AIBullisharXiv – CS AI · 2d ago7/10
🧠Researchers introduce Battery-Sim-Agent, an LLM-based framework that uses AI agents to estimate battery parameters by mimicking scientific reasoning rather than traditional black-box optimization. The system outperforms conventional methods like Bayesian optimization on benchmark tests and demonstrates practical applicability on real-world battery datasets, representing a novel approach to accelerating battery innovation through physics-informed AI reasoning.
AIBearisharXiv – CS AI · 2d ago7/10
🧠A physicist supervised Claude AI models over 12 days to build CLAX-PT, a physics simulation module, documenting how AI agents struggle with architectural redesign and distinguishing symptom-fixes from root-cause solutions. The study reveals that supervision design and human domain expertise, rather than model capability alone, determine whether AI-generated scientific code produces trustworthy results.
🧠 Claude
AIBullisharXiv – CS AI · 3d ago7/10
🧠Researchers present hybrid neural world models that use machine learning surrogates to accelerate physical dynamics simulations while maintaining accuracy at discontinuities like shocks and contacts. The approach achieves 26-72x speedups over traditional solvers while implicitly learning to identify uncertain regions without explicit training, with an optional fallback mode using classical solvers for high-confidence predictions.
AIBullisharXiv – CS AI · 4d ago7/10
🧠Researchers introduce Recursive Flow Matching (RecFM), a generative AI framework that significantly improves the speed and accuracy of physics simulations by enforcing self-consistency across computational scales. The method achieves high-fidelity predictions in 1-4 steps with up to 20× speedup over existing diffusion models while reducing error by 15%, addressing a critical bottleneck in scientific computing.
AIBullisharXiv – CS AI · 4d ago7/10
🧠Researchers introduce Domain-Gated Latent Diffusion (DGLD), an AI method that discovered 12 novel energetic materials using generative diffusion models with quality-gated training and multi-task guidance. The breakthrough identified two lead compounds with performance metrics rivaling HMX-class materials for the first time in 15 years, validated through DFT simulations and released with open-source code.
AIBullisharXiv – CS AI · May 127/10
🧠Researchers introduce NIAgent, a multi-agent AI system that automates end-to-end neuroimaging analysis by enabling specialist agents to collaboratively build and optimize executable programs. The system outperforms conventional static workflows like fMRIPrep by adapting dynamically to data and incorporating hierarchical quality control, addressing a critical bottleneck in clinical biomarker development.
AIBullisharXiv – CS AI · May 117/10
🧠Researchers introduce a novel training strategy for neural posterior estimation that decouples representation learning from posterior modeling, enabling amortized inference on large observation sets by training only on pairs of examples. The approach dramatically reduces computational requirements while maintaining or improving performance across diverse benchmarks, making scalable Bayesian inference practical for real-world applications.
AIBullisharXiv – CS AI · May 117/10
🧠ATHENA is an autonomous AI framework that automates scientific computing and machine learning research by autonomously selecting mathematical approaches, generating code, and iteratively improving solutions through a contextual bandit learning process. The system achieves validation errors as low as 10^-14 and demonstrates performance surpassing traditional foundation models in solving complex multiphysics problems.
AIBullisharXiv – CS AI · May 17/10
🧠Researchers introduce CARE, a systematic methodology for engineering LLM-based agents in scientific domains through collaboration between subject-matter experts, developers, and AI helper agents. The approach replaces ad-hoc development with stage-gated phases and reusable artifacts, demonstrating measurable improvements in development efficiency and performance on complex queries.
AIBullisharXiv – CS AI · May 17/10
🧠Researchers introduce Eywa, a heterogeneous agentic framework that enables large language models to coordinate and reason across specialized scientific foundation models beyond natural language. The system improves performance on domain-specific tasks by allowing language models to guide inference over non-linguistic data modalities in physical, life, and social sciences.
AIBullisharXiv – CS AI · Apr 157/10
🧠AutoSurrogate is an LLM-driven framework that automates the construction of deep learning surrogate models for subsurface flow simulation, enabling domain scientists without machine learning expertise to build high-quality models through natural language instructions. The system autonomously handles data profiling, architecture selection, hyperparameter optimization, and quality assessment while managing failure modes, demonstrating superior performance to expert-designed baselines on geological carbon storage tasks.
AINeutralarXiv – CS AI · Mar 57/10
🧠Researchers developed an end-to-end AI-based event reconstruction system for future particle colliders that uses geometric algebra transformer networks and object condensation clustering. The system outperforms traditional rule-based algorithms by 10-20% in reconstruction efficiency and improves energy resolution by 22%, while reducing fake-particle rates by up to two orders of magnitude.
AIBullisharXiv – CS AI · Mar 57/10
🧠Researchers have introduced Mozi, a dual-layer architecture designed to make AI agents more reliable for drug discovery by implementing governance controls and structured workflows. The system addresses critical issues of unconstrained tool use and poor long-term reliability that have limited LLM deployment in pharmaceutical research.
AIBullisharXiv – CS AI · Mar 46/102
🧠Researchers have developed a Bayesian adversarial multi-agent framework for AI-driven scientific code generation, featuring three coordinated LLM agents that work together to improve reliability and reduce errors. The Low-code Platform (LCP) enables non-expert users to generate scientific code through natural language prompts, demonstrating superior performance in benchmark tests and Earth Science applications.
AIBullisharXiv – CS AI · Mar 47/102
🧠Researchers developed Physics-Embedded PINNs (PE-PINN) that achieve 10x faster convergence than standard physics-informed neural networks and orders of magnitude memory reduction compared to traditional methods for large-scale wave field reconstruction. The breakthrough enables high-fidelity electromagnetic wave modeling for wireless communications, sensing, and room acoustics applications.
AIBullisharXiv – CS AI · Mar 47/102
🧠Researchers have developed DynFormer, a new Transformer-based neural operator that improves partial differential equation (PDE) solving by incorporating physics-informed dynamics. The system achieves up to 95% reduction in relative error compared to existing methods while significantly reducing GPU memory consumption through specialized attention mechanisms for different physical scales.
AIBullishGoogle DeepMind Blog · Feb 127/108
🧠Gemini 3 Deep Think represents an updated specialized reasoning mode designed to tackle complex challenges in modern science, research, and engineering. The advancement focuses on enhanced problem-solving capabilities for technical and scientific applications.
AIBullishOpenAI News · Dec 117/108
🧠OpenAI has released GPT-5.2, their most advanced model for mathematics and science applications, achieving state-of-the-art performance on benchmarks like GPQA Diamond and FrontierMath. The model demonstrates significant research capabilities, including solving open theoretical problems and generating reliable mathematical proofs.
AIBullishOpenAI News · Jan 307/107
🧠OpenAI is partnering with U.S. National Laboratories to deploy its latest reasoning AI models for scientific research and breakthroughs. This collaboration aims to strengthen America's artificial intelligence leadership by leveraging the nation's premier research institutions.
AIBullisharXiv – CS AI · 2d ago6/10
🧠Researchers introduce EvoMD-LLM, a framework that adapts large language models to predict molecular dynamics by treating chemical reactions as temporal sequences with duration-aware tokens. The model achieves 66.14% accuracy on prediction tasks and demonstrates the ability to generate explanations for its predictions without explicit supervision, suggesting LLMs can effectively ground themselves in physical simulations through symbolic temporal modeling.
AINeutralarXiv – CS AI · 2d ago6/10
🧠Researchers compared Claude Code and Codex on autonomously executing a gravitational wave analysis pipeline, revealing significant differences in speed, error handling transparency, and instruction interpretation despite converging scientific results. The study highlights critical considerations for deploying agentic AI in scientific workflows, including auditability trade-offs and the importance of precise data representation standards.
🏢 OpenAI🏢 Anthropic🧠 Claude
AINeutralarXiv – CS AI · 2d ago6/10
🧠Researchers introduce Stochastic Lifting, a machine learning technique that generates diverse trajectories of stochastic physical systems by attaching random labels to state transitions during training. The method enables single-network inference to produce multiple plausible outcomes without collapsing to average predictions, advancing physics-informed AI applications.
AINeutralarXiv – CS AI · 2d ago6/10
🧠Researchers propose Autoregression-Free Neural Operators (AFNO), a new approach for solving time-dependent partial differential equations that models continuous-time evolution in latent space rather than performing recursive predictions. By avoiding autoregressive rollout and using flow matching, AFNO reduces error accumulation over long-horizon predictions and demonstrates improved stability across six PDE benchmarks.
AINeutralarXiv – CS AI · 3d ago6/10
🧠Researchers propose SincKANs, a neural network architecture combining Sinc interpolation with Kolmogorov-Arnold Networks to improve function approximation and solve partial differential equations. The approach demonstrates superior performance compared to existing methods, particularly for functions with singularities, offering potential advances in physics-informed machine learning.
AINeutralarXiv – CS AI · 3d ago6/10
🧠Researchers propose LNN-PINN, an enhanced physics-informed neural network framework that integrates liquid residual gating architecture to improve predictive accuracy for complex scientific problems. The method maintains existing physics modeling pipelines while refining the hidden-layer architecture, demonstrating consistent error reductions across benchmark tests without requiring hyperparameter adjustments.
