SciTrue: Reliable Scientific Claim Validation with Frontier and Open Language Models at the NTCIR SciClaimEval Task

• URL: http://arxiv.org/abs/2609.00654v1
• Date: Tue, 01 Sep 2026 03:30:45 GMT
• Title: SciTrue: Reliable Scientific Claim Validation with Frontier and Open Language Models at the NTCIR SciClaimEval Task
• Abstract summary: We benchmark eleven frontier and open multimodal models under one honest, per-sample protocol.<n>SciTrue placed first by a clear margin in three of the four evidence-category/subtask combinations.
• Score: 15.259880314614117
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We describe the SciTrue team's participation in both subtasks of the NTCIR-19 SciClaimEval task~\cite{sciclaimeval}, which asks systems to verify scientific claims against the tables and figures of a paper. Rather than tuning a single model, we benchmark eleven frontier and open multimodal models under one honest, per-sample protocol and combine them with light, transparent post-processing. On the official, blind test leaderboard (Section~\ref{sec:results}), SciTrue placed first by a clear margin in three of the four evidence-category/subtask combinations, and tied for first on the primary metric in the fourth. Three findings explain the result. First, strong instruction-tuned models are already competitive: Claude Opus~4.8 and Gemma-4-31B each exceed the strongest public baseline (o4-mini), and GPT-5.5 and Claude Fable~5 lead both subtasks (97.7 on Subtask~2). Second, the task's pairing structure is the largest lever: a \emph{leak-free pair prior} that recovers the Supported/Refuted pairing from the claim text alone (a visible field) and assigns Supported to the higher-confidence evidence raises Subtask-1 pair-accuracy from 72.2 to 93.5, far more than any model swap or ensemble weighting. Third, a case-by-case audit finds that most residual errors are visually-undetectable label-mapping swaps or dataset label noise, so measured accuracy understates the true ability and the fixable-by-modeling headroom is small. Controlled fine-tuning, distillation, and agentic consistency-checking support the same conclusions, and we document throughout a measurement leak---label information reaching a system through the packaging of the data rather than its content---in which the released file ordering encodes the label, including one instance that briefly misled our own pipeline.

Related papers

• Admission Without Answers: Label-Free Certification and Experience Learning for LLM-Based Optimization Modeling [8.467936416935986]
  AdmitOR is an admission gate built on calibrated external behavioral evidence.<n>It is the first label-free admission mechanism designed around an explicitly calibrated false-discovery target.
  arXiv  Detail & Related papers  (2026-08-16T06:18:54Z)
• One Token Is Enough: Fingerprinting and Verifying Large Language Models from Single-Token Output Distributions [0.0]
  API aggregators, resellers, and inference providers consume large language models (LLMs)<n>Existing identification techniques require long generated texts, token-level log-probabilities, adversarially crafted prompts, or the model owner's cooperation.<n>We define a behavioral fingerprint of an LLM as the empirical distribution of its answers to trivial one-word prompts.
  arXiv  Detail & Related papers  (2026-07-11T10:34:17Z)
• ttda704 at SemEval-2026 Task 6: Structured Chain-of-Thought Prompting for Political Evasion Detection [0.006227637802384129]
  This paper describes our system for SemEval-2026 Task 6, which addresses the classification of political evasion strategies in English question-answer pairs extracted from U.S. presidential interviews.
  arXiv  Detail & Related papers  (2026-06-14T12:09:23Z)
• Knowledge Index of Noah's Ark [63.143852586221534]
  We introduce KINA, an 899-item benchmark across 261 fine-grained disciplines.<n>We prove a bonus-on-bar tournament weakly FOSD-dominates flat payment in released-review quality.<n>The top model, Gemini-3.1-Pro-Preview, reaches 53.17%, followed by Claude-Opus-4.6 at 49.92% and GPT-5.4 at 48.55%.
  arXiv  Detail & Related papers  (2026-06-03T17:06:49Z)
• AgentAtlas: Beyond Outcome Leaderboards for LLM Agents [0.025718125188898048]
  AgentAtlas reframes agent evaluation as a diagnostic vocabulary and audit protocol.<n>The paper contributes: (i) a six-state control-decision taxonomy (Act / Ask / Refuse / Stop / Confirm / Recover); (ii) a trajectory-failure vocabulary with primary error source and downstream impact; and (iv) an illustrative protocol study on a synthetic 1,342-item set evaluated with eight models.
  arXiv  Detail & Related papers  (2026-05-19T22:05:12Z)
• AgentCollabBench: Diagnosing When Good Agents Make Bad Collaborators [0.0]
  We introduce AgentCollabBench, a diagnostic benchmark of 900 human-validated tasks spanning software engineering, DevOps, and data engineering.<n>Each task isolates one of four behavioral risks: instruction decay, false-belief contagion, context leakage, and tracer durability.<n>We evaluate four modern LLMs (GPT 4.1 mini, Gemini 2.5 Flash Lite, Qwen-3.5-35B-A3B, and Llama 3.1 8B Instruct)<n> communication topology emerges as a primary risk factor that explains 7-40% of the variance in multi-hop information survival.
  arXiv  Detail & Related papers  (2026-05-09T03:35:09Z)
• Three Roles, One Model: Role Orchestration at Inference Time to Close the Performance Gap Between Small and Large Agents [0.4666493857924357]
  We study whether inference-time scaffolding alone, without any additional training compute, can improve the performance of a small model in complex multi-step environments.<n>We evaluate Qwen3-8B on the AppWorld benchmark under both full-precision and 4-bit quantized configurations.<n>On full-precision inference, our scaffolded 8B model surpasses DeepSeek-Coder 33B Instruct (7.1%) from the original AppWorld evaluation.
  arXiv  Detail & Related papers  (2026-04-13T13:40:33Z)
• DecepGPT: Schema-Driven Deception Detection with Multicultural Datasets and Robust Multimodal Learning [64.33887406863899]
  Multimodal deception detection aims to identify deceptive behavior by analyzing audiovisual cues for forensics and security.<n>Existing benchmarks provide only binary labels without intermediate reasoning cues.<n>We construct reasoning datasets with structured cue-level descriptions and reasoning chains.<n>With 1695 samples, it is the largest non-laboratory deception detection dataset.
  arXiv  Detail & Related papers  (2026-03-25T04:06:36Z)
• CLUE: Non-parametric Verification from Experience via Hidden-State Clustering [64.50919789875233]
  We show that correctness of a solution is encoded as a geometrically separable signature within the trajectory of hidden activations.<n>ClUE consistently outperforms LLM-as-a-judge baselines and matches or exceeds modern confidence-based methods in reranking candidates.
  arXiv  Detail & Related papers  (2025-10-02T02:14:33Z)
• Eigen-1: Adaptive Multi-Agent Refinement with Monitor-Based RAG for Scientific Reasoning [53.45095336430027]
  We develop a unified framework that combines implicit retrieval and structured collaboration.<n>On Humanity's Last Exam (HLE) Bio/Chem Gold, our framework achieves 48.3% accuracy.<n>Results on SuperGPQA and TRQA confirm robustness across domains.
  arXiv  Detail & Related papers  (2025-09-25T14:05:55Z)
• Document Attribution: Examining Citation Relationships using Large Language Models [62.46146670035751]
  We propose a zero-shot approach that frames attribution as a straightforward textual entailment task.<n>We also explore the role of the attention mechanism in enhancing the attribution process.
  arXiv  Detail & Related papers  (2025-05-09T04:40:11Z)
• TACRED Revisited: A Thorough Evaluation of the TACRED Relation Extraction Task [80.38130122127882]
  TACRED is one of the largest, most widely used crowdsourced datasets in Relation Extraction (RE) In this paper, we investigate the questions: Have we reached a performance ceiling or is there still room for improvement? We find that label errors account for 8% absolute F1 test error, and that more than 50% of the examples need to be relabeled.
  arXiv  Detail & Related papers  (2020-04-30T15:07:37Z)

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.