The paper introduces improvements to Distributional Diffusion Models (DDMs) by deferring particle expansion to later transformer layers and using time-dependent scoring rule schedules, enabling practical training on ImageNet-256^2 with a single model. It achieves low FID scores with DiT-XL/2, showing stable performance as sampling budget increases and transferring to text-to-image generation.
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This approach enables efficient training of DDMs for image generation without requiring complex setups like teachers or self-distillation, making it practical for real-world applications.
The paper introduces VisionHOPE, a novel visual backbone that functions as a self-modifying learning system, allowing the model to co-evolve what it remembers and how it learns within an image. It uses five coupled memories and a stability-matched step-size control scheme to ensure stable learning dynamics, achieving competitive results on benchmark datasets like ImageNet-1K, COCO, and ADE20K.
3 days agoDaily PapersThe paper explores the linearity in Large Language Models (LLMs) by showing that combining inputs from different text streams leads to a superposition of next-token distributions. It suggests that this linearity is an inherent property of the Transformer architecture and can be restored through fine-tuning, allowing for generating two coherent continuations from one forward pass.
6 days agoDaily PapersThis paper introduces FuseReg, a method that replaces heuristic layer fusion in representation autoencoders (RAEs) with training over random subsets of encoder layers. The approach reduces the reconstruction-generation gap by improving robustness to layer fusion choices, achieving higher PSNR and lower generation FID scores without modifying the pretrained encoder.
5 days agoDaily PapersThe paper introduces Omni-IO Skills, a plug-and-play agent harness that enables existing agents to handle multiple modalities through hierarchical skills, standardized execution interfaces, and dependency-aware orchestration. It demonstrates significant improvements in input support and semantic quality scores when applied to GPT-5.6 Sol and Claude Sonnet 5 on the UniM-90 dataset.
5 days agoDaily PapersThe paper introduces GAGAR, a framework for quality-aware credit redistribution in code agent reinforcement learning (RL). It uses dynamic sampling and an SFT-trained agentic grader to rank test-passing trajectories, adjusting advantages to prioritize higher-quality implementations. The method was evaluated on large-scale industrial code agents with significant parameter counts.
4 days agoDaily PapersThis paper introduces SentZero, a sentence-centric vision-language pretraining framework designed for zero-shot multi-task analysis of chest X-rays. It enhances positive-pair diversity and mitigates false negatives through LLM-based sentence structuring and visual embedding modulation.
2 days ago