Orionnnn/hub-stats huggingface dataset:
PAN: A World Model for General, Interactable, and Long-Horizon World Simulation
Authors: name: PAN Team, name: Jiannan Xiang, name: Yi Gu, name: Zihan Liu, name: Zeyu Feng, name: Qiyue Gao, name: Yiyan Hu, name: Benhao Huang, name: Guangyi Liu, name: Yichi Yang, name: Kun Zhou, name: Davit Abrahamyan, name: Arif Ahmad, name: Ganesh Bannur, name: Junrong Chen, name: Kimi Chen, name: Mingkai Deng, name: Ruobing Han, name: Xinqi Huang, name: Haoqiang Kang, name: Zheqi Li, name: Enze Ma, name: Hector Ren, name: Yashowardhan Shinde, name: Rohan Shingre, name: Ramsundar Tanikella, name: Kaiming Tao, name: Dequan Yang, name: Xinle Yu, name: Cong Zeng, name: Binglin Zhou, name: Zhengzhong Liu, name: Zhiting Hu, name: Eric P. Xing
Published: 2025-11-12 07:20:35

A world model enables an intelligent agent to imagine, predict, and reason about how the world evolves in response to its actions, and accordingly to plan and strategize. While recent video generation models produce realistic visual sequences, they typically operate in the prompt-to-full-video manner without causal control, interactivity, or long-horizon consistency required for purposeful reasoning. Existing world modeling efforts, on the other hand, often focus on restricted domains (e.g., physical, game, or 3D-scene dynamics) with limited depth and controllability, and struggle to generalize across diverse environments and interaction formats. In this work, we introduce PAN, a general, interactable, and long-horizon world model that predicts future world states through high-quality video simulation conditioned on history and natural language actions. PAN employs the Generative Latent Prediction (GLP) architecture that combines an autoregressive latent dynamics backbone based on a large language model (LLM), which grounds simulation in extensive text-based knowledge and enables conditioning on language-specified actions, with a video diffusion decoder that reconstructs perceptually detailed and temporally coherent visual observations, to achieve a unification between latent space reasoning (imagination) and realizable world dynamics (reality). Trained on large-scale video-action pairs spanning diverse domains, PAN supports open-domain, action-conditioned simulation with coherent, long-term dynamics. Extensive experiments show that PAN achieves strong performance in action-conditioned world simulation, long-horizon forecasting, and simulative reasoning compared to other video generators and world models, taking a step towards general world models that enable predictive simulation of future world states for reasoning and acting.
One Small Step in Latent, One Giant Leap for Pixels: Fast Latent Upscale Adapter for Your Diffusion Models
Authors: name: Aleksandr Razin, name: Danil Kazantsev, name: Ilya Makarov
Published: 2025-11-13 18:54:18

Diffusion models struggle to scale beyond their training resolutions, as direct high-resolution sampling is slow and costly, while post-hoc image super-resolution (ISR) introduces artifacts and additional latency by operating after decoding. We present the Latent Upscaler Adapter (LUA), a lightweight module that performs super-resolution directly on the generator's latent code before the final VAE decoding step. LUA integrates as a drop-in component, requiring no modifications to the base model or additional diffusion stages, and enables high-resolution synthesis through a single feed-forward pass in latent space. A shared Swin-style backbone with scale-specific pixel-shuffle heads supports 2x and 4x factors and remains compatible with image-space SR baselines, achieving comparable perceptual quality with nearly 3x lower decoding and upscaling time (adding only +0.42 s for 1024 px generation from 512 px, compared to 1.87 s for pixel-space SR using the same SwinIR architecture). Furthermore, LUA shows strong generalization across the latent spaces of different VAEs, making it easy to deploy without retraining from scratch for each new decoder. Extensive experiments demonstrate that LUA closely matches the fidelity of native high-resolution generation while offering a practical and efficient path to scalable, high-fidelity image synthesis in modern diffusion pipelines.
AffordBot: 3D Fine-grained Embodied Reasoning via Multimodal Large Language Models
Authors: name: Xinyi Wang, name: Xun Yang, name: Yanlong Xu, name: Yuchen Wu, name: Zhen Li, name: Na Zhao
Published: 2025-11-13 06:43:00

Effective human-agent collaboration in physical environments requires understanding not only what to act upon, but also where the actionable elements are and how to interact with them. Existing approaches often operate at the object level or disjointedly handle fine-grained affordance reasoning, lacking coherent, instruction-driven grounding and reasoning. In this work, we introduce a new task: Fine-grained 3D Embodied Reasoning, which requires an agent to predict, for each referenced affordance element in a 3D scene, a structured triplet comprising its spatial location, motion type, and motion axis, based on a task instruction. To solve this task, we propose AffordBot, a novel framework that integrates Multimodal Large Language Models (MLLMs) with a tailored chain-of-thought (CoT) reasoning paradigm. To bridge the gap between 3D input and 2D-compatible MLLMs, we render surround-view images of the scene and project 3D element candidates into these views, forming a rich visual representation aligned with the scene geometry. Our CoT pipeline begins with an active perception stage, prompting the MLLM to select the most informative viewpoint based on the instruction, before proceeding with step-by-step reasoning to localize affordance elements and infer plausible interaction motions. Evaluated on the SceneFun3D dataset, AffordBot achieves state-of-the-art performance, demonstrating strong generalization and physically grounded reasoning with only 3D point cloud input and MLLMs.
ResearchRubrics: A Benchmark of Prompts and Rubrics For Evaluating Deep Research Agents
Authors: name: Manasi Sharma, name: Chen Bo Calvin Zhang, name: Chaithanya Bandi, name: Clinton Wang, name: Ankit Aich, name: Huy Nghiem, name: Tahseen Rabbani, name: Ye Htet, name: Brian Jang, name: Sumana Basu, name: Aishwarya Balwani, name: Denis Peskoff, name: Marcos Ayestaran, name: Sean M. Hendryx, name: Brad Kenstler, name: Bing Liu
Published: 2025-11-10 23:07:14

Deep Research (DR) is an emerging agent application that leverages large language models (LLMs) to address open-ended queries. It requires the integration of several capabilities, including multi-step reasoning, cross-document synthesis, and the generation of evidence-backed, long-form answers. Evaluating DR remains challenging because responses are lengthy and diverse, admit many valid solutions, and often depend on dynamic information sources. We introduce ResearchRubrics, a standardized benchmark for DR built with over 2,800+ hours of human labor that pairs realistic, domain-diverse prompts with 2,500+ expert-written, fine-grained rubrics to assess factual grounding, reasoning soundness, and clarity. We also propose a new complexity framework for categorizing DR tasks along three axes: conceptual breadth, logical nesting, and exploration. In addition, we develop human and model-based evaluation protocols that measure rubric adherence for DR agents. We evaluate several state-of-the-art DR systems and find that even leading agents like Gemini's DR and OpenAI's DR achieve under 68% average compliance with our rubrics, primarily due to missed implicit context and inadequate reasoning about retrieved information. Our results highlight the need for robust, scalable assessment of deep research capabilities, to which end we release ResearchRubrics(including all prompts, rubrics, and evaluation code) to facilitate progress toward well-justified research assistants.
SliderEdit: Continuous Image Editing with Fine-Grained Instruction Control
Authors: name: Arman Zarei, name: Samyadeep Basu, name: Mobina Pournemat, name: Sayan Nag, name: Ryan Rossi, name: Soheil Feizi
Published: 2025-11-12 20:21:37

Instruction-based image editing models have recently achieved impressive performance, enabling complex edits to an input image from a multi-instruction prompt. However, these models apply each instruction in the prompt with a fixed strength, limiting the user's ability to precisely and continuously control the intensity of individual edits. We introduce SliderEdit, a framework for continuous image editing with fine-grained, interpretable instruction control. Given a multi-part edit instruction, SliderEdit disentangles the individual instructions and exposes each as a globally trained slider, allowing smooth adjustment of its strength. Unlike prior works that introduced slider-based attribute controls in text-to-image generation, typically requiring separate training or fine-tuning for each attribute or concept, our method learns a single set of low-rank adaptation matrices that generalize across diverse edits, attributes, and compositional instructions. This enables continuous interpolation along individual edit dimensions while preserving both spatial locality and global semantic consistency. We apply SliderEdit to state-of-the-art image editing models, including FLUX-Kontext and Qwen-Image-Edit, and observe substantial improvements in edit controllability, visual consistency, and user steerability. To the best of our knowledge, we are the first to explore and propose a framework for continuous, fine-grained instruction control in instruction-based image editing models. Our results pave the way for interactive, instruction-driven image manipulation with continuous and compositional control.
Benchmarking Diversity in Image Generation via Attribute-Conditional Human Evaluation
Authors: name: Isabela Albuquerque, name: Ira Ktena, name: Olivia Wiles, name: Ivana Kajić, name: Amal Rannen-Triki, name: Cristina Vasconcelos, name: Aida Nematzadeh
Published: 2025-11-13 17:48:38

Despite advances in generation quality, current text-to-image (T2I) models often lack diversity, generating homogeneous outputs. This work introduces a framework to address the need for robust diversity evaluation in T2I models. Our framework systematically assesses diversity by evaluating individual concepts and their relevant factors of variation. Key contributions include: (1) a novel human evaluation template for nuanced diversity assessment; (2) a curated prompt set covering diverse concepts with their identified factors of variation (e.g. prompt: An image of an apple, factor of variation: color); and (3) a methodology for comparing models in terms of human annotations via binomial tests.
  Furthermore, we rigorously compare various image embeddings for diversity measurement. Notably, our principled approach enables ranking of T2I models by diversity, identifying categories where they particularly struggle. This research offers a robust methodology and insights, paving the way for improvements in T2I model diversity and metric development.
Rubric-Based Benchmarking and Reinforcement Learning for Advancing LLM Instruction Following
Authors: name: Yun He, name: Wenzhe Li, name: Hejia Zhang, name: Songlin Li, name: Karishma Mandyam, name: Sopan Khosla, name: Yuanhao Xiong, name: Nanshu Wang, name: Selina Peng, name: Beibin Li, name: Shengjie Bi, name: Shishir G. Patil, name: Qi Qi, name: Shengyu Feng, name: Julian Katz-Samuels, name: Richard Yuanzhe Pang, name: Sujan Gonugondla, name: Hunter Lang, name: Yue Yu, name: Yundi Qian, name: Maryam Fazel-Zarandi, name: Licheng Yu, name: Amine Benhalloum, name: Hany Awadalla, name: Manaal Faruqui
Published: 2025-11-13 17:14:01

Recent progress in large language models (LLMs) has led to impressive performance on a range of tasks, yet advanced instruction following (IF)-especially for complex, multi-turn, and system-prompted instructions-remains a significant challenge. Rigorous evaluation and effective training for such capabilities are hindered by the lack of high-quality, human-annotated benchmarks and reliable, interpretable reward signals. In this work, we introduce AdvancedIF (we will release this benchmark soon), a comprehensive benchmark featuring over 1,600 prompts and expert-curated rubrics that assess LLMs ability to follow complex, multi-turn, and system-level instructions. We further propose RIFL (Rubric-based Instruction-Following Learning), a novel post-training pipeline that leverages rubric generation, a finetuned rubric verifier, and reward shaping to enable effective reinforcement learning for instruction following. Extensive experiments demonstrate that RIFL substantially improves the instruction-following abilities of LLMs, achieving a 6.7% absolute gain on AdvancedIF and strong results on public benchmarks. Our ablation studies confirm the effectiveness of each component in RIFL. This work establishes rubrics as a powerful tool for both training and evaluating advanced IF in LLMs, paving the way for more capable and reliable AI systems.
Superpositional Gradient Descent: Harnessing Quantum Principles for
  Model Training
Authors: name: Ahmet Erdem Pamuk, name: Emir Kaan Özdemir, name: Şuayp Talha Kocabay
Published: 2025-11-01 16:37:55

Large language models (LLMs) are increasingly trained with classical
optimization techniques like AdamW to improve convergence and generalization.
However, the mechanisms by which quantum-inspired methods enhance classical
training remain underexplored. We introduce Superpositional Gradient Descent
(SGD), a novel optimizer linking gradient updates with quantum superposition by
injecting quantum circuit perturbations. We present a mathematical framework
and implement hybrid quantum-classical circuits in PyTorch and Qiskit. On
synthetic sequence classification and large-scale LLM fine-tuning, SGD
converges faster and yields lower final loss than AdamW. Despite promising
results, scalability and hardware constraints limit adoption. Overall, this
work provides new insights into the intersection of quantum computing and deep
learning, suggesting practical pathways for leveraging quantum principles to
control and enhance model behavior.
Music Flamingo: Scaling Music Understanding in Audio Language Models
Authors: name: Sreyan Ghosh, name: Arushi Goel, name: Lasha Koroshinadze, name: Sang-gil Lee, name: Zhifeng Kong, name: Joao Felipe Santos, name: Ramani Duraiswami, name: Dinesh Manocha, name: Wei Ping, name: Mohammad Shoeybi, name: Bryan Catanzaro
Published: 2025-11-13 13:21:09

We introduce Music Flamingo, a novel large audio-language model designed to advance music (including song) understanding in foundational audio models. While audio-language research has progressed rapidly, music remains challenging due to its dynamic, layered, and information-dense nature. Progress has been further limited by the difficulty of scaling open audio understanding models, primarily because of the scarcity of high-quality music data and annotations. As a result, prior models are restricted to producing short, high-level captions, answering only surface-level questions, and showing limited generalization across diverse musical cultures. To address these challenges, we curate MF-Skills, a large-scale dataset labeled through a multi-stage pipeline that yields rich captions and question-answer pairs covering harmony, structure, timbre, lyrics, and cultural context. We fine-tune an enhanced Audio Flamingo 3 backbone on MF-Skills and further strengthen multiple skills relevant to music understanding. To improve the model's reasoning abilities, we introduce a post-training recipe: we first cold-start with MF-Think, a novel chain-of-thought dataset grounded in music theory, followed by GRPO-based reinforcement learning with custom rewards. Music Flamingo achieves state-of-the-art results across 10+ benchmarks for music understanding and reasoning, establishing itself as a generalist and musically intelligent audio-language model. Beyond strong empirical results, Music Flamingo sets a new standard for advanced music understanding by demonstrating how models can move from surface-level recognition toward layered, human-like perception of songs. We believe this work provides both a benchmark and a foundation for the community to build the next generation of models that engage with music as meaningfully as humans do.
AlphaResearch: Accelerating New Algorithm Discovery with Language Models
Authors: name: Zhaojian Yu, name: Kaiyue Feng, name: Yilun Zhao, name: Shilin He, name: Xiao-Ping Zhang, name: Arman Cohan
Published: 2025-11-11 18:03:22

Large language models have made significant progress in complex but easy-to-verify problems, yet they still struggle with discovering the unknown. In this paper, we present AlphaResearch, an autonomous research agent designed to discover new algorithms on open-ended problems. To synergize the feasibility and innovation of the discovery process, we construct a novel dual research environment by combining the execution-based verify and simulated real-world peer review environment. AlphaResearch discovers new algorithm by iteratively running the following steps: (1) propose new ideas (2) verify the ideas in the dual research environment (3) optimize the research proposals for better performance. To promote a transparent evaluation process, we construct AlphaResearchComp, a new evaluation benchmark that includes an eight open-ended algorithmic problems competition, with each problem carefully curated and verified through executable pipelines, objective metrics, and reproducibility checks. AlphaResearch gets a 2/8 win rate in head-to-head comparison with human researchers, demonstrate the possibility of accelerating algorithm discovery with LLMs. Notably, the algorithm discovered by AlphaResearch on the ``packing circles'' problem achieves the best-of-known performance, surpassing the results of human researchers and strong baselines from recent work (e.g., AlphaEvolve). Additionally, we conduct a comprehensive analysis of the remaining challenges of the 6/8 failure cases, providing valuable insights for future research.
Black-Box On-Policy Distillation of Large Language Models
Authors: name: Tianzhu Ye, name: Li Dong, name: Zewen Chi, name: Xun Wu, name: Shaohan Huang, name: Furu Wei
Published: 2025-11-13 18:58:37

Black-box distillation creates student large language models (LLMs) by learning from a proprietary teacher model's text outputs alone, without access to its internal logits or parameters. In this work, we introduce Generative Adversarial Distillation (GAD), which enables on-policy and black-box distillation. GAD frames the student LLM as a generator and trains a discriminator to distinguish its responses from the teacher LLM's, creating a minimax game. The discriminator acts as an on-policy reward model that co-evolves with the student, providing stable, adaptive feedback. Experimental results show that GAD consistently surpasses the commonly used sequence-level knowledge distillation. In particular, Qwen2.5-14B-Instruct (student) trained with GAD becomes comparable to its teacher, GPT-5-Chat, on the LMSYS-Chat automatic evaluation. The results establish GAD as a promising and effective paradigm for black-box LLM distillation.
MuSc-V2: Zero-Shot Multimodal Industrial Anomaly Classification and Segmentation with Mutual Scoring of Unlabeled Samples
Authors: name: Xurui Li, name: Feng Xue, name: Yu Zhou
Published: 2025-11-13 07:47:37

Zero-shot anomaly classification (AC) and segmentation (AS) methods aim to identify and outline defects without using any labeled samples. In this paper, we reveal a key property that is overlooked by existing methods: normal image patches across industrial products typically find many other similar patches, not only in 2D appearance but also in 3D shapes, while anomalies remain diverse and isolated. To explicitly leverage this discriminative property, we propose a Mutual Scoring framework (MuSc-V2) for zero-shot AC/AS, which flexibly supports single 2D/3D or multimodality. Specifically, our method begins by improving 3D representation through Iterative Point Grouping (IPG), which reduces false positives from discontinuous surfaces. Then we use Similarity Neighborhood Aggregation with Multi-Degrees (SNAMD) to fuse 2D/3D neighborhood cues into more discriminative multi-scale patch features for mutual scoring. The core comprises a Mutual Scoring Mechanism (MSM) that lets samples within each modality to assign score to each other, and Cross-modal Anomaly Enhancement (CAE) that fuses 2D and 3D scores to recover modality-specific missing anomalies. Finally, Re-scoring with Constrained Neighborhood (RsCon) suppresses false classification based on similarity to more representative samples. Our framework flexibly works on both the full dataset and smaller subsets with consistently robust performance, ensuring seamless adaptability across diverse product lines. In aid of the novel framework, MuSc-V2 achieves significant performance improvements: a +23.7% AP gain on the MVTec 3D-AD dataset and a +19.3% boost on the Eyecandies dataset, surpassing previous zero-shot benchmarks and even outperforming most few-shot methods. The code will be available at The code will be available at https://github.com/HUST-SLOW/MuSc-V2{https://github.com/HUST-SLOW/MuSc-V2}.
UniVA: Universal Video Agent towards Open-Source Next-Generation Video Generalist
Authors: name: Zhengyang Liang, name: Daoan Zhang, name: Huichi Zhou, name: Rui Huang, name: Bobo Li, name: Yuechen Zhang, name: Shengqiong Wu, name: Xiaohan Wang, name: Jiebo Luo, name: Lizi Liao, name: Hao Fei
Published: 2025-11-11 17:58:13

While specialized AI models excel at isolated video tasks like generation or understanding, real-world applications demand complex, iterative workflows that combine these capabilities. To bridge this gap, we introduce UniVA, an open-source, omni-capable multi-agent framework for next-generation video generalists that unifies video understanding, segmentation, editing, and generation into cohesive workflows. UniVA employs a Plan-and-Act dual-agent architecture that drives a highly automated and proactive workflow: a planner agent interprets user intentions and decomposes them into structured video-processing steps, while executor agents execute these through modular, MCP-based tool servers (for analysis, generation, editing, tracking, etc.). Through a hierarchical multi-level memory (global knowledge, task context, and user-specific preferences), UniVA sustains long-horizon reasoning, contextual continuity, and inter-agent communication, enabling interactive and self-reflective video creation with full traceability. This design enables iterative and any-conditioned video workflows (e.g., text/image/video-conditioned generation rightarrow multi-round editing rightarrow object segmentation rightarrow compositional synthesis) that were previously cumbersome to achieve with single-purpose models or monolithic video-language models. We also introduce UniVA-Bench, a benchmark suite of multi-step video tasks spanning understanding, editing, segmentation, and generation, to rigorously evaluate such agentic video systems. Both UniVA and UniVA-Bench are fully open-sourced, aiming to catalyze research on interactive, agentic, and general-purpose video intelligence for the next generation of multimodal AI systems. (https://univa.online/)