3/11/2026
Routing without Forgetting
Routing mechanism for online continual learning in transformers without forgetting, addressing non-stationary streaming data.
3/11/2026
Memorization capacity of deep ReLU neural networks characterized by width and depth
Theoretical analysis of memorization capacity in deep ReLU networks characterized by width and depth parameters.
3/11/2026
MM-algorithms for traditional and convex NMF with Tweedie and Negative Binomial cost functions and empirical evaluation
MM-algorithms for non-negative matrix factorization with Tweedie and Negative Binomial cost functions for unsupervised learning and feature extraction.
3/11/2026
FreqCycle: A Multi-Scale Time-Frequency Analysis Method for Time Series Forecasting
FreqCycle framework for time series forecasting using multi-scale time-frequency analysis to capture mid to high frequency patterns.
3/11/2026
No evaluation without fair representation : Impact of label and selection bias on the evaluation, performance and mitigation of classification models
Research on how label and selection bias impact ML classification model evaluation, performance, and mitigation strategies.
3/11/2026
GNNs for Time Series Anomaly Detection: An Open-Source Framework and a Critical Evaluation
Open-source framework evaluating graph neural networks for time series anomaly detection with critical benchmarking.
3/11/2026
On Catastrophic Forgetting in Low-Rank Decomposition-Based Parameter-Efficient Fine-Tuning
Empirical study of catastrophic forgetting in LoRA and parameter-efficient fine-tuning methods during sequential learning.
3/11/2026
ActiveUltraFeedback: Efficient Preference Data Generation using Active Learning
Active learning pipeline for efficiently generating preference data annotations for RLHF-based LLM alignment.
3/11/2026
A Multi-Prototype-Guided Federated Knowledge Distillation Approach in AI-RAN Enabled Multi-Access Edge Computing System
Federated knowledge distillation approach for AI-native radio access networks in multi-access edge computing systems.
3/11/2026
Exploiting Label-Aware Channel Scoring for Adaptive Channel Pruning in Split Learning
Adaptive channel pruning scheme for split learning to reduce communication overhead in distributed training.
3/11/2026
Information Theoretic Bayesian Optimization over the Probability Simplex
Bayesian optimization algorithm for optimizing probability distributions and mixtures on the probability simplex.
3/11/2026
Good Reasoning Makes Good Demonstrations: Implicit Reasoning Quality Supervision via In-Context Reinforcement Learning
In-context reinforcement learning approach that uses high-quality reasoning traces as better demonstrations for improving LLM reasoning.
3/11/2026
Correction of Transformer-Based Models with Smoothing Pseudo-Projector
Lightweight pseudo-projector modification for transformer-based language models to reduce noise sensitivity in hidden representations.
3/11/2026
GAST: Gradient-aligned Sparse Tuning of Large Language Models with Data-layer Selection
GAST combines gradient-aligned sparse tuning with data-layer selection for parameter-efficient fine-tuning of large language models.
3/11/2026
MSSR: Memory-Aware Adaptive Replay for Continual LLM Fine-Tuning
MSSR is a memory-aware replay strategy for continual LLM fine-tuning that reduces catastrophic forgetting during sequential task learning.
3/11/2026
OptEMA: Adaptive Exponential Moving Average for Stochastic Optimization with Zero-Noise Optimality
OptEMA optimizer improves exponential moving average with adaptive stepsizes, achieving zero-noise optimality for stochastic optimization.
3/11/2026
When Learning Rates Go Wrong: Early Structural Signals in PPO Actor-Critic
Study of learning rate sensitivity in PPO actor-critic methods, analyzing early structural signals to predict training stability.
3/11/2026
Towards a Neural Debugger for Python
Neural debugger for Python that trains LLMs on execution traces to predict line-by-line program execution for debugging workflows.
3/11/2026
On the Width Scaling of Neural Optimizers Under Matrix Operator Norms I: Row/Column Normalization and Hyperparameter Transfer
Analysis of neural network optimizers (AdamW, Muon) as steepest descent under matrix norms, addressing width scaling stability.
3/11/2026
Skip to the Good Part: Representation Structure & Inference-Time Layer Skipping in Diffusion vs. Autoregressive LLMs
Layer-wise representational analysis comparing diffusion language models and autoregressive LLMs, examining layer-skipping capabilities.
3/11/2026
Unveiling the Potential of Quantization with MXFP4: Strategies for Quantization Error Reduction
Novel techniques (OAS, MBS) improve MXFP4 quantization accuracy for efficient LLM inference, addressing gaps versus NVIDIA's NVFP4.
3/11/2026
KernelCraft: Benchmarking for Agentic Close-to-Metal Kernel Generation on Emerging Hardware
KernelCraft benchmarks agentic LLM systems for generating low-level kernels for novel AI accelerator instruction set architectures.
3/11/2026
ALADIN: Accuracy-Latency-Aware Design-space Inference Analysis for Embedded AI Accelerators
ALADIN framework for design-space analysis of mixed-precision quantized neural networks on resource-constrained embedded AI accelerators.
3/11/2026
Performance Analysis of Edge and In-Sensor AI Processors: A Comparative Review
Review of ultra-low-power edge AI processors including SoCs, neural accelerators, and in-sensor architectures for embedded inference.
3/11/2026
Data-Rate-Aware High-Speed CNN Inference on FPGAs
Research on dataflow-based CNN accelerators on FPGAs addressing data-rate inefficiencies in layers with reduced output dimensions.
3/11/2026
Autonomous Edge-Deployed AI Agents for Electric Vehicle Charging Infrastructure Management
Auralink SDC deploys autonomous edge AI agents for electric vehicle charging infrastructure management, achieving autonomous operation with edge computing latency requirements.
3/11/2026
Sensitivity-Guided Framework for Pruned and Quantized Reservoir Computing Accelerators
Sensitivity-guided compression framework for reservoir computing enabling design-space exploration of quantization, pruning, and hardware efficiency trade-offs.
3/11/2026
The AetherFloat Family: Block-Scale-Free Quad-Radix Floating-Point Architectures for AI Accelerators
AetherFloat family proposes block-scale-free quad-radix floating-point architectures reducing silicon area and power overhead in AI accelerators.
3/11/2026
Permutation-Equivariant 2D State Space Models: Theory and Canonical Architecture for Multivariate Time Series
Permutation-equivariant 2D state space models for multivariate time series, formalizing permutation symmetry principle for exchangeable variables.
3/11/2026
On the Formal Limits of Alignment Verification
Formal analysis proving that no verification procedure can simultaneously satisfy soundness, completeness, and decidability for AI alignment certification.
3/11/2026
MASEval: Extending Multi-Agent Evaluation from Models to Systems
MASEval extends multi-agent evaluation beyond model-centric benchmarks to evaluate LLM-based agentic system components including topology, orchestration, and error handling.
3/11/2026
APPLV: Adaptive Planner Parameter Learning from Vision-Language-Action Model
APPLV automates parameter tuning for autonomous navigation by learning from vision-language-action models, balancing safety assurances with learning flexibility.
3/11/2026
FedLECC: Cluster- and Loss-Guided Client Selection for Federated Learning under Non-IID Data
FedLECC proposes cluster and loss-guided client selection for federated learning under non-IID data, improving convergence in distributed AI systems.
3/11/2026
Vision-Language Models Encode Clinical Guidelines for Concept-Based Medical Reasoning
Vision-language models encode clinical guidelines for interpretable medical reasoning in Concept Bottleneck Models, enabling transparent AI in medical imaging.
3/11/2026
Optimizing Reinforcement Learning Training over Digital Twin Enabled Multi-fidelity Networks
Deep learning framework using digital network twin for optimizing reinforcement learning training in multi-fidelity 5G networks with antenna tilt adjustment.
3/11/2026
Interpretable Markov-Based Spatiotemporal Risk Surfaces for Missing-Child Search Planning with Reinforcement Learning and LLM-Based Quality Assurance
Guardian system combines reinforcement learning with LLM-based quality assurance to create spatiotemporal risk surfaces for missing-child search planning from unstructured case documents.
3/11/2026
BiCLIP: Domain Canonicalization via Structured Geometric Transformation
BiCLIP adapts vision-language models to specialized domains via structured geometric transformation, extending canonical transformation theory to domain adaptation.
3/11/2026
Towards Reliable Simulation-based Inference
Research on using machine learning for statistical inference with scientific simulators, focusing on hypothesis testing and model refinement.
3/11/2026
A Consensus-Driven Multi-LLM Pipeline for Missing-Person Investigations
Guardian system uses multi-LLM pipeline for intelligent information extraction in missing-person investigations, coordinating end-to-end execution across tasks.
3/11/2026
A Survey of Reinforcement Learning For Economics
Survey introducing reinforcement learning methods to economists, addressing curse of dimensionality in complex economic models.
3/11/2026
Statistical Inference via Generative Models: Flow Matching and Causal Inference
Reinterprets generative AI through statistical lens using flow matching, connecting generative models to causal inference and interpretability.
3/11/2026
FlexServe: A Fast and Secure LLM Serving System for Mobile Devices with Flexible Resource Isolation
LLM serving system for mobile devices with hardware-based isolation using ARM TrustZone to protect model weights and user data from kernel attacks.
3/11/2026
From Days to Minutes: An Autonomous AI Agent Achieves Reliable Clinical Triage in Remote Patient Monitoring
Autonomous AI agent for clinical triage in remote patient monitoring, using 21 medical tools to process vitals data 24/7 without physician bottleneck.
3/11/2026
Quality over Quantity: Demonstration Curation via Influence Functions for Data-Centric Robot Learning
Data curation method for robot learning using influence functions to select high-quality demonstrations from noisy human teleoperation data.
3/11/2026
Latent World Models for Automated Driving: A Unified Taxonomy, Evaluation Framework, and Open Challenges
Taxonomy and evaluation framework for latent world models and vision-language-action systems in autonomous driving.
3/11/2026
RubiCap: Rubric-Guided Reinforcement Learning for Dense Image Captioning
Reinforcement learning approach for dense image captioning using rubric-guided optimization to improve diversity and generalization.
3/11/2026
The Reasoning Trap -- Logical Reasoning as a Mechanistic Pathway to Situational Awareness
Examination of logical reasoning as mechanistic pathway to situational awareness in advanced AI systems, exploring emergent capabilities risks.
3/11/2026
Emotion is Not Just a Label: Latent Emotional Factors in LLM Processing
Study of emotion as latent representational factor in LLM reasoning and text processing, beyond sentiment classification tasks.
3/11/2026
MM-Zero: Self-Evolving Multi-Model Vision Language Models From Zero Data
Vision-language models that self-evolve from zero data without seed images, extending self-improvement paradigms from LLMs to multimodal systems.
3/11/2026
TrainDeeploy: Hardware-Accelerated Parameter-Efficient Fine-Tuning of Small Transformer Models at the Extreme Edge
TrainDeeploy: framework for hardware-accelerated parameter-efficient fine-tuning of transformer models on extreme-edge devices with memory constraints.