2/24/2026
Exact Discrete Stochastic Simulation with Deep-Learning-Scale Gradient Optimization
Enables gradient-based learning for continuous-time Markov chains by decoupling simulation from differentiation for optimization.
2/24/2026
Enhancing Automatic Chord Recognition via Pseudo-Labeling and Knowledge Distillation
Automatic chord recognition via pseudo-labeling and knowledge distillation using pre-trained models with unlabeled audio.
2/24/2026
Path-conditioned training: a principled way to rescale ReLU neural networks
Path-conditioned training approach for exploiting rescaling symmetries in ReLU neural network parameters.
2/24/2026
Depth-Structured Music Recurrence: Budgeted Recurrent Attention for Full-Piece Symbolic Music Modeling
Depth-Structured Music Recurrence model for long-context symbolic music generation on resource-limited devices.
2/24/2026
Meta-Learning and Meta-Reinforcement Learning - Tracing the Path towards DeepMind's Adaptive Agent
Survey on meta-learning and meta-reinforcement learning techniques for rapid adaptation to novel tasks with minimal data.
2/24/2026
Orthogonal Uplift Learning with Permutation-Invariant Representations for Combinatorial Treatments
Uplift learning method for estimating causal effects of combinatorial treatments using permutation-invariant representations.
2/24/2026
Rethinking Chronological Causal Discovery with Signal Processing
Causal discovery method using signal processing for time-series observations with misaligned sampling rates in biological/physical systems.
2/24/2026
Gradient based Severity Labeling for Biomarker Classification in OCT
arXiv paper proposing gradient-based severity labeling for contrastive learning in medical image biomarker classification.
2/24/2026
RobPI: Robust Private Inference against Malicious Client
arXiv paper on private inference protocols resilient to malicious client behavior in ML deployment.
2/24/2026
Janus-Q: End-to-End Event-Driven Trading via Hierarchical-Gated Reward Modeling
arXiv paper on event-driven trading using LLMs and hierarchical-gated reward modeling with textual market signals.
2/24/2026
Beyond Mimicry: Toward Lifelong Adaptability in Imitation Learning
arXiv paper proposing lifelong adaptability in imitation learning to improve compositional generalization beyond memorization.
2/24/2026
Multivariate time-series forecasting of ASTRI-Horn monitoring data: A Normal Behavior Model
arXiv paper on normal behavior modeling for time-series forecasting of telescope monitoring data.
2/24/2026
FairFS: Addressing Deep Feature Selection Biases for Recommender System
arXiv paper on feature importance estimation and selection biases in large-scale recommender systems.
2/24/2026
Closing the gap in multimodal medical representation alignment
arXiv paper addressing modality gap in CLIP-based multimodal learning for medical image representation alignment.
2/24/2026
The Invisible Gorilla Effect in Out-of-distribution Detection
arXiv paper on out-of-distribution detection in deep neural networks and how regional focus affects OOD detection performance.
2/24/2026
Descent-Guided Policy Gradient for Scalable Cooperative Multi-Agent Learning
Research on descent-guided policy gradients for multi-agent reinforcement learning addressing cross-agent noise scaling issues.
2/24/2026
Align When They Want, Complement When They Need! Human-Centered Ensembles for Adaptive Human-AI Collaboration
Human-AI collaboration framework using adaptive ensembles that align with human judgment when beneficial and complement when needed.
2/24/2026
ReSyn: Autonomously Scaling Synthetic Environments for Reasoning Models
Autonomous scaling method for synthetic training environments using verifier-based reinforcement learning for reasoning language models.
2/24/2026
NanoKnow: How to Know What Your Language Model Knows
Investigation of LLM knowledge encoding using nanochat family with fully open pre-training data to understand where parametric knowledge originates.
2/24/2026
JUCAL: Jointly Calibrating Aleatoric and Epistemic Uncertainty in Classification Tasks
Post-calibration uncertainty quantification method balancing aleatoric and epistemic uncertainty in classifier ensembles.
2/24/2026
Skill-Inject: Measuring Agent Vulnerability to Skill File Attacks
Security vulnerability analysis of LLM agents exploiting skill files for prompt injection attacks through the agent skills supply chain.
2/24/2026
A Very Big Video Reasoning Suite
Benchmark suite for evaluating video reasoning capabilities of models, focusing on spatiotemporal consistency and reasoning beyond visual quality.
2/24/2026
A simple connection from loss flatness to compressed neural representations
Investigation of loss surface sharpness relationship to neural representation compression through local volumetric ratio and activation concentration measures.
2/24/2026
Layer Collapse Can be Induced by Unstructured Pruning
Study demonstrating unstructured neural network pruning can induce structural effects and layer collapse, shortening computational paths.
2/24/2026
Mirror Bridges Between Probability Measures
Mathematical framework for resampling from target probability measures using learned maps from prior distributions without explicit density knowledge.
2/24/2026
Improving Discrete Optimisation Via Decoupled Straight-Through Estimator
Improved straight-through estimator for discrete optimization decoupling forward-pass stochasticity from backward-pass gradient dispersion in neural networks.
2/24/2026
Robust Time Series Causal Discovery for Agent-Based Model Validation
Robust causal discovery method for agent-based model validation using cross-validation approach to improve accuracy on complex time series data.
2/24/2026
Reducing Biases in Record Matching Through Scores Calibration
Threshold-independent fairness assessment for record matching models, introducing score bias measures beyond binary decision evaluation.
2/24/2026
A spectral mixture representation of isotropic kernels with application to random Fourier features
Spectral mixture representation framework for isotropic kernels extending Random Fourier Features beyond Gaussian kernels for machine learning applications.
2/24/2026
Revisiting Graph Neural Networks for Graph-level Tasks: Taxonomy, Empirical Study, and Future Directions
Comprehensive taxonomy and empirical study of Graph Neural Networks for graph-level prediction tasks in molecular and biological domains.
2/24/2026
VillageNet: Graph-based, Easily-interpretable, Unsupervised Clustering for Broad Biomedical Applications
Unsupervised clustering algorithm for high-dimensional biomedical datasets using two-phase K-Means and graph-based approach without requiring prior cluster count.
2/24/2026
Mamba-Based Graph Convolutional Networks: Tackling Over-smoothing with Selective State Space
Mamba-based graph convolutional network addressing over-smoothing in GNNs using selective state space mechanisms for improved depth handling.
2/24/2026
vCache: Verified Semantic Prompt Caching
Semantic prompt caching system using variable similarity thresholds in vector databases to reduce LLM inference latency and cost beyond static threshold approaches.
2/24/2026
Are We Measuring Oversmoothing in Graph Neural Networks Correctly?
Critical examination of oversmoothing metrics in Graph Neural Networks, arguing that Dirichlet energy and similarity measures fail to reliably capture the phenomenon.
2/24/2026
The Curse of Depth in Large Language Models
Research on 'Curse of Depth' phenomenon where ~50% of LLM layers underperform, analyzing root causes across Llama, Mistral, DeepSeek, Qwen families with theoretical and empirical analysis.
2/24/2026
Analysis of Off-Policy $n$-Step TD-Learning with Linear Function Approximation
Multi-step TD-learning analysis under linear function approximation, off-policy learning, and bootstrapping.
2/24/2026
From Contextual Combinatorial Semi-Bandits to Bandit List Classification: Improved Sample Complexity with Sparse Rewards
Contextual combinatorial semi-bandits with sparse rewards for recommendation systems.
2/24/2026
AdaGC: Improving Training Stability for Large Language Model Pretraining
AdaGC: gradient clipping method stabilizing LLM pretraining by addressing multi-factor loss spikes.
2/24/2026
Test-Time Training Provably Improves Transformers as In-context Learners
Test-time training provably enhances transformer in-context learning through gradient-based weight updates.
2/24/2026
Autonomous Learning with High-Dimensional Computing Architecture Similar to von Neumann's
High-dimensional vector computing architecture for modeling human and animal learning.
2/24/2026
Noise-Aware Generalization: Robustness to In-Domain Noise and Out-of-Domain Generalization
Noise-Aware Generalization: training methods handling label noise and domain shifts simultaneously.
2/24/2026
Towards A Universal Graph Structural Encoder
Universal graph encoder for learning transferable structural representations across diverse graph domains.
2/24/2026
GraphOmni: A Comprehensive and Extensible Benchmark Framework for Large Language Models on Graph-theoretic Tasks
GraphOmni: comprehensive benchmark for evaluating LLM reasoning on graph-theoretic tasks in natural language.
2/24/2026
Think2SQL: Reinforce LLM Reasoning Capabilities for Text2SQL
Think2SQL: reinforcement learning approach improving LLM reasoning for complex text-to-SQL generation.
2/24/2026
Learning to Rank Critical Road Segments via Heterogeneous Graphs with Origin-Destination Flow Integration
HetGL2R: heterogeneous graph framework for ranking critical road segments using origin-destination flows.
2/24/2026
FedSDAF: Leveraging Source Domain Awareness for Enhanced Federated Domain Generalization
Federated domain generalization leveraging source domain features for improved generalization.
2/24/2026
GRILL: Restoring Gradient Signal in Ill-Conditioned Layers for More Effective Adversarial Attacks on Autoencoders
GRILL: adversarial attack method for autoencoders addressing ill-conditioned gradient propagation.
2/24/2026
Performance Estimation in Binary Classification Using Calibrated Confidence
Performance estimation for binary classifiers without ground truth labels using calibrated confidence.
2/24/2026
CodePDE: An Inference Framework for LLM-driven PDE Solver Generation
CodePDE: LLM-based inference framework generating code for solving partial differential equations.
2/24/2026
FairSHAP: Preprocessing for Fairness Through Attribution-Based Data Augmentation
FairSHAP: preprocessing framework using Shapley values for identifying and mitigating bias in ML models.