Could robots finally achieve rapid, reliable real-world performance? GigaAI and the GigaWorld Team have broken new ground! Their GigaWorld-Policy model revolutionizes how robots learn. It trains by understanding future visual dynamics, but unlike older methods, it skips slow
@jiqizhixin
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GarmentPile++: Teaching Robots Chaotic Clothing Manipulation
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How do we teach robots to handle a chaotic pile of clothes?
— 机器之心 JIQIZHIXIN (@jiqizhixin) 10 avril 2026
Peking University, The University of Hong Kong, and Northwestern University present GarmentPile++.
This system intelligently combines vision-language AI with robotic dexterity. It identifies individual garments in a… pic.twitter.com/Nv3EpSKssKHow do we teach robots to handle a chaotic pile of clothes? Peking University, The University of Hong Kong, and Northwestern University present GarmentPile++. This system intelligently combines vision-language AI with robotic dexterity. It identifies individual garments in a
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DMax: Aggressive Parallel Decoding for Distributed Language Models
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DMax: Aggressive Parallel Decoding for dLLMs Paper: https://
huggingface.co/papers/2604.08
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Code: https://
github.com/czg1225/DMax
Models: https://
huggingface.co/collections/Zi
geng/dmax-models
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Datasets: https://
huggingface.co/collections/Zi
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DMax: Aggressive Parallel Decoding for Diffusion Language Models
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Wow, really fast!
— 机器之心 JIQIZHIXIN (@jiqizhixin) 10 avril 2026
Researchers at the National University of Singapore present DMax!
This new paradigm for diffusion language models (dLLMs) enables aggressive parallel decoding.
It cleverly mitigates error accumulation by reforming decoding as a progressive self-refinement… pic.twitter.com/T7ZS5jO4srWow, really fast! Researchers at the National University of Singapore present DMax! This new paradigm for diffusion language models (dLLMs) enables aggressive parallel decoding. It cleverly mitigates error accumulation by reforming decoding as a progressive self-refinement
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UC San Diego Unveils AIBuildAI Agent for Autonomous Development
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Is the era of AI building AI finally here?
— 机器之心 JIQIZHIXIN (@jiqizhixin) 10 avril 2026
University of California, San Diego researchers just unveiled a breakthrough.
Their new AIBuildAI agent autonomously manages the entire AI development workflow.
It uses a team of specialized sub-agents to design, code, tune, and… pic.twitter.com/ah61v2GDRrIs the era of AI building AI finally here? University of California, San Diego researchers just unveiled a breakthrough. Their new AIBuildAI agent autonomously manages the entire AI development workflow. It uses a team of specialized sub-agents to design, code, tune, and
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CoBRA: Controlling Cognitive Bias in AI Agents
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What if we could precisely control cognitive bias in AI agents? New research from UC San Diego and an independent researcher unveils CoBRA. This novel toolkit uses classic social science experiments as "gym" environments to measure and precisely adjust an AI agent's cognitive
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ReFTA: Tensor Algebra Accelerates AI Fine-Tuning Speed
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Is slow weight reconstruction bottlenecking your AI fine-tuning? A joint team from Peking University, The University of British Columbia, and Pazhou Laboratory introduce ReFTA, a groundbreaking method that leverages tensor algebraic properties to completely eliminate the slow,
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RAGEN-2: Reasoning Collapse in Agentic Reinforcement Learning
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RAGEN-2: Reasoning Collapse in Agentic RL
— 机器之心 JIQIZHIXIN (@jiqizhixin) 9 avril 2026
Paper: https://t.co/w4mCiZzCOp
Project: https://t.co/LFS5PpioMF
Code: https://t.co/f5bO13kGrn pic.twitter.com/dMzR3eZMwcRAGEN-2: Reasoning Collapse in Agentic RL Paper: huggingface.co/papers/2604.0… Project: ragen-ai.github.io/v2/ Code: github.com/mll-lab-nu/RAGEN
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RAGEN-2 Framework Tackles Template Collapse in LLM Agents
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Are your LLM agents truly reasoning, or just stuck repeating the same patterns?
— 机器之心 JIQIZHIXIN (@jiqizhixin) 9 avril 2026
Zihan Wang @wzenus and a stellar team from Northwestern, Stanford, Microsoft, Oxford, and Imperial College London have uncovered "template collapse", a hidden flaw where LLM agents appear diverse… pic.twitter.com/0XRXFuc7rxAre your LLM agents truly reasoning, or just stuck repeating the same patterns? Zihan Wang @wzenus and a stellar team from Northwestern, Stanford, Microsoft, Oxford, and Imperial College London have uncovered "template collapse", a hidden flaw where LLM agents appear diverse but fail to adapt to new inputs. Their RAGEN-2 framework introduces Mutual Information to accurately measure true "cross-input distinguishability" and proposes SNR-Aware Filtering to select high-signal training prompts. This new metric and method vastly outperform current approaches, boosting LLM agent performance and input dependence across critical tasks like planning, math reasoning, web navigation, and code execution! And this paper is also #1 Paper of the day on Hugging Face!
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Revela: Efficient AI Retrievers Without Expensive Annotated Datasets
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What if we could train powerful AI retrievers to find specialized information without needing huge, expensive datasets?
— 机器之心 JIQIZHIXIN (@jiqizhixin) 9 avril 2026
A team from TU Darmstadt, University of Washington, CMU, Microsoft, and Tencent AI Lab presents Revela!
Revela leverages self-supervised language modeling. It… pic.twitter.com/bC4emjEYdVWhat if we could train powerful AI retrievers to find specialized information without needing huge, expensive datasets? A team from TU Darmstadt, University of Washington, CMU, Microsoft, and Tencent AI Lab presents Revela! Revela leverages self-supervised language modeling. It teaches retrievers to understand semantic relationships between document segments by predicting "next chunks" of info, integrating retriever similarity scores directly into this learning process. Without any annotated query-document pairs, Revela surpasses larger supervised models and proprietary APIs in code and reasoning-intensive domains. It achieves unsupervised SoTA on general benchmarks with ~1000x less data and 10x less compute! Revela: Dense Retriever Learning via Language Modeling Paper: openreview.net/forum?id=e7pA… Code: github.com/TRUMANCFY/Revela Model: huggingface.co/trumancai/Rev… Our report: mp.weixin.qq.com/s/9TmVSNHMQ… 📬 #PapersAccepted by Jiqizhixin