Control Barrier Functions for Safety-Aware Latent-Space Motion Planning
Neural control barrier functions enable safe latent-space robotic motion planning without explicit collision classifiers.
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Neural control barrier functions enable safe latent-space robotic motion planning without explicit collision classifiers.
Guidewire acquires Hyperexponential, an AI insurance underwriting platform, for $1.7B cash plus $400M earnout.
Study evaluates LLM agents' ability to design therapeutic antibodies via task-guided and black-box protein design workflows.
Reflex addresses robot policy inference latency via action-chunk caching and denoising-step optimization for multi-robot control.
TAILOR framework uses Conditional Value at Risk to synthesize robust LLM heuristics, addressing tail-risk failures in average-case evals.
Infineon expands 300mm GaN power chip production to address power delivery and thermal bottlenecks in AI data center scaling.
Breunig embedded a working NES 6502 emulator into Qwen3.5-4B weights, executing ROM code via LLM inference.
OpenAI restricts ChatGPT's weapons-related responses to minors via age-gated safety controls, including ostensibly benign queries.
Big Tech returned only 9.3% cash to shareholders in 9M 2026, 15-year low, as capex redirects to AI infrastructure.
Theoretical analysis of gradient descent dynamics in two-neuron ReLU networks reveals phase transitions between learning regimes.
ACCT enables KV cache reuse across different-scale LLMs in same family, reducing context processing overhead in heterogeneous deployments.
Extension of hard constraint projection method from linear to nonlinear PDEs for physics-informed neural networks applied to Burgers' equation.
Neural network method optimizes knot placement in B-spline approximations for quadrotor trajectory planning.
Analysis of Jacobian regularization for stabilizing neural PDE emulators on chaotic systems.
TBC launches mischief 1, a bio-inspired image-to-video model claiming 5x speedup over baseline using AWS infrastructure.
Quantum measurement noise repurposed as randomness source for diffusion model sampling in quantum neural networks.
UpLoop pretraining schedule trains looped Transformers via weight reuse matching dense baseline FLOPs for improved reasoning depth.
Multi-Scale GNN predicts bulk, spatial, and single-cell transcriptomics from histopathology WSIs using pathology foundation models.
CXMT reports Q3 2026 revenue of $16.7B (+613% YoY) and $8.17B profit, driven by AI-related memory chip demand.
UK CMA opens antitrust investigation into Meta for allegedly imposing exploitative content-licensing terms on publishers and advertisers for AI training.
DeepWeave combines neural networks and symbolic logic rules for interpretable network intrusion detection with probabilistic reasoning.
Neuro-symbolic framework combines LLM decoding with grammar refinement to guarantee formal specification satisfaction.
Turn a PHP-to-Java migration from a one-time leap of faith into a measurable, repeatable process with Kiro. This post walks through a reusable engine: a mechanical discovery scanner and an eight-phase, parity-first workflow that treats the original PHP application as the specification and verifies the Java port with a parity harness.
Neural Thickets reasoning gains driven by output diversity, not weight perturbation structure.
NEOPIC applies neural operators to particle-based kinetic plasma simulations, avoiding mesh aliasing on empirical particle measures.
Ridge regression identifies brain-predictive subspace in LLM representations during naturalistic language listening via EEG and fMRI.
Neural triage decoder achieves fast, accurate decoding of high-rate quantum LDPC codes with latency-accuracy tradeoffs for fault-tolerant quantum computing.
Thirteen design patterns cataloging LLM-rule engine coupling methods across agent frameworks, guardrails, and neuro-symbolic AI with applicability guidance.
Mistral Large 4 (Le Chonk), 1T-param open-weight multimodal model trained in Europe, ranks best for coding; now available.
Opinion comparing connectionist AI energy demands to neuromorphic and local learning approaches as efficiency alternatives.