Model Science
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Related Articles from SNS
The Case for Model Science: Verify, Explore, Steer, Refine
arXiv:2606.01189v1 Announce Type: new Abstract: We argue that the AI community is now ready to move beyond benchmarking and consolidate scattered efforts in model analysis into a systematic discipline, a direction we term Model Science. Complex AI models now serve billions of users, yet our understanding of how they work lags far behind our ability to deploy them. Decades of benchmark-driven research have delivered remarkable progress: extensive leaderboards, a wide range of performance...
Advancing Heliophysics and Space Weather Modeling through Open Science
arXiv:2605.30626v1 Announce Type: new Abstract: We present a community-wide effort to develop a strategy and action plan to advance heliophysics and space weather modeling through open science. While open science has the potential to enhance the quality and pace of scientific discovery, its application to scientific modeling requires more careful consideration regarding open data and open software guidelines, as scientific models differ significantly from data analysis software. We gathered...
MatSciBench: Benchmarking the Reasoning Ability of Large Language Models in Materials Science
Announce Type: replace Abstract: Large Language Models have shown strong scientific reasoning ability, but their performance on materials science problems remains less studied. To fill this gap, we introduce MatSciBench, a comprehensive college-level benchmark comprising 1340 problems that span the essential subdisciplines of materials science. MatSciBench features a structured and fine-grained taxonomy that categorizes materials science questions into 6 primary fields and 31 subfields,...
How Far Can You Grow? Characterizing the Extrapolation Frontier of Graph Generative Models for Materials Science
Announce Type: replace-cross Abstract: Every generative model for crystalline materials harbors a critical structure size beyond which its outputs become unreliable; we call this the extrapolation frontier. Despite its consequences for nanomaterial design, this frontier has never been systematically measured. We introduce RADII, a radius-resolved benchmark of ~75,000 crystal-derived nanoparticle structures (33-11,298 atoms) that treats radius as a continuous scaling knob, tracing generation...
How Far Can You Grow? Characterizing the Extrapolation Frontier of Graph Generative Models for Materials Science
Announce Type: replace-cross Abstract: Every generative model for crystalline materials harbors a critical structure size beyond which its outputs become unreliable; we call this the extrapolation frontier. Despite its consequences for nanomaterial design, this frontier has never been systematically measured. We introduce RADII, a radius-resolved benchmark of ~75,000 crystal-derived nanoparticle structures (33-11,298 atoms) that treats radius as a continuous scaling knob, tracing generation...
Addressing Longstanding Challenges in Cognitive Science with Language Models
arXiv:2511.00206v3 Announce Type: replace Abstract: Cognitive science faces ongoing challenges in research integration, formalization, conceptual clarity, and other areas, in part due to its multifaceted and interdisciplinary nature. Recent advances in artificial intelligence, particularly the development of language models, offer tools that may help to address these longstanding issues. Specifically, they can help map fragmented literatures, formalize verbal theories, identify overlap among...
MatMind: A Structure-Activity Knowledge-Driven Generative Foundation Model for Materials Science
Announce Type: cross Abstract: Progress in AI-driven crystal materials science has so far been carried by narrow architectures purpose-built for individual tasks -- graph neural networks for property prediction, diffusion and flow-matching models for crystal generation -- each excelling within its niche yet unable to act as a shared backbone across the full spectrum of materials problems. Generative large language models offer a fundamentally different paradigm, in which structural...
Faithful, Enriched, and Precise: Benchmarking Natural-Science Illustration Generation by T2I models
Announce Type: replace Abstract: Scientific illustrations are essential tools for communicating research findings, especially in natural science, where they visualize complex concepts and processes. As Text-to-Image (T2I) models become increasingly capable, researchers have started to use them for scientific illustration generation. However, existing benchmarks often assess outputs at a holistic level, overlooking fine-grained elements, while scientific reasoning ability and output...
Faithful, Enriched, and Precise: Benchmarking Natural-Science Illustration Generation by T2I models
Announce Type: new Abstract: Scientific illustrations are essential tools for communicating research findings, especially in natural science, where they visualize complex concepts and processes. As Text-to-Image (T2I) models become increasingly capable, researchers have started to use them for scientific illustration generation. However, existing benchmarks often assess outputs at a holistic level, overlooking fine-grained elements, while scientific reasoning ability and output conciseness...
Efficient and Training-Free Single-Image Diffusion Models
Computer Science > Computer Vision and Pattern Recognition [Submitted on 3 Jun 2026] Title:Efficient and Training-Free Single-Image Diffusion Models View PDF HTML (experimental)Abstract:We consider the problem of generating images whose internal structure -- defined by the distribution of patches across multiple scales -- matches that of a single reference image. Recent approaches address this problem by training a diffusion model on a single image.