SocialMemBench: Are AI Memory Systems Ready for Social Group Settings?

• URL: http://arxiv.org/abs/2605.17789v1
• Date: Mon, 18 May 2026 03:11:48 GMT
• Title: SocialMemBench: Are AI Memory Systems Ready for Social Group Settings?
• Abstract summary: Memory systems for AI assistants were built for single-user dialogue and fail characteristically when applied to multi-party social group settings.<n>Existing memory benchmarks evaluate dyadic or workplace dialogue; none targets multi-party social groups.<n>We introduce SocialMemBench, a benchmark of human-verified synthetic social group networks.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Memory systems for AI assistants were built for single-user dialogue and fail characteristically when applied to multi-party social group settings. This gap matters for the social assistants being built today: group-acting agents embedded in chat platforms, and proactive personal-assistant agents whose holistic model of a user must include their social context. Existing memory benchmarks evaluate dyadic or workplace dialogue; none targets multi-party social groups, where memory must anchor facts in shared history rather than professional roles, separate group norms from individual exceptions, and correctly attribute even after member departure. We introduce SocialMemBench, a benchmark of human-verified synthetic social group networks across five archetypes (close friends, family, recreational, interest community, acquaintance network) and three group-size tiers (4-30 members), with 430 personas and 7,355 conversation turns, yielding 1,031 QA pairs across nine question categories. Each category isolates an architectural capability, and the five failure modes (single-stream conflation, temporal-state overwrite, entity merging at scale, missing cross-persona knowledge, norm-individual conflation) are testable hypotheses; our two research probes Subject-Mem and SMG provide evidence on two, three remain open. A full-context Gemini 2.5 Flash reference reaches only 0.721 against a blind-critic reasoning-model mean of 0.98 on small networks, indicating the benchmark is genuinely difficult even with complete access to the conversation. Across all 43 networks, the four open-source memory frameworks evaluated (Mem0, LangMem, Graphiti, Cognee) cluster in the 0.12-0.18 question-weighted range with overlapping 95% CIs, well below an uncompressed retrieval reference of 0.345 and a matched-answerer full-context reference of 0.369 (GPT-4o-mini). Current memory systems show a measurable gap.

Related papers

• ZifaMem: Structured Memory for Persona, Preference, and Emotional Continuity in AI Companions [2.959518534015279]
  ZifaMem is a structured memory system that organizes dialogue into session summaries, episodic memories, and a consolidated user model.<n>Under an identical preregistered protocol, three memory systems each improve significantly over raw-history deployment.<n>ZifaMem and Mem0 are statistically equivalent within +/-5 points on the preregistered primary preference endpoint.
  arXiv  Detail & Related papers  (2026-07-20T05:18:40Z)
• Toward Anthropomorphic Dialogue: A Closed-Loop Framework for Human-Like Chat Generation, Evaluation, and Preference Alignment [11.379426404872833]
  AnthroDial is a closed-loop framework that formulates anthropomorphic dialogue as a joint problem of system architecture, executable evaluation, and diagnostic alignment.<n>It combines a role-conditioned scheduled dialogue with persona and scenario cards, long-term memory, virtual time, and single-draft message decisions.<n>We show that anthropomorphic dialogue benefits when generation, evaluation, and reward shaping share the same behavioral dimensions.
  arXiv  Detail & Related papers  (2026-07-19T11:06:40Z)
• Let the Results Speak: A Replication-First Paradigm for LLM Behavioral Benchmarking [22.825786049667602]
  We propose a replication-first paradigm to anchor validity to a single human-rater consensus.<n>We certify the instrument via four properties -- reliability across K runs, cross-instrument replication across architecturally distinct judges, historical-footprint calibration via judges from earlier training cohorts, and pre-registered prediction.<n>We test it on emotional accompaniment by letting the self-evolve data-driven across: the dimensions are not pre-stipulated and the procedure stabilizes to a 9-dimension set.
  arXiv  Detail & Related papers  (2026-05-27T03:41:11Z)
• WBench: A Comprehensive Multi-turn Benchmark for Interactive Video World Model Evaluation [56.5415838759151]
  WBench is a comprehensive benchmark for evaluation of interactive world models.<n>It covers video quality, setting adherence, interaction adherence, consistency, and physics compliance.<n>It contains 289 test cases and 1,058 interaction turns, covering diverse scenes, styles, subjects, and both first- and third-person perspectives.
  arXiv  Detail & Related papers  (2026-05-25T14:01:31Z)
• GroupMemBench: Benchmarking LLM Agent Memory in Multi-Party Conversations [25.703133924514884]
  Large Language Model (LLM) agents increasingly serve as personal assistants and workplace collaborators.<n>Existing memory systems and benchmarks are built around the dyadic, single-user setup.<n>We introduce GroupMemBench, a benchmark that exposes three properties of group memory.
  arXiv  Detail & Related papers  (2026-05-14T07:38:29Z)
• MemReranker: Reasoning-Aware Reranking for Agent Memory Retrieval [37.54115468116941]
  This report introduces MemReranker, a reranking model family (0.6B/4B) built on Qwen3-Reranker through multi-stage LLM knowledge distillation.<n>On the memory retrieval benchmark, MemReranker-0.6B substantially outperforms BGE-Reranker and matches open-source 4B/8B models as well as GPT-4o-mini on key metrics.<n>MemReranker-4B further achieves 0.737 MAP, with several metrics on par with Gemini-3-Flash, while maintaining inference latency at only 10-20% of large models.
  arXiv  Detail & Related papers  (2026-05-07T12:33:57Z)
• ZenBrain: A Neuroscience-Inspired 7-Layer Memory Architecture for Autonomous AI Systems [51.56484100374058]
  On LongMemEval-500, ZenBrain matches a long-context oracle's binary-judge accuracy to within 4.5 pp.<n> ZenBrain is a 7-layer neuroscience-inspired memory architecture.
  arXiv  Detail & Related papers  (2026-04-26T20:39:19Z)
• ImplicitMemBench: Measuring Unconscious Behavioral Adaptation in Large Language Models [60.14219417402433]
  Existing memory benchmarks for LLM agents evaluate explicit recall of facts, yet overlook implicit memory where experience becomes automated behavior without conscious retrieval.<n>We introduce ImplicitMemBench, the first systematic benchmark evaluating implicit memory through three constructs drawn from standard cognitive-science accounts of non-declarative memory.<n>Our 300-item suite employs a unified Learning/Priming-Interfere-Test protocol with first-attempt scoring.
  arXiv  Detail & Related papers  (2026-04-09T10:26:32Z)
• Graph-Native Cognitive Memory for AI Agents: Formal Belief Revision Semantics for Versioned Memory Architectures [0.6091702876917279]
  Kumiho is a graph-native cognitive memory architecture grounded in formal belief revision semantics.<n>The architecture implements a dual-store model (Redis working memory, Neo4j long-term graph) with hybrid fulltext and vector retrieval.
  arXiv  Detail & Related papers  (2026-03-18T00:59:49Z)
• SocialOmni: Benchmarking Audio-Visual Social Interactivity in Omni Models [86.19617358080016]
  Social Omni is a benchmark that operationalizes the evaluation of conversational interactivity across three core dimensions.<n>Social Omni features 2,000 perception samples and a quality-controlled diagnostic set of 209 interaction-generation instances.<n>Our analysis reveals a pronounced decoupling between a model's perceptual accuracy and its ability to generate contextually appropriate interruptions.
  arXiv  Detail & Related papers  (2026-03-17T17:58:44Z)
• PersonaMem-v2: Towards Personalized Intelligence via Learning Implicit User Personas and Agentic Memory [56.81126490418336]
  Personalization is one of the next milestones in advancing AI capability and alignment.<n> PersonaMem-v2 simulates 1,000 realistic user-chatbot interactions on 300+ scenarios, 20,000+ user preferences, and 128k-token context windows.<n>We train Qwen3-4B to outperforms GPT-5, reaching 53% accuracy in implicit personalization.
  arXiv  Detail & Related papers  (2025-12-07T06:48:23Z)
• One Battle After Another: Probing LLMs' Limits on Multi-Turn Instruction Following with a Benchmark Evolving Framework [51.50565654314582]
  Large language models can follow users' instructions throughout a dialogue spanning multiple topics.<n>Existing benchmarks are often limited to a fixed number of turns, making them susceptible to saturation and failing to account for the user's interactive experience.<n>We propose a framework for assessing multi-turn instruction-following ability.
  arXiv  Detail & Related papers  (2025-11-05T14:39:59Z)
• SocialBench: Sociality Evaluation of Role-Playing Conversational Agents [85.6641890712617]
  Large language models (LLMs) have advanced the development of various AI conversational agents. SocialBench is the first benchmark designed to evaluate the sociality of role-playing conversational agents at both individual and group levels. We find that agents excelling in individual level does not imply their proficiency in group level.
  arXiv  Detail & Related papers  (2024-03-20T15:38:36Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.