---
title: "LiveFact — paper by Xu et al. (2026) — summary and ideas"
description: "Benchmarks for fake news detection are mostly fixed snapshots. 76 references. Among them: 2."
authors: ["Cheng Xu", "Changhong Jin", "Yingjie Niu", "Nan Yan", "Yuke Mei", "Shuhao Guan", "Liming Chen", "Tahar Kechadi"]
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.546/"
doi: "10.18653/v1/2026.acl-long.546"
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/xu-2026-livefact-dynamic-time-aware-benchmark/"
---
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# LiveFact: A Dynamic, Time-Aware Benchmark for LLM-Driven Fake News Detection

**Authors:** [Cheng Xu](https://md.research.elamur.ai/free/en/authors/cheng-xu/), Changhong Jin, Yingjie Niu, [Nan Yan](https://md.research.elamur.ai/free/en/authors/nan-yan/), [Yuke Mei](https://md.research.elamur.ai/free/en/authors/yuke-mei/), [Shuhao Guan](https://md.research.elamur.ai/free/en/authors/shuhao-guan/), Liming Chen and Tahar Kechadi · **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.546/) · [PDF](https://aclanthology.org/2026.acl-long.546.pdf) · [DOI 10.18653/v1/2026.acl-long.546](https://doi.org/10.18653/v1/2026.acl-long.546) · [OpenAlex](https://openalex.org/W7166855846)

**Citations:** 2 citations (OpenAlex, 2026-10-14)

## Summary
Benchmarks for fake news detection are mostly fixed snapshots. LLMs may have absorbed them during pre-training (benchmark data contamination, BDC), and they usually hand the model all the evidence at once, which does not match how verification works as a story is still unfolding. LiveFact addresses this with a benchmark rebuilt every month from fresh high-impact news. Its pipeline retrieves events through the Google News API, collects evidence bounded by timestamps before, at and after each event, generates background context and labelled claims, and has humans review the output. Models are tested in two ways. Classification Mode asks for a definitive verdict. Inference Mode asks for a judgement based only on the evidence available in a given time slice, so the correct answer may be that a claim cannot yet be verified. A Semantic Sensitivity Amplifier (SSA) with entity shifting tracks memorization risk. In the evaluation, open-source MoE models matched or beat proprietary systems. Stronger models also tended to flag early-stage claims as unverifiable. This skill is invisible to static benchmarks, so LiveFact offers a contamination-resistant, time-aware testbed.

## Key points
- **Temporal evidence:** time-sliced evidence sets (e.g., E(−3) vs. E(+3)) simulate how information develops around an event. For copyright reasons, only webpage titles, source names and publication timestamps are stored.
- **Results:** in the Nov. 2025 snapshot, Qwen3-235B-A22B-Instruct-2507 scored best at 72.4%. Qwen3-30B-A3B-Instruct-2507 reached 69.46%, within 3 points of the leader, at about 14× lower cost than gpt-5.2. Top models cost roughly $3.65–$9.27 per run.
- **Number of models:** the abstract reports 22 evaluated LLMs, while the conclusion says 18.
- **SSA monitoring:** the entity shift mechanism (e.g., swapping in an altered name such as "Trump" → "Wannetta") builds a parallel dataset. Comparing predictions on original and shifted versions (overturn rate, performance gap) reveals memorization. The mechanism was validated with a simulated BDC injection experiment.
- **Limitations:** the benchmark currently covers English, text-only sources, and human review limits how many claims can be released each month.
- **Resources:** code is at github.com/bebxy/livefact, and a regularly updated leaderboard is at livefact.bebxy.com.

*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: Cheng Xu, Changhong Jin, Yingjie Niu, Nan Yan, Yuke Mei, Shuhao Guan, Liming Chen and Tahar Kechadi. 2026. LiveFact: A Dynamic, Time-Aware Benchmark for LLM-Driven Fake News Detection. 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{xu2026livefact,
  title = {{LiveFact: A Dynamic, Time-Aware Benchmark for LLM-Driven Fake News Detection}},
  author = {Cheng Xu, Changhong Jin, Yingjie Niu, Nan Yan, Yuke Mei, Shuhao Guan, Liming Chen and Tahar Kechadi},
  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.546},
  url = {https://doi.org/10.18653/v1/2026.acl-long.546}
}
```

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## References

76 references, 19 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

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*Updated 31 October 2026*