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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

Emotional-support dialogue is sparse and temporally evolving, while fixed-unit memories may miss details or add redundant noise. FTA-Mem segments coherent situations into fact-time-affect units and improves long-term memory question answering across ES-MemEval and LoCoMo.

  • Problem

    Longitudinal emotional support requires personalized memory across sessions, but dialogue evidence is often sparse, indirect, temporally ambiguous, and affectively contextual.

    Source: Introduction · p. 1

  • Method

    FTA-Mem uses boundary-preserving segmentation to create situation-level Fact-Time-Affect units encoding factual, temporal, and affective context.

    Source: Introduction · p. 1, Fact-Time-Affect Memory Unit · p. 3

  • Results

    38.71 F1 with Qwen3-8B; FTA-Mem achieves the strongest average performance across ES-MemEval backbone settings and improves question answering on ES-MemEval and LoCoMo.

    Source: Overall Performance · p. 5, Conclusion · p. 7

  • Takeaways & Limitations

    Situation-level construction provides a better overall trade-off between evidence preservation and construction cost than session-level or turn-pair alternatives.

    Source: Granularity Analysis · p. 6

    FTA-Mem is a research framework, not a standalone clinical or counseling system; persistent memories can also retain incorrect or outdated inferences and raise privacy risks.

    Source: Ethical Statement · p. 11

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

Low-density emotional-support dialogue scatters incomplete evidence across sessions, while event meaning depends on temporal validity and affective interpretation. FTA-Mem addresses this with situation-level Fact-Time-Affect memory units and a boundary-preserving construction pipeline.

Introduction

  • Low-density dialogue scatters incomplete, indirect, or temporally ambiguous evidence across sessions, and event meaning also depends on validity over time and user appraisal.Simply retrieving more history or compressing longer contexts may therefore fail to produce reliable memory.Source: Introduction · p. 1
  • Session summaries can omit temporal updates, plans, relationship changes, and affective cues, while turn-level or turn-pair units can fragment coherent events into redundant pieces.The introduction argues that event grouping alone does not organize distributed factual, temporal, and affective cues within memory units.Source: Introduction · p. 1
  • FTA-Mem uses Boundary-preserving Window Segmentation to form coherent fragments, then constructs situation-level units jointly encoding factual evidence, temporal validity, and affective interpretation.It carries unresolved units across fragment boundaries, fuses adjacent-fragment units before ID assignment, links finalized units to historical memories, and synthesizes retrieved units into structured context packets.Source: Introduction · pp. 1–2
  • FTA-Mem improves question-answering performance on ES-MemEval and LoCoMo and offers a better granularity trade-off than session-level and turn-pair construction.The introduction identifies these benchmarks and comparison levels but reports no quantitative results here.Source: Introduction · p. 2

Related Work

Earlier counseling agents emphasize short-term response generation or organize continuity around profiles, summaries, planning, and skills, while long-term memory systems leave retrievable-unit granularity underexplored. FTA-Mem instead centers situation-level units that preserve factual evidence, temporal grounding, and affective state.

Psychological Counseling Agents

Long-Term Memory for LLM Agents

  • Long-term memory methods leave retrievable-unit granularity underexplored, motivating situation-level FTA units that retain facts, temporal grounding, and affective state.Existing approaches include graph organization (GraphRAG, Edge et al. 2025), summaries (MemoryBank, Zhong et al. 2024), hierarchical management (MemGPT, Packer et al. 2024; MemoryOS, Kang et al. 2025), linked notes (A-MEM, Xu et al. 2026), semantic units (FraCom, Ke et al. 2025), and event memories or maps (ES-Mem, Zou et al. 2026a; CompassMem, Hu et al. 2026b). Coarse session summaries may lose QA-sensitive details, while turn or turn-pair units may split coherent situations into redundant fragments.Source: Long-Term Memory for LLM Agents · p. 2

Methodology · Problem Definition

FTA-Mem frames long-term dialogue question answering as generating an answer grounded in a multi-session case, using retrieved structured memory because direct conditioning is infeasible for long, low-density histories. Its central design problem is to keep memory units compact for retrieval while preserving factual evidence, temporal validity, and affective context.

Problem Definition

  • A dialogue case C comprises timestamped turns grouped into multiple sessions, and the task is to answer a user query grounded in that case history.Each turn contains the speaker role, timestamp, and utterance text.Source: Problem Definition · p. 2
  • Directly conditioning on the case history is infeasible for long cases and unreliable when useful evidence is sparse, implicit, or distributed across sessions.Source: Problem Definition · p. 2
  • FTA-Mem introduces an external structured memory M as an intermediate representation for long-term dialogue answering.Source: Problem Definition · p. 2
  • At inference, the query retrieves a relevant subset Msub from M, which is used to generate the answer.Source: Problem Definition · p. 2
  • The central problem is constructing memory units compact enough for retrieval yet expressive enough to preserve factual evidence, temporal validity, and affective context.Source: Problem Definition · p. 2

Boundary-preserving Window Segmentation

Boundary-preserving Window Segmentation (BWS) turns low-density dialogue into coherent situation fragments for memory construction. It preserves uncertain content near window tails by carrying it into the next window for reprocessing.

Boundary-preserving Window Segmentation

  • BWS applies an LLM boundary detector to consecutive turn windows to identify continuous spans and cues such as topic changes or emotional shifts, drawing on event segmentation theory.A numeric turn index is attached to every turn before windowing.Source: Boundary-preserving Window Segmentation · p. 2
  • To avoid truncation, BWS starts the next window at the last detected segment’s start and keeps that segment provisional for reprocessing with following context.It finalizes earlier segments, enforces a minimum forward step if a window is detected as one full segment, and finalizes all detected segments at session end.Source: Boundary-preserving Window Segmentation · pp. 2–3
  • BWS outputs contiguous session fragments, each serving as an evidence span for constructing one or more memory units.Source: Boundary-preserving Window Segmentation · p. 3

Memory Unit Construction

FTA-Mem builds evidence-grounded episodic units from dialogue fragments and supplements them with longitudinal semantic-user and support-experience memory to maintain consistency.

Fact-Time-Affect Memory Unit

  • Each situation-level memory unit combines factual, temporal, and affective anchors with an evidence pointer and construction status.The anchors capture the factual claim and situation, dialogue and event timing and completion, and subjective context such as emotion, intention, and relation cues. Units can be complete, partial, pending, or unknown; unresolved partials carry forward, while finalized units receive persistent IDs and enter the store.Source: Fact-Time-Affect Memory Unit · p. 3

Unit Consolidation and Temporal-Link Maintenance

  • FTA-Mem reduces boundary-related incompleteness through local fusion, then links stored units to historical memories using classified relations.Fusion combines factual and temporal anchors, affective context, and evidence pointers without assigning the candidate an independent memory ID; otherwise, candidates are finalized and stored. The relation classifier labels pairs as same-situation, update, contradiction, follow-up, support, or unknown, adding non-unknown relations to the graph.Source: Unit Consolidation and Temporal-Link… · pp. 3–4

Longitudinal Memory Maintenance

  • After each session, FTA-Mem maintains auxiliary user-semantic and support-experience memories, while episodic FTA units remain the primary evidence source.Source: Longitudinal Memory Maintenance · p. 4

Retrieval and Context Synthesis

At inference time, FTA-Mem retrieves relevant memory units and synthesizes them into a structured packet for answer generation. It combines query rewriting and structured matching with linked evidence, source pointers, and auxiliary longitudinal memory.

Query Rewriting and Unit Retrieval

  • FTA-Mem rewrites queries to preserve entities, time, relations, and task intent, then retrieves FTA units using semantic similarity and structured cues.Retrieval uses thresholded top-K selection; K sets the retrieval budget and τ filters weakly related memories.Source: Query Rewriting and Unit Retrieval · p. 4

Linked Neighbor Expansion and Source Grounding

  • FTA-Mem expands retrieved units with linked neighbors, deduplicates and chronologically orders them, and retains dialogue-span pointers for source-grounded answers.It also retrieves user semantic and support-experience memories as auxiliary longitudinal context; the initial evidence set remains primary.Source: Linked Neighbor Expansion and Source… · p. 4

Structured Context Synthesis

Experiments

Experiments evaluate FTA-Mem across emotional-support and general long-conversation memory benchmarks, testing performance, construction granularity, retrieval budgets, and memory components. Results show strong benchmark performance and a situation-level design that balances evidence preservation and construction cost.

Datasets. / Metrics.

  • ES-MemEval contains 18 emotional-support user cases and 1,427 questions, while LoCoMo contains 10 conversation samples and 1,986 questions spanning single-hop, multi-hop, temporal, and open-domain reasoning.Source: Datasets · p. 4

Baselines

  • FTA-Mem is compared with MemoryBank, MemGPT, MemoryOS, A-Mem, and CompassMem, with retrieval budgets of 10 on ES-MemEval and 25 on LoCoMo.Implementation uses BWS window size 20, query rewriting, one linked neighbor per retrieved unit, and temperature 0 for answer generation.Source: Baselines · p. 5

Overall Performance

  • FTA-Mem achieves 38.71 F1 and 66.68 BERTScore with Qwen3-8B on ES-MemEval and the best average F1 and BLEU-1 across LoCoMo backbone settings.It also leads average performance across ES-MemEval backbone settings and performs best on temporal and open-domain LoCoMo questions, while CompassMem is slightly stronger on LoCoMo single-hop and multi-hop questions.Source: Overall Performance · pp. 5–6, Table 2 · p. 5, Table 3 · p. 6

Granularity Analysis

  • Situation-level construction outperforms session-level and turn-pair construction on ES-MemEval, while turn-pair construction is slightly stronger on LoCoMo at substantially higher construction cost.Session-level construction loses useful evidence; very fine-grained construction can help in denser factual settings but is less efficient and more redundant overall.Source: Granularity Analysis · p. 6, Table 6 · p. 7

Answer-Stage Cost

  • FTA-Mem constructs fewer units than turn-level or turn-pair methods but more than summary-based or coarse event-localization methods, keeping answer-stage Token/Q competitive despite richer units.Its situation-level design preserves sparse evidence more finely than summaries while avoiding the redundancy of indexing every turn.Source: Table 5 · p. 6, Answer-Stage Cost · p. 7

Density-Gain Correlation

  • On ES-MemEval, FTA-Mem’s gain over CompassMem correlates with lower factual density (r = −0.39) and higher implicitness (r = 0.41), a pattern not consistent on LoCoMo.Entity and time densities are less correlated with gains, suggesting the density-related advantage is more specific to ES-MemEval.Source: Table 4 · p. 6, Density-Gain Correlation · p. 7

Hyperparameter Analysis

  • ES-MemEval performance rises from K = 3 to K = 10, reaching 38.71 F1 and 66.68 BERTScore before saturating, while LoCoMo improves gradually and peaks around K = 25.The contrast indicates that ES-MemEval benefits from selective retrieval, whereas denser LoCoMo dialogue can use a larger retrieval set.Source: Hyperparameter Analysis · p. 7, Figure 2 · p. 7

Ablation Study

  • Removing temporal information causes the largest ablation degradation, especially on LoCoMo; removing links causes a smaller drop, and removing affect information also reduces performance.Fact-Only memory underperforms full FTA units on both benchmarks, and Question-only performs much worse, supporting the combined use of factual, temporal, affective, and lightweight link cues.Source: Ablation Study · p. 7, Figure 3 · p. 7

Conclusion

FTA-Mem is a structured memory framework for low-density long-term dialogue that preserves factual evidence with temporal and affective context in situation-level units. Experiments show improved long-term memory question answering across dialogue settings, with a favorable trade-off among evidence preservation, temporal consistency, and construction efficiency.

Conclusion

  • FTA-Mem forms situation-level Fact-Time-Affect units through boundary-preserving segmentation, preserving factual evidence alongside temporal and affective context.Source: Conclusion · p. 7
  • Temporal links and structured context synthesis organize the units to support grounded, personalized answer generation.Source: Conclusion · p. 7
  • Experiments on ES-MemEval and LoCoMo show improved long-term memory question answering across dialogue settings and a favorable trade-off among evidence preservation, temporal consistency, and construction efficiency.Source: Conclusion · p. 7

Supplementary Material … Paired Bootstrap Uncertainty Check

The supplementary material details FTA-Mem’s implementation and segmentation procedure, then reports ablations and diagnostics on boundary handling, dialogue density, and comparison uncertainty. These analyses indicate benefits from boundary preservation and structured retrieval/context, while the density and bootstrap findings warrant cautious interpretation.

Reproducibility Details

  • The implementation separates BWS, FTA-unit extraction, relation classification, query rewriting, and answer generation into fixed-prompt LLM calls, using Qwen3-8B for additional analyses unless otherwise specified.Answer generation uses temperature 0 and memory construction 0.7; retrieval uses all-MiniLM-L6-v2 cosine similarity, and compared methods share answer-backbone and generation settings while retaining native memory formats. Main comparative retrieval uses K = 7 on ES-MemEval and K = 10 on LoCoMo; subsequent analyses use K = 10 and K = 25, respectively.Source: Reproducibility Details · p. 9

Baseline Selection

  • The supplementary experiments exclude ES-Mem and SGMem because neither had a publicly available official implementation at submission, and their fine-grained construction is examined in the granularity comparison.Source: Baseline Selection · p. 9

Boundary-preserving Window Segmentation

  • BWS finalizes all segments at session end, but during processing retains the last detected segment provisionally so boundary-tail content can be reprocessed with following context.It enforces a minimum forward step when a window yields one full-window segment to prevent non-termination.Source: Boundary-preserving Window Segmentation · p. 9

Additional Comparative Experiments

  • Boundary-preserving segmentation outperforms fixed windows on F1 and B/B1 across both benchmarks, despite the fixed-window variant producing slightly more memory units.The comparison holds window size, memory-unit schema, retriever, and answer-generation pipeline constant; BWS carries the last boundary-uncertain segment forward to avoid incomplete units.Source: Additional Comparative Experiments · p. 9

Additional Ablation Studies

  • Removing query rewriting, using plain embedding retrieval, or replacing the structured memory packet with flat context each degrades ES-MemEval performance.Removing auxiliary memory causes little change, indicating episodic FTA units remain the primary evidence source while semantic and support-experience memories mainly provide background personalization.Source: Additional Ablation Studies · p. 10

Density Annotation and Statistical Analysis

  • On ES-MemEval, diagnostic trends associate larger FTA-Mem gains with lower factual density and higher implicitness; the pattern is weaker on LoCoMo and is not causal evidence.Turn annotations count factual, entity, temporal, and support-process anchors; implicitness is rated 1–3 and low-information status is binary, with dialogue-level statistics averaged over turns.Source: Density Annotation and Statistical… · p. 10

Paired Bootstrap Uncertainty Check

  • The paired cluster-bootstrap F1 gain over CompassMem remains clearly positive on ES-MemEval, while the LoCoMo interval is closer to zero and merits cautious interpretation.Cases or conversations are resampled with replacement and F1 differences averaged over sampled clusters, keeping constructed memories fixed.Source: Paired Bootstrap Uncertainty Check · p. 10

Memory Quality Audit … Prompt Templates

The supplementary sections audit FTA units, illustrate how they support temporal and contextual reasoning, and document ethical risks and implementation prompts. The audit finds factual and temporal grounding stronger than affect/context explicitness and linked-pair relatedness.

Memory Quality Audit

  • The manual audit scores factual grounding, temporal accuracy, affect/context accuracy, and linked-pair relatedness on a 0–2 scale using 100 units and 50 linked pairs.Two annotators independently evaluated units from Qwen3-8B runs balanced across ES-MemEval and LoCoMo; linked pairs were also balanced across the benchmarks.Source: Memory Quality Audit · p. 10
  • FTA units are strongly grounded in factual and temporal evidence, while affect/context is usually usable but less explicit and linked-pair relatedness is noisier.The authors therefore treat links as lightweight maintenance cues rather than a fully precise symbolic graph.Source: Memory Quality Audit · p. 10

Unit Case

  • A representative situation-level FTA unit stores factual, temporal, affective, evidence, and link-maintenance fields.Figure 5 presents an example unit with temporal links.Source: Unit Case · p. 10, Figure 5 · p. 11

Result Case

  • In three difficult ES-MemEval cases, FTA-Mem locates the time-specific argument, detects a coping-strategy contradiction, and preserves the Jimmy–George participant relation missed by A-Mem.The examples show that FTA units help answer generation use temporal validity, status cues, and affective context.Source: Result Case · pp. 10–11, Table 12 · p. 12

Ethical Statement

  • The experiments use public benchmarks under their original data-use conditions, collect no new real-user data, and position FTA-Mem as memory research rather than clinical diagnosis or treatment.Source: Ethical Statement · p. 11
  • Persistent memories can carry incorrect, outdated, or over-interpreted information across sessions, with particular ambiguity and sensitivity around affect, intentions, relationships, and privacy.Source: Ethical Statement · p. 11
  • Real-world deployment should inform users and support consent, inspection, correction, deletion, retention controls, secure storage, access control, data minimization, and safeguards for uncertain or conflicting memories.The authors characterize FTA-Mem as a research framework, not a standalone clinical or counseling system.Source: Ethical Statement · p. 11

Prompt Templates

  • FTA-Mem provides prompts for situation-fragment segmentation, FTA-unit extraction, and temporal-link maintenance, with full prompt files and runnable configurations in supplementary code.The supplementary materials also include a turn-level density annotation prompt for diagnostic information-density analysis.Source: Prompt Templates · pp. 11, 12, 15

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