---
title: "KnowMe-Bench — paper by Wu et al. (2026)"
description: "Digital companions meant to support a user for years must do more than recall facts. 29 references, cited by 1 paper here."
authors: ["Tingyu Wu", "Zhisheng Chen", "Ziyan Weng", "Shuhe Wang", "Shuo Zhang", "Sen Hu", "Silin Wu", "Qizhen Lan", "Huacan Wang", "Ronghao Chen"]
year: 2026
venue: "Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)"
source: "https://aclanthology.org/2026.acl-long.1394/"
doi: "10.18653/v1/2026.acl-long.1394"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/wu-2026-knowme-bench-benchmarking-person-understanding/"
---
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# KnowMe-Bench: Benchmarking Person Understanding for Lifelong Digital Companions

**Authors:** Tingyu Wu, Zhisheng Chen, Ziyan Weng, [Shuhe Wang](https://md.research.elamur.ai/free/en/authors/shuhe-wang/), [Shuo Zhang](https://md.research.elamur.ai/free/en/authors/shuo-zhang/), Sen Hu, Silin Wu, Qizhen Lan, [Huacan Wang](https://md.research.elamur.ai/free/en/authors/huacan-wang/) and [Ronghao Chen](https://md.research.elamur.ai/free/en/authors/ronghao-chen/) · **Year:** 2026 · **Venue:** [Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)](https://md.research.elamur.ai/free/en/venues/acl/) ([ACL 2026](https://md.research.elamur.ai/free/en/venues/acl/2026/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2026.acl-long.1394/) · [PDF](https://aclanthology.org/2026.acl-long.1394.pdf) · [DOI 10.18653/v1/2026.acl-long.1394](https://doi.org/10.18653/v1/2026.acl-long.1394) · [OpenAlex](https://openalex.org/W7166821523)

**Citations:** 0 citations (OpenAlex, 2026-10-14) · 1 in our library, of the 149,439 papers whose bibliographies we read

## Summary
Digital companions meant to support a user for years must do more than recall facts. They need to explain and anticipate behaviour from a person's motivations and values. Current long-term memory benchmarks mostly use chat logs or synthetic histories and mainly test retrieval. KnowMe-Bench instead draws on long autobiographical narratives (~4.7M tokens), which interleave actions, context and inner thoughts. A multi-agent pipeline with verify-and-revise checks and human expert validation turns each narrative into a time- and place-anchored cognitive stream. Flashbacks are moved to their original point in the chronology ("mnestic realignment"). Questions are evidence-linked and range from factual recall to attributing mental states and reasoning about decision principles. On several LLM backbones, retrieval-style memory (Naive RAG, Mem0) mostly boosts fact-level scores. Chronology and insight tasks remain weak, with MemOS helping more consistently. The benchmark shows that person understanding needs memory with explicit temporal provenance, not just a searchable fact store.

## Key points
- Each narrative is split into five textual fields: visual observations, auditory inputs, situational context, background knowledge, and inner monologue. The pipeline runs in four stages: segmentation, atomic unit (ANU) extraction, timeline generation, and narrative generation.
- Evaluation has seven tasks in three levels:
  - T1 Extract and T2 Abstain make up Level I, which focuses on Fact & Entity.
  - Level II, Narrative Logic: T3 Temporal, T4 Order, T5 Trigger.
  - Level III, Insight: T6 Mind–Body, T7 Principle.
- Tested backbones were Qwen3-32B, GPT-5-mini, DeepSeek-R1 and Gemini-3 Pro, each with Base, Naive RAG, Mem0 and MemOS variants. A-Mem was tested on Qwen3-32B only.
- An "update paradox" appears: state-updating memory can treat recalled past events as overwrites of the present state. For example, Qwen3-32B + A-Mem scores 55.7 on T6 but drops to 16.0 on T3.
- In closed-book contamination checks, Level II accuracy falls from 89.1 to 33.2 after de-identification. Level III stays at 0, so memorized literary knowledge does not explain the results.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

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## Cite

Cite as: Tingyu Wu, Zhisheng Chen, Ziyan Weng, Shuhe Wang, Shuo Zhang, Sen Hu, Silin Wu, Qizhen Lan, Huacan Wang and Ronghao Chen. 2026. KnowMe-Bench: Benchmarking Person Understanding for Lifelong Digital Companions. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers).

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{wu2026knowme,
  title = {{KnowMe-Bench: Benchmarking Person Understanding for Lifelong Digital Companions}},
  author = {Tingyu Wu, Zhisheng Chen, Ziyan Weng, Shuhe Wang, Shuo Zhang, Sen Hu, Silin Wu, Qizhen Lan, Huacan Wang and Ronghao Chen},
  booktitle = {Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  year = {2026},
  doi = {10.18653/v1/2026.acl-long.1394},
  url = {https://doi.org/10.18653/v1/2026.acl-long.1394}
}
```

- [← previous Knowledge Extraction on Semi-Structured Content: Does It Remain Relevant for Question Answering in the Era of LLMs?](https://md.research.elamur.ai/free/en/papers/sun-2026-knowledge-extraction-semi-structured-content/)
- [next → KV-Embedding: Training-free Text Embedding via Internal KV Re-routing in Decoder-only LLMs](https://md.research.elamur.ai/free/en/papers/tang-2026-kv-embedding-training-free-text/)
- [↑ back to papers of 2026](https://md.research.elamur.ai/free/en/papers/2026/page/14/)

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*Updated 1 November 2026*