3/16/2026
Invariant Graph Transformer for Out-of-Distribution Generalization
Graph transformer architecture designed for invariant learning to improve out-of-distribution generalization on graph-structured data.
3/16/2026
Intrinsic training dynamics of deep neural networks
Theoretical study of implicit bias in deep neural network training showing gradient flow induces learning of lower-dimensional parameter structures.
3/16/2026
UniPrompt-CL: Sustainable Continual Learning in Medical AI with Unified Prompt Pools
Continual learning framework with unified prompt pools for medical imaging tasks, addressing domain-specific challenges in adaptive AI.
3/16/2026
Local Mechanisms of Compositional Generalization in Conditional Diffusion
Analysis of compositional generalization mechanisms in conditional diffusion models, studying length generalization on controlled image generation tasks.
3/16/2026
LiLAW: Lightweight Learnable Adaptive Weighting to Meta-Learn Sample Difficulty, Improve Noisy Training, Increase Fairness, and Effectively Use Synthetic Data
Lightweight meta-learning method using three parameters to dynamically adjust sample loss weights for noisy training, fairness, and synthetic data utilization.
3/16/2026
PreLoRA: Hybrid Pre-training of Vision Transformers with Full Training and Low-Rank Adapters
Hybrid pre-training approach using low-rank adapters alongside full training to reduce computational cost for vision transformer training.
3/16/2026
Robust Fine-Tuning from Non-Robust Pretrained Models: Mitigating Suboptimal Transfer With Epsilon-Scheduling
Method for robust fine-tuning non-robust pretrained models using epsilon-scheduling to achieve adversarial robustness and task adaptation simultaneously.
3/16/2026
Disentangling Recall and Reasoning in Transformer Models through Layer-wise Attention and Activation Analysis
Analysis of transformer internals distinguishing recall from reasoning mechanisms through layer-wise attention and activation patterns for interpretability.
3/16/2026
Language Models are Injective and Hence Invertible
Mathematical proof that transformer language models are injective, enabling exact input recovery from representations despite nonlinear components.
3/16/2026
LLM Unlearning with LLM Beliefs
Method for unlearning harmful content from LLMs by analyzing belief redistribution in probability space, avoiding unwanted side effects of gradient ascent.
3/16/2026
Larger Datasets Can Be Repeated More: A Theoretical Analysis of Multi-Epoch Scaling in Linear Regression
Theoretical analysis of data scaling laws in linear regression when training multiple epochs on limited datasets, relevant to LLM training efficiency.
3/16/2026
Information-Consistent Language Model Recommendations through Group Relative Policy Optimization
Method to improve LLM consistency and reliability across semantically equivalent prompts using group relative policy optimization for business-critical applications.
3/16/2026
Epistemic diversity across language models mitigates knowledge collapse
Study demonstrating that ensemble diversity across language models mitigates knowledge collapse from training on model-generated outputs.
3/16/2026
MoHETS: Long-term Time Series Forecasting with Mixture-of-Heterogeneous-Experts
Mixture-of-experts approach with heterogeneous experts for capturing multi-scale temporal dynamics in long-horizon time series forecasting.
3/16/2026
Learnable Koopman-Enhanced Transformer-Based Time Series Forecasting with Spectral Control
Integration of Koopman operator theory with transformer architectures for time series forecasting with learnable spectral parameterizations.
3/16/2026
CORE: Context-Robust Remasking for Diffusion Language Models
Decoding strategy for masked diffusion language models that dynamically adjusts token retention based on context coverage.
3/16/2026
Hierarchical Concept Embedding & Pursuit for Interpretable Image Classification
Interpretable image classification using hierarchical concept embeddings recovered from vision-language model latent spaces.
3/16/2026
RooflineBench: A Benchmarking Framework for On-Device LLMs via Roofline Analysis
Benchmarking framework using roofline analysis to characterize performance of small language models on resource-constrained edge hardware.
3/16/2026
On the Geometric Coherence of Global Aggregation in Federated Graph Neural Networks
Federated learning approach addressing heterogeneous graph structures in distributed GNN training across multiple clients.
3/16/2026
Test-Time Adaptation via Many-Shot Prompting: Benefits, Limits, and Pitfalls
Study of many-shot in-context learning as test-time adaptation for LLMs, analyzing benefits and reliability limits with open-source models.
3/16/2026
Distributional Regression with Tabular Foundation Models: Evaluating Probabilistic Predictions via Proper Scoring Rules
Evaluation framework using proper scoring rules for assessing distributional predictions from tabular foundation models beyond point estimates.
3/16/2026
What do near-optimal learning rate schedules look like?
Search procedure to identify optimal learning rate schedule shapes for neural network training across different workloads.
3/16/2026
H2LooP Spark Preview: Continual Pretraining of Large Language Models for Low-Level Embedded Systems Code
Continual pretraining of LLMs specialized for low-level embedded systems code generation, targeting underrepresented hardware domains.
3/16/2026
Why Softmax Attention Outperforms Linear Attention
Comparative analysis of softmax vs linear attention mechanisms in transformer architectures, examining computational efficiency tradeoffs.
3/16/2026
Latent diffusion models for parameterization and data assimilation of facies-based geomodels
Latent diffusion models for geological parameterization and data assimilation, generating realistic geomodels with reduced variables for history matching.
3/16/2026
Superficial Safety Alignment Hypothesis
Analyzes brittleness of LLM safety alignment mechanisms, proposing superficial safety alignment hypothesis explaining why standard alignment approaches are vulnerable.
3/16/2026
Active Causal Structure Learning with Latent Variables: Towards Learning to Detour in Autonomous Robots
Active causal structure learning framework enabling autonomous robots and AGI agents to dynamically construct causal models of environmental interactions.
3/16/2026
Mask-Enhanced Autoregressive Prediction: Pay Less Attention to Learn More
Training paradigm integrating masked language modeling with next-token prediction to improve in-context retrieval in large language models.
3/16/2026
Understanding Dataset Distillation via Spectral Filtering
Spectral filtering framework unifying dataset distillation methods by interpreting them as filters affecting feature correlation eigenvalues.
3/16/2026
Minimax learning rates for estimating binary classifiers under margin conditions
Theoretical analysis of minimax learning rates for binary classification under geometric margin conditions with horizon function decision boundaries.
3/16/2026
AdaBoN: Adaptive Best-of-N Alignment
Prompt-adaptive Best-of-N alignment strategy using reward models to reduce computational cost of test-time alignment for language models.
3/16/2026
Integration of TinyML and LargeML: A Survey of 6G and Beyond
Survey on integrating TinyML and LargeML for 6G networks, covering deep learning applications in mobile systems, autonomous vehicles, and smart services.
3/16/2026
Token Distillation: Attention-aware Input Embeddings For New Tokens
Attention-aware embedding initialization method for new tokens in LLMs without expensive retraining, addressing vocabulary limitations in specialized domains.
3/16/2026
Towards Reliable Detection of Empty Space: Conditional Marked Point Processes for Object Detection
Conditional marked point processes for reliable object detection uncertainty quantification, addressing miscalibrated confidence scores in neural networks.
3/16/2026
From Video to EEG: Adapting Joint Embedding Predictive Architecture to Uncover Saptiotemporal Dynamics in Brain Signal Analysis
Self-supervised learning approach adapting joint embedding architecture from video to EEG signals for brain activity analysis with limited labeled data.
3/16/2026
Quantum-Informed Machine Learning for Predicting Spatiotemporal Chaos with Practical Quantum Advantage
Quantum-informed ML framework combining quantum generative models with classical predictors for long-term spatiotemporal chaos prediction.
3/16/2026
Aligning Large Language Model Agents with Rational and Moral Preferences: A Supervised Fine-Tuning Approach
Supervised fine-tuning method to align LLM agents with rational and moral preferences in strategic economic games, addressing systematic behavioral deviations.
3/16/2026
SegDAC: Visual Generalization in Reinforcement Learning via Dynamic Object Tokens
Object-centric representations for visual RL policies using dynamic tokens to improve generalization under visual condition changes without fixed-size slots.
3/16/2026
On the (In)Security of Loading Machine Learning Models
Security evaluation of ML model sharing frameworks and hubs, assessing vulnerabilities in loading shared models and security awareness gaps among practitioners.
3/16/2026
Neural-Quantum-States Impurity Solver for Quantum Embedding Problems
Neural quantum states impurity solver for quantum embedding problems. Graph transformer-based NQS for solving Hamiltonians in quantum chemistry.
3/16/2026
Dynamic Aware: Adaptive Multi-Mode Out-of-Distribution Detection for Trajectory Prediction in Autonomous Vehicles
Dynamic Aware: out-of-distribution detection for trajectory prediction in autonomous vehicles. Adaptive multi-mode approach for distribution shift in AVs.
3/16/2026
AutoClimDS: Climate Data Science Agentic AI -- A Knowledge Graph is All You Need
AutoClimDS: agentic AI system for climate data science. Knowledge graph-based workflows for discovering climate patterns from fragmented data sources.
3/16/2026
From Formal Language Theory to Statistical Learning: Finite Observability of Subregular Languages
Formal language theory applied to statistical learning. Proves subregular language classes are linearly separable with simple models.
3/16/2026
Scaling Generalist Data-Analytic Agents
DataMind: scalable data-analytic AI agents for automated discovery. Open-source agent framework handling diverse-format data files and multi-step reasoning.
3/16/2026
HoneyBee: Data Recipes for Vision-Language Reasoners
HoneyBee: data curation approaches for vision-language reasoning datasets. Analyzes impact of context, content, and format on VLM reasoning capabilities.
3/16/2026
CBF-RL: Safety Filtering Reinforcement Learning in Training with Control Barrier Functions
CBF-RL: integrates control barrier functions into reinforcement learning training. Enforces dynamic safety constraints during RL policy training, not just inference.
3/16/2026
RobotArena $\infty$: Scalable Robot Benchmarking via Real-to-Sim Translation
RobotArena ∞: scalable robot benchmarking via real-to-sim translation. Enables rigorous evaluation of robot policies across diverse tasks and environments.
3/16/2026
Verifying LLM Inference to Detect Model Weight Exfiltration
Verifying LLM inference to detect model weight exfiltration via steganography. Defends inference servers against model theft and anomalous behavior.
3/16/2026
AnatomiX, an Anatomy-Aware Grounded Multimodal Large Language Model for Chest X-Ray Interpretation
AnatomiX: anatomy-aware multimodal LLM for chest X-ray interpretation. Improves spatial reasoning and anatomical understanding in medical imaging.
3/16/2026
FARM: Few-shot Adaptive Malware Family Classification under Concept Drift
FARM framework for malware family classification under concept drift. Uses triplet autoencoder for few-shot adaptation to covariate and label drift.