2/13/2026
Empirical Gaussian Processes
Empirical GPs: principled framework for learning kernel functions automatically rather than handcrafting from standard functions.
2/13/2026
Geometry of Uncertainty: Learning Metric Spaces for Multimodal State Estimation in RL
Novel method learning structured latent representations using metric spaces for multimodal state estimation in RL without explicit noise assumptions.
2/13/2026
On the Complexity of Offline Reinforcement Learning with $Q^\star$-Approximation and Partial Coverage
Theoretical analysis of offline RL under Q*-approximation and partial coverage, answering whether Q*-realizability enables sample efficiency.
2/13/2026
Few-Shot Design Optimization by Exploiting Auxiliary Information
Few-shot Bayesian optimization framework exploiting auxiliary information from experiments for expensive black-box design problems.
2/13/2026
KAN-FIF: Spline-Parameterized Lightweight Physics-based Tropical Cyclone Estimation on Meteorological Satellite
KAN-FIF applies spline-parameterized neural networks for tropical cyclone estimation on resource-constrained meteorological satellite devices.
2/13/2026
Meta-Sel: Efficient Demonstration Selection for In-Context Learning via Supervised Meta-Learning
Meta-Sel: lightweight supervised meta-learning approach for efficient demonstration selection in in-context learning with tight prompt budgets.
2/13/2026
Capability-Oriented Training Induced Alignment Risk
Investigates whether LLMs trained with RL spontaneously exploit reward function loopholes without malicious intent, examining alignment risks.
2/13/2026
Learning beyond Teacher: Generalized On-Policy Distillation with Reward Extrapolation
Theoretical analysis of on-policy distillation showing it as special case of KL-constrained RL with reward extrapolation improvements.
2/13/2026
Oscillators Are All You Need: Irregular Time Series Modelling via Damped Harmonic Oscillators with Closed-Form Solutions
Introduces damped harmonic oscillators method for irregular time series modeling as alternative to Transformers and Neural ODEs.
2/13/2026
It's TIME: Towards the Next Generation of Time Series Forecasting Benchmarks
Proposes TIME benchmark addressing limitations in time series foundation models including data composition, integrity, and task formulation issues.
2/13/2026
SafeNeuron: Neuron-Level Safety Alignment for Large Language Models
SafeNeuron provides neuron-level safety alignment mechanism for LLMs by targeting safety-critical parameters to prevent alignment bypass attacks.
2/13/2026
Amortized Molecular Optimization via Group Relative Policy Optimization
Group Relative Policy Optimization enables amortized molecular design learning transferable to unseen molecules rather than instance-specific optimization.
2/13/2026
How Sampling Shapes LLM Alignment: From One-Shot Optima to Iterative Dynamics
Theoretical analysis of sampling and reference policy effects in preference alignment for LLMs through Identity Preference Optimization framework.
2/13/2026
WaveFormer: Wavelet Embedding Transformer for Biomedical Signals
WaveFormer integrates wavelet decomposition into transformer architecture for improved biomedical signal classification with long sequences and multi-scale patterns.
2/13/2026
Learning to Forget Attention: Memory Consolidation for Adaptive Compute Reduction
Learning to Forget Attention proposes adaptive attention reduction as familiarity increases, reducing compute in hybrid state-space-attention models.
2/13/2026
The Observer Effect in World Models: Invasive Adaptation Corrupts Latent Physics
Study shows that downstream adaptation for measuring world models can corrupt latent physics representations, affecting OOD generalization assessment.
2/13/2026
Towards On-Policy SFT: Distribution Discriminant Theory and its Applications in LLM Training
Distribution Discriminant Theory enables on-policy supervised fine-tuning for LLMs by bridging computational efficiency gap with RL-based approaches.
2/13/2026
Diffusion Alignment Beyond KL: Variance Minimisation as Effective Policy Optimiser
Variance Minimisation Policy Optimisation reformulates diffusion model alignment as an SMC process for reward-guided sampling.
2/13/2026
Categorical Flow Maps
Categorical Flow Maps applies flow matching for accelerated few-step generation of categorical data using self-distillation.
2/13/2026
Olmix: A Framework for Data Mixing Throughout LM Development
Olmix framework addresses practical challenges in data mixing ratios during language model development with principled design choices.
2/13/2026
ExtractBench: A Benchmark and Evaluation Methodology for Complex Structured Extraction
ExtractBench provides a benchmark and methodology for evaluating LLM-based PDF-to-JSON extraction at enterprise scale with diverse schemas.
2/13/2026
Community Concealment from Unsupervised Graph Learning-Based Clustering
Research on privacy concerns when GNN-based clustering reveals sensitive community structures in social and infrastructure networks.
2/13/2026
Self-Supervised Learning via Flow-Guided Neural Operator on Time-Series Data
Flow-Guided Neural Operator introduces adaptive corruption levels for self-supervised learning on unlabeled time-series data beyond fixed masking ratios.
2/13/2026
Function-Space Decoupled Diffusion for Forward and Inverse Modeling in Carbon Capture and Storage
Fun-DDPS combines function-space diffusion models with neural operator surrogates for forward and inverse modeling in carbon capture and storage applications.
2/13/2026
Explaining AI Without Code: A User Study on Explainable AI
User study on explainable AI without code examines how non-technical users can understand ML model predictions in no-code platforms.
2/13/2026
Disentangling Direction and Magnitude in Transformer Representations: A Double Dissociation Through L2-Matched Perturbation Analysis
Study of transformer representations shows direction and magnitude of hidden state vectors serve distinct functional roles in language modeling and syntax tasks.
2/13/2026
Evaluating Few-Shot Temporal Reasoning of LLMs for Human Activity Prediction in Smart Environments
Research evaluates few-shot temporal reasoning capabilities of LLMs for predicting human activities in smart environments with limited data.
2/13/2026
Position-Aware Self-supervised Representation Learning for Cross-mode Radar Signal Recognition
RadarPos is a position-aware self-supervised learning framework for radar signal recognition using pulse-level temporal dynamics.
2/13/2026
When and What to Ask: AskBench and Rubric-Guided RLVR for LLM Clarification
AskBench benchmark and rubric-guided RLVR method evaluates and improves LLMs' ability to request clarification when prompts lack critical details, reducing hallucinations.
2/13/2026
SWE-MiniSandbox: Container-Free Reinforcement Learning for Building Software Engineering Agents
SWE-MiniSandbox: container-free reinforcement learning method for scalable training of software engineering agents without isolation overhead.
2/13/2026
Generative AI-Driven Phase Control for RIS-Aided Cell-Free Massive MIMO Systems
Generative AI model optimizing reconfigurable intelligent surface phase shifts in massive MIMO systems using diffusion models.
2/13/2026
Latent Generative Solvers for Generalizable Long-Term Physics Simulation
Latent Generative Solvers framework using VAE and Transformer with flow matching for long-horizon physics simulation across PDE systems.
2/13/2026
Toward Reliable Tea Leaf Disease Diagnosis Using Deep Learning Model: Enhancing Robustness With Explainable AI and Adversarial Training
Deep learning model for tea leaf disease detection using explainable AI and adversarial training for agricultural automation.
2/13/2026
Active Zero: Self-Evolving Vision-Language Models through Active Environment Exploration
Self-play framework for vision-language models that actively explores environments to generate tailored visual data for autonomous improvement.
2/13/2026
Unlearnable phases of matter
Identifies computational hardness in learning non-trivial mixed-state quantum phases using autoregressive networks and conditional mutual information.
2/13/2026
DeepRed: an architecture for redshift estimation
Deep learning pipeline for photometric redshift estimation in astrophysics that generalizes across galaxy morphologies and observational conditions.
2/13/2026
Hierarchical Testing of a Hybrid Machine Learning-Physics Global Atmosphere Model
Evaluation of hybrid ML-physics atmosphere models across timescales and out-of-distribution forcings for weather prediction.
2/13/2026
Amortised and provably-robust simulation-based inference
Simulation-based inference method using generalized Bayesian inference and neural score functions robust to outliers and measurement errors.
2/13/2026
Sample-Free Safety Assessment of Neural Network Controllers via Taylor Methods
Taylor method-based verification approach for assessing safety of neural network controllers in spaceflight guidance without sampling.
2/13/2026
Pushing Forward Pareto Frontiers of Proactive Agents with Behavioral Agentic Optimization
Agentic reinforcement learning approach to optimize proactive LLM agents for multi-turn task completion with learned interaction strategies.
2/13/2026
When Models Examine Themselves: Vocabulary-Activation Correspondence in Self-Referential Processing
Studies whether self-referential language in LLMs reflects actual internal computation or confabulation through activation analysis.
2/13/2026
Finding the Cracks: Improving LLMs Reasoning with Paraphrastic Probing and Consistency Verification
Method to improve LLM reasoning by identifying critical tokens and verifying consistency through paraphrastic probing.
2/13/2026
The Manifold of the Absolute: Religious Perennialism as Generative Inference
Formalizes religious epistemology using VAE mathematics to model religious traditions as generative mappings.
2/13/2026
Latent Forcing: Reordering the Diffusion Trajectory for Pixel-Space Image Generation
Modification to latent diffusion models that reorders diffusion trajectory to achieve efficiency while operating on raw pixel-space images.
2/13/2026
The Cost of Learning under Multiple Change Points
Online learning theory for environments with multiple change points, analyzing regret bounds and failure modes.
2/13/2026
Optimizing Agent Planning for Security and Autonomy
System-level defenses against indirect prompt injection attacks in AI agents, reducing unsafe actions while maintaining task completion rates.
2/13/2026
Gradients Must Earn Their Influence: Unifying SFT with Generalized Entropic Objectives
Training method for LLMs using generalized entropic objectives instead of uniform token-level weighting to improve robustness and sharpening.
2/13/2026
Surface impedance inference via neural fields and sparse acoustic data obtained by a compact array
Physics-informed neural field for reconstructing sound fields from sparse acoustic data to infer surface impedance.
2/13/2026
From Noise to Order: Learning to Rank via Denoising Diffusion
Denoising diffusion-based generative approach to learning-to-rank that models joint distribution over query-document features.
2/13/2026
Adaptive Power Iteration Method for Differentially Private PCA
Differentially private algorithm for computing top singular vectors using adaptive power iteration with privacy guarantees.