mcp-proto-okn is a Python server that facilitates natural language access to complex scientific knowledge graphs, simplifying cross-domain knowledge analysis for biomedical research.

The paper introduces ProtStructQA, an executable benchmark that tests protein structural reasoning by requiring language models to generate measurable 3D coordinates, revealing a capability-dependent…

The paper introduces KnowledgeGain, a novel metric that measures the actual knowledge gained by readers from science news, and demonstrates its use in optimizing news generation to improve reader lear…

The paper introduces CORE, a contrastive evidence organization method, which significantly improves the accuracy of LLM-based predictions of gene expression changes following cellular perturbations by…

The paper introduces AMix-2, a novel protein-text foundation model that unifies protein understanding and sequence design by embedding both modalities in a shared token space, achieving state-of-the-a…

The paper proposes a steganographic mechanism, analogous to genetic inheritance, to track the lineage of synthetic information within a cyber ecosystem.

This paper measures the lower bound for the shortest program generating a sequence, proving a conservation law and providing a deterministic engine to recover generating programs for certain sequences…

The paper formally models structure-informed multiple sequence alignment (MSA-S) as an NP-complete optimization problem, establishing a strong computational complexity baseline for the field.

BIRDNet is a novel, sparse, and interpretable deep neural network that encodes Boolean implication knowledge mined directly from tabular data, achieving performance comparable to dense models while dr…

The paper advocates for the establishment of Model Science, a systematic discipline that moves beyond simple benchmarking to deeply analyze AI models' internal workings and failure modes.

The paper argues that current search agents often verify existing knowledge rather than genuinely searching, and introduces LiveBrowseComp, a new benchmark to measure true evidence-driven discovery.

MolLingo is a multi-agent system that significantly improves automated molecular design by integrating domain-specific chemical reasoning and structural context into LLMs, outperforming state-of-the-a…

The paper introduces FAM-Bench, a novel multimodal benchmark designed to test advanced, condition-aware reasoning for food-as-medicine applications.

This paper proposes using Answer-Set Programming (ASP) to implement and evaluate CARCASS abstractions, demonstrating a promising method for constructing powerful abstractions for Reinforcement Learnin…

The paper introduces a robust, four-mechanism LLM pipeline that generates auditable, evidence-grounded structured trait records for hundreds of thousands of diverse species across multiple taxa.

The paper introduces Science Earth, a planet-scale scientific runtime that enables diverse, siloed AI capabilities to connect and collaborate dynamically, demonstrating that scientific discovery can b…

Agentic-J is a containerized, multi-agent AI assistant designed to enable biologists to perform complex, reproducible biological microscopy image analysis by specifying tasks in natural language.

The paper introduces StemBind, a diagnostic benchmark that separates perception, rule induction, and answer selection in abstract visual reasoning, revealing that the primary failure point for MLLMs i…

The paper introduces ProjectionBench, a novel benchmark that progressively discloses information to evaluate LLMs' ability to generate scientific hypotheses, demonstrating that advanced models like GP…

The paper introduces Semantic Triplet Restoration (STR), a novel protocol that converts complex table structures into atomic semantic triplets, improving table question answering by providing explicit…