3/31/2026
MicroMix: Efficient Mixed-Precision Quantization with Microscaling Formats for Large Language Models
MicroMix: mixed-precision quantization method using microscaling formats for efficient LLM inference on NVIDIA Blackwell hardware.
3/31/2026
Forgetting: A New Mechanism Towards Better Large Language Model Fine-tuning
Novel fine-tuning mechanism for LLMs that addresses data quality/volume issues through controlled forgetting to improve domain adaptation.
3/31/2026
PENGUIN: Enhancing Transformer with Periodic-Nested Group Attention for Long-term Time Series Forecasting
PENGUIN: Transformer variant with periodic-nested group attention mechanism for improved long-term time series forecasting.
3/31/2026
Initialization Schemes for Kolmogorov-Arnold Networks: An Empirical Study
Empirical study of initialization schemes for Kolmogorov-Arnold Networks, proposing theory-driven approaches to improve training of spline-based KANs.
3/31/2026
No Need for Learning to Defer? A Training Free Deferral Framework to Multiple Experts through Conformal Prediction
Training-free framework for deferring predictions to multiple experts using conformal prediction without retraining.
3/31/2026
ReTrack: Data Unlearning in Diffusion Models through Redirecting the Denoising Trajectory
ReTrack enables data unlearning in diffusion models via importance sampling to remove memorized training data influence.
3/31/2026
GaussianPSL: Soft partitioning for complex PSL problem
GaussianPSL framework for multi-objective optimization with soft partitioning handling complex discontinuous and degenerate Pareto frontiers.
3/31/2026
Learning Genetic Circuit Modules with Neural Networks: Full Version
Neural network approach to learning modular genetic circuit functions in synthetic biology from input/output data.
3/31/2026
Asynchronous Policy Gradient Aggregation for Efficient Distributed Reinforcement Learning
Algorithms for distributed RL with policy gradients under asynchronous parallel computation and communication.
3/31/2026
LLM-Assisted Emergency Triage Benchmark: Bridging Hospital-Rich and MCI-Like Field Simulation
Benchmark for LLM-assisted emergency triage from MIMIC-IV-ED database with preprocessing for rapid patient deterioration prediction.
3/31/2026
To Augment or Not to Augment? Diagnosing Distributional Symmetry Breaking
Method for diagnosing when data augmentation and equivariant architectures improve or harm generalization under distribution asymmetry.
3/31/2026
Q-Learning with Shift-Aware Upper Confidence Bound in Non-Stationary Reinforcement Learning
Q-learning algorithm for non-stationary RL with distribution shifts under both episodic and infinite-horizon settings.
3/31/2026
LLM as an Algorithmist: Enhancing Anomaly Detectors via Programmatic Synthesis
Uses LLMs to programmatically synthesize anomaly detectors for tabular data without direct processing of raw data for privacy.
3/31/2026
Agentic Context Engineering: Evolving Contexts for Self-Improving Language Models
ACE framework evolves context for self-improving LLM agents, addressing brevity bias and context collapse in iterative refinement.
3/31/2026
Mitigating Premature Exploitation in Particle-based Monte Carlo for Inference-Time Scaling
Mitigates premature exploitation in particle filtering for inference-time scaling of language models using process reward models.
3/31/2026
TabPFN-Wide: Continued Pre-Training for Extreme Feature Counts
TabPFN-Wide extends prior-data fitted networks for tabular data with extreme feature counts in biomedicine applications.
3/31/2026
Constraints-of-Thought: A Framework for Constrained Reasoning in Language-Model-Guided Search
Constraints-of-Thought framework enables LLMs to perform constrained multi-step reasoning while satisfying symbolic constraints and user intent.
3/31/2026
PANTHER: Generative Pretraining Beyond Language for Sequential User Behavior Modeling
PANTHER applies generative pretraining to model user behavior sequences beyond language, using multi-dimensional action attributes.
3/31/2026
Learning When Not to Learn: Risk-Sensitive Abstention in Bandits with Unbounded Rewards
Bandit algorithm for high-stakes sequential decision-making that learns when to abstain from actions with irreparable consequences.
3/31/2026
Dense and Diverse Goal Coverage in Multi Goal Reinforcement Learning
RL algorithm for learning policies that maximize return while inducing dispersed state distributions across multiple reward sources.
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.