PDEAgent-Bench is the first multi-metric, multi-library benchmark for AI-generated PDE solvers, evaluating executability, numerical accuracy, and efficiency across DOLFINx, Firedrake, and deal.II.
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Phybench: Holistic evaluation of physical perception and reasoning in large language models
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CritPt benchmark shows state-of-the-art LLMs reach only 5.7% average accuracy on full-scale unpublished physics research tasks, rising to about 10% with coding tools.
PhysCodeBench benchmark and SMRF multi-agent framework enable better AI generation of physically accurate 3D simulation code, boosting performance by 31 points over baselines.
PolyReal benchmark shows leading MLLMs perform well on polymer knowledge reasoning but drop sharply on practical tasks like lab safety analysis and raw data extraction.
PhysNote lets VLMs externalize physical knowledge into hierarchical self-generated notes, stabilizing spatio-temporal reasoning and yielding 56.68% accuracy on PhysBench with a 4.96% gain over the best multi-agent baseline.
PRL-Bench evaluates frontier LLMs on 100 real physics research tasks and finds the best models score below 50, exposing a gap to autonomous discovery.
LLaDA2.0 scales discrete diffusion language models to 100B parameters via systematic conversion from autoregressive models using a 3-phase WSD training scheme and releases open-source 16B and 100B MoE variants.
Eywa enables language-based agentic AI systems to collaborate with specialized scientific foundation models for improved performance on structured data tasks.
Seed1.8 is a new foundation model that adds unified agentic capabilities for search, code execution, and GUI interaction to existing LLM and vision strengths.
OmniFysics is an omni-modal network using a dynamic physical data engine and evolutive tuning to improve performance on multimodal benchmarks and physics-oriented tasks.
Ministral 3 releases 3B/8B/14B parameter-efficient language models with base, instruction, and reasoning variants derived via iterative pruning and distillation, including image understanding capabilities.
citing papers explorer
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PRL-Bench: A Comprehensive Benchmark Evaluating LLMs' Capabilities in Frontier Physics Research
PRL-Bench evaluates frontier LLMs on 100 real physics research tasks and finds the best models score below 50, exposing a gap to autonomous discovery.
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LLaDA2.0: Scaling Up Diffusion Language Models to 100B
LLaDA2.0 scales discrete diffusion language models to 100B parameters via systematic conversion from autoregressive models using a 3-phase WSD training scheme and releases open-source 16B and 100B MoE variants.