arXiv Machine Learning
Jun 16
Adaptive Kernel Density Estimation with Pre-training
arXiv:2605. 13092v2 Announce Type: replace-cross Abstract: Density estimation in high-dimensional settings is an important and challenging statistical problem.
Fine-tuning and adaptation
LoRA, PEFT, instruction tuning and domain adaptation — adapting a pretrained model without paying to train one.
arXiv AI
Jun 16
Rational Sparse Autoencoder
arXiv:2606. 14990v1 Announce Type: cross Abstract: Sparse autoencoders (SAEs) are standard tools for mechanistic interpretability, but current SAE families are constrained by fixed encoder nonlinearities such as ReLU, JumpReLU, and TopK.
arXiv Machine Learning
Jun 16
Contrastive Regularization for Accent-Robust ASR
arXiv:2605. 03297v2 Announce Type: replace-cross Abstract: ASR systems based on self-supervised acoustic pretraining and CTC fine-tuning achieve strong performance on native speech but remain sensitive to accent variability.
arXiv AI
Jun 16
Skill-to-LoRA: From Using Skills to Learning Behaviors for Token-Efficient LLM Agents
arXiv:2606. 16769v1 Announce Type: new Abstract: Agent skills are commonly distributed as SKILL.
arXiv Machine Learning
Jun 16
Spatial Priors via Space Filling Curves for Small and Limited Data Vision Transformers
arXiv:2606. 14757v1 Announce Type: cross Abstract: Though Vision Transformers (ViTs) have become the dominant backbone in many computer vision tasks, due to permutation equivariance, their attention mechanism lacks explicit spatial inductive biases.
arXiv AI
Jun 16
Cordyceps: Covert Control Attacks on LLMs via Data Poisoning
arXiv:2605. 26595v2 Announce Type: replace-cross Abstract: Large language models (LLMs) are often fine-tuned on uncurated text datasets that adversaries can poison.
arXiv AI
Jun 16
The Truth Stays in the Family: Enhancing Contextual Grounding via Inherited Truthful Heads in Model Lineages
arXiv:2606. 15821v1 Announce Type: cross Abstract: Recent advances in large language models (LLMs) have produced many specialized multimodal LLMs (MLLMs) that share common foundational LLMs, forming distinct model lineages.
arXiv Machine Learning
Jun 16
Navigating Distribution Shifts in Medical Image Analysis: A Survey
arXiv:2411. 05824v4 Announce Type: replace-cross Abstract: Medical Image Analysis (MedIA) has become indispensable in modern healthcare, enhancing clinical diagnostics and personalized treatment.
arXiv AI
Jun 16
CIWI-CKT: Chaos-Informed Wave Interference Feature Fusion and Cross-City Knowledge Transfer for Traffic Flow Forecasting
arXiv:2606. 15642v1 Announce Type: cross Abstract: Accurate traffic flow prediction remains challenging in cross-city, data-scarce scenarios where limited historical data hinders model generalisation.
arXiv AI
Jun 16
CogGuard: Cognitive and Operational Profiling for Proactive Warning in Edge Intelligent Services
arXiv:2606. 15199v1 Announce Type: new Abstract: Proactive warning is an important capability for edge intelligent services, where the system predicts whether a subject will successfully complete an incoming task under strict latency and privacy constraints.
arXiv AI
Jun 16
QPILOTS: Efficient Test-Time Q-Steering for Flow Policies
arXiv:2606. 14801v1 Announce Type: cross Abstract: Flow-matching and diffusion policies are expressive action generators, but optimizing them with temporal-difference reinforcement learning (RL) remains difficult.
arXiv AI
Jun 16
DYNA : Dynamic Episodic Memory Networks for Augmenting Large Language Models with Temporal Knowledge Graphs in Continuous Learning
arXiv:2606. 15778v1 Announce Type: cross Abstract: Large Language Models (LLMs) struggle to incorporate new knowledge without forgetting or costly retraining.
arXiv AI
Jun 16
PVminerLLM2: Improving Structured Extraction of Patient Voice via Preference Optimization
arXiv:2606. 16074v1 Announce Type: cross Abstract: Motivation: Patient-generated text contains critical information on patients' lived experiences, social context, and care engagement, but remains largely unstructured, limiting its use in patient-centered outcomes research.
arXiv AI
Jun 16
Deep Residual Injection for Full-Spectrum Forensic Signal Perception in Multimodal Large Language Models
arXiv:2606. 15880v1 Announce Type: cross Abstract: Multimodal large language models (MLLMs) have been increasingly adopted in forensics for their robust semantic understanding.
arXiv Machine Learning
Jun 16
Privacy from Symmetry: Orthogonally Equivariant Transformers for LLM Inference
arXiv:2606. 16461v1 Announce Type: new Abstract: Running large language models locally is often impractical, pushing inference on sensitive text to third-party providers.
arXiv AI
Jun 16
From Tokens to Regions: CUDA-Sensitive Instruction Tuning for GPU Kernel Generation
arXiv:2606. 16231v1 Announce Type: cross Abstract: High-performance CUDA kernels are essential for scalable AI systems, while Large Language Models (LLMs) still struggle to generate correct kernels due to strict and implicit execution constraints.
arXiv AI
Jun 16
Domain-Guided Prompting of the Segment Anything Model for Seismic Interpretation: The Role of Attributes, Visualization, and Hybrid Prompts
arXiv:2606. 15786v1 Announce Type: cross Abstract: The advent of large pretrained foundation models for computer vision has significantly improved the efficiency of visual data interpretation.
arXiv AI
Jun 16
Do You Really Need a GPU to Guard Your LLM? CPU-Class Classifiers and Multi-Stage Pipelines for Safety Enforcement at Scale
arXiv:2512. 19011v3 Announce Type: replace-cross Abstract: Safety classifiers that screen LLM inputs for jailbreak attempts have become standard deployment components, yet almost all production systems rely on GPU-based models: fine-tuned transformers and LLM-as-a-judge pipelines.
arXiv AI
Jun 16
LLM4RTL: Tool-Assisted LLM for RTL Generation
arXiv:2606. 15500v1 Announce Type: cross Abstract: Large language models (LLMs) have facilitated impressive progress in software engineering, code generation, tooling, and systems.
arXiv Machine Learning
Jun 16
Towards a Unified Generative Model for Scarce Time Series with Domain Experts
arXiv:2606. 15172v1 Announce Type: new Abstract: Synthesizing realistic time series with generative models has wide-ranging applications in real-world scenarios.