FourierSpecNet hybrid framework combining Fourier spectral methods with neural networks to approximate collision operators for solving the Boltzmann equation efficiently.
Asynchronous-to-synchronous paradigm for event cameras using learned feature encoding to improve expressivity and generalizability for sparse sequential visual data.
DejaVu attack exploiting temporal misalignment vulnerabilities in multimodal fusion systems for autonomous driving by manipulating camera and LiDAR synchronization.
State Space Neural Operator for learning solution operators of time-dependent PDEs using structured state space models with adaptive damping and learnable frequency modulation.
arXiv paper providing theoretical analysis of GRPO (Group Relative Policy Optimization) for LLM fine-tuning from human feedback.
arXiv paper analyzing EM algorithm behavior under model misspecification in mixture models with excess components.
arXiv paper analyzing GNN-based SAT solvers through graph Ricci curvature geometric perspective to explain performance degradation.
arXiv paper on efficient world models for heterogeneous multi-task planning, addressing gradient conflicts and plasticity loss.
arXiv paper on assessing performance of language model applications in healthcare, addressing evaluation methodology.
arXiv paper introducing Answer-Then-Check safety alignment method to defend LLMs against jailbreak attacks using reasoning.
arXiv paper on prompt-based federated continual learning addressing class-wise and temporal forgetting across distributed clients.
arXiv paper applying auto-regressive U-Net for predicting time-dependent damage in concrete. Domain-specific, limited AI/ML generality.
arXiv paper on training diffusion language models with planner-aware path learning to optimize generation strategies.
arXiv paper formulating diffusion model alignment as variational EM to reduce reward over-optimization and mode collapse.
arXiv paper on opinion dynamics optimization using low-rank matrix bandits in online settings. Social dynamics focus, limited AI/ML relevance.
arXiv paper on adapting decoder-only LLMs to partial differential equations via cross-modal learning for scientific machine learning.
arXiv paper introducing KLASS sampling method for masked diffusion models using token-level KL divergence to accelerate inference.
SQDF applies soft Q-function RL to fine-tune diffusion models with KL regularization, mitigating reward over-optimization.
Analyzes diversity loss in RL-trained LLMs caused by mode-seeking reverse KL; proposes forward KL filtering for reasoning tasks.
A-3PO accelerates asynchronous LLM RL training with staleness-aware proximal policy approximation, improving over decoupled PPO.
Data-driven sensitivity analysis using Individual Conditional Expectations for interpreting black-box models in engineering design.
Analysis of optimization challenges in hyperbolic deep RL identifying gradient factors affecting training success for hierarchical state embeddings.
CARE failure-centric post-training framework uses contrastive learning on wrong rollouts to improve multimodal reasoning with verifiable rewards.
LLMTM benchmarks LLMs on temporal motif analysis in dynamic graphs for anomaly detection and structural understanding.
Spectral embedding approach for domain-invariant representations via optimal transport plans, addressing distributional shift.
CELM foundation model generates clinical notes from long-duration EEG recordings using multimodal learning.
EDIS analyzes token-level entropy trajectories during LLM generation to diagnose reasoning quality beyond aggregate confidence statistics.
Red-teaming framework to stress-test LLM alignment audits against strategic deception prompts in white-box and black-box settings.
LaPha trains AlphaZero-style LLM agents in hyperbolic space using Poincaré geometry for efficient tree search and dense reward shaping.
Aletheia agent iteratively generates, verifies and revises mathematical proofs using LLMs, advancing autonomous research capabilities.
Adaptive hybrid model selection framework for demand forecasting across multiple horizons and SKUs.
Flow-based generative model for predicting molecular crystal structures with periodic boundary conditions.
FLoRG enables parameter-efficient federated fine-tuning of LLMs using low-rank adaptation with Procrustes alignment across distributed clients.
Deep learning approach for antibody sequence engineering using phylogenetic models and affinity maturation data.
EMPO² framework combines on/off-policy RL with memory augmentation to improve exploration in LLM agents, addressing novel state discovery limitations.
Web-to-Knowledge-to-Web pipeline iteratively crawling domain-specific sources to discover SME suppliers in specialized industry sectors.
Framework establishing first-order equivalence between activation steering and weight updates for parameter-efficient LLM adaptation.
MatRIS: Foundation machine learning interatomic potentials with equivariant inductive bias for efficient material simulation.
Geometric pretraining approach for protein design combining structure learning and conformational ensembles with rigidity-aware representations.
mlx-vis: Python library implementing GPU-accelerated dimensionality reduction (UMAP, t-SNE, PaCMAP, etc.) on Apple Silicon via MLX.
Method for detecting visual hallucinations in VLM outputs on cartoon character images using pose information.
BInD: Diffusion model for multi-objective structure-based drug design balancing molecular generation with protein interaction requirements.
Philosophical investigation of LLMs' ontological status as agents, analyzing architecture, training, and extensions enabling agent-like behavior.
FALCON: Self-supervised video pretraining for UAV action recognition addressing spatial imbalance in aerial footage with object-centric learning.
Self-supervised seismic data reconstruction method using self-consistency learning for handling irregularly distributed seismic receiver data.
Survey on LLMs transforming scientific research covering literature search, hypothesis generation, experimentation, content generation, and peer review assistance.
Research on adversarial robustness of quantum classifiers using circuit cutting techniques for NISQ-era quantum computing.
Research paper examining governance failures in preventing AI-generated non-consensual intimate images using open-source face-swapping and nudification tools.
Research paper proposing reward modeling with chain-of-thought reasoning for improved LLM alignment with human preferences via reinforcement learning.
Entropic mirror descent optimization algorithm for linear systems with Polyak-type stepsizes and implicit bias analysis including convergence guarantees.