Learning Structural Convergence: A Neuro-Symbolic Benchmark for Temporal Reasoning
TRACTA benchmark for temporal reasoning in complex event-driven systems with neuro-symbolic approaches for early warning and pattern detection.
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TRACTA benchmark for temporal reasoning in complex event-driven systems with neuro-symbolic approaches for early warning and pattern detection.
Deep RL framework for coupled order dispatching and routing in last-mile logistics, combining learned routing oracle with real-time heuristics.
SEM-DNN neural estimator identifies bidirectional causal interactions from observational data via conditional variance methods.
Amazon is launching an update to its Alexa Plus assistant that will allow it to connect to smart home devices in new ways. With the update, Alexa Plus can link up with tech from Bosch, Delta, Ecovacs, iRobot, Yale Home, Whirlpool, Tapo, Eufy, and others, while automatically…
Neural networks solve coupled Dyson-Schwinger equations for Yang-Mills gauge theory with percent-level agreement to fixed-point solutions.
TriviaRoomQA benchmark evaluates multilingual LLM performance on 3,300 culturally-grounded trivia questions across 6 European languages and long-tail knowledge.
Euclid-MCP: open-source MCP server coupling LLMs with SWI-Prolog for deterministic logical reasoning in safety-critical domains.
Cycle-consistent neural surrogate for tokamak edge plasma prediction with uncertainty quantification for real-time control.
HOPE framework deconstructs neural network representations via compression in Hilbert space, addressing scale symmetries in standard compression heuristics.
SPORD applies simulation-propose-dispose heuristics to unified e-commerce supply chain planning across millions of SKUs and nodes.
Lawmakers are preparing to introduce an "AI Kill Switch Act" that would require AI companies to shut down or throttle their systems on orders from the Department of Homeland Security, according to a report from Politico. Reps. Ted Lieu (D-CA) and Nathaniel Moran (R-TX) are…
SoftReason: differentiable neuro-symbolic architecture for deductive reasoning over latent perceptual facts and knowledge graphs.
Interval and fuzzy physics-augmented neural networks (iPANN, fPANN) quantify uncertainty in hyperelastic constitutive modeling from sparse/noisy data.
Graph neural networks trained on finite-volume residuals for 3D thermo-fluid prediction without labeled data.
Deep neural network regression under covariate shift from dependent data using quantile and Huber loss.
Personal travel tip about activating mechanical instruments at a San Francisco museum.
Quadrilateral loss: differentiable penalty measuring additivity in neural networks via second-order mixed differences, connects to Shapley-GAM interaction quantification.
Learned finite volume solver for 2D Euler equations with entropy-stability guarantees and validated under frozen benchmarks.
Neuromorphic Receptron classifier for edge microcontrollers enabling non-linear decision boundaries without multi-layer networks.
Gaussian smoothing analysis of quantized ReLU and sign activation functions with local stability bounds for discontinuous networks.
Xaira Therapeutics builds causal models for drug discovery using synthetic data generation; Bo Wang and Ci Chu discuss data requirements for model training.
1-Lipschitz neural networks on Hadamard manifolds using Busemann functions for robustness in non-Euclidean geometry.
Deep CNN model of visual valence processing inspired by Rescorla-Wagner associative learning, applied to natural scene classification.
Framework generalizes batch normalization and neural modules to Riemannian manifolds, Lie groups, and gyrogroups for manifold-valued deep learning.
A federal judge has signed off on Anthropic's $1.5 billion class action settlement with authors who accused the company of training its AI models on copyrighted books, as reported earlier by Reuters. In an order on Monday, Judge Araceli Martínez-Olguín writes that the…
Neural network surrogates for thermodynamic property prediction in supercritical combustion; domain-specific physics simulation.
Investigates cost-quality tradeoffs in RL post-training for neural machine translation with reasoning verification.
ATLAS: diffusion-based sampler for generating Boltzmann-distributed amorphous material structures, addressing rare-event sampling in molecular systems.
Neural Kolmogorov Equations reformulate neural SDEs for parallelizable stochastic dynamics learning under general noise.
Physics-informed neural networks with boundary enforcement for elliptic PDEs and mean escape time computation.