arXiv AI
Jun 15
A Benchmark and Framework for Evaluating Next Action Predictions in Spreadsheets
arXiv:2606. 13802v1 Announce Type: cross Abstract: Predictive code completion greatly accelerates how quickly developers work.
Fine-tuning and adaptation
LoRA, PEFT, instruction tuning and domain adaptation — adapting a pretrained model without paying to train one.
arXiv AI
Jun 15
Unsupervised Learning of Efficient Exploration: Pre-training Adaptive Policies via Self-Imposed Goals
arXiv:2601. 19810v2 Announce Type: replace-cross Abstract: Unsupervised pre-training can equip reinforcement learning agents with prior knowledge and accelerate learning in downstream tasks.
arXiv Machine Learning
Jun 15
Multi-fidelity aerodynamic data fusion by autoencoder transfer learning
arXiv:2512. 13069v2 Announce Type: replace Abstract: Accurate aerodynamic prediction often relies on high-fidelity simulations; however, their prohibitive computational costs severely limit their applicability in data-driven modeling.
arXiv Machine Learning
Jun 15
PhysVLA: Towards Physically-Grounded VLA for Embodied Robotic Manipulation
arXiv:2606. 13886v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models excel at mapping visual inputs and natural language instructions directly to robotic control policies.
arXiv AI
Jun 15
Rethinking the Trust Region in LLM Reinforcement Learning
arXiv:2602. 04879v3 Announce Type: replace-cross Abstract: Reinforcement learning (RL) has become a cornerstone for fine-tuning Large Language Models (LLMs), with Proximal Policy Optimization (PPO) serving as the de facto standard algorithm.
arXiv AI
Jun 15
No Accidental Software Agent First Canonical Code for Human Code Entropy Reduction and 30 to 500 times Lower Frontier Model Requirements
arXiv:2606. 14357v1 Announce Type: cross Abstract: Frontier coding models may spend substantial capacity learning not only program behavior, but also accidental entropy in human repositories.
arXiv AI
Jun 15
Refusal Beyond a Single Direction: A Preliminary Comparison of Diff-in-Means and INLP
arXiv:2606. 13720v1 Announce Type: new Abstract: Arditi et al.
arXiv AI
Jun 15
Clay-CNN Hybrids: Leveraging Geo-Foundational Models as Auxiliary Context for Landslide Detection
arXiv:2606. 14081v1 Announce Type: cross Abstract: Rapid post-event landslide mapping is essential for disaster response but remains difficult to automate due to extreme class imbalance.
arXiv AI
Jun 15
GMN4AD: Graph Matching Network for Alzheimer's Disease Diagnosis with Test-Time Domain Adaptation using Multi-centered Structure Magnetic Resonance Imaging
arXiv:2606. 13919v1 Announce Type: cross Abstract: Alzheimer's Disease (AD) is a progressive neurodegenerative disorder that affects millions of older adults, with prevalence expected to rise significantly in the coming years.
arXiv AI
Jun 15
STaR-DRO: Stateful Tsallis Reweighting for Group-Robust Structured Prediction
arXiv:2604. 09737v2 Announce Type: replace-cross Abstract: Structured prediction with large language models requires outputs that are label-accurate, ontology-constrained, structurally valid, and evidence-grounded under label imbalance and heterogeneous group difficulty.
arXiv Machine Learning
Jun 15
Muon$^p$: Muon with Fractional Spectral Powers
arXiv:2606. 13867v1 Announce Type: new Abstract: Muon is an increasingly widely used optimizer that replaces a gradient $G=USV^\top$ with its polar factor $UV^\top$, thereby flattening the singular spectrum.
arXiv AI
Jun 15
3D-RFT: Reinforcement Fine-Tuning for Video-based 3D Scene Understanding
arXiv:2603. 04976v2 Announce Type: replace-cross Abstract: Reinforcement Learning with Verifiable Rewards ( RLVR ) has emerged as a transformative paradigm for enhancing the reasoning capabilities of Large Language Models ( LLMs), yet its potential in 3D scene understanding remains under-explored.
arXiv AI
Jun 15
VeriGeo: Controllable Geometry Question Generation with Numerical and Analytical Verification
arXiv:2606. 14176v1 Announce Type: new Abstract: Geometry problem generation is useful for AI-assisted education and multimodal mathematical reasoning, but reliable synthesis remains difficult because the problem statement, diagram, constraints, and solution should be mutually consistent.
arXiv Machine Learning
Jun 15
Towards Steering without Sacrifice: Principled Training of Steering Vectors for Prompt-only Interventions
arXiv:2605. 05983v2 Announce Type: replace Abstract: Recently, steering vectors (SVs) have emerged as an effective and lightweight approach to steer behaviors of large language models (LLMs), among which fine-tuned SVs are more effective than optimization-free ones.
arXiv Machine Learning
Jun 15
Towards Efficient Large Language Reasoning Models via Extreme-Ratio Chain-of-Thought Compression
arXiv:2602. 08324v5 Announce Type: replace Abstract: Chain-of-Thought (CoT) reasoning successfully enhances the reasoning capabilities of Large Language Models (LLMs), yet it incurs substantial computational overhead for inference.
arXiv AI
Jun 15
ClinHallu: A Benchmark for Diagnosing Stage-Wise Hallucinations in Medical MLLM Reasoning
arXiv:2606. 14697v1 Announce Type: cross Abstract: Building trustworthy medical multimodal large language models (MLLMs) is critical for reliable clinical decision support.
arXiv AI
Jun 15
Achieving Precise Text-To-Cypher Via Grounded Knowledge Graph Data Generation
arXiv:2606. 14325v1 Announce Type: cross Abstract: Property Graphs are rapidly being adopted as database frameworks for representing heterogeneous data sources.
arXiv AI
Jun 15
From Prompts to Responses: Dual-Sided Data Leakage and Defense in Split Large Language Models
arXiv:2606. 14210v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly deployed in privacy-sensitive domains, where users must balance the risk of data exposure through external APIs against the high computational cost of local deployment.
arXiv AI
Jun 15
Towards Direct Latent-Space Synthesis for Parallel Branches in LLM-Agent Workflows
arXiv:2606. 14672v1 Announce Type: new Abstract: Large language models increasingly serve as execution engines for agentic systems, yet they still consume context through a sequential text interface.
arXiv Machine Learning
Jun 15
DeepJEB++: Foundation Model-Driven Large-Scale 3D Engineering Dataset via 2D Latent Space Augmentation
arXiv:2606. 12994v2 Announce Type: replace Abstract: Data-driven engineering design is constrained by the lack of large-scale 3D datasets that pair geometry with physics-based performance labels.