3/2/2026
General Bayesian Policy Learning
General Bayes framework for policy learning where decision rules are the target rather than outcome prediction.
3/2/2026
Any Model, Any Place, Any Time: Get Remote Sensing Foundation Model Embeddings On Demand
Platform for standardized access to remote sensing foundation model embeddings across heterogeneous model formats and interfaces.
3/2/2026
From Static Benchmarks to Dynamic Protocol: Agent-Centric Text Anomaly Detection for Evaluating LLM Reasoning
Dynamic benchmarking protocol where AI agents autonomously generate, validate, and solve problems to evaluate LLM reasoning capabilities beyond static datasets.
3/2/2026
GLUScope: A Tool for Analyzing GLU Neurons in Transformer Language Models
Open-source interpretability tool for analyzing gated activation functions (SwiGLU) in transformer neurons across recent language models.
3/2/2026
Enhancing Continual Learning for Software Vulnerability Prediction: Addressing Catastrophic Forgetting via Hybrid-Confidence-Aware Selective Replay for Temporal LLM Fine-Tuning
Addresses catastrophic forgetting in continual fine-tuning of LLMs for vulnerability detection in source code, using selective replay with LoRA on temporal distribution shifts.
3/2/2026
MI$^2$DAS: A Multi-Layer Intrusion Detection Framework with Incremental Learning for Securing Industrial IoT Networks
MI²DAS: multi-layer intrusion detection framework for IIoT with incremental learning to detect novel attacks in dynamic environments.
3/2/2026
Exploring Robust Intrusion Detection: A Benchmark Study of Feature Transferability in IoT Botnet Attack Detection
Benchmark study evaluating cross-domain transferability of flow-based feature sets across IoT and IIoT datasets for intrusion detection.
3/2/2026
RF-Agent: Automated Reward Function Design via Language Agent Tree Search
RF-Agent: automated reward function design for reinforcement learning using LLM-based tree search to optimize low-level control tasks.
3/2/2026
Experience-Guided Self-Adaptive Cascaded Agents for Breast Cancer Screening and Diagnosis with Reduced Biopsy Referrals
BUSD-Agent: cascaded multi-agent framework for breast ultrasound screening and diagnosis reducing biopsy referrals through selective decision-making.
3/2/2026
Learning to Build: Autonomous Robotic Assembly of Stable Structures Without Predefined Plans
Autonomous robotic assembly framework using reinforcement learning for constructing stable structures without predefined plans.
3/2/2026
Benchmarking BERT-based Models for Sentence-level Topic Classification in Nepali Language
Benchmarking 10 BERT variants for Nepali sentence-level topic classification, evaluating multilingual and Indic-specific models.
3/2/2026
Jailbreak Foundry: From Papers to Runnable Attacks for Reproducible Benchmarking
Jailbreak Foundry: multi-agent system translating jailbreak papers into executable modules for unified benchmarking and reproducible LLM robustness evaluation.
3/2/2026
Data Driven Optimization of GPU efficiency for Distributed LLM Adapter Serving
Data-driven optimization pipeline for GPU efficiency in distributed LLM adapter serving, maximizing throughput with concurrent adapter hosting.
3/2/2026
Unsupervised Baseline Clustering and Incremental Adaptation for IoT Device Traffic Profiling
Two-stage unsupervised pipeline for IoT device traffic profiling with incremental model adaptation using density-based clustering.
3/2/2026
The Subjectivity of Monoculture
Analysis of monoculture in LLMs showing agreement metrics depend on subjective baseline assumptions for independence.
3/2/2026
Artificial Agency Program: Curiosity, compression, and communication in agents
Artificial Agency Program: research agenda for building resource-bounded AI agents driven by curiosity-as-learning-progress and human-tool integration.
3/2/2026
A multimodal slice discovery framework for systematic failure detection and explanation in medical image classification
Automated auditing framework for detecting systematic failures in medical image classifiers using multimodal features and slice discovery.
3/2/2026
Uncertainty Quantification for Multimodal Large Language Models with Incoherence-adjusted Semantic Volume
Proposes uncertainty quantification method for multimodal LLMs using incoherence-adjusted semantic volume for reliable deployment.
3/2/2026
SenCache: Accelerating Diffusion Model Inference via Sensitivity-Aware Caching
SenCache: training-free acceleration for diffusion model inference via sensitivity-aware caching of model outputs across timesteps.
3/2/2026
Compositional Generalization Requires Linear, Orthogonal Representations in Vision Embedding Models
Formalizes desiderata showing compositional generalization requires linear, orthogonal representations in vision embedding models.
3/2/2026
Mode Seeking meets Mean Seeking for Fast Long Video Generation
Proposes training paradigm decoupling local fidelity from long-term coherence for scaling video generation from seconds to minutes.
3/2/2026
TimeMAE: Self-Supervised Representations of Time Series with Decoupled Masked Autoencoders
TimeMAE: self-supervised framework for learning transferable time series representations using decoupled masked autoencoders.
3/2/2026
Less is more -- the Dispatcher/ Executor principle for multi-task Reinforcement Learning
Proposes dispatcher/executor principle for multi-task reinforcement learning that abstracts unnecessary details for better generalization.
3/2/2026
Joint Distribution-Informed Shapley Values for Sparse Counterfactual Explanations
Introduces COLA framework for generating sparse counterfactual explanations using optimal transport and Shapley-based attribution methods.
3/2/2026
Quantifying Climate Change Impacts on Renewable Energy Generation: A Super-Resolution Recurrent Diffusion Model
Super-resolution recurrent diffusion model for renewable energy generation under climate change impacts.
3/2/2026
Rethinking Uncertainty Estimation in LLMs: A Principled Single-Sequence Measure
Single-sequence uncertainty estimation method for LLMs addressing computational expense of multi-sequence approaches.
3/2/2026
The Sample Complexity of Online Reinforcement Learning: A Multi-model Perspective
Sample complexity analysis for online reinforcement learning in nonlinear continuous state/action spaces.
3/2/2026
InfoBridge: Mutual Information estimation via Bridge Matching
Mutual information estimation method using diffusion bridge models for improved domain transfer problems.
3/2/2026
Semantic Parallelism: Redefining Efficient MoE Inference via Model-Data Co-Scheduling
Novel semantic parallelism approach for efficient MoE model inference via co-scheduling of model and data placement.
3/2/2026
Probabilistic Neural Networks (PNNs) with t-Distributed Outputs: Adaptive Prediction Intervals Beyond Gaussian Assumptions
Probabilistic neural networks using t-distribution outputs for improved prediction intervals beyond Gaussian assumptions.
3/2/2026
What Makes a Reward Model a Good Teacher? An Optimization Perspective
Optimization perspective on reward model quality for RLHF, analyzing factors beyond accuracy that make effective teachers.
3/2/2026
Operator Learning with Domain Decomposition for Geometry Generalization in PDE Solving
Domain decomposition approach for neural operators solving PDEs with improved geometry generalization capabilities.
3/2/2026
Sparsity Forcing: Reinforcing Token Sparsity of MLLMs
Research on enforcing token sparsity in multimodal LLMs to reduce computational overhead while maintaining accuracy.
3/2/2026
PAPN: Proximity Attention Encoder and Pointer Network Decoder for Parcel Pickup Route Prediction
Novel attention mechanism and pointer network for parcel pickup route prediction in logistics optimization.
3/2/2026
Manifold Learning with Normalizing Flows: Towards Regularity, Expressivity and Iso-Riemannian Geometry
Theoretical research on manifold learning with normalizing flows for Riemannian geometry in high-dimensional data.
3/2/2026
Continuous Optimization for Feature Selection with Permutation-Invariant Embedding and Policy-Guided Search
Research on feature selection using permutation-invariant embeddings and policy-guided search with generative models.
3/2/2026
Multi-View Encoders for Performance Prediction in LLM-Based Agentic Workflows
Lightweight prediction model for LLM-based agentic workflow performance across agent configurations and prompting strategies.
3/2/2026
From Generator to Embedder: Harnessing Innate Abilities of Multimodal LLMs via Building Zero-Shot Discriminative Embedding Model
Framework converting multimodal LLM generative capabilities into zero-shot discriminative embedding models without extensive pre-training.
3/2/2026
DisTaC: Conditioning Task Vectors via Distillation for Robust Model Merging
Model merging technique using task vector distillation to improve robustness of multi-task learning across diverse settings.
3/2/2026
On the Generalization of SFT: A Reinforcement Learning Perspective with Reward Rectification
Theoretically motivated improvement to supervised fine-tuning for LLMs by rectifying reward structure to match RL generalization.
3/2/2026
OM2P: Offline Multi-Agent Mean-Flow Policy
Offline multi-agent reinforcement learning using efficient flow-based policies for time-sensitive deployment.
3/2/2026
Beyond Na\"ive Prompting: Strategies for Improved Context-aided Forecasting with LLMs
Framework of strategies for improving LLM-based forecasting by integrating historical data and textual context with reduced computational cost.
3/2/2026
Context and Diversity Matter: The Emergence of In-Context Learning in World Models
Investigation of in-context learning in world models for embodied AI to adapt to novel environmental configurations.
3/2/2026
ProtoTS: Learning Hierarchical Prototypes for Explainable Time Series Forecasting
Interpretable time series forecasting method using hierarchical prototypes to explain model decision-making.
3/2/2026
In-Context Learning of Temporal Point Processes with Foundation Inference Models
Foundation inference model using in-context learning to predict marked temporal point process event sequences across different systems.
3/2/2026
Linking Process to Outcome: Conditional Reward Modeling for LLM Reasoning
Process Reward Models that capture step-by-step reasoning dependencies in LLMs to improve reasoning alignment with final outcomes.
3/2/2026
CMT-Benchmark: A Benchmark for Condensed Matter Theory Built by Expert Researchers
Benchmark dataset of 50 condensed matter theory problems for evaluating LLMs on advanced research-level physics problem-solving.
3/2/2026
Permutation-Invariant Representation Learning for Robust and Privacy-Preserving Feature Selection
Permutation-invariant representation learning for privacy-preserving feature selection using generative intelligence.
3/2/2026
Carr\'e du champ flow matching: better quality-generalisation tradeoff in generative models
Carré du champ flow matching: geometry-aware regularization technique for generative models improving quality-generalization tradeoff.
3/2/2026
Learning Mixtures of Linear Dynamical Systems via Hybrid Tensor-EM Method
Hybrid tensor-EM method for learning mixtures of linear dynamical systems with improved performance on noisy time-series data.