The Archive

Identifiability theory for continuous-time latent SDEs using diffusion covariance shifts in causal representation learning.

DEFAR framework reduces exposure bias in Flow Matching generative models by exploiting dynamic signals during training.

Analysis of estimation-prediction tradeoff in probabilistic temporal graph models for link prediction.

Physics-informed neural networks with transfer learning applied to lithium-ion battery state estimation.

Framework for value-constrained credit assignment and reward allocation in delegated AI cooperatives using traversal learning.

HAT-4D agentic framework reconstructs 4D multi-object interactions from monocular video using VLMs for embodied AI data collection.

As context windows grow longer, moving large model weights efficiently becomes critical to performance. A common way to address this is quantization, an... As context windows grow longer, moving large model weights efficiently becomes critical to performance. A common way to address this is quantization, an optimization technique that compresses model weights into a smaller data format. One quantization format is NVFP4, an innovative 4-bit floating point introduced with NVIDIA Blackwell architecture. That’s the approach behind our new Nemotron 3… Source

Lagrangian duality-based method approximates best-response in non-linear strategic classification settings.

User critique of Anthropic's customer support responsiveness following Fable suspension due to US government directive.

COCOLogic-V2 benchmark dataset for visual logical reasoning on real-world images with fine-grained hard negatives for concept bottleneck model evaluation.

Time-series and ML models predict Sri Lankan remittances using exchange rates and oil prices; domain-specific economics work, not AI frontier.

Uses LLM reasoning traces to predict item difficulty in educational assessment via interpretable process evidence extraction.

LLawCo framework enables embodied multi-agent cooperation by learning coordination laws through reflection on failures.

CPAgents uses agentic iteration to auto-generate cardiac phenotypes for disease association studies via composite feature discovery.

Tandem RL training pairs weak and strong agents to maintain verifiable reasoning quality while improving compatibility and readability.

EchoSonar-R is a vision-language model for multi-view echocardiography that generates diagnostic reports with anatomical reasoning.

Physics-informed neural networks with wavelet encoding for inverse conductivity problems; applied math, not AI systems frontier.

Mechanistic analysis reveals jailbreak attacks suppress specific attention heads (ACHs) while safety-aligned heads (SAHs) remain robust.

KL-Coupled Policy Regularization framework for reward-punishment RL with coupled soft-optimal policies via KL-regularized Bellman operators.

Concept-Guided In-Context Segmentation improves robustness of few-shot segmentation via high-level semantic concept extraction.

Dimensionality reduction via autoencoders and PCA for compressing wearable runner telemetry into scalar performance scores.

Low-rank cross-channel mixing module for test-time adaptation under distribution shift in normalization layers.

ADC-GNN: diffusion-guided graph neural network for fraud detection with sparse, imbalanced labels on transaction graphs.

PhysisForcing: physics constraints in video diffusion models to correct implausible robot manipulations and object deformations.

Conceptual analysis: LLMs as degenerate world models; latent-space architectures as strict generalization of autoregressive token prediction.

Automation-in-design framework using deep learning and generative AI for high-tech system design synthesis.

Theoretical result: non-affine gradient aggregation cannot preserve monotonicity in convex learning, blocking last-iterate convergence.

Higher-order Fourier Neural Operator: mode mixing for nonlinear PDE surrogate models beyond linear constant-coefficient regimes.

Physics-constrained neural networks projecting onto Stiefel manifold for energy-conserving surrogate modeling of optical structures.

Low-rank redundancy in task-specific LoRA adapters; single adapters can represent multiple continual learning tasks effectively.