4/21/2026
Symmetry Guarantees Statistic Recovery in Variational Inference
Research on symmetry properties in variational inference guaranteeing statistic recovery in approximate target density distributions.
4/21/2026
Learning from Less: Measuring the Effectiveness of RLVR in Low Data and Compute Regimes
Study of RLVR fine-tuning effectiveness for LLMs in low-data and low-compute regimes, evaluating practical applicability in resource-constrained settings.
4/21/2026
Spectral bandits for smooth graph functions
Bandit algorithm framework for online learning on graph-structured payoffs with applications to content recommendation systems.
4/21/2026
Using large language models for embodied planning introduces systematic safety risks
DESPITE benchmark evaluates safety risks of LLMs used as planners for robotic systems across 12,279 tasks, finding planning ability doesn't guarantee safety.
4/21/2026
Safe Control using Learned Safety Filters and Adaptive Conformal Inference
Method combining learned safety filters with adaptive conformal inference to guarantee safety in control systems with learned components.
4/21/2026
Learning the Riccati solution operator for time-varying LQR via Deep Operator Networks
Deep operator network approach for learning Riccati equation solutions to replace repeated numerical solving in LQR control problems.
4/21/2026
UDM-GRPO: Stable and Efficient Group Relative Policy Optimization for Uniform Discrete Diffusion Models
Framework for stable integration of reinforcement learning with uniform discrete diffusion models, addressing training instability in GRPO application.
4/21/2026
FUSE: Ensembling Verifiers with Zero Labeled Data
Unsupervised method for improving LLM verification by ensembling verifiers without labeled data, useful for training and deployment.
4/21/2026
GSQ: Highly-Accurate Low-Precision Scalar Quantization for LLMs via Gumbel-Softmax Sampling
Gumbel-Softmax based scalar quantization technique for LLM weight compression achieving sub-4-bit accuracy without vector quantization overhead.
4/21/2026
ConforNets: Latents-Based Conformational Control in OpenFold3
Method for controlling protein conformational states in OpenFold3 via latent variable manipulation to capture biologically relevant alternate conformations.
4/21/2026
Benchmarking System Dynamics AI Assistants: Cloud Versus Local LLMs on CLD Extraction and Discussion
Benchmark comparing cloud-based and open-source LLMs on causal loop diagram extraction and system dynamics discussion tasks.
4/21/2026
Back into Plato's Cave: Examining Cross-modal Representational Convergence at Scale
Empirical study examining whether neural networks trained on different modalities converge to shared representations, challenging the Platonic Representation Hypothesis.
4/21/2026
MathNet: a Global Multimodal Benchmark for Mathematical Reasoning and Retrieval
Large-scale multimodal multilingual benchmark of Olympiad-level math problems for evaluating reasoning in LLMs and embedding-based retrieval systems.
4/21/2026
RAYEN: Imposition of Hard Convex Constraints on Neural Networks
Framework for enforcing hard convex constraints on neural network outputs during training and inference for robotics applications.
4/21/2026
Neural Network-Based Score Estimation in Diffusion Models: Optimization and Generalization
Theoretical analysis of score estimation optimization and generalization in diffusion models trained via gradient descent on neural networks.
4/21/2026
Condense, Don't Just Prune: Enhancing Efficiency and Performance in MoE Layer Pruning
Method for pruning Mixture-of-Experts layers in LLMs to reduce memory requirements while maintaining performance through condensation rather than removal.
4/21/2026
Improving Dynamic Object Interactions in Text-to-Video Generation with AI Feedback
Technique using AI feedback to improve text-to-video model generation, specifically addressing dynamic object interactions and physics violations.
4/21/2026
Test-Time Alignment via Hypothesis Reweighting
Method for real-time LLM personalization via hypothesis reweighting that adapts model outputs to individual user preferences with minimal labeled examples.
4/21/2026
Rethinking Uncertainty Estimation in LLMs: A Principled Single-Sequence Measure
Research on efficient uncertainty estimation in LLMs using single-sequence measures instead of computationally expensive multi-sequence methods for trustworthiness evaluation.
4/21/2026
On the Interpolation Effect of Score Smoothing in Diffusion Models
Theoretical study of score smoothing in diffusion models enabling interpolation and generation of new data.
4/21/2026
Judge a Book by its Cover: Investigating Multi-Modal LLMs for Multi-Page Handwritten Document Transcription
Evaluation of multi-modal LLM prompting strategies for zero-shot handwritten document transcription without fine-tuning.
4/21/2026
Two-Stage Regularization-Based Structured Pruning for LLMs
Two-stage pruning method for reducing LLM parameters with regularization to preserve knowledge during deployment.
4/21/2026
PiCa: Parameter-Efficient Fine-Tuning with Column Space Projection
Parameter-efficient fine-tuning method using column space projection as alternative to LoRA for foundation models.
4/21/2026
Saddle-To-Saddle Dynamics in Deep ReLU Networks: Low-Rank Bias in the First Saddle Escape
Theoretical analysis of gradient descent dynamics in deep ReLU networks escaping saddle points with low-rank bias.
4/21/2026
Vision Language Models are Biased
Analysis of bias in vision-language models on objective visual tasks like counting and identification.
4/21/2026
LIFT the Veil for the Truth: Principal Weights Emerge after Rank Reduction for Reasoning-Focused Supervised Fine-Tuning
Parameter-efficient fine-tuning method using rank reduction for LLM reasoning tasks with reduced computational cost.
4/21/2026
Rethinking Post-Unlearning Behavior of Large Vision-Language Models
Study of vision-language model privacy concerns and degenerate outputs after machine unlearning.
4/21/2026
End-to-End Optimization of LLM-Driven Multi-Agent Search Systems via Heterogeneous-Group-Based Reinforcement Learning
Framework for optimizing multi-agent search systems coordinating LLM agents with tools using reinforcement learning.
4/21/2026
Textual Bayes: Quantifying Prompt Uncertainty in LLM-Based Systems
Method for quantifying uncertainty in LLM-based systems and prompt sensitivity without access to model internals.
4/21/2026
Time Series Forecasting as Reasoning: A Slow-Thinking Approach with Reinforced LLMs
LLM-based time series forecasting using reinforcement learning and reasoning approach instead of fast pattern extraction.
4/21/2026
"Faithful to What?" On the Limits of Fidelity-Based Explanations
Research on limitations of fidelity-based explanations in XAI, introducing linearity score to measure linear decodability of neural network behavior.
4/21/2026
From 2:4 to 8:16 sparsity patterns in LLMs for Outliers and Weights with Variance Correction
Demonstrates 8:16 semi-structured sparsity for LLM compression, outperforming N:M sparsification methods while handling outlier weights.
4/21/2026
EvoCoT: Overcoming the Exploration Bottleneck in Reinforcement Learning
Introduces EvoCoT to address exploration bottleneck in RL with verifiable rewards for LLM post-training, improving reasoning on hard problems without teacher models.
4/21/2026
Beyond Memorization: Extending Reasoning Depth with Recurrence, Memory and Test-Time Compute Scaling
Studies multi-step reasoning in LLMs using cellular automata framework to understand how models learn and execute reasoning beyond memorization.
4/21/2026
A Ridge Too Far: Correcting Over-Shrinkage via Negative Regularization
Negative regularization technique addressing over-shrinkage in small-data regression through controlled anti-shrinkage.
4/21/2026
Bi-LoRA: Efficient Sharpness-Aware Minimization for Fine-Tuning Large-Scale Models
Bi-LoRA integrates Sharpness-Aware Minimization with LoRA for efficient fine-tuning of large models with improved generalization.
4/21/2026
RefineStat: Efficient Exploration for Probabilistic Program Synthesis
RefineStat method for probabilistic program synthesis combining small language models with statistical exploration under domain constraints.
4/21/2026
Low-rank Orthogonalization for Large-scale Matrix Optimization with Applications to Foundation Model Training
Low-rank orthogonalization technique for matrix optimization improving foundation model training efficiency.
4/21/2026
PiERN: Token-Level Routing for Integrating High-Precision Computation and Reasoning
PiERN architecture enables token-level routing to integrate high-precision numerical computation within LLM reasoning.
4/21/2026
Flow marching for a generative PDE foundation model
Flow Marching algorithm combining neural operators with flow matching for generative PDE foundation models.
4/21/2026
Central Limit Theorems for Asynchronous Averaged Q-Learning
Theoretical analysis establishing central limit theorems for asynchronous Q-learning with convergence rate bounds.
4/21/2026
CaTS-Bench: Can Language Models Describe Time Series?
CaTS-Bench benchmark for evaluating language models on time series captioning and temporal reasoning across 11 domains.
4/21/2026
Semantic-Space Exploration and Exploitation in RLVR for LLM Reasoning
Framework for LLM reasoning via reinforcement learning that optimizes semantic space exploration rather than token-level proxies.
4/21/2026
Downgrade to Upgrade: Optimizer Simplification Enhances Robustness in LLM Unlearning
Method for improving robustness of LLM unlearning by simplifying optimizers, addressing fragility of privacy/safety interventions.
4/21/2026
OptunaHub: A Platform for Black-Box Optimization
OptunaHub platform for distributed black-box optimization components with unified interface, supporting AutoML and hyperparameter tuning.
4/21/2026
On the Convergence and Size Transferability of Continuous-depth Graph Neural Networks
Theoretical convergence analysis of Graph Neural Differential Equations combining GNNs with continuous-depth architectures.
4/21/2026
RACE Attention: A Strictly Linear-Time Attention Layer for Training on Outrageously Large Contexts
RACE Attention proposes linear-time attention layer to handle contexts exceeding 4M tokens, addressing quadratic complexity of softmax attention.
4/21/2026
Why Low-Precision Transformer Training Fails: An Analysis on Flash Attention
Mechanistic analysis of training failures with low-precision transformers and Flash Attention, revealing root causes of loss explosion.
4/21/2026
The Potential of Second-Order Optimization for LLMs: A Study with Full Gauss-Newton
Research on full Gauss-Newton preconditioning for LLM training, establishing practical iteration complexity bounds up to 150M parameters.
4/21/2026
PaTaRM: Bridging Pairwise and Pointwise Signals via Preference-Aware Task-Adaptive Reward Modeling
PaTaRM: Reward modeling approach bridging pairwise and pointwise preference signals for improved RLHF alignment of LLMs with human preferences.