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5.0 Anthropic CEO Amodei calls for slower AI development and shared safety rules — 11 src2.7 Agility Robotics unveils Digit 5 humanoid for warehouses and factories — 2 src2.5 Apple ships rebuilt Siri with Google Gemini, but not in the EU — 2 src2.2 Siri AI in macOS 27 Golden Gate: FAQ, Germany availability, privacy questions — 2 src1.8 Sam Altman says OpenAI will not go public in 2026, citing AI safety concerns — 5 src1.4 OpenAI contractors review real ChatGPT conversations to rate responses, report says — 2 src1.4 Anthropic data retention policy prompts firms to limit Claude use for sensitive work — 1 src1.4 VoiceCodeBench arXiv paper proposes benchmark for exact structured-token recovery in speech recognition — 1 src1.4 Arabic-Russian Parallel Corpus and LLM Benchmark for Scientific Text — 1 src1.4 Study Analyzes Self-Reported Limitations in NLP Research — 1 src5.0 Anthropic CEO Amodei calls for slower AI development and shared safety rules — 11 src2.7 Agility Robotics unveils Digit 5 humanoid for warehouses and factories — 2 src2.5 Apple ships rebuilt Siri with Google Gemini, but not in the EU — 2 src2.2 Siri AI in macOS 27 Golden Gate: FAQ, Germany availability, privacy questions — 2 src1.8 Sam Altman says OpenAI will not go public in 2026, citing AI safety concerns — 5 src1.4 OpenAI contractors review real ChatGPT conversations to rate responses, report says — 2 src1.4 Anthropic data retention policy prompts firms to limit Claude use for sensitive work — 1 src1.4 VoiceCodeBench arXiv paper proposes benchmark for exact structured-token recovery in speech recognition — 1 src1.4 Arabic-Russian Parallel Corpus and LLM Benchmark for Scientific Text — 1 src1.4 Study Analyzes Self-Reported Limitations in NLP Research — 1 src
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#scientific-ml

7 curated events
papersTODAY 04:00 UTC

Gradient Repair Method Aimed at Stabilizing Neural ODE Training

A new arXiv paper introduces GradRepair-ODE, a technique that certifies and repairs gradients when neural ordinary differential equations are trained. Because neural ODEs embed a numerical solver in the training loop, solver choices shape both the forward trajectory and the gradients sent to the optimizer, which the authors flag as a reliability issue for scientific machine learning. The approach is presented as a way to keep those gradients trustworthy during training.

neural-ode/training/research/scientific-ml
papersTODAY 04:00 UTC

Conflict-Free Gradients Target Failure Modes in PINNs and PIKANs

A new arXiv preprint examines why physics-informed neural networks (PINNs) and their Kolmogorov-Arnold counterparts (PIKANs) often fail when used with domain decomposition to solve partial differential equations over complex geometries. The authors attribute these problems to conflicting gradient signals and propose a conflict-free gradient approach to improve training stability and scalability.

research/pinns/scientific-ml/training
papersTODAY 04:00 UTC

Pullback-corrected auxiliary variable optimizer targets multi-term scientific ML losses

A new arXiv paper proposes a pullback-corrected scalar auxiliary variable (PB-SAV) optimizer that adds momentum and adaptive mobility. The method is aimed at scientific machine learning objectives that combine several loss terms, such as the residual, boundary, initial, and data losses used in physics-informed neural networks. The abstract frames the work as addressing optimization challenges specific to these composite objectives.

optimization/scientific-ml/pinns/arxiv
papersTODAY 04:00 UTC

GRPO-QM Uses Reinforcement Learning to Guide Quantum Tomography Without Distorting Posteriors

A new arXiv preprint presents GRPO-QM, a method that applies group-relative policy optimization to quantum tomography while leaving the target posterior distribution unchanged. Instead of letting reward-based learning reshape the inference target, the approach learns only an exploration policy that selects measurements. This separates the strategy for gathering data from the statistical estimate itself.

quantum/reinforcement-learning/arxiv/scientific-ml
papersSEP 10 04:00 UTC

Convolutional autoencoder and neural ODE framework for transient counterflow flame modeling

Researchers propose a reduced-order modeling framework that combines a convolutional autoencoder with a neural ordinary differential equation to serve as a surrogate for simulating transient two-dimensional counterflow flames. The approach extends autoencoder–neural ODE techniques, previously applied to homogeneous reactive systems, to spatially resolved combustion problems. Such surrogates can cut the computational cost of modeling reactive flows.

scientific-ml/neural-ode/combustion/surrogate-models
papersSEP 10 04:00 UTC

Kolmogorov-Arnold Networks Applied to Refine Nuclear Mass Models

A new arXiv preprint uses Kolmogorov-Arnold Networks, an interpretable neural architecture, to improve theoretical models that predict the masses of atomic nuclei. The authors address the difficulty of learning from limited and highly complex nuclear datasets, aiming to blend physics-based theory with data-driven corrections.

kolmogorov-arnold-networks/interpretability/nuclear-physics/scientific-ml
papersSEP 12 04:00 UTC

PINN Framework Infers Perpendicular Heat Conductivity in Stellarator Scrape-Off Layer

Researchers present an inverse physics-informed neural network that estimates how the scrape-off layer's perpendicular heat conductivity varies with plasma density and temperature in stellarator devices. The approach embeds physical constraints into the learning process rather than relying solely on labeled data, allowing the conductivity function to be recovered from available measurements. This is an arXiv preprint on fusion plasma modeling and has not yet been peer reviewed.

physics/neural-networks/fusion/scientific-ml