KnowMe-Bench: Benchmarking Person Understanding for Lifelong Digital Companions

• URL: http://arxiv.org/abs/2601.04745v1
• Date: Thu, 08 Jan 2026 09:11:33 GMT
• Title: KnowMe-Bench: Benchmarking Person Understanding for Lifelong Digital Companions
• Abstract summary: We present BenchName, a publicly releasable benchmark built from long-form autobiographical narratives.<n>BenchNamereconstructs each narrative into a flashback-aware, time-anchored stream and evaluates models with evidence-linked questions.
• Score: 13.246922338917601
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Existing long-horizon memory benchmarks mostly use multi-turn dialogues or synthetic user histories, which makes retrieval performance an imperfect proxy for person understanding. We present \BenchName, a publicly releasable benchmark built from long-form autobiographical narratives, where actions, context, and inner thoughts provide dense evidence for inferring stable motivations and decision principles. \BenchName~reconstructs each narrative into a flashback-aware, time-anchored stream and evaluates models with evidence-linked questions spanning factual recall, subjective state attribution, and principle-level reasoning. Across diverse narrative sources, retrieval-augmented systems mainly improve factual accuracy, while errors persist on temporally grounded explanations and higher-level inferences, highlighting the need for memory mechanisms beyond retrieval. Our data is in \href{KnowMeBench}{https://github.com/QuantaAlpha/KnowMeBench}.

Related papers

• UTILMEM: Benchmarking Evidence Utilization in Long-Term Conversational Memory [32.625416782173204]
  We introduce UtilMem, a diagnostic benchmark to evaluate memory utilization.<n>We find that strong performance on conventional factual-memory benchmarks does not reliably translate into effective memory utilization.<n>These findings expose a substantial gap between accessing stored information and using it effectively, and suggest that progress in long-term conversational memory will require architectures that explicitly support evidence integration and robustness to retrieval interference.
  arXiv  Detail & Related papers  (2026-08-31T09:41:26Z)
• REVEAL: A Rubric-Guided Agent for Explicit Evidence Sufficiency Verificationin Long-Video Question Answering [25.519927761448603]
  REVEAL is a rubric-guided agent framework for long-video question answering.<n>We introduce an adaptive visual-similarity-based preprocessing pipeline that groups visually coherent adjacent frames into natural event units.<n>Built upon this structured memory, REVEAL uses an automatically constructed rubric library to explicitly verify whether retrieved evidence satisfies sufficiency criteria.
  arXiv  Detail & Related papers  (2026-08-09T09:55:42Z)
• MINTEval: Evaluating Memory under Multi-Target Interference in Long-Horizon Agent Systems [69.06764269022925]
  We study how current memory-augmented agents perform in realistic, interference-heavy, long-horizon settings.<n> MINTEval is a benchmark featuring long, highly interconnected contexts with frequently updated information.<n>Overall, MINTEval has 15.6k question-answering pairs over long-horizon contexts averaging 138.8k tokens and extending to 1.8M tokens per instance.
  arXiv  Detail & Related papers  (2026-05-18T15:43:35Z)
• AdaMem: Adaptive User-Centric Memory for Long-Horizon Dialogue Agents [49.63422082885992]
  We propose AdaMem, an adaptive user-centric memory framework for long-horizon dialogue agents.<n>AdaMem organizes dialogue history into working, episodic, persona, and graph memories.<n>We evaluate AdaMem on the LoCoMo and PERSONAMEM benchmarks for long-horizon reasoning and user modeling.
  arXiv  Detail & Related papers  (2026-03-17T13:22:54Z)
• LifeBench: A Benchmark for Long-Horizon Multi-Source Memory [22.24847456134897]
  We introduce Lifebench, which features densely connected, long-horizon event simulation.<n>Lifebench pushes AI agents beyond simple recall, requiring the integration of declarative and non-declarative memory reasoning.<n>Performance results show that top-tier, state-of-the-art memory systems reach just 55.2% accuracy.
  arXiv  Detail & Related papers  (2026-03-04T06:42:17Z)
• EverMemBench: Benchmarking Long-Term Interactive Memory in Large Language Models [16.865998112859604]
  We introduce EverMemBench, a benchmark featuring multi-party, multi-group conversations spanning over 1 million tokens.<n>EverMemBench evaluates memory systems across three dimensions through 1,000+ QA pairs.
  arXiv  Detail & Related papers  (2026-02-01T16:13:08Z)
• MemWeaver: Weaving Hybrid Memories for Traceable Long-Horizon Agentic Reasoning [26.119505362626338]
  We propose a unified memory framework that consolidates long-term agent experiences into three interconnected components.<n>MemWeaver employs a dual-channel retrieval strategy that jointly retrieves structured knowledge and supporting evidence to construct compact yet information-dense contexts.
  arXiv  Detail & Related papers  (2026-01-26T06:39:27Z)
• EMemBench: Interactive Benchmarking of Episodic Memory for VLM Agents [52.567469286881426]
  We introduce EMemBench, a programmatic benchmark for evaluating long-term memory of agents through interactive games.<n>Rather than using a fixed set of questions, EMemBench generates questions from each agent's own trajectory.<n>Each template computes verifiable ground truth from underlying game signals.
  arXiv  Detail & Related papers  (2026-01-23T12:09:59Z)
• Amory: Building Coherent Narrative-Driven Agent Memory through Agentic Reasoning [14.368376032599437]
  Amory is a working memory framework that actively constructs structured memory representations during offline time.<n>Amory organizes conversational fragments into episodic narratives, consolidates memories with momentum, and semanticizes peripheral facts into semantic memory.<n>Amory achieves considerable improvements over previous state-of-the-art, with performance comparable to full context reasoning while reducing response time by 50%.
  arXiv  Detail & Related papers  (2026-01-09T19:51:11Z)
• Memory Matters More: Event-Centric Memory as a Logic Map for Agent Searching and Reasoning [55.251697395358285]
  Large language models (LLMs) are increasingly deployed as intelligent agents that reason, plan, and interact with their environments.<n>To effectively scale to long-horizon scenarios, a key capability for such agents is a memory mechanism that can retain, organize, and retrieve past experiences.<n>We propose CompassMem, an event-centric memory framework inspired by Event Theory.
  arXiv  Detail & Related papers  (2026-01-08T08:44:07Z)
• Evaluating Long-Term Memory for Long-Context Question Answering [100.1267054069757]
  We present a systematic evaluation of memory-augmented methods using LoCoMo, a benchmark of synthetic long-context dialogues annotated for question-answering tasks.<n>Our findings show that memory-augmented approaches reduce token usage by over 90% while maintaining competitive accuracy.
  arXiv  Detail & Related papers  (2025-10-27T18:03:50Z)
• KnowMT-Bench: Benchmarking Knowledge-Intensive Long-Form Question Answering in Multi-Turn Dialogues [58.305425399644086]
  Multi-Turn Long-Form Question Answering (MT-LFQA) is a key application paradigm of Large Language Models (LLMs) in knowledge-intensive domains.<n>We introduce textbfKnowMT-Bench, the textitfirst-ever benchmark designed to systematically evaluate MT-LFQA for LLMs across knowledge-intensive fields.
  arXiv  Detail & Related papers  (2025-09-26T04:32:29Z)
• MADial-Bench: Towards Real-world Evaluation of Memory-Augmented Dialogue Generation [15.64077949677469]
  We present a novel Memory-Augmented Dialogue Benchmark (MADail-Bench) to evaluate the effectiveness of memory-augmented dialogue systems (MADS) The benchmark assesses two tasks separately: memory retrieval and memory recognition with the incorporation of both passive and proactive memory recall data. Results from cutting-edge embedding models and large language models on this benchmark indicate the potential for further advancement.
  arXiv  Detail & Related papers  (2024-09-23T17:38:41Z)
• SOK-Bench: A Situated Video Reasoning Benchmark with Aligned Open-World Knowledge [60.76719375410635]
  We propose a new benchmark (SOK-Bench) consisting of 44K questions and 10K situations with instance-level annotations depicted in the videos. The reasoning process is required to understand and apply situated knowledge and general knowledge for problem-solving. We generate associated question-answer pairs and reasoning processes, finally followed by manual reviews for quality assurance.
  arXiv  Detail & Related papers  (2024-05-15T21:55:31Z)
• Evaluating Long-Term Memory in 3D Mazes [10.224858246626171]
  Memory Maze is a 3D domain of randomized mazes designed for evaluating long-term memory in agents. Unlike existing benchmarks, Memory Maze measures long-term memory separate from confounding agent abilities. We find that current algorithms benefit from training with truncated backpropagation through time and succeed on small mazes, but fall short of human performance on the large mazes.
  arXiv  Detail & Related papers  (2022-10-24T16:32:28Z)
• Fact-driven Logical Reasoning for Machine Reading Comprehension [82.58857437343974]
  We are motivated to cover both commonsense and temporary knowledge clues hierarchically. Specifically, we propose a general formalism of knowledge units by extracting backbone constituents of the sentence. We then construct a supergraph on top of the fact units, allowing for the benefit of sentence-level (relations among fact groups) and entity-level interactions.
  arXiv  Detail & Related papers  (2021-05-21T13:11:13Z)

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.