# KnowMe-Bench: Benchmarking Person Understanding for Lifelong Digital Companions

arXiv: https://arxiv.org/abs/2601.04745 · HTML: https://mlsanity.com/paper/2601.04745/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 29 references that are in the map.
- First posted on arXiv 8 Jan 2026.

## Builds on (most cited first, 16 of 16)

- [Generative Agents: Interactive Simulacra of Human Behavior](https://mlsanity.com/paper/2304.03442/) (2023)
- [MemGPT: Towards LLMs as Operating Systems](https://mlsanity.com/paper/2310.08560/) (2023)
- [A-MEM: Agentic Memory for LLM Agents](https://mlsanity.com/paper/2502.12110/) (2025)
- [Evaluating Very Long-Term Conversational Memory of LLM Agents](https://mlsanity.com/paper/2402.17753/) (2024)
- [LongMemEval: Benchmarking Chat Assistants on Long-Term Interactive Memory](https://mlsanity.com/paper/2410.10813/) (2024)
- [Persona Vectors: Monitoring and Controlling Character Traits in Language Models](https://mlsanity.com/paper/2507.21509/) (2025)
- [Memory in the Age of AI Agents](https://mlsanity.com/paper/2512.13564/) (2025)
- [Evaluating Memory in LLM Agents via Incremental Multi-Turn Interactions](https://mlsanity.com/paper/2507.05257/) (2025)
- [Limitations of the LLM-as-a-Judge Approach for Evaluating LLM Outputs in Expert Knowledge Tasks](https://mlsanity.com/paper/2410.20266/) (2024)
- [Test of Time: A Benchmark for Evaluating LLMs on Temporal Reasoning](https://mlsanity.com/paper/2406.09170/) (2024)
- [MemBench: Towards More Comprehensive Evaluation on the Memory of LLM-based Agents](https://mlsanity.com/paper/2506.21605/) (2025)
- [VisionArena: 230K Real World User-VLM Conversations with Preference Labels](https://mlsanity.com/paper/2412.08687/) (2024)
- [Persona-L has Entered the Chat: Leveraging LLM and Ability-based Framework for Personas of People with Complex Needs](https://mlsanity.com/paper/2409.15604/) (2024)
- [OmniChat: Enhancing Spoken Dialogue Systems with Scalable Synthetic Data for Diverse Scenarios](https://mlsanity.com/paper/2501.01384/) (2025)
- [The influence of persona and conversational task on social interactions with a LLM-controlled embodied conversational agent](https://mlsanity.com/paper/2411.05653/) (2024)
- [Just What You Desire: Constrained Timeline Summarization with Self-Reflection for Enhanced Relevance](https://mlsanity.com/paper/2412.17408/) (2024)

## Built on by (most cited first, 11 of 11)

- [PERMA: Benchmarking Personalized Memory Agents via Event-Driven Preference and Realistic Task Environments](https://mlsanity.com/paper/2603.23231/) (2026)
- [LMEB: Long-horizon Memory Embedding Benchmark](https://mlsanity.com/paper/2603.12572/) (2026)
- [DynamicMem: A Long-Horizon Memory Benchmark in Real-World Settings](https://mlsanity.com/paper/2606.22877/) (2026)
- [MedMemoryBench: Benchmarking Agent Memory in Personalized Healthcare](https://mlsanity.com/paper/2605.11814/) (2026)
- [PersonaTree: Structured Lifecycle Memory for Person Understanding in LLM Agents](https://mlsanity.com/paper/2606.04780/) (2026)
- [HEART-Bench: Do LLM Agents Exhibit Human-like Psychology?](https://mlsanity.com/paper/2605.30058/) (2026)
- [Connecting the Dots: Benchmarking Reflective Memory in Long-Horizon Dialogue](https://mlsanity.com/paper/2606.01223/) (2026)
- [FlowTime: Towards Continuous Generative Watch Time Prediction via Flow-based Personalized Priors](https://mlsanity.com/paper/2606.01352/) (2026)
- [BehaviorBench: Modeling Real-World User Decisions from Behavioral Traces](https://mlsanity.com/paper/2606.02798/) (2026)
- [Setoka: A Benchmark for Hierarchical User Understanding in Personalized Agents over Heterogeneous Data](https://mlsanity.com/paper/2607.27056/) (2026)
- [Know It, Act on It: Investigating Memory Utilization in LLM Personalization](https://mlsanity.com/paper/2607.29433/) (2026)

## Abstract (arXiv)

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 hrefKnowMeBenchhttps://github.com/QuantaAlpha/KnowMeBench.