5/6/2026
Structured Diffusion Bridges: Inductive Bias for Denoising Diffusion Bridges
Diffusion bridge framework for unpaired modality translation with structured constraints to restrict solution space.
5/6/2026
TCD-Arena: Assessing Robustness of Time Series Causal Discovery Methods Against Assumption Violations
TCD-Arena benchmark for evaluating robustness of time series causal discovery algorithms against violation of assumptions.
5/6/2026
Neuron-Anchored Rule Extraction for Large Language Models via Contrastive Hierarchical Ablation
MechaRule framework extracting symbolic rules from LLMs by grounding rule extraction in neuron-level mechanistic interpretability via contrastive ablation.
5/6/2026
OGPO: Sample Efficient Full-Finetuning of Generative Control Policies
Sample-efficient fine-tuning algorithm for diffusion and flow-based robot control policies using off-policy critic networks and modified PPO.
5/6/2026
Adaptive Negative Scheduling for Graph Contrastive Learning
Adaptive negative sampling scheduler for graph contrastive learning improving computational efficiency in self-supervised representation learning.
5/6/2026
Attribution-Guided Masking for Robust Cross-Domain Sentiment Classification
Attribution-guided masking method for improving transformer sentiment classification transfer across domains by identifying spurious tokens.
5/6/2026
When Prompts Interact: Assessing Prompt Arithmetic for Deconfounding under Distribution Shift
Prompt arithmetic technique for improving LLM robustness under distribution shift without full fine-tuning via task arithmetic and prompt manipulation.
5/6/2026
Pose Tracking with a Foundation Pose Model and an Ensemble Directional Kalman Filter
arXiv paper introducing Ensemble Directional Kalman Filter for pose tracking using unit-quaternion attitude representation beyond standard Kalman assumptions.
5/6/2026
Gated Subspace Inference for Transformer Acceleration
arXiv paper on Gated Subspace Inference accelerating transformer inference by exploiting low-rank activation manifolds with cached weights and per-token gating.
5/6/2026
Taming the Curses of Multiagency in Robust Markov Games with Large State Space through Linear Function Approximation
arXiv paper on distributionally robust Markov games for multi-agent RL addressing robustness and data efficiency in uncertain environments with large state spaces.
5/6/2026
Instance-Level Costs for Nuanced Classifier Evaluation
arXiv paper proposing Normalized Excess Cost (NEC) metric for classification evaluation that weights errors by per-example costs for safety-critical applications.
5/6/2026
OCRR: A Benchmark for Online Correction Recovery under Distribution Shift
Benchmark for measuring classifier recovery speed under distribution shift and online corrections, addressing real-world deployment scenarios.
5/6/2026
Pairwise matrices for sparse autoencoders: single-feature inspection mislabels causal axes
Pairwise matrix protocol for sparse autoencoder interpretability reveals limitations of standard single-feature inspection on language model feature analysis.
5/6/2026
Moral Sensitivity in LLMs: A Tiered Evaluation of Contextual Bias via Behavioral Profiling and Mechanistic Interpretability
Framework for evaluating bias in LLMs through behavioral profiling and mechanistic interpretability, introducing Moral Sensitivity Index metric.
5/6/2026
Beyond Activation Alignment: The Geometry of Neural Sensitivity
Theoretical analysis of activation alignment methods (RSA, CCA, CKA) for comparing neural representations in biological and artificial systems.
5/6/2026
Self-Mined Hardness for Safety Fine-Tuning
Self-Mined Hardness method for LLM safety fine-tuning that scores prompt difficulty by model jailbreak frequency, then trains on hardest prompts.
5/6/2026
Text-Conditional JEPA for Learning Semantically Rich Visual Representations
Text-Conditional JEPA uses image captions to reduce prediction uncertainty in masked visual representation learning.
5/6/2026
Ortho-Hydra: Orthogonalized Experts for DiT LoRA
Ortho-Hydra addresses style bleed in LoRA fine-tuning of diffusion transformers using orthogonalized mixture-of-experts architecture.
5/6/2026
Do LLMs have core beliefs?
Examines whether LLMs exhibit core beliefs—foundational commitments that resist change—parallel to human cognition.
5/6/2026
The Right Answer, the Wrong Direction: Why Transformers Fail at Counting and How to Fix It
Investigates why transformers fail at counting tasks, finding failures stem from token generation rather than internal representation, with proposed fixes.
5/6/2026
RFPrompt: Prompt-Based Expert Adaptation of the Large Wireless Model for Modulation Classification
RFPrompt enables efficient adaptation of wireless foundation models to out-of-distribution modulation classification tasks via prompt-based expert adaptation.
5/6/2026
Stable Multimodal Graph Unlearning via Feature-Dimension Aware Quantile Selection
Graph unlearning technique using feature-dimension aware quantile selection for privacy-preserving multimodal graph learning.
5/6/2026
Distributed Learning with Adversarial Gradient Perturbations
Studies convex optimization under adversarial gradient perturbations in distributed learning, analyzing privacy-utility tradeoffs.
5/6/2026
DGPO: Distribution Guided Policy Optimization for Fine Grained Credit Assignment
DGPO algorithm for fine-grained credit assignment in LLM reinforcement learning, improving reasoning step isolation in chain-of-thought generations.
5/6/2026
LLM-ADAM: A Generalizable LLM Agent Framework for Pre-Print Anomaly Detection in Additive Manufacturing
LLM-ADAM agent framework for detecting anomalies in additive manufacturing pre-prints, helping users without manufacturing expertise catch process-planning errors.
5/6/2026
Toward Structural Multimodal Representations: Specialization, Selection, and Sparsification via Mixture-of-Experts
S3 framework decomposes multimodal inputs into semantic experts with selective routing and sparsification via mixture-of-experts approach.
5/6/2026
Population-Aware Imitation Learning in Mean-field Games with Common Noise
Addresses imitation learning in mean-field games with stochastic population distribution, developing algorithms for population-aware agent policies.
5/6/2026
Learning Dynamics of Zeroth-Order Optimization: A Kernel Perspective
Investigates zeroth-order optimization methods for fine-tuning large language models, explaining why they work despite theoretical dimension-dependent slowdown predictions.
5/6/2026
GRAFT: Auditing Graph Neural Networks via Global Feature Attribution
Proposes GRAFT, a global explanation framework for Graph Neural Networks that identifies which input node attributes drive model predictions.
5/6/2026
Two Calls, Two Moments, and the Vote-Accuracy Curve of Repeated LLM Inference
Studies how repeated LLM inference sampling improves accuracy beyond single-call performance, analyzing correctness correlation across examples through latent probability distributions.
5/6/2026
Graph Reconstruction from Differentially Private GNN Explanations
Research shows differential privacy-perturbed GNN explanations can leak graph structure through adversarial reconstruction attacks.
5/6/2026
Discovering Reinforcement Learning Interfaces with Large Language Models
Uses LLMs to automatically synthesize complete RL task interfaces including observations and rewards from natural language.
5/6/2026
Learning to Theorize the World from Observation
Proposes learning-to-theorize approach where agents build internal theories from observations inspired by cognitive science.
5/6/2026
FIBER: A Differentially Private Optimizer with Filter-Aware Innovation Bias Correction
Introduces FIBER optimizer correcting bias from filtering in differentially private training with adaptive optimizers.
5/6/2026
DynaTab: Dynamic Feature Ordering as Neural Rewiring for High-Dimensional Tabular Data
Proposes DynaTab, architecture with dynamic feature ordering for high-dimensional tabular data using neural rewiring.
5/6/2026
Quantum Hierarchical Reinforcement Learning via Variational Quantum Circuits
Studies hierarchical reinforcement learning using variational quantum circuits.
5/6/2026
Learning Generalizable Action Representations via Pre-training AEMG
Proposes AEMG, first large-scale self-supervised framework for learning generalizable EMG representations across subjects and devices.
5/6/2026
Adaptive graph-based algorithms for conditional anomaly detection and semi-supervised learning
Develops fast approximate online graph-based algorithms for semi-supervised learning and anomaly detection.
5/6/2026
GRIFDIR: Graph Resolution-Invariant FEM Diffusion Models in Function Spaces over Irregular Domains
Proposes resolution-invariant diffusion models for function spaces over irregular domains using score-based approach.
5/6/2026
Meta-Inverse Physics-Informed Neural Networks for High-Dimensional Ordinary Differential Equations
Applies physics-informed neural networks with meta-learning to solve inverse problems in high-dimensional ODEs.
5/6/2026
Understanding Self-Supervised Learning via Latent Distribution Matching
Provides unifying theoretical framework for self-supervised learning via latent distribution matching principle.
5/6/2026
PerFlow: Physics-Embedded Rectified Flow for Efficient Reconstruction and Uncertainty Quantification of Spatiotemporal Dynamics
Combines physics constraints with rectified flow for reconstructing spatiotemporal PDE-governed fields from sparse measurements.
5/6/2026
HeadQ: Model-Visible Distortion and Score-Space Correction for KV-Cache Quantization
Presents HeadQ, a KV-cache quantization method optimizing for model-visible distortion in LLM inference.
5/6/2026
Most ReLU Networks Admit Identifiable Parameters
Studies parameter identifiability in deep ReLU networks using weighted polyhedral complexes framework.
5/6/2026
A Few-Step Generative Model on Cumulative Flow Maps
Proposes few-step generative modeling framework using cumulative flow maps for probability space transport, inspired by physical dynamics.
5/6/2026
Information Plane Analysis of Binary Neural Networks
Information plane analysis of binary neural networks using discrete activations to overcome MI estimation challenges in deep network training dynamics.
5/6/2026
ELAS: Efficient Pre-Training of Low-Rank Large Language Models via 2:4 Activation Sparsity
ELAS combines low-rank training and 2:4 activation sparsity for efficient LLM pre-training with reduced memory and computation.
5/6/2026
Uni-OPD: Unifying On-Policy Distillation with a Dual-Perspective Recipe
Uni-OPD framework unifies on-policy distillation theory, identifying bottlenecks in consolidating expert models into single student models.
5/6/2026
From Code to Prediction: Fine-Tuning LLMs for Neural Network Performance Classification in NNGPT
Fine-tuned LLMs for predicting neural network performance across datasets in AutoML frameworks, bridging code generation and performance reasoning.
5/6/2026
Graph Neural Network based Hierarchy-Aware Embeddings of Knowledge Graphs: Applications to Yeast Phenotype Prediction
GNN-based method for hierarchy-aware knowledge graph embeddings using semantic loss from ontologies, applied to yeast gene deletion phenotype prediction.