Neural Petri flows for chemical reactions
Neural Petri flows preserves chemical reaction semantics (valence rules, bond conservation) in learned neural architectures.
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Neural Petri flows preserves chemical reaction semantics (valence rules, bond conservation) in learned neural architectures.
Neuro-semantic verification framework ensures biomedical AI explanations in glioblastoma radiogenomics are grounded in patient-specific evidence.
CNet C++/CUDA framework for training complex-valued neural networks with Wirtinger autodifferentiation.
OpenAI will watermark ChatGPT and Codex text in the EU to comply with the AI Act. Editing can make the invisible marks harder to detect, it says.
An invisible, machine-readable watermark in text output is rolling out to ChatGPT and Codex, but only for users in the European Union at first. OpenAI says its textGrain watermarking "matched or exceeded" other approaches like Google DeepMind's SynthID for text, which is also…
Learned heuristics (CNN, GATv2 with DQL) select optimal agent corners in orthogonal guard art galleries while preserving formal coverage guarantees.
Analysis of missing limits in neural PDE solvers shows unbounded parameters prevent attaining best loss; kernel derivatives recover missing functions.
OpenAI details watermarking implementation for EU text provenance compliance, including detection mechanisms and researcher access protocols.
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RepICL: reusable in-context few-shot predictor learning across 1,218 text/image/audio encoder-dataset tasks via RepShiftBench.
KV-cache reuse in rolling agents exhibits history-dependent behavior; selective recomputation alignment reduces answer variation across request orders.
Physics-constrained neural operators reconstruct full coupled-system fields and identify unknown parameters from sparse single-field observations.
Dale Caldwell, former lieutenant governor for New Jersey. | Bloomberg via Getty Images New Jersey's lieutenant governor Dale Caldwell was forced to resign on September 25th after an investigation found he had sexually harassed a staffer and repeatedly violated ethics rules. The…
Latent Lagrangian neural networks reduce order modeling of nonlinear dynamical systems using learned kinetic/potential energies.
Conceptual framework for neurodiversity-aware multimodal affective computing in developmental assessment, challenging norm-referenced classification assumptions.
Neural eigenvalue solvers accelerated via low-fidelity finite-difference method guidance, reducing training steps for high-dimensional operator problems.
Neural surrogates for linear radiation transport equations accelerate simulations across nuclear engineering and fusion applications.
RETRACE reconstructs problem-level repair experience from messy CI histories to enable LLM agents to reuse debugged solutions.
PathLapPE introduces spectral positional encodings for directed graphs via path Laplacian to improve graph neural network learning.
GS-Codec replaces VQ-VAE quantizers with Gaussian splatting parametric decomposition for neural speech codec compression.
Systematic benchmark reveals neural Graph Edit Distance models generalize poorly across structurally different graph collections.
RDA2C reuses historical advantage estimates via regularized dual averaging for improved sample efficiency in actor-critic learning.
Controlled benchmark comparing neural vs. classical baselines on health misinformation detection across COVID-19 corpora with leakage controls.
Single detached projection step enables exact constraint satisfaction in neural networks with reduced compute vs. iterative unrolling.
NeutronGym provides executable physics-graded environment for LLM agents to design neutron instruments via McStas simulation.
Solver-aware target selection for caching in few-step diffusion transformers optimizes tensor reuse post-distillation.
GRIP/TRIP framework for principled long-range interaction certification in Graph Neural Network benchmarks.
Weave Forcing: training-free framework for compositional memory routing in long-form interactive video generation, selectively reusing historical visual components.
S²-PINN applies separable neural networks to uncertainty quantification in random PDEs using Gaussian dictionaries and Fourier features.
SPEAR applies spectral-disentangled mixture-of-experts to PDE foundation models to mitigate knowledge interference and expert redundancy.