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arxiv: 2601.02845 · v2 · submitted 2026-01-06 · 💻 cs.CL · cs.AI

TiMem: Temporal-Hierarchical Memory Consolidation for Long-Horizon Conversational Agents

Kai Li , Xuanqing Yu , Ziyi Ni , Yi Zeng , Yao Xu , Zheqing Zhang , Xin Li , Jitao Sang
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Xiaogang Duan Xuelei Wang Chengbao Liu Jie Tan
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Pith reviewed 2026-05-16 17:46 UTC · model grok-4.3

classification 💻 cs.CL cs.AI
keywords temporal memory treememory consolidationconversational agentslong-horizon memorypersona representationmemory efficiencyLLM memory management
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The pith

TiMem organizes conversation histories into a temporal memory tree to reach higher accuracy with 52 percent shorter recalled memory.

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

The paper introduces TiMem to handle ever-growing conversation histories that exceed LLM context windows in long-horizon agents. It structures interactions via a Temporal Memory Tree that consolidates raw observations into progressively abstracted persona representations. Semantic-guided integration across tree levels builds these representations without fine-tuning, while complexity-aware recall selects the right level of detail for each query. This yields state-of-the-art accuracy on LoCoMo and LongMemEval-S while cutting recalled memory length substantially. The work positions temporal continuity as a primary organizing principle for stable memory in conversational systems.

Core claim

TiMem organizes conversations through a Temporal Memory Tree that enables systematic consolidation from raw observations to abstracted persona representations, using semantic-guided integration across hierarchical levels without fine-tuning and complexity-aware recall that balances precision and efficiency for queries of varying depth, resulting in state-of-the-art accuracy on both benchmarks and a 52.20 percent reduction in recalled memory length on LoCoMo.

What carries the argument

The Temporal Memory Tree (TMT), which arranges memory in temporal-hierarchical levels to support semantic-guided consolidation and complexity-aware recall.

Load-bearing premise

Semantic-guided consolidation across temporal-hierarchical levels produces stable persona representations without any fine-tuning of the underlying language model.

What would settle it

If disabling the temporal hierarchy or semantic guidance causes accuracy to drop below all baselines on LoCoMo and LongMemEval-S while increasing recalled memory length, the central claim would be falsified.

Figures

Figures reproduced from arXiv: 2601.02845 by Chengbao Liu, Jie Tan, Jitao Sang, Kai Li, Xiaogang Duan, Xin Li, Xuanqing Yu, Xuelei Wang, Yao Xu, Yi Zeng, Zheqing Zhang, Ziyi Ni.

Figure 1
Figure 1. Figure 1: TiMem framework overview. The framework organizes conversational streams through a five-level TMT, consolidating memories from factual segments to persona profiles, with adaptive memory recall guided by query complexity. et al., 2025). Supporting such interactions requires two capabilities: maintaining temporal coherence as user states evolve, and forming stable represen￾tations by distilling consistent pe… view at source ↗
Figure 2
Figure 2. Figure 2: TiMem architecture overview: a five-layer TMT from level 1 segments to level 5 profiles, with a [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: illustrates UMAP visualization of TiMem memory embeddings on LoCoMo and LongMemEval-S through different hierarchies. It shows that consolidation reshapes memory geome￾try differently across datasets. On LoCoMo, higher￾level memories separate users more clearly, with clustering quality improving 6.2×, indicating effec￾tive persona feature distillation. On LongMemEval￾S, consolidation reduces spatial dispers… view at source ↗
Figure 4
Figure 4. Figure 4: contrasts TiMem’s hierarchical consolida￾tion against Mem0 fragmented memories. Profile for Caroline ( May 2023 ) 1. Basic Identity Role Positioning: Female / Aspiring Counselor Life Background: Single, no children, lives in an urban setting. Main Social Contacts: Melanie (weekly), LGBTQ support group members (monthly). 2. Key Events This Month May 8: Attended an LGBTQ support group, where she felt a sense… view at source ↗
read the original abstract

Long-horizon conversational agents have to manage ever-growing interaction histories that quickly exceed the finite context windows of large language models (LLMs). Existing memory frameworks provide limited support for temporally structured information across hierarchical levels, often leading to fragmented memories and unstable long-horizon personalization. We present TiMem, a temporal--hierarchical memory framework that organizes conversations through a Temporal Memory Tree (TMT), enabling systematic memory consolidation from raw conversational observations to progressively abstracted persona representations. TiMem is characterized by three core properties: (1) temporal--hierarchical organization through TMT; (2) semantic-guided consolidation that enables memory integration across hierarchical levels without fine-tuning; and (3) complexity-aware memory recall that balances precision and efficiency across queries of varying complexity. Under a consistent evaluation setup, TiMem achieves state-of-the-art accuracy on both benchmarks, reaching 75.30% on LoCoMo and 76.88% on LongMemEval-S. It outperforms all evaluated baselines while reducing the recalled memory length by 52.20% on LoCoMo. Manifold analysis indicates clear persona separation on LoCoMo and reduced dispersion on LongMemEval-S. Overall, TiMem treats temporal continuity as a first-class organizing principle for long-horizon memory in conversational agents. The code is available at https://github.com/TiMEM-AI/timem.

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

3 major / 1 minor

Summary. The paper introduces TiMem, a temporal-hierarchical memory framework for long-horizon conversational agents organized around a Temporal Memory Tree (TMT). It claims three core properties—temporal-hierarchical organization, semantic-guided consolidation across levels without fine-tuning, and complexity-aware recall—and reports state-of-the-art accuracies of 75.30% on LoCoMo and 76.88% on LongMemEval-S while reducing recalled memory length by 52.20% on LoCoMo, with supporting manifold analysis of persona separation.

Significance. If the empirical claims hold under rigorous validation, TiMem would provide a structured alternative to flat or retrieval-only memory systems by elevating temporal continuity to a first-class principle, potentially improving long-horizon personalization and efficiency. The public release of code at https://github.com/TiMEM-AI/timem is a clear strength that supports reproducibility.

major comments (3)
  1. [Evaluation] Evaluation section: the reported accuracies (75.30% LoCoMo, 76.88% LongMemEval-S) and memory reduction (52.20%) are given as single point estimates without error bars, standard deviations, or results from multiple independent runs, which is required to assess statistical significance given the stochastic LLM-based consolidation steps.
  2. [Method] Method description of semantic-guided consolidation: repeated LLM abstraction from raw turns to persona summaries at successive TMT levels is presented as producing stable representations without fine-tuning, yet no quantification of variance across temperatures, prompt paraphrases, or random seeds is supplied, leaving the robustness of the central stability claim unverified.
  3. [Experiments] Experiments: no ablation studies isolate the contributions of TMT hierarchy, semantic-guided consolidation, and complexity-aware recall, so it is impossible to determine whether the reported gains are attributable to the proposed framework or to other implementation choices.
minor comments (1)
  1. [Abstract] The abstract states that 'Manifold analysis indicates clear persona separation' but provides no corresponding figure, table, or quantitative metric (e.g., silhouette score) in the main text.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive and detailed feedback, which highlights important aspects of statistical rigor and experimental validation. We address each major comment below and will incorporate the suggested improvements in the revised manuscript.

read point-by-point responses

  1. Referee: [Evaluation] Evaluation section: the reported accuracies (75.30% LoCoMo, 76.88% LongMemEval-S) and memory reduction (52.20%) are given as single point estimates without error bars, standard deviations, or results from multiple independent runs, which is required to assess statistical significance given the stochastic LLM-based consolidation steps.

    Authors: We agree that single-point estimates are insufficient for assessing statistical significance in the presence of stochastic LLM components. In the revised manuscript, we will rerun all evaluations across 5 independent random seeds and report mean accuracies with standard deviations for both LoCoMo and LongMemEval-S, as well as for the recalled memory length reduction. This will be added to the Evaluation section. revision: yes

  2. Referee: [Method] Method description of semantic-guided consolidation: repeated LLM abstraction from raw turns to persona summaries at successive TMT levels is presented as producing stable representations without fine-tuning, yet no quantification of variance across temperatures, prompt paraphrases, or random seeds is supplied, leaving the robustness of the central stability claim unverified.

    Authors: We acknowledge the value of quantifying robustness for the stability claim. We will add a dedicated analysis subsection that measures variance in persona summary embeddings across temperatures (0.0, 0.5, 1.0), prompt paraphrases, and seeds, using metrics such as average pairwise cosine similarity. This will be included in the revised Experiments section to support the no-fine-tuning consolidation property. revision: yes

  3. Referee: [Experiments] Experiments: no ablation studies isolate the contributions of TMT hierarchy, semantic-guided consolidation, and complexity-aware recall, so it is impossible to determine whether the reported gains are attributable to the proposed framework or to other implementation choices.

    Authors: We agree that explicit ablations would better isolate component contributions. While baseline comparisons already contrast against flat and non-hierarchical systems, we will add three targeted ablations in the revised Experiments section: (1) flat memory without TMT hierarchy, (2) non-semantic (direct) consolidation, and (3) uniform rather than complexity-aware recall. Results will be reported on both benchmarks to attribute performance gains. revision: yes

Circularity Check

0 steps flagged

No circularity: TiMem is an independent engineering framework with empirical results

full rationale

The paper describes TiMem via a Temporal Memory Tree (TMT) with semantic-guided consolidation and complexity-aware recall as an engineering proposal for long-horizon memory. No equations, fitted parameters, or self-citations appear in the provided text that would reduce the claimed accuracies (75.30% LoCoMo, 76.88% LongMemEval-S) or memory reduction (52.20%) to inputs by construction. Results are presented as benchmark evaluations under a consistent setup, with no derivation chain that renames, fits, or self-defines the outputs from the inputs.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 1 invented entities

The central claim rests on the unstated assumption that semantic similarity plus temporal distance suffice to decide consolidation levels without introducing contradictions or loss of critical facts.

axioms (1)
  • domain assumption Semantic similarity between conversation segments can be reliably computed by an off-the-shelf embedding model without domain-specific calibration.
    Invoked when describing semantic-guided consolidation across TMT levels.
invented entities (1)
  • Temporal Memory Tree (TMT) no independent evidence
    purpose: Organize raw conversational observations into progressively abstracted persona representations while preserving temporal order.
    New data structure introduced by the paper; no independent falsifiable prediction (e.g., predicted tree depth or branching factor) is supplied.

pith-pipeline@v0.9.0 · 5578 in / 1187 out tokens · 21323 ms · 2026-05-16T17:46:12.197604+00:00 · methodology

discussion (0)

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Forward citations

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Reference graph

Works this paper leans on

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    Hierarchical Recall (no LLM calls) Leaf Activa- tion Score L1 leaves by s(m, q, K) =λs sem + (1−λ)s lex with λ=0.9 (cosine similarity + BM25), then select top-k1=20. Ancestor Col- lection For each activated leaf, collect ancestors whose levels satisfy ℓ(m)∈ S(c) (determin- istic traversal). Budgeting Keep up to:Simple( L1:20, L2:4, L5:1);Hy- brid( L1:20, ...

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    Carefully analyze all provided memories from both speakers

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    Pay special attention to the timestamps to determine the answer

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    If the question asks about a specific event or fact, look for direct evidence in the memories

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    If the memories contain contradictory information, prioritize the most recent memory

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    last year

    If there is a question about time references (like "last year", "two months ago", etc.), calculate the actual date based on the memory timestamp. For example, if a memory from 4 May 2022 mentions "went to India last year," then the trip occurred in 2021

    work page 2022

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    last year

    Always convert relative time references to specific dates, months, or years. For example, convert "last year" to "2022" or "two months ago" to "March 2023" based on the memory timestamp. Ignore the reference while answering the question

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    Do not confuse character names mentioned in memories with the actual users who created those memories

    Focus only on the content of the memories from both speakers. Do not confuse character names mentioned in memories with the actual users who created those memories

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    # APPROACH (Think step by step):

    The answer should be less than 5-6 words. # APPROACH (Think step by step):

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    First, examine all memories that contain information related to the question

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  21. [21]

    Examine the timestamps and content of these memories carefully

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  22. [22]

    Look for explicit mentions of dates, times, locations, or events that answer the question

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  23. [23]

    If the answer requires calculation (e.g., converting relative time references), show your work

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    Formulate a precise, concise answer based solely on the evidence in the memories

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    Double-check that your answer directly addresses the question asked

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    last Tuesday

    Ensure your final answer is specific and avoids vague time references Relevant Memories: {context_memories} Question: {question} Answer: E.1.2 LLM-as-Judge Evaluation Prompt Your task is to label an answer to a question as ’CORRECT’ or ’WRONG’. You will be given the following data: (1) a question (posed by one user to another user), (2) a ’gold’ (ground t...

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  27. [27]

    KEEP if memory directly answers the question

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    KEEP if memory provides essential context (time/location of the fact)

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    EXCLUDE if related but does not contribute to answer

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    relevant_ids

    EXCLUDE if different topic entirely ## Instructions - Be strict: Only keep memories that help answer the specific question - Remove noise: Exclude tangentially related memories - Aim for 3-8 memories total Question: {question} Candidate memories ({total_count} total): {numbered_memories} Return IDs to keep (JSON format): {{"relevant_ids": [1, 2, 3, ...]}}...

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    First identify: Does the question require user’s preferences/habits/personality/values? If yes →Deep Retrieval (2)

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    Second identify: Does the question require reasoning/prediction/evaluation/subjective judgment? If yes→Deep Retrieval (2)

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    Third identify: Does the question require summarizing multiple fact fragments? If yes→Hybrid Retrieval (1)

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    Finally: If only single explicit fact needed→Simple Retrieval (0) Keyword extraction requirements:

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    Extract 1-3 most important keywords from the question

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    Exclude common stopwords (such as: the, a, in, is, have, and, or, with, etc.)

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    STRICTLY FORBIDDEN: Never include any personal names, usernames, or names

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    complexity

    FOCUS ONLY ON: Action words, object names, location types, concept words, adjectives and other non-name key concepts Question: {question} Please carefully analyze the essential needs of the question and output in the following JSON format: {\n "complexity": 0/1/2,\n "keywords": ["keyword1", "keyword2", "keyword3"]\n } 16

    work page