3/26/2026
Upper Entropy for 2-Monotone Lower Probabilities
Theoretical analysis of upper entropy computation for credal sets and uncertainty quantification. Pure mathematics focus.
3/26/2026
Synthetic Mixed Training: Scaling Parametric Knowledge Acquisition Beyond RAG
Training method combining synthetic QA and document generation to improve LLM knowledge beyond RAG performance ceiling.
3/26/2026
Safe Reinforcement Learning with Preference-based Constraint Inference
Safe reinforcement learning framework inferring constraints from user preferences with minimal expert demonstrations.
3/26/2026
AscendOptimizer: Episodic Agent for Ascend NPU Operator Optimization
RL agent optimizing operator kernels on Huawei Ascend NPUs. Addresses knowledge gap in alternative hardware ecosystem.
3/26/2026
Causal Reconstruction of Sentiment Signals from Sparse News Data
Causal signal reconstruction approach for converting sparse news sentiment into reliable time series for financial/tech analysis.
3/26/2026
StateLinFormer: Stateful Training Enhancing Long-term Memory in Navigation
StateLinFormer model using linear attention for navigation agents with long-term memory. Addresses context window limitations in Transformers.
3/26/2026
Dual-Criterion Curriculum Learning: Application to Temporal Data
Research on curriculum learning with dual criteria for temporal data. Proposes improved difficulty-based training scheduling.
3/26/2026
PoiCGAN: A Targeted Poisoning Based on Feature-Label Joint Perturbation in Federated Learning
PoiCGAN: Poisoning attack method against federated learning systems using feature-label joint perturbation.
3/26/2026
APreQEL: Adaptive Mixed Precision Quantization For Edge LLMs
APreQEL: Adaptive mixed precision quantization technique for deploying large language models on edge devices with reduced memory and computational requirements.
3/26/2026
The Geometric Price of Discrete Logic: Context-driven Manifold Dynamics of Number Representations
Research on how LLMs form discrete decision boundaries within continuous semantic spaces through context-driven topological distortion of number representations.
3/26/2026
MetaKube: An Experience-Aware LLM Framework for Kubernetes Failure Diagnosis
MetaKube: LLM framework for Kubernetes failure diagnosis with Episodic Pattern Memory Network that learns from operational history to improve diagnostic accuracy over time.
3/26/2026
A Theory of LLM Information Susceptibility
Theoretical framework analyzing fundamental performance limits when deploying fixed LLMs as optimization modules in agentic systems.
3/26/2026
Steering Code LLMs with Activation Directions for Language and Library Control
Method for steering code LLMs via activation space manipulation to control programming language and library preferences at inference time.
3/26/2026
CDMT-EHR: A Continuous-Time Diffusion Framework for Generating Mixed-Type Time-Series Electronic Health Records
Continuous-time diffusion model for generating synthetic electronic health records with mixed numerical and categorical features.
3/26/2026
BXRL: Behavior-Explainable Reinforcement Learning
Framework for generating interpretable explanations of learned behaviors in RL agents with formal behavior definition.
3/26/2026
Self Paced Gaussian Contextual Reinforcement Learning
Curriculum learning approach for contextual RL using closed-form updates for self-paced task sequencing.
3/26/2026
Lightweight Fairness for LLM-Based Recommendations via Kernelized Projection and Gated Adapters
Lightweight fairness method for LLM-based recommenders using kernelized projection and adapters without fine-tuning.
3/26/2026
Probabilistic Geometric Alignment via Bayesian Latent Transport for Domain-Adaptive Foundation Models
Domain adaptation framework for foundation models using probabilistic geometric alignment and Bayesian transport.
3/26/2026
Latent Algorithmic Structure Precedes Grokking: A Mechanistic Study of ReLU MLPs on Modular Arithmetic
Mechanistic analysis of grokking phenomenon in ReLU MLPs on modular arithmetic revealing algorithmic structure.
3/26/2026
Manifold Generalization Provably Proceeds Memorization in Diffusion Models
Theoretical study showing diffusion models learn manifold geometry before memorization under manifold hypothesis.
3/26/2026
Resolving gradient pathology in physics-informed epidemiological models
Solution for training instability in physics-informed neural networks on epidemiological models by addressing gradient pathology.
3/26/2026
Deep Neural Regression Collapse
Analysis of neural collapse phenomenon in regression models across multiple layers showing low-rank structure.
3/26/2026
Unveiling Hidden Convexity in Deep Learning: a Sparse Signal Processing Perspective
Theoretical analysis revealing convex equivalences in ReLU neural networks from sparse signal processing perspective.
3/26/2026
Symbolic--KAN: Kolmogorov-Arnold Networks with Discrete Symbolic Structure for Interpretable Learning
Kolmogorov-Arnold networks combining neural learning with symbolic structure for interpretable scientific equation discovery.
3/26/2026
Why the Maximum Second Derivative of Activations Matters for Adversarial Robustness
Analysis of activation function curvature role in adversarial robustness using parameterized activation family.
3/26/2026
Can VLMs Reason Robustly? A Neuro-Symbolic Investigation
Study of vision-language models' robustness to distribution shifts in visual deductive reasoning tasks.
3/26/2026
HDPO: Hybrid Distillation Policy Optimization via Privileged Self-Distillation
Training method for LLMs on mathematical reasoning combining RL with privileged self-distillation to improve learning on hard problems.
3/26/2026
The Luna Bound Propagator for Formal Analysis of Neural Networks
C++ implementation of neural network verification tool supporting bound propagation methods for DNN formal analysis.
3/26/2026
Off-Policy Safe Reinforcement Learning with Constrained Optimistic Exploration
Safe reinforcement learning method addressing constraint violations in off-policy exploration through constrained optimistic exploration Q-learning.
3/26/2026
Diet Your LLM: Dimension-wise Global Pruning of LLMs via Merging Task-specific Importance Score
DIET: structured pruning method for LLMs using dimension-wise global importance scores that adapt to task-specific requirements.
3/26/2026
Can we generate portable representations for clinical time series data using LLMs?
Uses LLMs to generate portable patient embeddings from clinical time series that transfer across hospitals with minimal retraining.
3/26/2026
Understanding the Challenges in Iterative Generative Optimization with LLMs
Analysis of design challenges in iterative generative optimization using LLMs for self-improving agents; identifies hidden choices engineers must make.
3/26/2026
Stochastic Dimension-Free Zeroth-Order Estimator for High-Dimensional and High-Order PINNs
Dimension-free zeroth-order estimator for PINNs addressing spatial derivative complexity and memory overhead in high-dimensional PDEs.
3/26/2026
i-IF-Learn: Iterative Feature Selection and Unsupervised Learning for High-Dimensional Complex Data
Iterative unsupervised framework for feature selection and clustering in high-dimensional data by recovering influential features.
3/26/2026
Lagrangian Relaxation Score-based Generation for Mixed Integer linear Programming
Generative framework using Lagrangian relaxation-guided score-based generation to solve mixed-integer linear programming with diverse solutions.
3/26/2026
MoE-Sieve: Routing-Guided LoRA for Efficient MoE Fine-Tuning
MoE-Sieve: routing-guided LoRA fine-tuning framework for MoE models that adapts to skewed expert routing patterns for efficiency.
3/26/2026
The impact of sensor placement on graph-neural-network-based leakage detection
Investigates optimal sensor placement for GNN-based leakage detection in water distribution networks.
3/26/2026
Towards Effective Experiential Learning: Dual Guidance for Utilization and Internalization
Dual guidance approach for RL-based LLM training combining external verification and internal experience to improve reasoning task performance.
3/26/2026
Causality-Driven Disentangled Representation Learning in Multiplex Graphs
Causal inference framework for learning disentangled representations from multiplex graphs by separating shared and layer-specific information.
3/26/2026
The Alignment Tax: Response Homogenization in Aligned LLMs and Its Implications for Uncertainty Estimation
RLHF-aligned LLMs exhibit response homogenization limiting uncertainty estimation; analyzes alignment tax impact across different tasks and sampling methods.
3/26/2026
On Gossip Algorithms for Machine Learning with Pairwise Objectives
Gossip-based distributed machine learning algorithms for IoT networks with privacy constraints and limited computation/communication resources.
3/26/2026
Reservoir-Based Graph Convolutional Networks
Graph convolutional networks using reservoir computing to address challenges with complex and dynamic graph data and long-range dependencies.
3/26/2026
Efficient Controller Learning from Human Preferences and Numerical Data Via Multi-Modal Surrogate Models
Bayesian optimization framework for tuning control policies using human preferences and pairwise comparisons instead of quantitative evaluations.
3/26/2026
TsetlinWiSARD: On-Chip Training of Weightless Neural Networks using Tsetlin Automata on FPGAs
FPGA-based implementation of weightless neural networks using Tsetlin automata for on-chip training and inference with low latency and complexity.
3/26/2026
A Deep Dive into Scaling RL for Code Generation with Synthetic Data and Curricula
Scalable RL pipeline for improving LLM code generation through synthetic data and curriculum learning, addressing data diversity challenges at scale.
3/26/2026
IPatch: A Multi-Resolution Transformer Architecture for Robust Time-Series Forecasting
Transformer architecture for multivariate time series forecasting using multi-resolution representations to capture short-term and long-range dependencies.
3/26/2026
Large Language Model Guided Incentive Aware Reward Design for Cooperative Multi-Agent Reinforcement Learning
Framework uses LLMs to automatically design reward functions for cooperative multi-agent reinforcement learning, synthesizing executable reward programs from environment instrumentation.
3/26/2026
CoordLight: Learning Decentralized Coordination for Network-Wide Traffic Signal Control
Multi-agent reinforcement learning approach for decentralized adaptive traffic signal control using learned coordination in partially observable environments.
3/26/2026
MolEvolve: LLM-Guided Evolutionary Search for Interpretable Molecular Optimization
MolEvolve framework uses LLM guidance with evolutionary search for interpretable molecular optimization, addressing activity cliffs and lack of interpretability.
3/26/2026
CUA-Suite: Massive Human-annotated Video Demonstrations for Computer-Use Agents
CUA-Suite dataset provides massive human-annotated continuous video demonstrations for training computer-use agents on desktop automation tasks, addressing data bottleneck.