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.
Fine-tuning and adaptation
LoRA, PEFT, instruction tuning and domain adaptation — adapting a pretrained model without paying to train one.
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
ACC: Compiling Agent Trajectories for Long-Context Training
arXiv:2605. 21850v2 Announce Type: replace-cross Abstract: Recent development of agents has renewed demand for long-context reasoning capacity of LLMs.
arXiv Machine Learning
Jun 16
Conflict-Aware Federated Fine-Tuning of Large Language Models with Mixture-of-Experts
arXiv:2606. 15625v1 Announce Type: new Abstract: The continuous scaling of large language models (LLMs) incurs prohibitive computational costs, making Mixture-of-Experts (MoE) a scalable alternative for efficient fine-tuning via sparse activation.
arXiv Machine Learning
Jun 16
Towards Data-Efficient Cross-Device Generalization of Grad-Shafranov Equilibria via Transfer Learning Neural Operator
arXiv:2606. 15512v1 Announce Type: new Abstract: Real-time reconstruction of magnetohydrodynamic equilibria is essential for plasma shaping, stability assessment and feedback control in magnetic confinement fusion.
arXiv Machine Learning
Jun 16
ReQAT: Achieving Full-Precision Reasoning Accuracy with 4-bit Floating-Point Quantization-Aware Training
arXiv:2606. 15682v1 Announce Type: new Abstract: Large Reasoning Models (LRMs) achieve strong problem-solving through long chain-of-thought, but their deployment is constrained by the high cost of full-precision inference and growing KV cache footprints.
arXiv Machine Learning
Jun 16
PhysGuard: Fisher-Guided Gradient Projection for Sim-to-Real Neural PDE Surrogates
arXiv:2606. 16602v1 Announce Type: new Abstract: Neural operator models trained on simulation data often lose accuracy when applied to experimental measurements due to the sim-to-real gap.
arXiv AI
Jun 16
Haiku to Opus in Just 10 bits: LLMs Unlock Large Compression Gains
arXiv:2604. 02343v2 Announce Type: replace-cross Abstract: We study the compression of LLM-generated text across lossless and lossy regimes, characterizing a compression-compute frontier where more compression is possible at the cost of more compute.
arXiv AI
Jun 16
ArtBoost: Synthetic Articulatory Data Augmentation for Acoustic-to-Articulatory Inversion
arXiv:2606. 16327v1 Announce Type: cross Abstract: Recent acoustic-to-articulatory inversion (AAI) models rely on electromagnetic articulography (EMA) data, which are costly and limited in scale.
arXiv AI
Jun 16
FusionRS: A Large-Scale RGB-Infrared Remote Sensing Dataset for Dual-Modal Vision-Language Foundation Models
arXiv:2606. 17020v1 Announce Type: cross Abstract: Remote sensing vision-language models have advanced Earth observation understanding, but most existing work remains centered on RGB imagery, leaving the complementary information in infrared data underexplored.
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
Multi-Granular Node Pruning for Causal Circuit Discovery
arXiv:2512. 10903v2 Announce Type: replace Abstract: Circuit discovery aims to identify minimal subnetworks that are responsible for specific behaviors in large language models (LLMs).
arXiv Machine Learning
Jun 16
Exploring Extrinsic and Intrinsic Properties for Effective Reasoning with Code Interpreter
arXiv:2606. 16934v1 Announce Type: cross Abstract: Reasoning with a Code Interpreter (CI) has emerged as an effective paradigm for enhancing the reasoning capabilities of large language models (LLMs) through executable computation and iterative verification.
arXiv Machine Learning
Jun 16
Transfer Learning for FHIR Questionnaire Terminology Binding
arXiv:2606. 15449v1 Announce Type: cross Abstract: Electronic prior authorization workflows require FHIR Questionnaire items to carry LOINC codes, yet most items in the HL7 Da Vinci CDS-Library lack these bindings.
arXiv AI
Jun 16
AdaMame: A Training Recipe for Adaptive Multilingual Reasoning
arXiv:2606. 15080v1 Announce Type: cross Abstract: While Large Reasoning Models (LRMs) show strong performance in English, they often fail to reason in the language of the query, a phenomenon known as language collapse.
arXiv AI
Jun 16
DualGauge: Automated Joint Security-Functionality Benchmarking of Specification-Only Code Generation by LLMs and Coding Agents
arXiv:2511. 20709v2 Announce Type: replace-cross Abstract: Large language models (LLMs) and LLM-based coding agents are now used to generate code from natural-language specifications, yet ensuring such code is both functionally correct and secure remains a challenge.
arXiv Machine Learning
Jun 16
Efficient Reinforcement Learning by Guiding World Models with Non-Curated Data
arXiv:2502. 19544v3 Announce Type: replace Abstract: Leveraging offline data is a promising way to improve the sample efficiency of online reinforcement learning (RL).
arXiv Machine Learning
Jun 16
Learn-to-learn on Arbitrary Textual Conditioning: A Hypernetwork-Driven Meta-Gated LLM
arXiv:2605. 01973v2 Announce Type: replace-cross Abstract: Conventional LLMs may suffer from corpus heterogeneity and subtle condition changes.
arXiv Machine Learning
Jun 16
Design and Scheduling of an AI-based Queueing System
arXiv:2406. 06855v3 Announce Type: replace-cross Abstract: To leverage prediction models to make optimal scheduling decisions in service systems, we must understand how predictive errors impact congestion due to externalities on the delay of other jobs.
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.