Orionnnn/hub-stats huggingface dataset:
Provable Benefits of In-Tool Learning for Large Language Models
Authors: name: Sam Houliston, name: Ambroise Odonnat, name: Charles Arnal, name: Vivien Cabannes
Published: 2025-08-28 13:12:19

Tool-augmented language models, equipped with retrieval, memory, or external
APIs, are reshaping AI, yet their theoretical advantages remain underexplored.
In this paper, we address this question by demonstrating the benefits of
in-tool learning (external retrieval) over in-weight learning (memorization)
for factual recall. We show that the number of facts a model can memorize
solely in its weights is fundamentally limited by its parameter count. In
contrast, we prove that tool-use enables unbounded factual recall via a simple
and efficient circuit construction. These results are validated in controlled
experiments, where tool-using models consistently outperform memorizing ones.
We further show that for pretrained large language models, teaching tool-use
and general rules is more effective than finetuning facts into memory. Our work
provides both a theoretical and empirical foundation, establishing why
tool-augmented workflows are not just practical, but provably more scalable.
Dress&Dance: Dress up and Dance as You Like It - Technical Preview
Authors: name: Jun-Kun Chen, name: Aayush Bansal, name: Minh Phuoc Vo, name: Yu-Xiong Wang
Published: 2025-08-28 17:59:55

We present Dress&Dance, a video diffusion framework that generates high
quality 5-second-long 24 FPS virtual try-on videos at 1152x720 resolution of a
user wearing desired garments while moving in accordance with a given reference
video. Our approach requires a single user image and supports a range of tops,
bottoms, and one-piece garments, as well as simultaneous tops and bottoms
try-on in a single pass. Key to our framework is CondNet, a novel conditioning
network that leverages attention to unify multi-modal inputs (text, images, and
videos), thereby enhancing garment registration and motion fidelity. CondNet is
trained on heterogeneous training data, combining limited video data and a
larger, more readily available image dataset, in a multistage progressive
manner. Dress&Dance outperforms existing open source and commercial solutions
and enables a high quality and flexible try-on experience.
FakeParts: a New Family of AI-Generated DeepFakes
Authors: name: Gaetan Brison, name: Soobash Daiboo, name: Samy Aimeur, name: Awais Hussain Sani, name: Xi Wang, name: Gianni Franchi, name: Vicky Kalogeiton
Published: 2025-08-28 17:55:14

We introduce FakeParts, a new class of deepfakes characterized by subtle,
localized manipulations to specific spatial regions or temporal segments of
otherwise authentic videos. Unlike fully synthetic content, these partial
manipulations, ranging from altered facial expressions to object substitutions
and background modifications, blend seamlessly with real elements, making them
particularly deceptive and difficult to detect. To address the critical gap in
detection capabilities, we present FakePartsBench, the first large-scale
benchmark dataset specifically designed to capture the full spectrum of partial
deepfakes. Comprising over 25K videos with pixel-level and frame-level
manipulation annotations, our dataset enables comprehensive evaluation of
detection methods. Our user studies demonstrate that FakeParts reduces human
detection accuracy by over 30% compared to traditional deepfakes, with similar
performance degradation observed in state-of-the-art detection models. This
work identifies an urgent vulnerability in current deepfake detection
approaches and provides the necessary resources to develop more robust methods
for partial video manipulations.
OnGoal: Tracking and Visualizing Conversational Goals in Multi-Turn
  Dialogue with Large Language Models
Authors: name: Adam Coscia, name: Shunan Guo, name: Eunyee Koh, name: Alex Endert
Published: 2025-08-28 17:58:29

As multi-turn dialogues with large language models (LLMs) grow longer and
more complex, how can users better evaluate and review progress on their
conversational goals? We present OnGoal, an LLM chat interface that helps users
better manage goal progress. OnGoal provides real-time feedback on goal
alignment through LLM-assisted evaluation, explanations for evaluation results
with examples, and overviews of goal progression over time, enabling users to
navigate complex dialogues more effectively. Through a study with 20
participants on a writing task, we evaluate OnGoal against a baseline chat
interface without goal tracking. Using OnGoal, participants spent less time and
effort to achieve their goals while exploring new prompting strategies to
overcome miscommunication, suggesting tracking and visualizing goals can
enhance engagement and resilience in LLM dialogues. Our findings inspired
design implications for future LLM chat interfaces that improve goal
communication, reduce cognitive load, enhance interactivity, and enable
feedback to improve LLM performance.
CogVLA: Cognition-Aligned Vision-Language-Action Model via
  Instruction-Driven Routing & Sparsification
Authors: name: Wei Li, name: Renshan Zhang, name: Rui Shao, name: Jie He, name: Liqiang Nie
Published: 2025-08-28 17:50:58

Recent Vision-Language-Action (VLA) models built on pre-trained
Vision-Language Models (VLMs) require extensive post-training, resulting in
high computational overhead that limits scalability and deployment.We propose
CogVLA, a Cognition-Aligned Vision-Language-Action framework that leverages
instruction-driven routing and sparsification to improve both efficiency and
performance. CogVLA draws inspiration from human multimodal coordination and
introduces a 3-stage progressive architecture. 1) Encoder-FiLM based
Aggregation Routing (EFA-Routing) injects instruction information into the
vision encoder to selectively aggregate and compress dual-stream visual tokens,
forming a instruction-aware latent representation. 2) Building upon this
compact visual encoding, LLM-FiLM based Pruning Routing (LFP-Routing)
introduces action intent into the language model by pruning
instruction-irrelevant visually grounded tokens, thereby achieving token-level
sparsity. 3) To ensure that compressed perception inputs can still support
accurate and coherent action generation, we introduce V-L-A Coupled Attention
(CAtten), which combines causal vision-language attention with bidirectional
action parallel decoding. Extensive experiments on the LIBERO benchmark and
real-world robotic tasks demonstrate that CogVLA achieves state-of-the-art
performance with success rates of 97.4% and 70.0%, respectively, while reducing
training costs by 2.5-fold and decreasing inference latency by 2.8-fold
compared to OpenVLA. CogVLA is open-sourced and publicly available at
https://github.com/JiuTian-VL/CogVLA.
AWorld: Orchestrating the Training Recipe for Agentic AI
Authors: name: Chengyue Yu, name: Siyuan Lu, name: Chenyi Zhuang, name: Dong Wang, name: Qintong Wu, name: Zongyue Li, name: Runsheng Gan, name: Chunfeng Wang, name: Siqi Hou, name: Gaochi Huang, name: Wenlong Yan, name: Lifeng Hong, name: Aohui Xue, name: Yanfeng Wang, name: Jinjie Gu, name: David Tsai, name: Tao Lin
Published: 2025-08-28 04:04:30

The learning from practice paradigm is crucial for developing capable Agentic
AI systems, yet it is severely hampered by inefficient experience generation, a
bottleneck especially pronounced in complex benchmarks like GAIA. To address
this, we introduce AWorld, an open-source system engineered for large-scale
agent-environment interaction. By distributing tasks across a cluster, AWorld
accelerates experience collection by 14.6x compared to standard single-node,
sequential execution. This critical speedup makes extensive reinforcement
learning practical and scalable. Leveraging this capability, we trained a
Qwen3-32B-based agent that significantly outperforms its base model, increasing
its overall GAIA accuracy from 21.59% to 32.23%. On the benchmark's most
challenging levels, our agent achieves a score of 16.33%, surpassing the
performance of leading proprietary models. Our open-source system and resulting
agent provide a practical blueprint for a complete agentic AI training
pipeline, from efficient interaction to demonstrable model improvement.
TCIA: A Task-Centric Instruction Augmentation Method for Instruction
  Finetuning
Authors: name: Simin Ma, name: Shujian Liu, name: Jun Tan, name: Yebowen Hu, name: Song Wang, name: Sathish Reddy Indurthi, name: Sanqiang Zhao, name: Liwei Wu, name: Jianbing Han, name: Kaiqiang Song
Published: 2025-08-28 02:42:10

Diverse instruction data is vital for effective instruction tuning of large
language models, as it enables the model to generalize across different types
of inputs . Building such diversified instruction dataset is an essential step
in this process. Existing approaches often leverage large language models to
automatically explore and generate diverse instructions, ensuring both data
diversity and quality. However, they tend to overlook an important factor in
real-world applications: on-task relevance. In practice, only a few real-world
applications require a truly general-purpose model; most benefit from
task-specific knowledge tailored to their particular use case. Therefore, it is
vital to develop instruction augmentation methods that not only maintain
diversity but are also optimized for specific, real-world scenarios.
  We thus introduce Task Centric Instruction Augmentation (TCIA), a framework
that systematically expands instructions while preserving both diversity and
task alignment. By representing instructions in a discrete query-constraints
space, TCIA creates a rich set of task-relevant instructions and enables models
to generalize to these task-specific instructions without sacrificing overall
performance. Experiments show that TCIA improves open-source LLMs' performance
by an average of 8.7% across four real-world, task-specific applications, and
in some cases outperforming leading closed-source models. These improvements do
not compromise general instruction-following ability, making TCIA a scalable
and efficient solution for adapting LLMs to real-world, task-focused
applications.
USO: Unified Style and Subject-Driven Generation via Disentangled and
  Reward Learning
Authors: name: Shaojin Wu, name: Mengqi Huang, name: Yufeng Cheng, name: Wenxu Wu, name: Jiahe Tian, name: Yiming Luo, name: Fei Ding, name: Qian He
Published: 2025-08-26 12:10:24

Existing literature typically treats style-driven and subject-driven
generation as two disjoint tasks: the former prioritizes stylistic similarity,
whereas the latter insists on subject consistency, resulting in an apparent
antagonism. We argue that both objectives can be unified under a single
framework because they ultimately concern the disentanglement and
re-composition of content and style, a long-standing theme in style-driven
research. To this end, we present USO, a Unified Style-Subject Optimized
customization model. First, we construct a large-scale triplet dataset
consisting of content images, style images, and their corresponding stylized
content images. Second, we introduce a disentangled learning scheme that
simultaneously aligns style features and disentangles content from style
through two complementary objectives, style-alignment training and
content-style disentanglement training. Third, we incorporate a style
reward-learning paradigm denoted as SRL to further enhance the model's
performance. Finally, we release USO-Bench, the first benchmark that jointly
evaluates style similarity and subject fidelity across multiple metrics.
Extensive experiments demonstrate that USO achieves state-of-the-art
performance among open-source models along both dimensions of subject
consistency and style similarity. Code and model:
https://github.com/bytedance/USO
Collaborative Multi-Modal Coding for High-Quality 3D Generation
Authors: name: Ziang Cao, name: Zhaoxi Chen, name: Liang Pan, name: Ziwei Liu
Published: 2025-08-21 04:31:14

3D content inherently encompasses multi-modal characteristics and can be
projected into different modalities (e.g., RGB images, RGBD, and point clouds).
Each modality exhibits distinct advantages in 3D asset modeling: RGB images
contain vivid 3D textures, whereas point clouds define fine-grained 3D
geometries. However, most existing 3D-native generative architectures either
operate predominantly within single-modality paradigms-thus overlooking the
complementary benefits of multi-modality data-or restrict themselves to 3D
structures, thereby limiting the scope of available training datasets. To
holistically harness multi-modalities for 3D modeling, we present TriMM, the
first feed-forward 3D-native generative model that learns from basic
multi-modalities (e.g., RGB, RGBD, and point cloud). Specifically, 1) TriMM
first introduces collaborative multi-modal coding, which integrates
modality-specific features while preserving their unique representational
strengths. 2) Furthermore, auxiliary 2D and 3D supervision are introduced to
raise the robustness and performance of multi-modal coding. 3) Based on the
embedded multi-modal code, TriMM employs a triplane latent diffusion model to
generate 3D assets of superior quality, enhancing both the texture and the
geometric detail. Extensive experiments on multiple well-known datasets
demonstrate that TriMM, by effectively leveraging multi-modality, achieves
competitive performance with models trained on large-scale datasets, despite
utilizing a small amount of training data. Furthermore, we conduct additional
experiments on recent RGB-D datasets, verifying the feasibility of
incorporating other multi-modal datasets into 3D generation.
ROSE: Remove Objects with Side Effects in Videos
Authors: name: Chenxuan Miao, name: Yutong Feng, name: Jianshu Zeng, name: Zixiang Gao, name: Hantang Liu, name: Yunfeng Yan, name: Donglian Qi, name: Xi Chen, name: Bin Wang, name: Hengshuang Zhao
Published: 2025-08-26 03:18:31

Video object removal has achieved advanced performance due to the recent
success of video generative models. However, when addressing the side effects
of objects, e.g., their shadows and reflections, existing works struggle to
eliminate these effects for the scarcity of paired video data as supervision.
This paper presents ROSE, termed Remove Objects with Side Effects, a framework
that systematically studies the object's effects on environment, which can be
categorized into five common cases: shadows, reflections, light, translucency
and mirror. Given the challenges of curating paired videos exhibiting the
aforementioned effects, we leverage a 3D rendering engine for synthetic data
generation. We carefully construct a fully-automatic pipeline for data
preparation, which simulates a large-scale paired dataset with diverse scenes,
objects, shooting angles, and camera trajectories. ROSE is implemented as an
video inpainting model built on diffusion transformer. To localize all
object-correlated areas, the entire video is fed into the model for
reference-based erasing. Moreover, additional supervision is introduced to
explicitly predict the areas affected by side effects, which can be revealed
through the differential mask between the paired videos. To fully investigate
the model performance on various side effect removal, we presents a new
benchmark, dubbed ROSE-Bench, incorporating both common scenarios and the five
special side effects for comprehensive evaluation. Experimental results
demonstrate that ROSE achieves superior performance compared to existing video
object erasing models and generalizes well to real-world video scenarios. The
project page is https://rose2025-inpaint.github.io/.
Pref-GRPO: Pairwise Preference Reward-based GRPO for Stable
  Text-to-Image Reinforcement Learning
Authors: name: Yibin Wang, name: Zhimin Li, name: Yuhang Zang, name: Yujie Zhou, name: Jiazi Bu, name: Chunyu Wang, name: Qinglin Lu, name: Cheng Jin, name: Jiaqi Wang
Published: 2025-08-28 13:11:24

Recent advancements highlight the importance of GRPO-based reinforcement
learning methods and benchmarking in enhancing text-to-image (T2I) generation.
However, current methods using pointwise reward models (RM) for scoring
generated images are susceptible to reward hacking. We reveal that this happens
when minimal score differences between images are amplified after
normalization, creating illusory advantages that drive the model to
over-optimize for trivial gains, ultimately destabilizing the image generation
process. To address this, we propose Pref-GRPO, a pairwise preference
reward-based GRPO method that shifts the optimization objective from score
maximization to preference fitting, ensuring more stable training. In
Pref-GRPO, images are pairwise compared within each group using preference RM,
and the win rate is used as the reward signal. Extensive experiments
demonstrate that PREF-GRPO differentiates subtle image quality differences,
providing more stable advantages and mitigating reward hacking. Additionally,
existing T2I benchmarks are limited by coarse evaluation criteria, hindering
comprehensive model assessment. To solve this, we introduce UniGenBench, a
unified T2I benchmark comprising 600 prompts across 5 main themes and 20
subthemes. It evaluates semantic consistency through 10 primary and 27
sub-criteria, leveraging MLLM for benchmark construction and evaluation. Our
benchmarks uncover the strengths and weaknesses of both open and closed-source
T2I models and validate the effectiveness of Pref-GRPO.
Mixture of Contexts for Long Video Generation
Authors: name: Shengqu Cai, name: Ceyuan Yang, name: Lvmin Zhang, name: Yuwei Guo, name: Junfei Xiao, name: Ziyan Yang, name: Yinghao Xu, name: Zhenheng Yang, name: Alan Yuille, name: Leonidas Guibas, name: Maneesh Agrawala, name: Lu Jiang, name: Gordon Wetzstein
Published: 2025-08-28 17:57:55

Long video generation is fundamentally a long context memory problem: models
must retain and retrieve salient events across a long range without collapsing
or drifting. However, scaling diffusion transformers to generate long-context
videos is fundamentally limited by the quadratic cost of self-attention, which
makes memory and computation intractable and difficult to optimize for long
sequences. We recast long-context video generation as an internal information
retrieval task and propose a simple, learnable sparse attention routing module,
Mixture of Contexts (MoC), as an effective long-term memory retrieval engine.
In MoC, each query dynamically selects a few informative chunks plus mandatory
anchors (caption, local windows) to attend to, with causal routing that
prevents loop closures. As we scale the data and gradually sparsify the
routing, the model allocates compute to salient history, preserving identities,
actions, and scenes over minutes of content. Efficiency follows as a byproduct
of retrieval (near-linear scaling), which enables practical training and
synthesis, and the emergence of memory and consistency at the scale of minutes.
rStar2-Agent: Agentic Reasoning Technical Report
Authors: name: Ning Shang, name: Yifei Liu, name: Yi Zhu, name: Li Lyna Zhang, name: Weijiang Xu, name: Xinyu Guan, name: Buze Zhang, name: Bingcheng Dong, name: Xudong Zhou, name: Bowen Zhang, name: Ying Xin, name: Ziming Miao, name: Scarlett Li, name: Fan Yang, name: Mao Yang
Published: 2025-08-28 12:45:25

We introduce rStar2-Agent, a 14B math reasoning model trained with agentic
reinforcement learning to achieve frontier-level performance. Beyond current
long CoT, the model demonstrates advanced cognitive behaviors, such as thinking
carefully before using Python coding tools and reflecting on code execution
feedback to autonomously explore, verify, and refine intermediate steps in
complex problem-solving. This capability is enabled through three key
innovations that makes agentic RL effective at scale: (i) an efficient RL
infrastructure with a reliable Python code environment that supports
high-throughput execution and mitigates the high rollout costs, enabling
training on limited GPU resources (64 MI300X GPUs); (ii) GRPO-RoC, an agentic
RL algorithm with a Resample-on-Correct rollout strategy that addresses the
inherent environment noises from coding tools, allowing the model to reason
more effectively in a code environment; (iii) An efficient agent training
recipe that starts with non-reasoning SFT and progresses through multi-RL
stages, yielding advanced cognitive abilities with minimal compute cost. To
this end, rStar2-Agent boosts a pre-trained 14B model to state of the art in
only 510 RL steps within one week, achieving average pass@1 scores of 80.6% on
AIME24 and 69.8% on AIME25, surpassing DeepSeek-R1 (671B) with significantly
shorter responses. Beyond mathematics, rStar2-Agent-14B also demonstrates
strong generalization to alignment, scientific reasoning, and agentic tool-use
tasks. Code and training recipes are available at
https://github.com/microsoft/rStar.
MCP-Bench: Benchmarking Tool-Using LLM Agents with Complex Real-World
  Tasks via MCP Servers
Authors: name: Zhenting Wang, name: Qi Chang, name: Hemani Patel, name: Shashank Biju, name: Cheng-En Wu, name: Quan Liu, name: Aolin Ding, name: Alireza Rezazadeh, name: Ankit Shah, name: Yujia Bao, name: Eugene Siow
Published: 2025-08-28 05:58:57

We introduce MCP-Bench, a benchmark for evaluating large language models
(LLMs) on realistic, multi-step tasks that demand tool use, cross-tool
coordination, precise parameter control, and planning/reasoning for solving
tasks. Built on the Model Context Protocol (MCP), MCP-Bench connects LLMs to 28
representative live MCP servers spanning 250 tools across domains such as
finance, traveling, scientific computing, and academic search. Unlike prior
API-based benchmarks, each MCP server provides a set of complementary tools
designed to work together, enabling the construction of authentic, multi-step
tasks with rich input-output coupling. Tasks in MCP-Bench test agents' ability
to retrieve relevant tools from fuzzy instructions without explicit tool names,
plan multi-hop execution trajectories for complex objectives, ground responses
in intermediate tool outputs, and orchestrate cross-domain workflows -
capabilities not adequately evaluated by existing benchmarks that rely on
explicit tool specifications, shallow few-step workflows, and isolated domain
operations. We propose a multi-faceted evaluation framework covering tool-level
schema understanding and usage, trajectory-level planning, and task completion.
Experiments on 20 advanced LLMs reveal persistent challenges in MCP-Bench. Code
and data: https://github.com/Accenture/mcp-bench.
Training a Foundation Model for Materials on a Budget
Authors: name: Teddy Koker, name: Tess Smidt
Published: 2025-08-22 03:38:06

Foundation models for materials modeling are advancing quickly, but their
training remains expensive, often placing state-of-the-art methods out of reach
for many research groups. We introduce Nequix, a compact E(3)-equivariant
potential that pairs a simplified NequIP design with modern training practices,
including equivariant root-mean-square layer normalization and the Muon
optimizer, to retain accuracy while substantially reducing compute
requirements. Built in JAX, Nequix has 700K parameters and was trained in 500
A100-GPU hours. On the Matbench-Discovery and MDR Phonon benchmarks, Nequix
ranks third overall while requiring less than one quarter of the training cost
of most other methods, and it delivers an order-of-magnitude faster inference
speed than the current top-ranked model. We release model weights and fully
reproducible codebase at https://github.com/atomicarchitects/nequix