3/31/2026
SPORE: Skeleton Propagation Over Recalibrating Expansions
SPORE is a classical clustering algorithm handling arbitrary geometry without rigid assumptions on cluster structure.
3/31/2026
Decomposable Neuro Symbolic Regression
Transformer-based symbolic regression method for discovering interpretable mathematical expressions from observed data.
3/31/2026
From Exploration to Exploitation: A Two-Stage Entropy RLVR Approach for Noise-Tolerant MLLM Training
Two-stage entropy approach for noise-tolerant multimodal LLM training using reinforcement learning with verifiable rewards.
3/31/2026
Object-Centric World Models for Causality-Aware Reinforcement Learning
Object-centric world models for reinforcement learning using decomposed representations to improve sample efficiency in multi-object environments.
3/31/2026
UniGame: Turning a Unified Multimodal Model Into Its Own Adversary
UniGame addresses inconsistency in unified multimodal models between understanding and generation through adversarial framework.
3/31/2026
Single-Round Scalable Analytic Federated Learning
SAFLe framework enabling scalable non-linear federated learning in a single round with heterogeneous data distribution invariance.
3/31/2026
Prototype-Based Semantic Consistency Alignment for Domain Adaptive Retrieval
Domain adaptive retrieval using prototype-based semantic consistency alignment to transfer knowledge from labeled to unlabeled domains.
3/31/2026
NeuralCrop: Combining physics and machine learning for improved crop yield projections
Hybrid physics and ML approach for crop yield projections combining gridded crop models with machine learning to improve agricultural forecasting.
3/31/2026
Measuring all the noises of LLM Evals
Research on measuring noise in LLM evaluations using statistical methods to separate signal from noise in prediction, data, and combined noise.
3/31/2026
Electricity Price Forecasting: Bridging Linear Models, Neural Networks and Online Learning
Day-ahead electricity price forecasting combining linear models, neural networks and online learning for volatile market prediction.
3/31/2026
Symbolic Regression for Shared Expressions: Introducing Partial Parameter Sharing
Symbolic regression with partial parameter sharing for discovering expressions describing related phenomena with varying parameters.
3/31/2026
Hellinger Multimodal Variational Autoencoders
Hellinger multimodal VAEs using probabilistic opinion pooling to aggregate unimodal inference distributions.
3/31/2026
Sparse-RL: Breaking the Memory Wall in LLM Reinforcement Learning via Stable Sparse Rollouts
Sparse-RL addresses memory bottleneck in LLM reinforcement learning by reducing KV cache overhead during long-horizon rollouts.
3/31/2026
Dual-Prototype Disentanglement: A Context-Aware Enhancement Framework for Time Series Forecasting
Dual-prototype disentanglement framework for context-aware time series forecasting using dynamic temporal pattern learning.
3/31/2026
A Dynamic Framework for Grid Adaptation in Kolmogorov-Arnold Networks
Generalized framework for adaptive grid allocation in Kolmogorov-Arnold Networks accounting for target function complexity.
3/31/2026
Smoothing the Score Function for Generalization in Diffusion Models: An Optimization-based Explanation Framework
Theoretical framework explaining memorization in diffusion models through weighted sum of empirical score functions.
3/31/2026
TextBFGS: A Case-Based Reasoning Approach to Code Optimization via Error-Operator Retrieval
TextBFGS applies case-based reasoning to iterative code generation with LLMs, using past solutions to guide optimization.
3/31/2026
Echo State Networks for Time Series Forecasting: Hyperparameter Sweep and Benchmarking
Benchmarks Echo State Networks for univariate time series forecasting against traditional statistical methods on M4 dataset.
3/31/2026
DADP: Domain Adaptive Diffusion Policy
Domain adaptive diffusion policy for control that generalizes to unseen transition dynamics through domain representation learning.
3/31/2026
Unveiling Implicit Advantage Symmetry: Why GRPO Struggles with Exploration and Difficulty Adaptation
Analyzes GRPO limitations in exploration and difficulty adaptation for LLM reasoning, proposing improvements to advantage symmetry.
3/31/2026
Joint Embedding Variational Bayes
VJE framework for self-supervised learning using reconstruction-free latent variables with symmetric conditional ELBO optimization.
3/31/2026
Live Knowledge Tracing: Real-Time Adaptation using Tabular Foundation Models
Applies tabular foundation models to knowledge tracing for real-time student learning prediction without extensive offline training.
3/31/2026
Hierarchical Concept Embedding & Pursuit for Interpretable Image Classification
Interpretable image classification using hierarchical concept embeddings recovered from vision-language models.
3/31/2026
$\phi$-DPO: Fairness Direct Preference Optimization Approach to Continual Learning in Large Multimodal Models
φ-DPO addresses fairness in continual learning for multimodal models when training data is imbalanced across tasks.
3/31/2026
Thin Keys, Full Values: Reducing KV Cache via Low-Dimensional Attention Selection
Reduces transformer KV cache by using low-dimensional keys for attention selection while maintaining full-dimensional values.
3/31/2026
Distributional Regression with Tabular Foundation Models: Evaluating Probabilistic Predictions via Proper Scoring Rules
Proposes using proper scoring rules to evaluate probabilistic predictions from tabular foundation models instead of point-estimate metrics.
3/31/2026
Rethinking Attention Output Projection: Structured Hadamard Transforms for Efficient Transformers
Replaces dense attention projections with Walsh Hadamard Transforms to reduce transformer parameters by 25% while maintaining performance.
3/31/2026
Few Batches or Little Memory, But Not Both: Simultaneous Space and Adaptivity Constraints in Stochastic Bandits
Theoretical analysis of multi-armed bandits under memory and batch constraints, studying regret bounds.
3/31/2026
Deconfounded Lifelong Learning for Autonomous Driving via Dynamic Knowledge Spaces
Autonomous driving framework using Dirichlet process mixture models and causal adjustment to address catastrophic forgetting and spurious correlations in lifelong learning.
3/31/2026
CausalEvolve: Towards Open-Ended Discovery with Causal Scratchpad
LLM agent framework with causal scratchpad for open-ended scientific discovery through iterative program evolution.
3/31/2026
Dataset Distillation Efficiently Encodes Low-Dimensional Representations from Gradient-Based Learning of Non-Linear Tasks
Theoretical analysis of dataset distillation for encoding low-dimensional task representations from gradient-based learning.
3/31/2026
SpecMoE: Spectral Mixture-of-Experts Foundation Model for Cross-Species EEG Decoding
Mixture-of-Experts foundation model for cross-species EEG signal decoding using spectral analysis.
3/31/2026
Off-Policy Learning with Limited Supply
Off-policy learning framework for contextual bandits with constrained item supply in recommendation and advertising.
3/31/2026
Wasserstein Propagation for Reverse Diffusion under Weak Log-Concavity: Exploiting Metric Mismatch via One-Switch Routing
Theoretical analysis of reverse diffusion sampling error under weak log-concavity using metric mismatch exploitation.
3/31/2026
Graph-Aware Stealthy Poison-Text Backdoors for Text-Attributed Graphs
Backdoor attack method on text-attributed graphs by injecting malicious cues into node text.
3/31/2026
SmaAT-QMix-UNet: A Parameter-Efficient Vector-Quantized UNet for Precipitation Nowcasting
Parameter-efficient vector-quantized UNet for weather precipitation nowcasting using deep learning.
3/31/2026
Decoupling Exploration and Policy Optimization: Uncertainty Guided Tree Search for Hard Exploration
RL approach decoupling exploration and policy optimization using uncertainty-guided tree search for hard exploration problems.
3/31/2026
Scaling Attention via Feature Sparsity
Sparse Feature Attention (SFA) method reducing transformer attention complexity via feature-level sparsity for ultra-long contexts.
3/31/2026
Problems with Chinchilla Approach 2: Systematic Biases in IsoFLOP Parabola Fits
Analysis of systematic biases in Chinchilla Approach 2 neural scaling law fits for compute-optimal LLM allocation.
3/31/2026
SkillRouter: Skill Routing for LLM Agents at Scale
Skill routing system for LLM agents managing and selecting from large skill ecosystems at inference time.
3/31/2026
Synthetic Mixed Training: Scaling Parametric Knowledge Acquisition Beyond RAG
Synthetic Mixed Training method combining synthetic QAs and documents to scale LLM knowledge acquisition beyond RAG limitations.
3/31/2026
Can LLMs Beat Classical Hyperparameter Optimization Algorithms? A Study on autoresearch
Study comparing LLM agents against classical hyperparameter optimization algorithms using autoresearch framework for hyperparameter tuning.
3/31/2026
PEANUT: Perturbations by Eigenvector Alignment for Attacking Graph Neural Networks Under Topology-Driven Message Passing
Attack method (PEANUT) exploiting GNN vulnerabilities through graph topology perturbations in message passing.
3/31/2026
Shapley meets Rawls: an integrated framework for measuring and explaining unfairness
Framework using Shapley values to measure and explain unfairness in ML models under group fairness criteria.
3/31/2026
Image-Adaptive GAN based Reconstruction
Image reconstruction using adaptive GANs to improve sample quality for complex image classes like human faces.
3/31/2026
Evasion Adversarial Attacks Remain Impractical Against ML-based Network Intrusion Detection Systems, Especially Dynamic Ones
Study on adversarial evasion attacks against ML-based network intrusion detection systems, showing attacks remain impractical against dynamic systems.
3/31/2026
LSM-GNN: Large-scale Storage-based Multi-GPU GNN Training by Optimizing Data Transfer Scheme
Multi-GPU GNN training system optimizing data transfer for large-scale graphs exceeding GPU memory capacity via storage-based approaches.
3/31/2026
NARVis: Neural Accelerated Rendering for Real-Time Scientific Point Cloud Visualization
Neural renderer for real-time visualization of large-scale scientific point cloud datasets using neural deferred rendering.
3/31/2026
Predictive variational inference: Learn the predictively optimal posterior distribution
Framework for variational inference that optimizes posterior distributions under model misspecification for better predictive accuracy.
3/31/2026
Trans-Glasso: A Transfer Learning Approach to Precision Matrix Estimation
Transfer learning method for precision matrix estimation leveraging related source data with limited target samples.