On the Safety of Graph Representation Learning
arXiv paper on safety and robustness of graph representation learning under distribution shifts. ML robustness research.
arXiv paper on safety and robustness of graph representation learning under distribution shifts. ML robustness research.
arXiv paper on discrete tokenization framework for audio via self-alignment. Research on audio representation learning.
arXiv paper on distributionally robust hyperparameter optimization for convex methods. Theoretical optimization research.
arXiv paper on weight decay effects on transformer loss landscapes using functional analysis. Theoretical optimization research for LLMs.
arXiv paper on efficient multi-turn LLM evaluation and jailbreak prediction with adaptive budgets. Research on LLM robustness evaluation.
arXiv paper on sparse autoencoders with dynamic sparsity selection for LLM interpretability. Research on mechanistic interpretability tools.
arXiv paper analyzing mechanistic origins of attention sink phenomenon in LLMs. Original research on transformer internals.
arXiv paper on Bayesian calibration for digital twins under system changes. Machine learning research with theoretical contribution.
Theoretical analysis of why sign-based optimization algorithms outperform SGD in training foundation models.
Method for creating reversible SFT behaviors in LLMs with causal control over induced behaviors.
Reinforcement learning approach for training recursive agents that spawn sub-tasks to solve longer context problems.
Concept-based explanation method for vision models using causal abductive and contrastive explanations.
Framework for validating LLM safety scores without labeled benchmarks using scenario-based audits.
Study showing using same optimizer in finetuning as pretraining reduces catastrophic forgetting in LLMs.
Analysis showing global LLM leaderboards are statistically misleading due to large vote cancellation across languages.
Method for generating hard mathematical problems using LLM verifiers for improved training datasets.
Review of LLM applications in quantitative finance including sentiment analysis, multi-agent trading systems, and practical pitfalls.
Manifold pooling network for efficient EEG signal decoding with Riemannian geometry approaches.
Mamba-based architecture adapted for medical time series classification tasks like ECG and EEG analysis.
Open-set fraud detection for identity documents using layout-aware representation learning and adapted vision models.
Graph neural network approach for recommender systems using dynamic graphs and similarity-aware attention.
Embedding models for bidirectional code search between source and decompiled code without identifiers.
Tree search optimization for tool-use agent RL via submodular maximization of rollout informativeness in GRPO.
Advanced gradient-based optimizers derived from evolutionary computation first principles.
Interpretability analysis of annotator safety policies identifying sources of disagreement in AI safety annotation.
ViTok-v2: Scaled Vision Transformer autoencoders to 5B parameters for image tokenization at native resolution.
Meta-learning approach for sample-efficient Bayesian optimization of fed-batch chemical processes.
BALAR: Bayesian agentic loop algorithm for LLMs to actively determine missing information and ask next questions.
Method for estimating implicit regularization in deep learning models without analytical derivation.
PAS mechanism for location privacy in spatial retrieval-augmented generation systems using anchor-based encoding.
LLM reasoning improvement via prompt space perturbation to address zero-advantage problem in Group Relative Policy Optimization training.
Pretrained transformer (PUICL) for in-context positive-unlabeled learning enabling quick binary classification with only positive labels available.
Analysis of recorruption failure mode in multimodal RAG systems where accurate retrieved context causes MLLMs to abandon correct predictions.
Variational inference method for calibrating stochastic differential equations from sparse observations using dynamic neural flows.
Synthetic dataset (Gen4Regen) using image generators to address training data scarcity for forest regeneration mapping with deep learning.
Generative model for discrete sequences using spherical flows and von Mises-Fisher distributions with closed-form conditional scores.
Evaluation of RAG system architectures including multi-agent debate and agentic retrieval under adversarial knowledge base poisoning attacks.
Residual adapter method (EGA) for frozen vision encoders in vector search with robustness to out-of-distribution queries.
Spectral analysis framework using activation and gradient covariance to diagnose internal mechanisms during language model training.
Saliency-aware quantization calibration method for post-training quantization of large language models with improved generalization.
Position-independent caching system (Irminsul) for agentic LLM serving that prevents cache invalidation from token shifts during multi-turn interactions.
Active learning method for optimizing communication structures in LLM multi-agent systems to reduce token usage and improve performance.
Framework combining LLMs with reinforcement learning for unified 3D scene generation and interactive user interaction in multimedia systems.
Research on linear decodability versus correction of medical LLM failure modes, showing limitations of fixed residual-stream steering.
Research on Fourier feature methods for nonlinear causal discovery in mixed data combining scoring and constraint-based approaches.
Research on machine learning interatomic potentials with polarizable atomic multipoles for modeling long-range electrostatics.
Theoretical work proving transformers can implement in-context reinforcement learning with policy improvement via explicit constructions.
Research on supremum-norm generalization error and uniform inference bounds for kernel gradient flow methods.
Survey of ratio-based loss functions for supervised and unsupervised learning algorithms.
Research on anytime-valid statistical inference for controlling error in LLM self-consistency aggregation methods.