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FTA-Mem: Fact-Time-Affect Anchored Memory for Low-Density Long-Term Dialogue

Chang Liu, Shuyi Zhang, Changsheng Ma, Yongfeng Tao, Minqiang Yang, Bin Hu

arXiv:2608.16303v1cs.CL

TL;DR

Long-term emotional-support dialogue requires reliable personalized memory despite sparse, distributed, and temporally ambiguous evidence. FTA-Mem addresses this with situation-level Fact-Time-Affect units and improves long-term memory question answering across ES-MemEval and LoCoMo.

  • Problem

    Long-term emotional-support agents need memory that preserves sparse, distributed evidence and evolving temporal and affective context across sessions.

  • Method

    FTA-Mem uses boundary-preserving segmentation to construct situation-level Fact-Time-Affect units encoding factual, temporal, and affective context for structured retrieval and generation.

  • Results

    FTA-Mem improves long-term memory question answering across ES-MemEval and LoCoMo, achieving 38.71 F1 and 66.68 BERTScore with Qwen3-8B on ES-MemEval.

  • Takeaways & Limitations

    Situation-level construction offers a favorable overall trade-off among evidence preservation, temporal consistency, and construction efficiency across dialogue settings.

  • Takeaways & Limitations

    The authors frame FTA-Mem as a research framework rather than a standalone clinical or counseling system because persistent memories may be incorrect, outdated, ambiguous, or privacy-sensitive.

Abstract

from arXiv · show

Long-term emotional-support agents require memory mechanisms for personalized understanding across sessions. However, emotional-support dialogue is often low-density: turns are incomplete, evidence is scattered, and user states evolve over time. Existing memory methods usually rely on fixed units, such as turn-level notes or session summaries, which may lose details or introduce redundant noise. We propose FTA-Mem, a structured memory framework for low-density long-term dialogue. FTA-Mem uses Boundary-preserving Window Segmentation (BWS) to form coherent situation fragments, and constructs Fact-Time-Affect Memory Units (FTA Units) that jointly encode factual content, temporal grounding, and affective context. Retrieved units are then synthesized into structured context for answer generation. Experiments on ES-MemEval and LoCoMo show that FTA-Mem improves overall long-term memory question answering across benchmarks with different information-density characteristics. On ES-MemEval, FTA-Mem achieves 0.3871 F1 and 0.6668 BERTScore. Further analysis shows that situation-level FTA construction better balances evidence preservation and construction cost than coarse session-level or overly fine-grained turn-pair construction, providing an effective granularity trade-off for long-term dialogue memory.

Introduction

FTA-Mem addresses the difficulty of remembering low-density emotional-support dialogue, where evidence is sparse, distributed, temporally ambiguous, and affectively dependent. It constructs situation-level Fact-Time-Affect Memory Units through boundary-preserving segmentation and synthesizes retrieved units into structured context for generation.

  • Motivation: Low-density emotional-support dialogue distributes sparse, indirect, and temporally ambiguous evidence across sessions, while event meaning depends on validity and user appraisal.Retrieving more history or compressing longer contexts does not necessarily produce reliable memory.
  • Problem: Session-level summaries may omit temporal updates, unresolved plans, relationship changes, and affective cues, whereas turn-level units can fragment coherent events into redundant pieces.The introduction frames memory granularity and retrievable-unit definition as underexplored design questions.
  • Contribution: FTA-Mem represents low-density long-term dialogue with situation-level Fact-Time-Affect Memory Units.The framework is designed specifically to address the granularity and unit-construction challenges of long-term dialogue memory.
  • Contribution: Boundary-preserving Window Segmentation maintains contextual continuity by carrying unresolved units across fragments, fusing adjacent-fragment units, and linking finalized units to historical memories.These operations occur before persistent identifier assignment and temporal-link maintenance.
  • Method: Each FTA Unit jointly grounds factual evidence, temporal validity, and affective interpretation, and retrieved units are synthesized into structured context packets rather than flat passage lists.The structured packets are used during answer generation.
  • Evaluation: FTA-Mem is evaluated on ES-MemEval and LoCoMo, with the introduction reporting improved question-answering performance and a better construction trade-off.The supplied contribution passage names both benchmarks but does not provide numerical results or specify the trade-off in full.

Related Work

Prior work has advanced counseling response generation and multi-session support, while long-term memory systems vary in how they define and organize retrievable units. However, memory granularity remains underexplored for low-density long-term dialogue, where coarse units may lose question-answering details.

  • Counseling Agents: Early counseling agents improve single-session or short-term emotional-support responses but do not explicitly model long-term memory.Examples include CBT-LLM and HealMe.
  • Counseling Agents: Recent work has begun addressing multi-session psychological counseling, including MusPsy.The supplied passage identifies MusPsy as a recent multi-session counseling system.
  • Long-Term Memory: Long-term LLM-agent memory systems differ in their memory units and organization, spanning graph-based retrieval, summaries, turn-derived semantic units, event memories, and event-centric maps.Examples include GraphRAG, MemoryBank, FraCom, ES-Mem, and CompassMem.
  • Long-Term Memory: Existing systems often rely on raw turns, summaries, or turn-derived semantic units, leaving retrievable-memory granularity underexplored in low-density long-term dialogue.Session summaries are compact but may lose question-answering-sensitive details.

Methodology

FTA-Mem introduces structured external memory for low-density long-term dialogue, using boundary-preserving situation fragments and Fact-Time-Affect units to preserve factual, temporal, and affective evidence. It maintains, links, and retrieves these units to synthesize structured context for grounded answer generation.

  • Structured memory: FTA-Mem uses an external structured memory to retrieve relevant units before generating answers, addressing sparse, implicit, and cross-session evidence in long cases.The retrieved subset Msub serves as an intermediate representation between dialogue history and answer generation.
  • Situation segmentation: Boundary-preserving Window Segmentation forms coherent situation fragments by retaining boundary context instead of treating isolated turns or entire sessions as fixed units.The next window starts at the last detected segment, allowing uncertain tail content to be reprocessed with following context.
  • FTA unit construction: Each Fact-Time-Affect Memory Unit stores factual, temporal, affective, evidence, and construction-status information for a situation-level memory representation.Anchors capture claims and participants, temporal validity and completion, and subjective context such as emotion, intention, and relation cues.
  • Unit maintenance: FTA-Mem carries unresolved partial units across fragments, fuses compatible boundary-spanning candidates, and assigns persistent IDs only to finalized memories.This process reduces segmentation-induced incompleteness and avoids independently retrieving unresolved partial units.
  • Retrieval and linking: FTA-Mem links newly stored units to historical neighbors through classified relations, then retrieves and expands linked evidence using rewritten, structure-aware queries.Retrieval scores combine embedding similarity with cues over time, situation type, participants, and affective context; linked neighbors can add updates, contradictions, or follow-up evidence.

Experiments

FTA-Mem is evaluated on emotional-support and general long-conversational memory benchmarks, achieving strong performance across both sparse and denser dialogue settings. Analyses show that situation-level FTA units balance evidence preservation, retrieval, construction cost, and the integration of factual, temporal, affective, and linked information.

  • Benchmarks: FTA-Mem is evaluated on ES-MemEval and LoCoMo, which contain 1,427 and 1,986 questions respectively and cover complementary long-term memory challenges.ES-MemEval contains 18 user cases and focuses on emotional-support interactions; LoCoMo contains 10 conversation samples and covers single-hop, multi-hop, temporal, and open-domain reasoning.
  • ES-MemEval Results: 38.71 F1 and 66.68 BERTScore are achieved by FTA-Mem with Qwen3-8B on ES-MemEval, alongside the best average rank.With GPT-4o-mini, FTA-Mem also achieves the best overall F1 and BERTScore; gains are most visible in information extraction and conflict detection.
  • LoCoMo Results: FTA-Mem achieves the best average F1 and BLEU-1 across all backbone settings on LoCoMo, with its strongest Qwen3-8B performance on temporal and open-domain questions.CompassMem is slightly stronger on Qwen3-8B single-hop and multi-hop questions, but FTA-Mem obtains the best overall average.
  • Granularity and Cost: Situation-level construction provides a better overall trade-off than session-level or turn-pair construction by preserving useful evidence while reducing redundancy and construction cost.Session-level construction loses evidence, whereas turn-pair construction can perform slightly better on LoCoMo but requires substantially higher cost.
  • Retrieval Budget: 38.71 F1 and 66.68 BERTScore are reached at K = 10 on ES-MemEval before performance saturates, whereas LoCoMo peaks around K = 25.The contrast indicates that low-density emotional-support dialogue benefits from selective retrieval, while denser factual dialogue can use larger retrieval sets.
  • Ablation Analysis: Removing temporal information causes the largest ablation degradation, while removing affect information, links, or factual-only restrictions also reduces performance on the benchmarks.The Question-only baseline performs much worse, supporting the contribution of retrieved memory and the combination of factual, temporal, affective, and linked information.

Conclusion

FTA-Mem is a structured memory framework for low-density long-term dialogue that builds situation-level Fact-Time-Affect units through boundary-preserving segmentation. It preserves factual, temporal, and affective context, then uses temporal links and structured synthesis to support grounded, personalized answer generation.

  • Framework: FTA-Mem constructs situation-level Fact-Time-Affect units through boundary-preserving segmentation.This preserves factual evidence together with temporal and affective context.
  • Framework: Temporal links and structured context synthesis organize memory units for grounded, personalized answer generation.
  • Empirical findings: Experiments on ES-MemEval and LoCoMo show that FTA-Mem improves long-term memory question answering.

Supplementary Material … Boundary-preserving Window Segmentation

The supplementary material specifies FTA-Mem’s implementation and retrieval protocols, explains baseline exclusions, and details Boundary-preserving Window Segmentation (BWS), which preserves provisional boundary content for reprocessing with subsequent context.

  • Reproducibility Details: FTA-Mem separates BWS, FTA-unit extraction, relation classification, query rewriting, and answer generation into fixed-prompt LLM calls.Answer generation uses temperature 0, whereas memory construction uses temperature 0.7.
  • Reproducibility Details: Retrieval uses all-MiniLM-L6-v2 embeddings with cosine similarity and the same top-K protocol across methods when applicable.Main experiments use K = 7 for ES-MemEval and K = 10 for LoCoMo; later analyses use K = 10 and K = 25, respectively.
  • Baseline Selection: ES-Mem and SGMem are excluded because neither had an officially available implementation at submission.Both are also closely related to fine-grained turn- or sentence-level construction already examined in the granularity comparison.
  • Boundary-preserving Window Segmentation: BWS detects segment boundaries using global numeric turn indices and finalizes completed segments while retaining the last detected segment provisionally.This design allows boundary-tail content to be reprocessed with following context.
  • Boundary-preserving Window Segmentation: Each subsequent BWS window begins at the start of the provisional segment, while session-end windows finalize all remaining segments.The procedure therefore carries unresolved boundary content forward rather than permanently closing it in the current window.
  • Boundary-preserving Window Segmentation: When a detector returns one full-window segment, BWS enforces a minimum forward step to prevent non-termination.At the session end, all detected segments are added to the finalized set before termination.

Additional Comparative Experiments · Additional Ablation Studies · Density Annotation and Statistical Analysis

Additional experiments show that boundary-preserving segmentation and structured FTA-based retrieval and organization improve memory construction and downstream question answering. Density analyses further indicate that FTA-Mem’s advantage is more evident in lower-density, more implicit ES-MemEval dialogues than in LoCoMo.

  • Additional Comparative Experiments: BWS is compared with fixed-window segmentation while holding window size, memory schema, retriever, and answer-generation pipeline constant.The comparison isolates boundary preservation during memory construction.
  • Additional Comparative Experiments: The fixed-window variant creates slightly more memory units but achieves lower F1 and B/B1 on both benchmarks.Preserving boundary-uncertain tail content helps avoid incomplete memory units and improves downstream question answering.
  • Additional Ablation Studies: Removing query rewriting moderately reduces performance, indicating that evidence-oriented reformulation helps align questions with structured FTA units.This ablation supports query rewriting as a useful component of FTA-Mem.
  • Additional Ablation Studies: Plain embedding retrieval further reduces performance, showing that structured matching over time, situation type, participants, and affect contributes beyond semantic similarity.The result highlights the role of structured cue matching in retrieval.
  • Additional Ablation Studies: Replacing structured memory packets with flat context degrades performance, while removing auxiliary memory causes little change.Structured packets organize primary evidence, linked neighbors, source spans, and auxiliary context; episodic FTA units remain the primary evidence source.
  • Density Annotation and Statistical Analysis: Dialogue density is annotated through factual, entity, temporal, and support-process anchor counts, alongside implicitness and binary low-information labels.Turn-level annotations are aggregated into dialogue-level statistics by averaging over turns.
  • Density Annotation and Statistical Analysis: Relative F1 gains are correlated with dialogue-level density fields using Pearson r on ES-MemEval cases and LoCoMo conversations.The analysis reports two main diagnostic fields together with auxiliary robustness statistics.
  • Density Annotation and Statistical Analysis: FTA-Mem gains more when factual density is lower and implicitness is higher on ES-MemEval, while the pattern is weaker on LoCoMo.The finding is diagnostic evidence rather than a causal claim, with the density-related advantage more evident in the low-density emotional-support benchmark.

Paired Bootstrap Uncertainty Check · Memory Quality Audit

A paired cluster-bootstrap analysis finds that FTA-Mem’s advantage over CompassMem is clearly positive on ES-MemEval but less certain on LoCoMo. A 100-unit manual audit further finds strong factual and temporal grounding, while affect/context and relation relatedness are more limited.

  • Paired Bootstrap Uncertainty Check: Paired cluster bootstrap resamples ES-MemEval cases and LoCoMo conversations with replacement while keeping constructed memories fixed.The F1 difference is averaged over sampled dialogue-level clusters to reflect each case’s multiple questions.
  • Paired Bootstrap Uncertainty Check: The uncertainty analysis directly compares FTA-Mem with CompassMem across both ES-MemEval and LoCoMo.The comparison uses paired cluster resampling rather than treating individual questions as independent observations.
  • Paired Bootstrap Uncertainty Check: The ES-MemEval gain over CompassMem remains clearly positive, whereas the LoCoMo interval lies much closer to zero.The result indicates greater stability on ES-MemEval and warrants cautious interpretation of the LoCoMo difference.
  • Memory Quality Audit: 100 generated FTA units from Qwen3-8B runs are manually audited, balanced across ES-MemEval and LoCoMo.Two annotators independently assess factual grounding, temporal accuracy, and affect/context accuracy against cited evidence spans.
  • Memory Quality Audit: Each audit criterion uses a 0–2 scale, distinguishing fully correct and grounded, partially correct but usable, and incorrect or unsupported units.Table 11 reports the mean score for each criterion.
  • Memory Quality Audit: FTA units are strongly grounded in factual and temporal evidence, while affect/context is usually usable but less often fully explicit.Emotional state and intentions are sometimes inferred from local context, making these fields less explicit.
  • Memory Quality Audit: Relation relatedness is useful but noisier than unit-level anchors, supporting lightweight maintenance cues rather than a fully precise symbolic graph.This reflects the audit’s distinction between reliable core fields and less precise relational links.

Case Study

The case study shows FTA-Mem resolving difficult temporal, conflict-detection, and user-modeling cases by preserving situation-specific relations and contextual cues. A representative FTA unit stores factual, temporal, affective, evidence, and link-maintenance fields.

  • Memory Unit Structure: A representative situation-level FTA unit stores factual, temporal, affective, evidence, and link-maintenance fields.The unit illustrates the structured fields used to preserve situation-level information.
  • Case Examples: FTA-Mem locates a time-specific argument with Jack when A-Mem retrieves a plausible but incorrect work-related event.This demonstrates improved temporal reasoning through time-specific evidence retrieval.
  • Case Examples: FTA-Mem detects the contradiction between painting as helpful coping and Sarah’s lack of consistent time, which A-Mem misses.The case illustrates conflict detection using status and temporal cues.
  • Case Examples: FTA-Mem preserves the participant relation between Jimmy and George, whereas A-Mem identifies the wrong person.This example highlights user modeling through relational memory preservation.
  • Case Examples: These cases show that FTA units help the answer model use relevant content together with temporal validity, status cues, and affective context.The examples span temporal reasoning, conflict detection, and user modeling.

Ethical Statement

The paper reports no new real-user or private-conversation data collection and frames FTA-Mem as a research framework, not a clinical or counseling system. It highlights risks from persistent, uncertain, sensitive memories and calls for informed consent, user controls, and deployment safeguards.

  • Experiments use public benchmarks, follow their data-use conditions, and collect neither new real-user data nor private conversations.
  • FTA-Mem targets evidence grounding and continuity in long-term dialogue memory, not clinical diagnosis, treatment, or standalone counseling.
  • Persistent incorrect, outdated, or over-interpreted memories may influence later responses, with affective inferences especially ambiguous or sensitive.
  • Real-world deployment should provide informed consent and control over inspection, correction, deletion, retention, access, storage, minimization, and uncertain or conflicting memories.

Prompt Templates

The section provides prompt templates implementing FTA-Mem’s construction pipeline: coherent situation segmentation, structured Fact-Time-Affect unit extraction, and temporal-link maintenance. It also includes prompts for turn-level information-density annotation, with full files and runnable configurations supplied as supplementary code.

  • Core Construction Prompts: FTA-Mem uses prompts for boundary-preserving window segmentation, Fact-Time-Affect unit extraction, and temporal-link maintenance.These correspond to forming situation fragments, converting fragments into structured units, and maintaining relations among finalized units.
  • Information-Density Analysis: A turn-level density annotation prompt labels information density for long-term memory construction using only the current turn, speaker role, and session time.The full prompt files and runnable configurations are included in the supplementary code.
  • Boundary-preserving Window Segmentation: The segmentation prompt divides one complete session into continuous small segments suitable for memory construction and requires JSON-only output without memory extraction or question answering.It receives raw dialogue turns containing system-generated numeric metadata.
  • Fact-Time-Affect Unit Extraction: The extraction prompt constructs memory units from segment dialogue and incomplete events, requiring strict JSON, “unknown” for missing information, and no unsupported inferences.Inputs may include turn_index, original turn_id, role, timestamp, and text.
  • Temporal Link Maintenance: The temporal-link prompt maintains relations among episodic memory units using a newly generated memory unit and candidate neighbor units.The prompt is designed for relation maintenance after memory-unit generation.
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