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Heterogeneous Decentralized Diffusion Models
Announce Type: replace Abstract: Training frontier-scale diffusion models often requires substantial computational resources concentrated in tightly-coupled clusters, limiting participation to well-resourced institutions. While Decentralized Diffusion Models (DDM) enable training multiple experts in isolation, existing approaches require 1176 GPU-days and homogeneous training objectives across all experts. We present an efficient framework that dramatically reduces resource requirements...
Adapting Diffusion Language Models for Lossless Pixel-Level Image Transmission
arXiv:2606.06273v1 Announce Type: new Abstract: Lossless pixel-level image transmission is a fundamental regime beyond semantic communications, because exact recovery requires both accurate symbol probability modeling and reliable delivery over noisy channels. This paper proposes DDM-SSCC, a discrete-diffusion-model-based separate source-channel coding framework for lossless image transmission. Different from raster-order autoregressive coding, the proposed source codec adapts a diffusion...
CAPE: Control Algorithm Performance Evaluation under Learned Vehicle Dynamics Models
arXiv:2606.05480v1 Announce Type: new Abstract: We propose the Control Algorithm Performance Evaluation (CAPE) framework, a systematic methodology for benchmarking racing controllers under our proposed learned enhanced physics model (EPM). The proposed framework enables cross-controller comparison by evaluating five closed-loop control architectures. We further compare our proposed EPM with two state-of-the-art learned vehicle dynamics models:
UniD$^3$: A Knowledge Graph-Enhanced RAG Framework for Drug-Disease Discovery and Reasoning
Announce Type: new Abstract: Systematic characterization of drug-disease relationships is essential for drug discovery and repurposing, yet is hindered by the heterogeneity and rapid growth of biomedical literature. Existing datasets rely on labor-intensive curation and are often incomplete, while LLM-only approaches suffer from hallucination and weak evidence grounding. We introduce UniD$^3$, a unified framework that integrates Large Language Models with Knowledge Graph-enhanced...