arxiv: 2604.04815 · v2 · submitted 2026-04-06 · 💻 cs.CL · cs.AI

Recognition: no theorem link

LiveFact: A Dynamic, Time-Aware Benchmark for LLM-Driven Fake News Detection

Changhong Jin, Cheng Xu, Liming Chen, M-Tahar Kechadi, Nan Yan, Shuhao Guan, Yingjie Niu, Yuke Mei

Authors on Pith no claims yet

Pith reviewed 2026-05-10 20:27 UTC · model grok-4.3

classification 💻 cs.CL cs.AI

keywords fake news detectionLLM evaluationdynamic benchmarktemporal reasoningepistemic humilitymixture of expertsbenchmark contaminationfact checking

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The pith

LiveFact benchmark reveals that capable LLMs recognize unverifiable claims early when given evolving evidence on misinformation events.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper presents LiveFact as a continuously updated evaluation framework that supplies LLMs with time-stamped, incomplete evidence sets instead of fixed snapshots. This setup tests whether models can reason about fake news under the same uncertainty that occurs in real events, rather than relying on memorized training data. The authors run the benchmark on 22 models and report that open-source mixture-of-experts systems reach or exceed the accuracy of leading proprietary models. They also document a consistent pattern in which stronger models decline to classify claims as true or false during the earliest stages of an event. The work therefore argues that temporal dynamics expose a form of epistemic caution that static benchmarks have hidden.

Core claim

LiveFact supplies dynamic temporal evidence sets that evolve over successive time slices for each news event, paired with a dual-mode protocol that separately scores final classification and step-by-step inference. When applied to 22 LLMs, the protocol shows open-source mixture-of-experts models such as Qwen3-235B-A22B matching or surpassing proprietary systems on both modes while also demonstrating greater reluctance to render judgments on claims that remain unverifiable in early slices. An explicit contamination monitor tracks whether models exploit prior knowledge of the events.

What carries the argument

LiveFact's dynamic temporal evidence sets with dual-mode evaluation (classification and inference) and built-in benchmark data contamination monitoring.

If this is right

Stronger models will increasingly refuse early judgments on breaking claims rather than guessing from incomplete data.
Open-source mixture-of-experts architectures become viable substitutes for proprietary systems on time-sensitive verification tasks.
Static benchmarks will systematically overestimate model reliability by allowing reliance on leaked future information.
Evaluation pipelines must incorporate explicit checks for benchmark data contamination to remain trustworthy.
Dual-mode scoring separates final accuracy from the quality of intermediate reasoning steps.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

Adoption of similar dynamic slices could extend to other domains where information arrives incrementally, such as scientific claim verification or legal evidence review.
The observed refusal behavior suggests training objectives that explicitly reward uncertainty detection may improve real-world deployment safety.
If LiveFact-style updates become standard, model developers would need continuous access to fresh event streams rather than one-time dataset releases.

Load-bearing premise

The time-ordered evidence slices supplied by LiveFact reproduce the actual incompleteness and uncertainty of real misinformation events without injecting artificial inconsistencies or biases.

What would settle it

If the same set of events is presented to models once as static full-context documents and once as the paper's successive time slices, and no measurable difference appears in either accuracy or refusal rates, the value of the temporal structure would be refuted.

Figures

Figures reproduced from arXiv: 2604.04815 by Changhong Jin, Cheng Xu, Liming Chen, M-Tahar Kechadi, Nan Yan, Shuhao Guan, Yingjie Niu, Yuke Mei.

**Figure 2.** Figure 2: The overall framework of the LiveFact Benchmark. (A) The Monthly Development Pipeline illustrates the [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗

**Figure 3.** Figure 3: Temporal performance evolution for select [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗

**Figure 4.** Figure 4: Analysis of the "Reasoning Gap" (Inference [PITH_FULL_IMAGE:figures/full_fig_p008_4.png] view at source ↗

**Figure 6.** Figure 6: Prompt for Real Claim Generation • Llama Family: Includes both the standard Dense architecture (Llama 3.1 70B/8B) and recent lightweight variants (Llama 3.2 3B/1B), serving as the baseline for open-weights performance (Grattafiori et al., 2024). • DeepSeek & Kimi: Represent specialized massive-scale MoE models, with Kimi-K2 exceeding 1 trillion parameters (DeepSeek-AI et al., 2025; Team et al., 2025). • … view at source ↗

**Figure 5.** Figure 5: Prompt for Context Generation producing high-quality initial outputs that required minimal manual correction. This finding validates the universality of the LiveFact construction pipeline, suggesting that future updates can be sustainably generated even with lower-cost models. However, to ensure the highest possible baseline for this inaugural release, we utilized the outputs from o4-mini for all subsequ… view at source ↗

**Figure 7.** Figure 7: Prompt for Fake Claim Generation general-purpose models to the absolute peak of computational power. B.2 Evaluation Settings To ensure a fair and reproducible evaluation, all models were subjected to a standardized inference protocol. We set the decoding parameters to TEMPERATURE = 0.0 and TOP_P = 1.0 to minimize randomness and ensure deterministic outputs. The maximum generation length was capped at MA… view at source ↗

**Figure 8.** Figure 8: Prompt for Ambiguous Claim Generation set during training—a direct emulation of realworld BDC. We then fine-tuned three representative models (Qwen3-30B-A3B-Instruct-2507, Qwen3- 4B-Instruct-2507, and Llama-3.1-8B-Instruct) on this contaminated corpus using Low-Rank Adaptation (LoRA). The specific hyperparameters used for this process are detailed in [PITH_FULL_IMAGE:figures/full_fig_p018_8.png] view at source ↗

**Figure 9.** Figure 9: Prompt for Evaluation pared to the robust Instruct-2507 version (69.46%). However, an interesting anomaly emerges with Qwen3-8B-Base. Unlike its Llama counterpart, this base model exhibits surprisingly strong reasoning capabilities, achieving an average score of 61.10%, which is competitive with the Qwen3-8B hybrid model (63.62%). Notably, in the δ = −3 Inference Mode, the Base model actually outperforms… view at source ↗

**Figure 10.** Figure 10: Prompt for Entity Shift Processing 25 [PITH_FULL_IMAGE:figures/full_fig_p025_10.png] view at source ↗

**Figure 11.** Figure 11: Comparison of raw model outputs. Kimi-K2 fails to produce the required classification label, getting [PITH_FULL_IMAGE:figures/full_fig_p026_11.png] view at source ↗

**Figure 12.** Figure 12: Confusion Matrices (Part 1/4) - Qwen Series. Note the sharp contrast in diagonal clarity between Instruct [PITH_FULL_IMAGE:figures/full_fig_p027_12.png] view at source ↗

read the original abstract

The rapid development of Large Language Models (LLMs) has transformed fake news detection and fact-checking tasks from simple classification to complex reasoning. However, evaluation frameworks have not kept pace. Current benchmarks are static, making them vulnerable to benchmark data contamination (BDC) and ineffective at assessing reasoning under temporal uncertainty. To address this, we introduce LiveFact a continuously updated benchmark that simulates the real-world "fog of war" in misinformation detection. LiveFact uses dynamic, temporal evidence sets to evaluate models on their ability to reason with evolving, incomplete information rather than on memorized knowledge. We propose a dual-mode evaluation: Classification Mode for final verification and Inference Mode for evidence-based reasoning, along with a component to monitor BDC explicitly. Tests with 22 LLMs show that open-source Mixture-of-Experts models, such as Qwen3-235B-A22B, now match or outperform proprietary state-of-the-art systems. More importantly, our analysis finds a significant "reasoning gap." Capable models exhibit epistemic humility by recognizing unverifiable claims in early data slices-an aspect traditional static benchmarks overlook. LiveFact sets a sustainable standard for evaluating robust, temporally aware AI verification.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

LiveFact adds a time-evolving evidence setup and contamination check to fake news benchmarks, which is useful, but the real test is whether the temporal slices hold up without new biases.

read the letter

LiveFact stands out because it moves fake news evaluation away from fixed datasets toward evidence that changes over time. The benchmark builds temporal slices so models face incomplete information early on and fuller context later, plus it tracks benchmark data contamination directly. That setup targets a clear weakness in existing static tests where models can rely on memorization instead of reasoning under uncertainty. The dual-mode split between final classification and evidence-based inference is a practical addition that lets them measure both outcomes and the process. Running 22 models shows open-source Mixture-of-Experts systems like Qwen3-235B-A22B reaching parity with proprietary ones, and the reported reasoning gap, where stronger models flag unverifiable early claims, follows logically if the slices are built cleanly. The paper earns credit for making the contamination monitor explicit rather than assuming it away. The main soft spot is the construction of those temporal evidence sets. If the way events are sliced or evidence is selected introduces inconsistencies that do not appear in actual misinformation timelines, the claimed simulation of the fog of war weakens. The abstract states the gap findings but leaves the exact quantitative breakdowns and example traces for the full text to show. Minor gaps in reported metrics could be fixed in revision. This work is aimed at groups that build or audit LLM fact-checking pipelines and want evaluation that stays current. A reader focused on temporal robustness or contamination issues will find concrete ideas to test. It deserves peer review because the core proposal is coherent and the evaluation scope is broad enough for referees to check the construction details and strengthen the numbers.

Referee Report

2 major / 2 minor

Summary. The paper introduces LiveFact, a continuously updated, dynamic benchmark for evaluating LLMs on fake news detection that uses temporal evidence sets to simulate real-world uncertainty and avoid benchmark data contamination issues in static datasets. It proposes a dual-mode evaluation (Classification Mode for final verification and Inference Mode for evidence-based reasoning) with explicit BDC monitoring, and reports results from tests on 22 LLMs showing that open-source Mixture-of-Experts models (e.g., Qwen3-235B-A22B) match or outperform proprietary systems, while identifying a 'reasoning gap' in which capable models exhibit epistemic humility by recognizing unverifiable claims in early data slices.

Significance. If the temporal evidence construction holds, LiveFact could establish a more sustainable and realistic standard for assessing LLM robustness in misinformation detection, directly addressing limitations of static benchmarks like data contamination and lack of temporal reasoning evaluation. The explicit BDC monitoring component and the identification of the reasoning gap (epistemic humility on early slices) represent valuable contributions that traditional evaluations overlook. The finding that open-source MoE models can compete with proprietary ones is noteworthy if supported by rigorous metrics.

major comments (2)

[Evaluation and Results] The central claims rest on results from 22 LLMs and the reasoning gap observation, but the abstract and available text provide no quantitative metrics, tables, statistical details, or full methodology for dataset construction and temporal slicing (e.g., how evidence sets evolve across time periods or how unverifiable claims are labeled). This is load-bearing for validating performance comparisons and the epistemic humility finding.
[Benchmark Design] The dynamic temporal evidence sets are presented as accurately simulating the 'fog of war' without artificial biases, but no details are given on construction, validation against real events, or controls for inconsistencies (as highlighted in the weakest assumption). This directly affects the benchmark's claimed superiority over static methods.

minor comments (2)

[Abstract] The abstract could include at least one key quantitative result or metric (e.g., accuracy delta or BDC rate) to strengthen the summary of findings.
[Dual-mode evaluation] Clarify the exact operational definitions and differences between Classification Mode and Inference Mode, including any scoring rubrics or examples, to improve reproducibility.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive and detailed review of our manuscript. We have addressed each major comment below and revised the paper to incorporate additional details on results and benchmark design.

read point-by-point responses

Referee: [Evaluation and Results] The central claims rest on results from 22 LLMs and the reasoning gap observation, but the abstract and available text provide no quantitative metrics, tables, statistical details, or full methodology for dataset construction and temporal slicing (e.g., how evidence sets evolve across time periods or how unverifiable claims are labeled). This is load-bearing for validating performance comparisons and the epistemic humility finding.

Authors: We agree that the abstract and the version of the text provided for review lack the quantitative metrics, tables, and granular methodological details needed to fully substantiate the central claims. In the revised manuscript we have added a results table reporting accuracy, F1, and other metrics across all 22 models, included statistical significance tests and confidence intervals, and expanded Section 3 to describe the temporal slicing procedure, evidence evolution rules, and the exact criteria used to label unverifiable claims. These additions make the performance comparisons and the epistemic-humility observation directly verifiable. revision: yes
Referee: [Benchmark Design] The dynamic temporal evidence sets are presented as accurately simulating the 'fog of war' without artificial biases, but no details are given on construction, validation against real events, or controls for inconsistencies (as highlighted in the weakest assumption). This directly affects the benchmark's claimed superiority over static methods.

Authors: We accept that the original submission did not supply sufficient documentation of the evidence-set construction pipeline. The revised manuscript now contains an expanded subsection that details the data-collection timeline, the protocol for validating evidence slices against contemporaneous real-world records, and the specific consistency checks and bias-mitigation steps applied at each temporal boundary. These additions directly support the claim that LiveFact provides a more realistic evaluation setting than static benchmarks. revision: yes

Circularity Check

0 steps flagged

No significant circularity

full rationale

The paper introduces a new dynamic benchmark (LiveFact) for evaluating LLMs on fake news detection under temporal uncertainty. Its core contributions are the benchmark construction itself, a dual-mode evaluation protocol, and empirical results from testing 22 independent LLMs. No derivation chain, mathematical prediction, or first-principles claim is present that reduces to fitted inputs or self-citations by construction. The reported performance patterns and 'reasoning gap' observations are direct outputs of applying the new benchmark to external models, with no self-definitional loops, renamed known results, or load-bearing self-citations. The work is self-contained as a proposal plus independent evaluation.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 2 invented entities

The central contribution is the invention of the LiveFact benchmark and its evaluation protocol. The paper relies on domain assumptions about the limitations of static benchmarks and the benefits of temporal dynamics, without introducing new physical entities or fitted parameters in the abstract.

axioms (2)

domain assumption Static benchmarks are vulnerable to benchmark data contamination (BDC)
Explicitly stated as a problem with current frameworks in the abstract.
domain assumption Dynamic temporal evidence sets can effectively evaluate reasoning under uncertainty
Central to the design of LiveFact as described.

invented entities (2)

LiveFact no independent evidence
purpose: A continuously updated benchmark for LLM fake news detection
Newly created and introduced in this paper.
Dual-mode evaluation no independent evidence
purpose: Classification Mode for verification and Inference Mode for reasoning assessment
Proposed as part of the evaluation framework.

pith-pipeline@v0.9.0 · 5529 in / 1456 out tokens · 46727 ms · 2026-05-10T20:27:23.009345+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

40 extracted references · 8 canonical work pages · 2 internal anchors

[1]

arXiv preprint arXiv:2502.17521 , year=

Enhancing multi-hop fact verification with structured knowledge-augmented large language models.Proceedings of the AAAI Conference on Artificial Intelligence, 39(22):23514–23522. Jianhong Chen, Wenyi Zhang, Hongcai Ma, and Shan Yang. 2023. Rumor detection in social media based on multi-hop graphs and differential time series. Mathematics, 11(16). Sanxing ...

work page arXiv 2023
[2]

arXiv:2307.12639

Unveiling the spectrum of data contamination in language model: A survey from detection to reme- diation. InFindings of the Association for Compu- tational Linguistics ACL 2024, pages 16078–16092, Bangkok, Thailand and virtual meeting. Association for Computational Linguistics. Shuzhi Gong, Richard O. Sinnott, Jianzhong Qi, and Cecile Paris. 2023. Fake ne...

work page arXiv 2024
[3]

gpt-oss-120b & gpt-oss-20b Model Card

Nela-gt-2018: A large multi-labelled news 11 dataset for the study of misinformation in news arti- cles.Proceedings of the International AAAI Confer- ence on Web and Social Media, 13(01):630–638. OpenAI, :, Sandhini Agarwal, Lama Ahmad, Jason Ai, Sam Altman, Andy Applebaum, Edwin Arbus, Rahul K. Arora, Yu Bai, Bowen Baker, Haiming Bao, Boaz Barak, Ally Be...

work page internal anchor Pith review Pith/arXiv arXiv 2018
[4]

InFindings of the Association for Computational Linguistics: EMNLP 2023, pages 10776–10787, Sin- gapore

NLP evaluation in trouble: On the need to mea- sure LLM data contamination for each benchmark. InFindings of the Association for Computational Linguistics: EMNLP 2023, pages 10776–10787, Sin- gapore. Association for Computational Linguistics. Mohammadamin Shafiei, Hamidreza Saffari, and Nafise Sadat Moosavi. 2025. MultiHoax: A dataset of multi-hop false-p...

work page arXiv 2023
[5]

Gemini: A family of highly capable multi- modal models.Preprint, arXiv:2312.11805. Kimi Team, Yifan Bai, Yiping Bao, Guanduo Chen, Jiahao Chen, Ningxin Chen, Ruijue Chen, Yanru Chen, Yuankun Chen, Yutian Chen, Zhuofu Chen, Jialei Cui, Hao Ding, Mengnan Dong, Angang Du, Chenzhuang Du, Dikang Du, Yulun Du, Yu Fan, and 150 others. 2025. Kimi k2: Open agentic...

work page internal anchor Pith review Pith/arXiv arXiv 2025
[6]

arXiv preprint arXiv:2406.04244 , year=

Piecing it all together: Verifying multi-hop multimodal claims. InProceedings of the 31st Inter- national Conference on Computational Linguistics, pages 7453–7469, Abu Dhabi, UAE. Association for Computational Linguistics. William Yang Wang. 2017. “liar, liar pants on fire”: A new benchmark dataset for fake news detection. InProceedings of the 55th Annual...

work page arXiv 2017
[7]

Don’t make your llm an evaluation benchmark cheater

Mrr-fv: Unlocking complex fact verification with multi-hop retrieval and reasoning.Proceedings of the AAAI Conference on Artificial Intelligence, 39(24):26066–26074. Kun Zhou, Yutao Zhu, Zhipeng Chen, Wentong Chen, Wayne Xin Zhao, Xu Chen, Yankai Lin, Ji-Rong Wen, and Jiawei Han. 2023. Don’t make your llm an evaluation benchmark cheater.Preprint, arXiv:23...

work page arXiv 2023
[8]

Focus ONL Y on identifying who/what the main entities are
[9]

Provide brief, factual descriptions (roles, posi- tions, basic facts)
[10]

Keep it concise - 2-3 sentences maximum
[11]

Do NOT explain the event itself, only the enti- ties involved
[12]

Trump to meet with Xi as he travels to Asia to contain trade war

Do NOT add unnecessary details or back- ground **Example**: Event: "Trump to meet with Xi as he travels to Asia to contain trade war" Context: "Trump is a businessman and politician, currently serving as the President of the United States. Xi is the President of China." **Output Format** (JSON): {{ "context": "your context text here" }} **Y our JSON outpu...

2025
[13]

Directly supported by the evidence above
[14]

Verifiable and accurate
[16]

Uses specific details from the evidence
[17]

claim_text

Does NOT add any unverified information **Output Format** (JSON): {{ "claim_text": "your factual claim here" }} **Y our JSON output**: Figure 6: Prompt for Real Claim Generation • Llama Family:Includes both the standard Dense architecture (Llama 3.1 70B/8B) and recent lightweight variants (Llama 3.2 3B/1B), serving as the baseline for open-weights per- fo...

2024
[18]

Related to the event but NOT supported by evidence
[19]

Contains fabricated details (false numbers, fake quotes, wrong people, etc.)
[20]

Sounds plausible but is factually incorrect
[21]

Written as a standalone news statement (1-2 sentences)
[22]

claim_text

Could mislead someone unfamiliar with the real story **Important**: Make it realistic fake news, not obviously absurd. **Output Format** (JSON): {{ "claim_text": "your fabricated claim here" }} **Y our JSON output**: Figure 7: Prompt for Fake Claim Generation general-purpose models to the absolute peak of computational power. B.2 Evaluation Settings To en...

2023
[23]

He signed the bill *specifically to appease his donors*

**Imputed Intent**: State a public figure’s private, internal motivation for a verifiable action as an objective fact (e.g., "He signed the bill *specifically to appease his donors*")
[24]

The stock drop *was a calculated response to* the leaked memo

**False Causality**: Connect two unrelated real facts as direct cause-and-effect without proof (e.g., "The stock drop *was a calculated response to* the leaked memo")
[25]

The negotiations ended in *an atmosphere of mutual distrust*

**Subjective Framing**: Use unverifiable, qualitative descriptors to characterize a verifiable event (e.g., "The negotiations ended in *an atmosphere of mutual distrust*")
[26]

The diplomat *refused to shake hands* before the meeting started

**Micro-Events**: Insert a plausible but unrecorded specific physical action into a real event (e.g., "The diplomat *refused to shake hands* before the meeting started")
[27]

The CEO *arrived 15 minutes late* to the conference

**Unfolding Details**: Add unverifiable specifics about the timing, location, or participants of a verifiable event (e.g., "The CEO *arrived 15 minutes late* to the conference", "Trump extended a dinner invitation to the Chinese delegation after the meeting", but this detail was not included in the evidence). **Goal**: The claim should sound like a confid...

work page arXiv 2024
[28]

Replace ALL named entities (people, organizations, cities, locations, nationalities, races) with fictional alternatives, ensure these names are not widely recognized
[29]

Trump" →

Use simple, uncommon but plausible names (e.g., "Trump" → "Korwin", "United States" → "Northland", "BBC"→"GBN")
[30]

Keep entity types consistent (person→person, country→country, organization→organization)
[31]

Preserve ALL other information (dates, numbers, events, relationships, facts)
[32]

Ensure the SAME entity always maps to the SAME fictional name across ALL fields
[33]

"" ENTITY_SHIFT_PROMPT=

Output valid JSON only, no markdown code blocks, no explanations""" ENTITY_SHIFT_PROMPT= """Perform entity shifting on the following news data. Replace all named entities with fictional alternatives while preserving semantic meaning. **Original Event Title**: {event_title} **Original Context**: {context} **Original Claim**: {claim} **Original Evidence Tit...
[34]

Identify ALL named entities: PERSON names, ORGANIZATION names, LOCATION names, NATIONALITY terms
[35]

Replace each with a LESS RECOGNIZABLE fictional alternative
[36]

Prince Andrew

Use simple, uncommon but plausible names (e.g., "Prince Andrew" → "Lord Harwick", "Buckingham Palace"→"Thornfield Manor", "United Kingdom"→"Alberia")
[37]

Keep entity types consistent (person→person, palace→palace, country→country)
[38]

PRESERVE all roles, positions, relationships, dates, numbers, and facts - ONL Y change entity names
[39]

Ensure CONSISTENT mapping: the SAME original entity ALWAYS maps to the SAME fictional name in ALL fields
[40]

Do NOT change news source names (BBC, Reuters, etc.) - these are metadata, not content entities
[41]

entity_mapping

Evidence titles should use the SAME entity mapping as claim, context, and event_title **Output Format** (JSON only, NO markdown, NO code blocks, NO explanations): {{ "entity_mapping": {{ "Original Entity 1": "Fictional Name 1", "Original Entity 2": "Fictional Name 2" }}, "event_title_shifted": "shifted event title here", "context_shifted": "shifted contex...