sometimes conflicts. Think of an AI keeping a long, messy notebook where facts are added, corrected, or contradicted. The test checks if the AI can find the right facts later—especially older ones—and combine several pieces to answer harder questions.

What the researchers wanted to find out

Here are the main questions they asked:

  • Can today’s AI systems remember and find the right information when facts are updated many times and sometimes conflict?
  • Can they answer questions that need older information, not just the latest updates?
  • Can they combine multiple pieces of information (like counting, ordering events, or comparing versions) to get a correct answer?
  • Do these skills work across different real-world areas, not just one?
  • Where do the systems fail more: finding the right information, or using it correctly to answer?

How they tested it (in simple terms)

They built MINTEVAL, a big benchmark (a standardized test) with very long, evolving stories made from real sources. Imagine four kinds of “memory timelines,” each with lots of updates:

  • State tracking: simple facts that change over time (like “the cat is in the kitchen,” later changed to “the cat is in the garden”).
  • Multi-turn dialogue: long conversations where a person’s preferences change or get clarified across many sessions.
  • Wikipedia revisions: articles being edited many times, with facts added, changed, or removed.
  • GitHub commits: code being updated over many versions, changing file contents, function names, or rules.

They asked two kinds of questions:

  • Single-target recall: find one specific piece of information.
    • Simple: What’s true now?
    • History (lookback): What was true some versions ago?
  • Multi-target aggregation: combine multiple pieces to answer.
    • Ordering: In what order did things happen?
    • Counting: How many times did something happen?
    • Multihop: Compare or connect facts across versions.

To run the test, they tried seven popular AI setups:

  • Full-context readers (give the model everything—very slow/expensive and often too long).
  • Retrieval-Augmented Generation (RAG): like a librarian that fetches a few relevant pages for the model to read.
  • Memory-augmented agents: systems that try to store, update, and retrieve memories over time (like a smart notebook that the AI maintains).

They measured accuracy with exact-match answers.

Key terms explained:

  • Interference: When new info and old info get mixed up, making it hard to remember the right thing (like writing corrections over old notes and then trying to recall what the old notes said).
  • Retrieval: Finding the right pieces of the “big notebook” to read before answering.
  • Aggregation: Pulling together several pieces of info to solve a question (like counting appearances or comparing versions).

What they found (the main results)

  • Overall, performance was low: on average, only about 28% of answers were correct; the best system reached about 33%.
  • Easier now-questions were much better than harder ones:
    • “Simple” (latest facts): about 47.5% accuracy.
    • “History” (older facts): about 21% accuracy.
    • “Aggregation” (ordering, counting, multihop): about 26.5% accuracy.
  • The biggest bottleneck is finding the right information (retrieval/memory-building), not just generating the answer. Even when the needed info exists somewhere in the history, systems often fail to pull it in.
  • The farther back you have to look (more edits/updates in between), the worse systems do—especially for full-context and RAG. Adding clear time markers (like dates) helps.
  • RAG struggles when there are distracting, off-topic sentences mixed in; it often fetches the wrong things.
  • Many “memory” systems mostly append new notes instead of editing or removing old ones, which leads to clutter and confusion.
  • Heavy memory compression can throw away important “which version did this come from?” details, especially harmful for revision-heavy tasks.
  • No single method worked well across all domains; cross-domain generalization was limited.

Why this matters (so what?)

In real life, information changes constantly: documents are revised, code evolves, and people’s preferences shift. This benchmark shows that current AI agents are far from reliable at:

  • Keeping track of what’s changed and when,
  • Finding older information when asked,
  • Combining multiple pieces spread across time.

To make AI agents more useful and trustworthy in the real world, researchers need to build better memory systems that:

  • Retrieve the right evidence even in long, messy histories,
  • Keep links to when/where each fact came from,
  • Update and delete information cleanly, not just keep adding,
  • Handle interference and long-range lookback,
  • Reason over multiple pieces at once (order, count, compare).

MINTEVAL gives a tough, realistic testbed to measure progress on these problems and guide future improvements.

View Paper Prompt  View All Prompts 

Knowledge Gaps

Knowledge gaps, limitations, and open questions

Below is a concise, actionable list of what remains missing, uncertain, or unexplored in the paper and its evaluation.

  • Dataset construction and validation
    • Limited human validation of LLM-generated questions: only 20% of Wiki/Git sessions were checked, leaving potential undetected errors or biases in the remaining 80%.
    • Ambiguity around ground-truth derivation for aggregation tasks (ordering, counting) where answers may not be explicitly stated in text; unclear procedures for verifying correctness and handling paraphrases or ties.
    • Lack of provenance annotations for answers (e.g., exact revision/commit and span) that would enable deterministic evidence auditing instead of LLM-based judgments.
    • Unclear handling of ambiguous lookback queries (e.g., multiple edits affecting the same fact in close succession); no error analysis for mis-specified or ambiguous prompts.
    • Potential dataset bias from using a single LLM family (Gemini-3.1-Pro) for question generation in two domains; no comparison to human-authored or multi-model generated questions.
    • Distractor robustness studied only on bAbI; no systematic distractor/noise injection for Wiki revisions and Git commits, where interference is most realistic.
    • No quantification of domain-specific “interference strength” beyond update depth (e.g., contradiction density, semantic similarity between conflicting states, or degree of revision churn).
  • Evaluation metrics and scoring
    • Heavy reliance on Exact Match (and a candidate set for HorizonBench) risks under-crediting semantically correct answers; no semantic or span-based evaluation is reported.
    • No partial credit or graded scoring for aggregation tasks like ordering (e.g., Kendall tau or edit distance), masking incremental improvements or near-misses.
    • The LLM-based check for “evidence exists in memory/retrieval” (used to decompose error sources) is itself noisy and unvalidated; no inter-annotator agreement or calibration is provided.
  • Experimental setup and fairness
    • Context-length constraints for “Full Context” are not fully specified for very long instances (up to 1.8M tokens); unclear truncation policy (head, tail, sliding window) and its impact on results.
    • RAG chunking is fixed at “one revision per chunk” for Wiki/Git despite variable revision sizes; no ablation of finer-grained chunking or hybrid segmentation that might improve retrieval.
    • Retrieval settings mostly fixed to top-5; limited exploration of retrieval hyperparameters (k, re-ranking, hybrid lexical+dense retrieval, temporal filtering).
    • Cross-model evaluation is narrow: most results use Qwen3.6-35B-A3B; frontier models are tested only in SimpleMem; no systematic comparison across a diverse set of answering agents and embedders.
    • Off-the-shelf memory systems are evaluated without adapting/training on MINTEVAL; domain transfer is discussed but not tested via train-on-some, test-on-held-out-domain settings.
    • Efficiency and resource costs (compute, memory footprint, latency) are not measured, limiting assessment of real-world feasibility for long-horizon settings.
  • Analysis coverage
    • Lookback-distance degradation is analyzed only for Wiki Revisions; no analogous analysis for dialogues or code commits where temporal dynamics differ.
    • Temporal cues help in a controlled setting, but the paper lacks an evaluation of realistic metadata (e.g., commit timestamps, edit IDs) as structured features in memory/retrieval pipelines.
    • The insertion bias in memory operations is observed, but there is no causal analysis linking operation distributions to error types or task categories across domains.
    • Chunk-size effects are explored only for MemAgent on bAbI; no cross-domain or cross-method study to generalize conclusions about update frequency vs. stability.
  • Scope and task design
    • Benchmark focuses on QA; it does not evaluate memory use in action-oriented or tool-using agents (e.g., planning, multi-step program synthesis, or environment interaction).
    • Aggregation tasks are limited to ordering, counting, and multihop comparisons; more complex numeric/temporal aggregations (e.g., durations, sums, min/max across states) are not included.
    • No tests of memory consistency across sequences of queries (e.g., does the agent answer consistently about the same past facts over multiple questions?).
    • No adversarial or poisoning scenarios beyond simple distractors (e.g., near-duplicate conflicting edits, subtle paraphrased contradictions, templated misinformation).
  • Generalizability and coverage
    • Language and modality limitations: all domains appear English and text-only; no multilingual or multimodal long-horizon interference settings are included.
    • GitHub evaluation does not exploit code structure (ASTs, symbol tables, cross-file dependency graphs); no measurement of agents’ ability to track renames, refactors, or API evolution via code-aware indexing.
    • Dialogue data are formed by concatenating sessions; the realism of long-term user preference drift and inter-session context continuity is not validated with user studies or real longitudinal logs.
    • Domain generalization is inferred from zero-shot performance, but not tested in a controlled way (e.g., train on two domains, test on the other two).
  • Measurement of “interference” and memory behavior
    • Interference is described qualitatively (proactive/retroactive) and approximated by update depth; there is no formal, instance-level interference metric or labels to benchmark progress on specific interference phenomena.
    • No per-instance difficulty labels (e.g., number of conflicting revisions per fact, similarity of conflicting mentions, temporal distance) to support targeted benchmarking and ablations.
    • No assessment of long-term consistency or catastrophic forgetting beyond accuracy drops; e.g., whether agents maintain coherent internal memory states over time.
  • Reproducibility and ethics
    • Use of proprietary models (Gemini) for question generation and evaluation steps may hinder reproducibility; prompts, seeds, and sampling parameters are not detailed in-text.
    • Licensing and privacy considerations for GitHub repositories and Wikipedia revisions are not discussed; unclear whether any repos restrict redistribution or include sensitive content.
  • Open methodological questions
    • How do structured temporal indexes (e.g., time-keyed databases, time-travel retrieval, or bitemporal stores) compare to dense embedding RAG and current memory agents on MINTEVAL?
    • Can provenance-preserving memory (e.g., explicit linkage of facts to revision IDs and diffs) mitigate interference-driven failures, especially for lookback queries?
    • What is the optimal granularity for memory edits (token-, sentence-, or fact-level) to reduce insertion bias and improve update/delete precision under heavy interference?
    • How do hybrid retrieval strategies (lexical+dense, graph+temporal filters, code-aware retrieval) affect aggregation tasks in Wiki/Git?
    • Can training or fine-tuning memory agents on interference-heavy data improve cross-domain generalization, or do gains remain domain-specific?
    • To what extent do different compression strategies trade off between retaining historical provenance and enabling efficient retrieval at scale?
View Paper Prompt  View All Prompts 

Practical Applications

Immediate Applications

The paper’s benchmark, analyses, and design recommendations can be applied today to improve memory-augmented systems in dynamic, evolving contexts.

  • Software engineering (software)
    • Use case: Version-aware code/documentation assistants that answer “what changed between tag v1.2.30 and current?” or “what was the API signature three commits ago?”
    • Tools/workflows: Git-aware RAG with temporal metadata; diff-based retrieval; provenance-preserving summaries that cite commit IDs; prompt templates that inject temporal cues (e.g., “two commits prior”).
    • Assumptions/dependencies: Access to repo histories; embeddings/index that store timestamps and commit ranges; minimal privacy constraints for source code.
  • Enterprise knowledge portals and documentation search (software/enterprise)
    • Use case: Time-travel Q&A across wiki/revision histories (e.g., “what did the policy say in Rev 53?”) and change-comparison helpers for migration planning.
    • Tools/workflows: Temporal indexing; query rewriting to include lookback distance or dates; reranking tuned to reduce distractor sensitivity; avoid aggressive compression that drops provenance.
    • Assumptions/dependencies: Availability of clean revision metadata; storage overhead for provenance; integration with existing wiki/KB systems.
  • Customer support and CRM assistants (software/retail/services)
    • Use case: Preference-consistent chatbots that reconcile evolving customer preferences and can answer lookback questions (“what did I set as my preference earlier this year?”).
    • Tools/workflows: Intent-aware retrieval with explicit timestamps; memory policies that limit overwrite bias and preserve historical state; lower frequency of memory updates (larger chunk sizes).
    • Assumptions/dependencies: User consent/PII governance; session segmentation; stable identity resolution across sessions.
  • ML engineering and evaluation (software/academia)
    • Use case: Incorporate MINTEVAL into CI/CD to stress-test agents on interference-heavy tasks; select chunk sizes and update iterations that minimize overwrite errors.
    • Tools/workflows: Benchmark harnesses; two-stage diagnostics that separate retrieval/memory construction failures from answer-generation failures.
    • Assumptions/dependencies: Compute for long-context tests; availability of open-source MINTEVAL code/data.
  • Retrieval system hardening (software)
    • Use case: Make RAG pipelines more robust to interference and distractors.
    • Tools/workflows: Time-aware ANN/BM25 indexes; cross-encoder rerankers that leverage temporal markers; negative OOD sampling to reduce distractor retrieval; per-query “lookback” filters.
    • Assumptions/dependencies: Access to time metadata; retriever/reranker stack that supports metadata filtering; monitoring for retrieval quality.
  • Provenance-preserving summarization (software/enterprise)
    • Use case: Summaries that retain links to source revisions to support accurate lookback queries.
    • Tools/workflows: Structured memory units with citation fields (revision_id, timestamp); snapshot pointers instead of paraphrased facts; configurable compression thresholds per domain.
    • Assumptions/dependencies: Extra storage and latency budgets; adherence to citation format in downstream UIs.
  • Governance and compliance checks (policy/enterprise/legal)
    • Use case: Audit tests for “right-to-be-forgotten” and update/delete capability in agent memory, given observed insertion bias.
    • Tools/workflows: Policy test suites that validate modify/delete operations on request; logs of memory actions (CRUD) with timestamps for audits.
    • Assumptions/dependencies: Memory frameworks must expose CRUD operations and logs; legal guidance on acceptable retention.
  • Robotics and long-horizon planning logs (robotics)
    • Use case: Task-memory that maintains historical world states and can answer lookback queries about earlier plans or observations.
    • Tools/workflows: Time-coded event stores; fewer, larger memory updates to reduce interference; ordering/temporal reasoning checks in validation.
    • Assumptions/dependencies: Reliable timestamps/sensors; integration with robot middleware (ROS, etc.).
  • Personal productivity (daily life/software)
    • Use case: Note-taking/email/journaling assistants that answer “what was the decision before the last revision?” or “how did my goals change since March?”
    • Tools/workflows: Local temporal index; time-cued prompts; provenance kept in summaries; distractor-aware retrieval for noisy inboxes.
    • Assumptions/dependencies: User data access; device storage; privacy-preserving local indexing.
  • Academic research and teaching (academia)
    • Use case: Study interference, lookback, and aggregation reasoning; teach best practices for memory system design (temporal cues, chunking, CRUD balance).
    • Tools/workflows: Use MINTEVAL splits (bAbI, dialogue, Wiki, Git) in coursework and ablation studies; replicate retrieval vs. answer-stage error decomposition.
    • Assumptions/dependencies: Access to benchmark and baseline systems; compute for long contexts.

Long-Term Applications

The paper highlights gaps that motivate new architectures, products, and standards that require further R&D, scaling, or validation.

  • Temporal-robust memory architectures (software/AI platforms)
    • Use case: Memory layers that natively encode time, support fine-grained diffing, and balance insert/update/delete to mitigate interference.
    • Tools/products: Temporal knowledge graphs; bitemporal databases integrated with LLMs; RL objectives that reward correct lookback and aggregation; provenance-aware compression.
    • Assumptions/dependencies: New training data with temporal supervision; system support for efficient time-travel queries; evaluation at scale on interference-heavy corpora.
  • Autonomous software maintenance and migration assistants (software)
    • Use case: Agents that track codebase evolution, propose safe migrations across versions, and justify changes with historical evidence.
    • Tools/products: Repo-scale temporal RAG; multi-target aggregation planners (ordering, counting, multihop); CI-integrated change explainers.
    • Assumptions/dependencies: High-precision code understanding; permissioned access; guardrails for reliability.
  • EHR time-travel assistants (healthcare)
    • Use case: Clinician copilots that can accurately reconstruct patient states at specific times (e.g., pre/post medication change) and aggregate longitudinal signals.
    • Tools/products: Provenance-preserving EHR summarization; temporal retrieval with strict audit trails; interference-aware memory validated for safety.
    • Assumptions/dependencies: Regulatory approval; strict privacy; alignment with clinical ontologies; near-zero tolerance for errors.
  • Lifelong learning tutors and student modeling (education)
    • Use case: Tutors that track evolving misconceptions/goals, answer lookback questions about prior lessons, and aggregate progress over time.
    • Tools/products: Temporal student models; curriculum-aware memory stores; ordering/counting evaluators for educational sequences.
    • Assumptions/dependencies: Consent and data integration across sessions; pedagogical validation; bias mitigation.
  • Compliance, audit, and risk for finance/energy (finance/energy/policy)
    • Use case: Agents that reconstruct historical configurations, policies, and risk positions “as of date” and compare across periods.
    • Tools/products: Time-indexed policy/configuration KBs; aggregation modules for counts/durations; explainable provenance chains for auditors.
    • Assumptions/dependencies: High-quality archival data; tamper-evident logs; model governance frameworks.
  • Standards for agent memory provenance and evaluation (policy/standards)
    • Use case: Sector-wide requirements for provenance retention, editable memory (CRUD), and interference stress-testing pre-deployment.
    • Tools/products: Benchmark-based certification (MINTEVAL-like suites); reporting formats for memory operations; temporal-cue best practices.
    • Assumptions/dependencies: Multi-stakeholder consensus; regulatory adoption; third-party auditors.
  • Scalable long-context infrastructure (software/hardware)
    • Use case: Serving stacks that combine million-token contexts with fast temporal retrieval and low-latency reranking.
    • Tools/products: Streaming context managers; hierarchical caches; index-time bucketing by time and entity; memory-compaction schedulers.
    • Assumptions/dependencies: Hardware budgets; efficient attention mechanisms; cost controls.
  • Multi-agent shared temporal memory (software/robotics)
    • Use case: Teams of agents coordinating via shared, versioned memory with conflict resolution and event ordering.
    • Tools/products: Shared temporal graphs; consensus on memory edits; arbitration for conflicting updates; lineage-aware merging.
    • Assumptions/dependencies: Communication protocols; consistency models; robustness to partial observability.
  • Training curricula for interference resilience (academia/industry)
    • Use case: Pretraining/finetuning regimes that explicitly target proactive/retroactive interference, lookback, and aggregation.
    • Tools/products: Synthetic and real temporal datasets; objectives for ordering/counting/multihop over evolving states; evaluation loops tied to MINTEVAL.
    • Assumptions/dependencies: Data generation pipelines; compute for curriculum learning; careful generalization studies.
  • User-facing “time-travel” UX patterns (software/daily life)
    • Use case: Interfaces that let users specify “as-of” dates or “two versions ago” and display answers with linked provenance.
    • Tools/products: Temporal query widgets; provenance visualizations; side-by-side diff explainers integrated with assistants.
    • Assumptions/dependencies: Consistent metadata; user education; performance acceptable for interactive use.
View Paper Prompt  View All Prompts 

Glossary

  • Ablation study: A controlled analysis that varies components or hyperparameters to assess their impact on performance. "We additionally provide an ablation study on chunk size in Section 4.4."
  • Answering agent: The model that generates the final answer given the retrieved context or constructed memory. "Answering agent takes either the full context, retrieved context, or managed memory as input and generates the final answer."
  • Bridge reasoning: Reasoning that connects multiple pieces of interdependent information to derive an answer. "performing bridge reasoning over interdependent events."
  • Chunk granularity: The chosen size of chunks used to process long inputs, affecting how often memory is updated. "different chunk granularity"
  • CRUD (Create, Read, Update, Delete): The standard set of operations for modifying stored information or memory. "atomic CRUD (Create, Read, Update, Delete) operations"
  • Deduplication: The process of removing redundant information during compression or storage. "aggressive compression and deduplication are prone to discarding important provenance information"
  • Dense vector similarity: A retrieval technique that compares learned vector embeddings to find relevant documents. "dense vector similarity [Lewis et al., 2021]"
  • Distractors: Irrelevant sentences or content inserted to test robustness of retrieval and reasoning. "different types and numbers of distractors are inserted"
  • Embedding model: A model that converts text into vectors for similarity-based retrieval. "Embedding model is used in retrieval-based systems to retrieve relevant contexts by computing similarity scores."
  • Exact Match: An evaluation metric that checks whether the predicted answer matches the gold answer exactly after normalization. "We evaluate using Exact Match after standard text normalization"
  • Frontier model: A state-of-the-art model used to generate new data (e.g., questions) for evaluation. "For questions that are generated by the frontier model"
  • Full Context: A baseline where the entire context is provided to the model without explicit memory. "Under the Full Context setting"
  • Graph-structured retrieval: A retrieval method that leverages graph relationships among documents or facts. "a graph-structured retrieval mechanism"
  • In-Domain (ID) distractors: Distractors that match the style and structure of the target domain, making them harder to filter. "In-Domain (ID) distractors"
  • Intent-aware retrieval: A retrieval approach that adapts the retrieval scope based on the user’s query intent. "intent-aware retrieval, which dynamically determines retrieval scope and constructs targeted retrieval contexts."
  • Interdependent inputs: Inputs whose elements depend on each other, increasing reasoning complexity. "interdependent inputs (Interdep.)"
  • Interference-heavy input contexts: Contexts with frequent conflicting or overlapping updates that make retrieval and reasoning difficult. "interference-heavy input contexts"
  • LLM-based evaluation: Using a LLM to assess whether evidence is present rather than relying on lexical matching. "We use an LLM-based evaluation for analysis instead of lexical matching"
  • Lookback distance: The number of updates between the queried information and the current state. "lookback distance"
  • Long-horizon contexts: Very long sequences of interactions or revisions spanning many updates. "long-horizon contexts averaging 138.8k tokens"
  • Memory-augmented agents: Systems that maintain, update, and retrieve memories over time to aid reasoning. "Memory-augmented agents powered by LLMs"
  • Memory compression: Techniques that compact or summarize stored information to fit within resource limits. "aggressive memory compression strategy"
  • Memory construction: The process of building or organizing a representation of past information for later retrieval. "retrieval and memory construction capabilities"
  • Memory manager: The component responsible for constructing and maintaining a compact memory representation from long inputs. "Memory manager constructs a compact memory representation"
  • Multihop questions: Questions requiring reasoning across multiple pieces of information or updates. "Multihop questions require reasoning over multiple targets"
  • Multi-target aggregation: Tasks that require combining evidence from multiple relevant locations to answer a query. "Multi-target aggregation tasks require models to identify and perform aggregated reasoning over multiple relevant pieces of context"
  • Online semantic synthesis: Incrementally merging semantically related contexts to reduce redundancy while preserving meaning. "online semantic synthesis, which incrementally merges related contexts to reduce redundancy"
  • Out-of-Domain (OOD) distractors: Distractors drawn from a different domain or style, often more disruptive to retrieval. "Out-of-Domain (OOD) distractors"
  • Provenance: Metadata about the origin or source of information, such as which revision introduced a fact. "revision provenance"
  • Proactive interference: When older memories hinder the encoding or recall of newer information. "proactive interference, where old memories affect encoding of new information"
  • Query-specific memory representations: Memory structures tailored to a particular question to surface the most relevant information. "constructing query- specific memory representations."
  • Question-agnostic memory structure: A shared memory representation built independent of any single question. "question-agnostic memory structure"
  • Retroactive interference: When newer information disrupts or overwrites the recall of older information. "retroactive interference occurs when new information disrupts recall of older information"
  • Retrieval pool: The set of documents or memory segments available for retrieval when answering a question. "exists by design in the retrieval pool"
  • Retrieval-augmented generation (RAG): A framework that retrieves external documents to condition the generation of answers. "RAG denotes the standard retrieval-augmented generation framework"
  • Semantic structured compression: Converting unstructured interactions into compact, structured memory units based on meaning. "semantic structured compression, which converts unstructured interactions into compact multi-view memory units"
  • Sequential decision-making: Framing memory management as a sequence of actions to optimize over time. "formulates memory management as a sequential decision-making problem"
  • Temporal cues: Explicit time markers (e.g., dates) added to help models distinguish between revisions or events. "we augment facts and questions with temporal cues such as dates or timestamps"
  • Temporal conflicts: Inconsistencies across time that must be resolved to maintain coherent memory. "resolving temporal conflicts"
View Paper Prompt  View All Prompts 

Open Problems

We haven't generated a list of open problems mentioned in this paper yet.

Generate Now

Collections

Sign up for free to add this paper to one or more collections.

Sign Up

Tweets

Sign up for free to view the 10 tweets with 161 likes about this paper.

Sign Up for Free