This paper presents an overview of \emph{SciClaimEval}, a pilot task at NTCIR-19 on verifying scientific claims against the tables and figures of the papers in which they appear. Our dataset pairs aut...
arXiv:2609.00654v1 Announce Type: new Abstract: We describe the SciTrue team's participation in both subtasks of the NTCIR-19 SciClaimEval task~\cite{sciclaimeval}, which asks systems to verify scien...
The study evaluates hybrid retrieval, NLI, and open‑source LLMs for scientific claim verification. Using a benchmark of 2,035 claims, the authors compare a DeBERTa‑based NLI baseline with Llama 3.1, Qwen3, and Mistral LLMs, finding that Qwen3 achieves the highest Macro‑F1 score. A conservative fusion rule that labels a claim as Support or Contradict only when both models agree actually degrades performance, and error analysis reveals that disagreements often overturn correct LLM predictions.
arXiv:2607. 16131v1 Announce Type: cross Abstract: Multimodal Scientific Claim Verification (MSCV) requires models to verify scientific claims using visually grounded evidence from papers, including figures, tables, charts, and textual context.
arXiv:2603. 05308v3 Announce Type: replace-cross Abstract: Assessing whether an article supports an assertion is essential for hallucination detection and claim verification.
MedRAGChecker is a claim-level verification framework designed for biomedical retrieval‑augmented generation (RAG). It decomposes generated answers into atomic claims and assesses each claim’s support by combining evidence‑grounded natural language inference with biomedical knowledge‑graph consistency signals. The aggregated claim decisions provide diagnostics that distinguish retrieval and generation failures, such as faithfulness, under‑evidence, contradiction, and safety‑critical errors, and the system is distilled into compact models for scalable evaluation.
Fact-checking systems built on LLMs achieve high verdict accuracy on standard benchmarks, yet routinely output Supports labels whose cited evidence does not license the claim. Structured decomposition is the natural way to inspect those warrants, but rigid extraction protocols strip the full-claim context that facets need.
ScienceJudge introduces Pattern Perturbation Augmentation (PPA) to generate hard negative examples for sentence‑level scientific claim verification by modeling reporting errors as faithfulness‑breaking transformations. PPA applies these patterns to supported claims, creating controlled hard negatives and expanding coverage through pattern‑level, operation‑level, and cross‑domain augmentation. The resulting ScienceJudge system, which ensembles PPA‑trained vision‑language predictors with instruction‑guided LLM predictors, achieves state‑of‑the‑art performance, improving the average score from 0.8124 to 0.8692 and ranking first on the NLPCC 2026 Task 10 Track 1 leaderboard.
arXiv:2609.06192v1 Announce Type: new Abstract: Scientific coding agents produce interdependent code, results, figures, and claims, yet evaluating final outputs alone does not establish whether the...
The paper investigates how stylistic changes introduced by large language models (LLMs) affect multimodal claim verification, a task that determines whether a textual claim is supported by given evidence. Two rewriting strategies are used: natural rewriting, mimicking typical academic polishing, and controlled injection, adding a single LLM-associated word. Across 11 open‑weight models (2B–38B parameters) from five VLM families, the study finds that most models remain robust to these modifications, showing no significant accuracy drop, though consistent probability shifts—especially under hedging conditions—are observed.
arXiv:2609.15964v1 Announce Type: new Abstract: Large language models (LLMs) have been widely adopted for clinical question answering (QA). Current systems can attach citations to their answers, but...
AtomCite is an agentic framework that verifies and corrects page‑level citations in multi‑page documents by parsing answers into claims, checking each claim against the cited page image, and applying a deterministic repair policy. The authors introduce DocCite, the first benchmark for this task, built on MP‑DocVQA and DUDE, containing 928 injected instances and 1,909 verified natural errors. Across Gemini, Claude, and GPT models, AtomCite achieves about 93% verification accuracy and improves citation precision from 34% to 87‑90%, while also enhancing hallucination detection in open‑source models.