Frontier LLM-based agents can effectively overcome the manual bottleneck of phenotype annotation by achieving consistency comparable to human experts, significantly outperforming existing NLP tools.

The paper introduces BioConCal, a supervised scoring mechanism that evaluates biomedical NER candidates surfaced by multiple LLMs, significantly improving the quality of the candidate pool for human c…

The paper introduces Ryze, an automated system that synthesizes evidence-enriched Question-Answering (QA) pairs from raw biomedical papers, resulting in a specialized VLM (BioVLM-8B) that significantl…

This paper shows that large language models can automate reproducibility assessments in the social and behavioral sciences.

This paper conducts a large-scale audit of human annotation reporting in NLP, finding that while reporting has improved, critical details needed to assess annotation validity, such as training and agr…

The paper identifies a universal, statistically predictable distribution (Mandelbrot) governing LLM outputs, enabling a highly efficient, model-agnostic scoring primitive for provenance and quality as…

The paper introduces Chunk-Level Guided Generation, a training-free method that uses an off-the-shelf large language model (LLM) as a process scorer to guide small model generation, achieving performa…

The paper introduces BioRefusalAudit, a method that audits the structural soundness of language model biosecurity refusals, finding that refusal behavior is highly unstable, often collapsing under min…

The paper audits the structural soundness of LLM biosecurity refusals, finding that refusal behavior is highly unstable, often collapsing under minor prompt changes, and may track legal salience rathe…

AutoForest is an end-to-end system that automatically generates publication-ready forest plots directly from biomedical papers, streamlining the labor-intensive process of meta-analysis.

The paper introduces SciIntBench, an adversarial benchmark that reveals that LLMs' adherence to research integrity norms is highly sensitive to how the misconduct is framed, often failing when the mis…

The paper introduces SciIntBench, an adversarial benchmark that reveals that LLMs' adherence to research integrity norms is highly sensitive to how the misconduct is framed, failing particularly when…

The paper introduces CultureForest, a new benchmark for evaluating Cultural Norm Grounded Reasoning in LLMs, demonstrating that models struggle to apply their cultural knowledge effectively in realist…

This paper evaluates multiple LLMs (DeepSeek-R1, OpenBioLLM-Llama3, Qwen 3.5) for generating privacy-safe, high-quality synthetic mental health reports, demonstrating their effectiveness in expanding…

The authors created ImmigrationQA, a large source-grounded QA dataset for U.S. immigration law, and fine-tuned a small language model (Llama 3.2 3B) on it, achieving a significant performance boost ov…

EviLink addresses the ambiguity of schema linking in Text-to-SQL by treating it as an uncertainty-aware inference over multiple plausible SQL paths, significantly improving recall and efficiency.

The paper introduces RefWalk, a novel framework designed to improve regulatory compliance question answering by ensuring rigorous citation traceability and explicit per-rule attribution across complex…

The paper introduces Multi-Legal-Bench, a novel cross-jurisdictional benchmark evaluating LLMs on five standardized legal reasoning tasks across six diverse countries, demonstrating that cross-lingual…

The paper introduces REBench, a comprehensive, standardized benchmark dataset designed to enable fair and rigorous evaluation of Large Language Models (LLMs) on complex binary reverse engineering task…

The paper introduces Compass, an expert-guided LLM agent framework that successfully extracts and integrates thousands of previously inaccessible marine lead records from vast corpora of scientific pa…