2/13/2026
Provably Efficient Algorithms for S- and Non-Rectangular Robust MDPs with General Parameterization
Provably efficient algorithms for robust MDPs with general policy parameterization, reducing to entropy-regularized formulations.
2/13/2026
Sparse Semantic Dimension as a Generalization Certificate for LLMs
Mathematical framework explaining LLM generalization through sparse low-dimensional manifold geometry of activation states rather than parameter count.
2/13/2026
General and Efficient Steering of Unconditional Diffusion
Efficient steering method for unconditional diffusion models enabling controllable generation without gradient guidance or retraining.
2/13/2026
Can We Really Learn One Representation to Optimize All Rewards?
Investigates whether a single learned representation can optimize multiple reward functions in RL, studying representation learning for multi-objective policies.
2/13/2026
CADET: Context-Conditioned Ads CTR Prediction With a Decoder-Only Transformer
CADET applies decoder-only transformer architecture to CTR prediction in ads systems, addressing challenges of contextual post-scoring constraints.
2/13/2026
TimeSynth: A Framework for Uncovering Systematic Biases in Time Series Forecasting
TimeSynth framework for evaluating time series forecasting models and uncovering systematic biases in benchmarking nonlinear vs linear approaches.
2/13/2026
Hierarchical Concept Embedding & Pursuit for Interpretable Image Classification
Interpretable image classification using hierarchical concept embeddings derived from vision-language models for sparse concept recovery.
2/13/2026
Assessing Low Back Movement with Motion Tape Sensor Data Through Deep Learning
Deep learning approach for classifying lower back movements from motion tape sensor data to support remote physical therapy monitoring.
2/13/2026
External Division of Two Bregman Proximity Operators for Poisson Inverse Problems
Novel sparse recovery method for Poisson inverse problems using Bregman proximity operators and NoLips algorithm.
2/13/2026
Exploring Multiple High-Scoring Subspaces in Generative Flow Networks
Research on Generative Flow Networks: methods for biasing GFlowNets toward high-reward solutions in combinatorial optimization.
2/13/2026
Partial GFlowNet: Accelerating Convergence in Large State Spaces via Strategic Partitioning
Partial GFlowNet accelerates convergence in large state spaces via planner-based partitioning, improving generative flow network scalability.
2/13/2026
A Generic Framework for Fair Consensus Clustering in Streams
Framework for fair consensus clustering in multi-agent streaming environments under proportionate fairness constraints.
2/13/2026
Calibrating an Imperfect Auxiliary Predictor for Unobserved No-Purchase Choice
Calibrates auxiliary predictors for multinomial logit models with missing consumer choice data in market estimation.
2/13/2026
RooflineBench: A Benchmarking Framework for On-Device LLMs via Roofline Analysis
RooflineBench framework benchmarks small language models on edge hardware using roofline analysis for performance characterization across architectures.
2/13/2026
Unifying Stable Optimization and Reference Regularization in RLHF
Unified framework addressing reward hacking and optimization instability in RLHF by combining KL regularization and policy ratio clipping.
2/13/2026
Adaptive Milestone Reward for GUI Agents
Adaptive Milestone Reward addresses temporal credit assignment in RL-trained GUI agents by balancing outcome and process reward with adaptive thresholds.
2/13/2026
PASCAL: A Phase-Aware Scheduling Algorithm for Serving Reasoning-based Large Language Models
PASCAL phase-aware scheduling algorithm optimizes serving of reasoning-based LLMs by distinguishing reasoning and answering phases for improved latency.
2/13/2026
AltTS: A Dual-Path Framework with Alternating Optimization for Multivariate Time Series Forecasting
AltTS dual-path framework with alternating optimization separates autoregressive dynamics from cross-dimension interactions in multivariate time series forecasting.
2/13/2026
Krause Synchronization Transformers
Krause Attention mechanism prevents representation collapse in transformers by decoupling softmax normalization inspired by bounded-confidence dynamics.
2/13/2026
Real-Time Proactive Anomaly Detection via Forward and Backward Forecast Modeling
Proactive anomaly detection using forward and backward forecast modeling for early warning signals in industrial, financial, cybersecurity applications.
2/13/2026
Native Reasoning Models: Training Language Models to Reason on Unverifiable Data
Native Reasoning Models trains language models to reason without external verifiers or fully-annotated data, extending RLVR paradigm to unverifiable domains.
2/13/2026
TS-Memory: Plug-and-Play Memory for Time Series Foundation Models
TS-Memory adds plug-and-play memory module to time series foundation models for efficient adaptation under distribution shift without catastrophic forgetting.
2/13/2026
The Implicit Bias of Steepest Descent with Mini-batch Stochastic Gradient
Studies implicit bias of mini-batch stochastic steepest descent in multiclass classification under various norm geometries.
2/13/2026
Brain4FMs: A Benchmark of Foundation Models for Electrical Brain Signal
Benchmark evaluating foundation models pretrained on brain electrical signals for EEG and intracranial recordings in neuroscience.
2/13/2026
Gradient Compression May Hurt Generalization: A Remedy by Synthetic Data Guided Sharpness Aware Minimization
Analyzes gradient compression effects on loss landscapes in federated learning, proposing sharpness-aware minimization remedy for generalization.
2/13/2026
Learn from Your Mistakes: Self-Correcting Masked Diffusion Models
Framework enabling masked diffusion models to perform token correction after unmasking, reducing error accumulation in parallel generation.
2/13/2026
SkillRater: Untangling Capabilities in Multimodal Data
SkillRater decomposes data quality into multidimensional capabilities rather than single scores, improving data curation for model training.
2/13/2026
How Well Do Large-Scale Chemical Language Models Transfer to Downstream Tasks?
Evaluates transfer learning performance of large-scale chemical language models across downstream molecular property prediction tasks.
2/13/2026
TreeGrad-Ranker: Feature Ranking via $O(L)$-Time Gradients for Decision Trees
TreeGrad-Ranker uses probabilistic values to rank features in decision trees for local prediction explanation via efficient gradient computation.
2/13/2026
ArGEnT: Arbitrary Geometry-encoded Transformer for Operator Learning
ArGEnT transformer learns solution operators for physical systems with complex geometries, enabling surrogate modeling across varying parametric settings.
2/13/2026
GP2F: Cross-Domain Graph Prompting with Adaptive Fusion of Pre-trained Graph Neural Networks
Graph prompt learning framework adapting pre-trained GNNs across domains via adaptive fusion and cross-domain knowledge transfer.
2/13/2026
TIP: Resisting Gradient Inversion via Targeted Interpretable Perturbation in Federated Learning
Defense against gradient inversion attacks in federated learning using targeted interpretable perturbation preserving model utility.
2/13/2026
Both Topology and Text Matter: Revisiting LLM-guided Out-of-Distribution Detection on Text-attributed Graphs
LLM-guided out-of-distribution detection for text-attributed graphs combining topology and text features for unseen patterns.
2/13/2026
Fully First-Order Algorithms for Online Bilevel Optimization
First-order algorithms for online bilevel optimization eliminating need for Hessian-vector product computation.
2/13/2026
Explainable Machine-Learning based Detection of Knee Injuries in Runners
ML-based system detecting knee injuries in runners using optical motion capture and explainable machine learning.
2/13/2026
DRACO: a Cross-Domain Benchmark for Deep Research Accuracy, Completeness, and Objectivity
Benchmark of complex deep research tasks across 10 domains from real-world usage patterns, measuring accuracy, completeness, objectivity.
2/13/2026
ANML: Attribution-Native Machine Learning with Guaranteed Robustness
Framework weighting training samples by quality factors including gradient consistency and verification status for specialized expert data.
2/13/2026
SpiralFormer: Looped Transformers Can Learn Hierarchical Dependencies via Multi-Resolution Recursion
Recursive Transformer architecture learning hierarchical dependencies through multi-resolution recursion with shared layers.
2/13/2026
TabSieve: Explicit In-Table Evidence Selection for Tabular Prediction
Framework for tabular prediction using explicit in-table evidence selection, making row context auditable and interpretable.
2/13/2026
Potential-energy gating for robust state estimation in bistable stochastic systems
Physics-based state estimation method using potential-energy gating for robust filtering in bistable stochastic systems.
2/13/2026
DICE: Diffusion Large Language Models Excel at Generating CUDA Kernels
Diffusion LLM approach for CUDA kernel code generation leveraging parallel token generation and non-sequential refinement.
2/13/2026
Dopamine: Brain Modes, Not Brains
Parameter-efficient fine-tuning method viewing adaptation as neuromodulation-inspired mode selection and rescaling of pretrained computations.
2/13/2026
U-Former ODE: Fast Probabilistic Forecasting of Irregular Time Series
U-Former ODE architecture for fast probabilistic forecasting of irregularly sampled time series using neural differential equations.
2/13/2026
TUBO: A Tailored ML Framework for Reliable Network Traffic Forecasting
ML framework for reliable network traffic forecasting using deep learning adapted for traffic characteristics.
2/13/2026
MUSE: Multi-Tenant Model Serving With Seamless Model Updates
Multi-tenant model serving system handling seamless model updates with dynamic decision threshold management.
2/13/2026
Temperature as a Meta-Policy: Adaptive Temperature in LLM Reinforcement Learning
Framework treating temperature as adaptive meta-policy in LLM reinforcement learning to improve exploration-exploitation tradeoff.
2/13/2026
Safe Fairness Guarantees Without Demographics in Classification: Spectral Uncertainty Set Perspective
Method for fair classification without demographic information using spectral uncertainty sets.
2/13/2026
Evaluating LLM Safety Under Repeated Inference via Accelerated Prompt Stress Testing
Benchmark for evaluating LLM safety under repeated inference via prompt stress testing, addressing consistency failures in deployment.
2/13/2026
Latent-Variable Learning of SPDEs via Wiener Chaos
Method for learning stochastic partial differential equations from spatiotemporal observations using latent-variable formulation and deep learning.
2/13/2026
Temporal Difference Learning with Constrained Initial Representations
Enhances off-policy RL sample efficiency by constraining initial representations to address distribution shift and stabilize training.