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Finding Is Not Returning, Returning Is Not Reconstructing: A Multi-Dimensional Evaluation of Agent Social Re-entry

Sheng Wan, Cong Ma, Sichen Dong, Qian'Ang Mao, Chenhan Zhang, Daning Hu, Qiang Yang

arXiv:2610.39781v1cs.AIcs.SI

TL;DR

LLM agents need to do more than access past conversations: they must identify when to revisit one, choose to return, and reconstruct its context. This paper evaluates those dimensions independently and finds that resurfacing improves Finding and memory is associated with higher Returning, without corresponding gains in Reconstructing.

  • Problem

    Memory retrieval evaluations do not establish whether an agent can autonomously return to a previously inactive conversation and continue it with contextual continuity.

    Source: §1 · p. 2, §3.1 · p. 4

  • Method

    A shared-snapshot framework evaluates Finding, Returning, and Reconstructing in separate branches and examines memory and resurfacing interventions across six configurations.

    Source: §1 · p. 2, §7 · p. 16

  • Results

    Conversational resurfacing improves Finding and persistent memory is associated with higher Returning, but these improvements are not accompanied by corresponding gains in Reconstructing.

    Source: §7 · p. 16

  • Takeaways & Limitations

    Separately evaluating identification, autonomous re-engagement, and contextual continuity extends persistent-agent evaluation beyond memory retrieval and isolated responses.

    Source: §7 · p. 16

    The independent-branch design does not measure how Finding, Returning, and Reconstructing progress within one continuous interaction.

    Source: §6.3 · p. 16

Abstract

from arXiv · show

As Large Language Model (LLM) agents increasingly participate in persistent social environments, understanding their long-term social interaction capabilities becomes increasingly important. While existing research has advanced long-term memory and conversational context retrieval, less attention has been paid to whether agents can autonomously re-engage with previously inactive conversations. In particular, remembering a past interaction does not necessarily mean that an agent can identify when to revisit it, decide to return, or reconstruct the context needed for a coherent continuation. To investigate this gap, we introduce agent social re-entry as a multi- dimensional evaluation problem, distinguishing three complementary capabilities: Finding, Returning, and Reconstructing. We develop a shared-snapshot evaluation framework that measures these dimensions independently, allowing us to examine how conversational resurfacing and persistent memory relate to different aspects of social re-entry. Evaluations across six LLM configurations show that resurfacing substantially improves target conversation identification, while persistent memory is associated with higher rates of autonomous target-directed replying. However, neither improvement is accompanied by consistent gains in contextual reconstruction. These results highlight the distinction between accessing previous conversations, choosing to re-engage, and maintaining conversational continuity. Our framework provides a way to evaluate these capabilities separately rather than relying on memory retrieval or conversational response quality alone.

1 Introduction

The introduction frames social re-entry as more than retrieving past information: agents must find a conversation, choose to return, and reconstruct its context. A shared-snapshot framework evaluates these capabilities separately; across six configurations, gains in Finding and Returning are not matched by consistent gains in Reconstructing.

1 Introduction

  • Memory and retrieval advances address historical information access, but social re-entry also requires deciding when and where to resume and maintaining continuity.The introduction argues retrieval accuracy or conversational memory alone cannot establish successful re-engagement.Source: §1 · p. 2
  • The paper distinguishes Finding, Returning, and Reconstructing as connected but separately measurable capabilities; success at one stage does not ensure success at the next.Finding identifies a relevant prior conversation, Returning re-engages with it, and Reconstructing recovers context for coherent continuation.Source: §1 · pp. 2–3
  • A shared-snapshot framework measures the three dimensions in separate branches and examines persistent memory and prioritized resurfacing across six service/model configurations.Finding selects among ten candidate threads, Returning measures an unprompted target-directed reply, and Reconstructing measures continuity in a prompted continuation.Source: §1 · pp. 2–3
  • Prioritized resurfacing improves Finding, persistent memory is associated with higher Returning rates, and neither brings consistent gains in Reconstructing.The patterns are consistent across six evaluated configurations, though reconstruction differences vary in magnitude and statistical significance.Source: §1 · p. 2

2 Related Work

Related work spans human conversational return, observed interaction patterns in agent communities, memory and context access, and broader evaluations of autonomous agents. This paper distinguishes its focus on resuming inactive conversations by measuring Finding, Returning, and Reconstructing separately.

Conversational re-entry and social continuity

  • Human re-entry research predicts whether participants return to earlier discussions, while work on grounding, interruptions, and prospective memory studies related continuity and resumption challenges.Wang et al. [15] jointly model conversation context and user interaction history.Source: §2 · p. 3
  • Studies of Moltbook describe shallow conversational structures, limited sustained interaction, and infrequent returns by original post authors [4] [18].Those works characterize observed agent-network behavior; this paper evaluates three separately measurable re-entry capabilities.Source: §2 · p. 3

Memory and context in LLM agents

  • Memory research supports storing and accessing conversational history, but access does not ensure effective use or answer whether an agent autonomously returns to a conversation [5] [7] [8] [11].LoCoMo [8] evaluates long-term conversational memory through question answering, event summarization, and dialogue generation.Source: §2 · p. 3

Evaluation of autonomous LLM agents

  • SocialBench [1] and ChronosBench [12] evaluate social interaction and proactive follow-up, respectively, whereas this work studies re-entry into previously inactive conversations.Its separate branches start from a common snapshot, avoiding the assumption that success in one stage establishes end-to-end re-entry.Source: §2 · pp. 3–4, Figure 1 · p. 4

3 Method

The paper frames agent social re-entry as three connected but separately measurable capabilities, then evaluates them in independent branches initialized from a shared observation state. The branches distinguish instructed target identification, autonomous re-engagement, and prompted contextual continuity.

3.1 Problem Formulation

  • Social re-entry comprises Finding, Returning, and Reconstructing: identifying a prior conversation, autonomously resuming it, and continuing with its relevant context.Success at one stage does not guarantee success at the next, and the stages are observable capabilities rather than independent internal mechanisms.Source: §3.1 · pp. 4–5

3.2 Social Re-entry Evaluation Framework

  • The framework models an initial interaction, inactivity, and a shared observation state, then evaluates each capability in a separate branch from that state.Independent branches prevent actions in one task from changing the environment or history used in another; the measurements do not estimate end-to-end re-entry success.Source: §3.2 · pp. 5–6

3.3 Three Dimensions of Social Re-entry

  • Finding measures whether an agent selects the target conversation from candidate threads when explicitly asked, rather than whether it spontaneously returns.A successful outcome is selection of the target conversation.Source: §3.3 · p. 6
  • Returning measures whether an agent autonomously produces a non-empty reply directed to the target conversation, without an explicit instruction to resume it.This observable measure does not establish whether the reply reconstructs the earlier discussion accurately.Source: §3.3 · p. 6
  • Reconstructing measures contextual continuity in a prompted continuation, using an ordinal score whose higher values indicate stronger continuity with the prior interaction.Finding and Returning are binary outcomes, whereas Reconstructing is an ordinal quality score; the measures are not interchangeable.Source: §3.3 · pp. 6–7

4 Experimental Design

The experimental design uses frozen synthetic scenarios to compare persistent-memory and conversational-resurfacing conditions. Separate branches assess the three re-entry dimensions, with analyses based on available outcomes and paired comparisons where possible.

4.1 Social Interaction Scenarios and Data Construction

  • The benchmark contains 30 frozen synthetic conversation scenarios across four categories, with five rounds of unrelated activity before an observation state containing ten candidate threads.Each candidate set contains the original target and nine distractors; the stimuli are reused across configurations.Source: §4.1 · p. 7

4.2 Experimental Conditions and Controls

  • A0 omits persistent social history while A2 provides recent thread messages and the target's full history; B3 combines a target notification with first-position promotion, unlike B0.For reconstruction under A0, the prompt still includes the target title, topic, and latest reply.Source: §4.2 · p. 7
  • The distractor-memory comparison is exploratory because its blocks differ from A2 in length by 15–22% and also differ in composition and context.It therefore does not isolate the contribution of target-relevant memory content.Source: §4.2 · p. 7

4.3 Evaluation Protocol

  • Three independent branches assess instructed target selection, unprompted target-directed replying, and prompted continuation; each branch's information and instructions vary with its objective and condition.The three measurements characterize separate dimensions, not one end-to-end execution.Source: §4.3 · p. 8

4.4 Models, Implementation, and Statistical Analysis

  • Six service/model configurations use the same frozen stimuli and protocol, but available outcomes vary by condition because of incomplete or invalid API calls.Condition denominators range from 20 to 30, against 30 intended paired-comparison stimuli.Source: §4.4 · p. 8, Table 1 · p. 9
  • Finding and Returning are binary outcomes analyzed with paired exact McNemar tests and Holm–Bonferroni correction; Reconstructing uses ordinal scores and exploratory paired tests.Mean continuity scores are interpreted descriptively, and nonsignificant differences are not treated as evidence of equivalence.Source: §4.4 · p. 9

5 Experimental Results

Across six configurations, the results distinguish target identification, autonomous replying, and contextual continuity: resurfacing improves Finding, and persistent memory is associated with higher Returning rates, but comparable gains in Reconstructing are not observed. The section examines these patterns across the three stages and an exploratory distractor-memory comparison.

5.1 Finding: Effects of Conversational Resurfacing

  • 100% Finding success under B3 contrasted with B0 rates of 13.3%–53.3%, with risk differences of +46.7 to +86.7 percentage points.Because B3 combines notification and first-position promotion, the observed improvement reflects the combined intervention.Source: §5.1 · pp. 9–10, Figure 2 · p. 10

5.2 Returning: Effects of Persistent Memory

  • A2B0 reply rates ranged from 30.0% to 70.4%, versus 0.0% to 7.7% under A0B0, with positive risk differences in all six configurations.Resurfacing produced smaller, more heterogeneous reply shifts than the A0-to-A2 differences; the A2 association does not establish a specific internal memory mechanism.Source: §5.2 · p. 11, Figure 3 · p. 11

5.3 Exploratory Distractor-Memory Comparison

  • Distractor-memory reply rates exceeded A0B0 in all six configurations but varied around A2B0, with no consistent direction in that comparison.The exploratory conditions were not matched in length and structure, so the comparison cannot establish whether memory-block presence or target-relevant content accounts for the observed differences; lack of significance does not establish equivalence.Source: §5.3 · p. 12, Figure 4 · p. 12

5.4 Reconstructing: Contextual Continuity

  • Mean Reconstructing scores were lower under A2 than A0 across all six configurations, but only Qwen3.5 remained significant after correction.The mean differences varied substantially, and the Oracle condition did not establish a uniform empirical performance ceiling under these evaluation settings.Source: §5.4 · pp. 12–13, Figure 5 · p. 13

5.5 Cross-Model Consistency

  • Across configurations, resurfacing increased Finding and A2B0 increased Returning, but A2 did not consistently improve Reconstructing; the distractor-memory comparison was less consistent.Finding and Returning effects were positive in every configuration, whereas Reconstruction means favored A0 and only one configuration remained significant after correction.Source: Figure 6 · p. 14, §5.5 · p. 14

6 Discussion

The discussion shows that Finding, Returning, and Reconstructing are distinct outcomes, and that improvements in one do not necessarily carry over to another. It draws implications for agent design and identifies limits of the controlled, independently branched evaluation.

6.1 Finding, Returning, and Reconstructing as Separate Evaluation Targets

  • Under B3, all six configurations achieved 100% Finding success, yet target-directed replies remained less frequent without persistent social history.Making a conversation easy to identify does not necessarily lead an agent to return autonomously; explicit-selection evaluations may overestimate re-engagement ability.Source: §6.1 · pp. 14–15
  • A2 was associated with higher Returning rates across all six configurations, but not consistent gains in contextual continuity; distractor-memory results also resist attribution to target-relevant history alone.The three dimensions are evaluated in independent branches, so observed differences do not establish a causal sequence or distinct internal mechanisms.Source: §6.1 · p. 15

6.2 Implications for Persistent Social Agents

  • Conversation resurfacing improved target identification, but making an interaction salient does not necessarily produce autonomous engagement or an appropriate reply.Agent design must consider both which conversations are surfaced and how the agent determines whether to reply.Source: §6.2 · p. 15
  • Persistent memory should be assessed for its contribution to conversational continuity, since increased target-directed replies do not by themselves show that memory content supports coherent continuation.The experiments do not identify whether memory content, length, structure, or contextual cues drive observed behavior; the discussion calls for distinguishing relevant-history retrieval from its presentation and use in action selection.Source: §6.2 · p. 15
  • Separately reporting whether agents locate an interaction, act on it, and continue it with sufficient continuity can reveal limits hidden by a single re-entry score.The discussion recommends evaluating contextual reconstruction independently of both target discovery and replying.Source: §6.2 · p. 15

6.3 Limitations and Future Work

  • The evaluation uses controlled scenarios with fixed interruption lengths and candidate conversation sets, leaving performance in dynamically evolving, open-world interactions for future study.Such settings would test the three dimensions under more complex long-term social interactions.Source: §6.3 · p. 16
  • Current conditions combine multiple factors, and memory configurations differ in length and structure, limiting conclusions about resurfacing strategies and memory relevance or presentation.Future experiments could separate resurfacing strategies and compare more closely matched memory configurations.Source: §6.3 · p. 16
  • The independent-branch design does not measure how Finding, Returning, and Reconstructing progress within one continuous interaction.The authors propose longitudinal evaluation to trace these capabilities over time.Source: §6.3 · p. 16

7 Conclusion

The conclusion frames agent social re-entry as three complementary capabilities measured independently by a shared-snapshot framework. Across six LLM service/model configurations, gains in Finding or Returning did not guarantee stronger Reconstructing, supporting separate evaluation of these outcomes.

7 Conclusion

  • The shared-snapshot framework evaluates Finding, Returning, and Reconstructing independently rather than treating social re-entry as one success outcome.The paper formulates agent social re-entry as a multidimensional evaluation problem.Source: §7 · p. 16
  • Across six LLM service/model configurations, successful Finding did not necessarily imply Returning, and increased Returning did not guarantee stronger Reconstructing.Conversational resurfacing improved Finding and persistent memory was associated with higher Returning rates, without corresponding gains in Reconstructing.Source: §7 · p. 16
  • The framework extends evaluation beyond memory retrieval and isolated responses by distinguishing identification, autonomous re-engagement, and contextual continuity.The authors present it as a foundation for evaluating how agents sustain social interactions over time.Source: §7 · p. 16

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