arXiv Machine Learning
Jul 28
The K-SCAN Clustering Algorithm
arXiv:2607. 24537v1 Announce Type: new Abstract: In the Big Data era, the scalability of clustering algorithms constitutes a key challenge.
Inference efficiency
Quantization, distillation, pruning and serving work aimed at the same accuracy for less memory, latency and money.
arXiv AI
Jul 28
The Physics of Multi-Turn Long-Horizon Planning: From Pre-training to Post-training via Single- and Multi-Teacher On-Policy Agentic Distillation
arXiv:2607. 24720v1 Announce Type: cross Abstract: Multi-turn long-horizon planning is critical for foundation model agents, yet how to fundamentally improve it remains unclear.
arXiv Machine Learning
Jul 28
Approximate reservoir computing with a semiconductor laser for reducing energy consumption
arXiv:2607. 23288v1 Announce Type: cross Abstract: Photonic reservoir computing is a promising physical machine-learning technique for predicting time-series data.
arXiv Machine Learning
Jul 28
Learning Reusable Hybrid Motion Priors for Humanoid Locomotion from Motion Imitation
arXiv:2607. 24083v1 Announce Type: new Abstract: Reinforcement learning can produce robust humanoid controllers, but each new task is typically trained as a separate policy with its own reward design and training process.
arXiv Machine Learning
Jul 28
Visual Token Compression Enhances Robustness of MLLMs
arXiv:2607. 22716v1 Announce Type: cross Abstract: In this paper, we show for the first time that visual token pruning enhances the robustness of Multimodal Large Language Models (MLLMs), mitigating vulnerabilities such as jailbreak attacks and hallucinations.
arXiv AI
Jul 28
Impute On-Demand: Adaptive Correlated Time Series Imputation for Changing Environments
arXiv:2607. 23503v1 Announce Type: cross Abstract: Internet of Things (IoT) applications generate vast amounts of Correlated Time Series (CTS) data that often contain missing values and require imputation.
arXiv AI
Jul 28
xMIx: High-Performance Serving-Time Platform for Mechanistic Interpretability Apps
arXiv:2607. 22595v1 Announce Type: new Abstract: Mechanistic interpretability (MI) has emerged as a powerful approach for analyzing and intervening in inference computations, with a growing number of applications such as jailbreak attempt detection, truthfulness evaluation, and hallucination detection.
arXiv AI
Jul 28
DraftExpert: Expansion-Aware Self-Speculative Decoding for End-Device MoE Inference
arXiv:2607. 24434v1 Announce Type: cross Abstract: Large Mixture-of-Experts (MoE) language models are attractive for end-device deployment because only a small subset of experts is active per token, but their routed expert weights often exceed accelerator memory.
arXiv AI
Jul 28
MXAttention: Data-Free Optimal Scaling and Pre-Normalization Quantization for MXFP4 Attention
arXiv:2607. 24377v1 Announce Type: cross Abstract: The quadratic cost of attention is a major bottleneck in diffusion-based video generation models.
arXiv Machine Learning
Jul 28
Self-Distillation of Hidden Layers for Self-Supervised Representation Learning
arXiv:2603. 15553v2 Announce Type: replace-cross Abstract: The landscape of self-supervised learning (SSL) is currently dominated by generative approaches (e.
arXiv Machine Learning
Jul 28
FlowCTS: On-policy Continuous Trajectory Supervision of Flow Models
arXiv:2607. 24522v1 Announce Type: new Abstract: While on-policy distillation (OPD) effectively addresses sparse rewards and exposure bias in large language model post-training, its extension to flow models remains underexplored.
arXiv Machine Learning
Jul 28
SPRKD: Effective Knowledge Distillation for Deep Neural Networks via Saddle Region Approximation
arXiv:2607. 23346v1 Announce Type: new Abstract: Modern deep neural networks are potent catalysts for scientific and industrial impact, yet excessive parameter counts impede deployment in low-compute settings such as hospital equipment and energy infrastructure.
arXiv Machine Learning
Jul 28
Explainable Reinforcement Learning via Physics-Aware Policy Distillation
arXiv:2607. 24672v1 Announce Type: new Abstract: In safety-critical sectors such as robotics and automotive engineering, the deployment of Deep Reinforcement Learning (DRL) is often hindered by the black-box nature of deep neural networks.
arXiv Machine Learning
Jul 28
Uncertainty Modeling for Multi-Objective RTA Interception with Distillation Acceleration
arXiv:2511. 05582v3 Announce Type: replace Abstract: Real-Time Auction (RTA) interception decides which incoming advertising requests reach downstream systems, and therefore controls the quality of the data those systems learn from.
arXiv Machine Learning
Jul 28
DynaCalKV: Key-Value Cache Compression via Head Grouping and Adaptive Rank Allocation
arXiv:2607. 24331v1 Announce Type: new Abstract: As the inference phase of Large Language Models (LLMs) requires handling long context windows, the Key-Value (KV) cache initially appears to address this challenge but eventually becomes a significant bottleneck as the context window continues to grow.
arXiv AI
Jul 28
Variational-Ising-Attention (VIA):TailoredAttentionMattersfor Science
arXiv:2607. 23634v1 Announce Type: cross Abstract: Attention enables context modeling via query-key scoring with softmax normalization.
arXiv AI
Jul 28
DynaResize: Runtime GPU Reallocation for Disaggregated LLM Post-Training
arXiv:2607. 22614v1 Announce Type: new Abstract: RL-based LLM post-training increasingly disaggregates Rollout and Training across separate GPU resources, but static GPU partitioning suffers from severe pipeline bubbles under long-tail rollout latency.
arXiv AI
Jul 28
TriSP: Tri-Signal Structured Pruning for Large Language Models
arXiv:2607. 22587v1 Announce Type: new Abstract: Large language models (LLMs) achieve strong performance across diverse tasks but their deployment is constrained by the memory and compute cost of their parameters.
arXiv AI
Jul 28
Multi-Objective Structured Pruning of LLMs for Latency and Model Size Optimization
arXiv:2607. 22583v1 Announce Type: new Abstract: Large Language Models (LLMs) have achieved widespread adoption because of their strong reasoning and query-response capabilities.
arXiv AI
Jul 28
Hierarchical Group-Conditional Conformal Risk Control for Selective Prediction in Language Models
arXiv:2607. 24562v1 Announce Type: new Abstract: Large language models serve heterogeneous populations structured by domain, topic difficulty, and linguistic style.