arXiv AI
Jun 30
HARD-KV: Head-Adaptive Regularization for Decoding-time KV Compression
arXiv:2606. 28831v1 Announce Type: cross Abstract: Long-context LLM inference faces a fundamental conflict: head-adaptive compression algorithms (e.
Inference efficiency
Quantization, distillation, pruning and serving work aimed at the same accuracy for less memory, latency and money.
arXiv Machine Learning
Jun 30
DreamForge-World 0.1 Preview: A Low-Compute Real-Time Controllable World Model
arXiv:2606. 30292v1 Announce Type: new Abstract: We present DreamForge-World 0.
arXiv AI
Jun 30
SwarmX: Agentic Scheduling for Low-Latency Agentic Systems
arXiv:2606. 21401v2 Announce Type: replace-cross Abstract: Agentic AI applications compose multiple model calls and tool executions, creating new scheduling challenges for GPU-CPU clusters.
arXiv AI
Jun 30
Process Advantage Signal Shaping: A Paradigm-Agnostic Middleware for Process-Supervised RL in LLM Reasoners
arXiv:2606. 29296v1 Announce Type: new Abstract: Group Relative Policy Optimization (GRPO) is a default recipe for process-supervised reinforcement learning of LLM reasoners, and dense process supervision -- via learned process reward models (PRMs) or on-policy-distillation KL signals -- is a common way to densify its otherwise weak outcome reward.
arXiv AI
Jun 30
Behavior Uncloning: Distilling Mode Redirection into Policy Weights without Inference-Time Steering
arXiv:2606. 29201v1 Announce Type: cross Abstract: Behavior-cloned policies often learn multiple behavior modes from demonstration datasets, including modes that are unsafe or otherwise undesired at deployment.
arXiv Machine Learning
Jun 30
SparsePixels: Efficient Convolution for Sparse Data on FPGAs
arXiv:2512. 06208v3 Announce Type: replace-cross Abstract: Inference of standard convolutional neural networks (CNNs) on FPGAs often incurs high latency and a long initiation interval due to the deep nested loops required to densely convolve every input pixel regardless of its feature value.
arXiv Machine Learning
Jun 30
HiAP: A Multi-Granular Stochastic Auto-Pruning Framework for Vision Transformers
arXiv:2603. 12222v2 Announce Type: replace-cross Abstract: Vision Transformers require significant computational resources and memory bandwidth, severely limiting their deployment on resource-constraint hardware.
arXiv AI
Jun 30
UCOB: Learning to Utilize and Evolve Agentic Skills via Credit-Aware On-Policy Bidirectional Self-Distillation
arXiv:2606. 29502v1 Announce Type: new Abstract: Skill memories can improve agentic reinforcement learning by reusing past experience as textual guidance, but retrieved skills are not oracular: they may help in one state while misleading the same policy in another.
arXiv Machine Learning
Jun 30
Depth Exploration for LLM Decoding
arXiv:2606. 29223v1 Announce Type: new Abstract: Autoregressive LLM decoding evaluates every generated token through the full layer stack, even though many tokens become predictable at intermediate depths.
arXiv AI
Jun 30
CRAFT: Counterfactual Credit Assignment from Free Sibling Rollouts for Self-Distilled Agentic Reinforcement Learning
arXiv:2606. 29476v1 Announce Type: cross Abstract: Self-distilled agentic reinforcement learning augments trajectory-level reward with a token-level distillation loss, using as its teacher the same policy conditioned on privileged context.
arXiv AI
Jun 30
Physics-Informed Distillation of Diffusion Models for PDE-Constrained Generation
arXiv:2505. 22391v2 Announce Type: replace-cross Abstract: Modeling physical systems in a generative manner offers several advantages, including the ability to handle partial observations, generate diverse solutions, and address both forward and inverse problems.
arXiv AI
Jun 30
Neuromorphic Energy-Aware Learning for Adaptive Deep Brain Stimulation
arXiv:2606. 28600v1 Announce Type: cross Abstract: Neuromorphic and edge computing research has focused on reducing the inference cost of neural network controllers, yet in physical closed-loop systems the actuator can rival or exceed an efficient controller in energy.
arXiv AI
Jun 30
Instance-Conditioned Adaptation for Large-scale Generalization of Neural Routing Solver
arXiv:2405. 01906v3 Announce Type: replace Abstract: In modern intelligent transportation systems (ITS), particularly in freight transportation and logistics, real-time route planning is crucial.
arXiv AI
Jun 30
Automating the Design of Embodied AgentArchitectures
arXiv:2606. 30111v1 Announce Type: cross Abstract: Embodied agents are typically built as hand-designed compositions of perception, memory, planning, and action modules.
arXiv AI
Jun 30
Neural Subspace Reallocation: Continual Learning as Retrieval-Based Subspace Memory Management
arXiv:2606. 30067v1 Announce Type: cross Abstract: We introduce Neural Subspace Reallocation (NSR), which reframes continual learning as memory management over parameter subspaces.
arXiv Machine Learning
Jun 30
Physical Analogue Kolmogorov-Arnold Networks based on Reconfigurable Nonlinear-Processing Units
arXiv:2602. 07518v3 Announce Type: replace-cross Abstract: Kolmogorov-Arnold Networks (KANs) shift neural computation from linear layers to learnable nonlinear edge functions, but implementing these nonlinearities efficiently in hardware remains an open challenge.
arXiv AI
Jun 30
The Speedup Paradox: Rethinking Inference Speed-Quality Trade-off in Embodied Tasks
arXiv:2606. 28529v1 Announce Type: cross Abstract: Embodied foundation models have recently been widely used to improve robot generalization and task success rates.
arXiv Machine Learning
Jun 30
TimeLAVA: Learning-Agnostic Valuation for Time Series Data
arXiv:2606. 18729v3 Announce Type: replace-cross Abstract: Data valuation quantifies the intrinsic quality of individual samples to enable principled data curation, quality control, and robust learning.
arXiv AI
Jun 30
Coverage-Driven KV Cache Eviction for Efficient and Improved Inference of LLM
arXiv:2606. 29563v1 Announce Type: cross Abstract: Large language models (LLMs) excel at complex tasks like question answering and summarization, thanks to their ability to handle long-context inputs.
arXiv AI
Jun 30
Learning to Adaptively Allocate Gaussians for Arbitrary-Scale Image Super-Resolution
arXiv:2606. 29400v1 Announce Type: cross Abstract: In computer graphics, visual content is continuously warped, zoomed and resampled.