Orionnnn/hub-stats huggingface dataset:
STAR-Bench: Probing Deep Spatio-Temporal Reasoning as Audio 4D
  Intelligence
Authors: name: Zihan Liu, name: Zhikang Niu, name: Qiuyang Xiao, name: Zhisheng Zheng, name: Ruoqi Yuan, name: Yuhang Zang, name: Yuhang Cao, name: Xiaoyi Dong, name: Jianze Liang, name: Xie Chen, name: Leilei Sun, name: Dahua Lin, name: Jiaqi Wang
Published: 2025-10-28 17:50:34

Despite rapid progress in Multi-modal Large Language Models and Large
Audio-Language Models, existing audio benchmarks largely test semantics that
can be recovered from text captions, masking deficits in fine-grained
perceptual reasoning. We formalize audio 4D intelligence that is defined as
reasoning over sound dynamics in time and 3D space, and introduce STAR-Bench to
measure it. STAR-Bench combines a Foundational Acoustic Perception setting (six
attributes under absolute and relative regimes) with a Holistic Spatio-Temporal
Reasoning setting that includes segment reordering for continuous and discrete
processes and spatial tasks spanning static localization, multi-source
relations, and dynamic trajectories. Our data curation pipeline uses two
methods to ensure high-quality samples. For foundational tasks, we use
procedurally synthesized and physics-simulated audio. For holistic data, we
follow a four-stage process that includes human annotation and final selection
based on human performance. Unlike prior benchmarks where caption-only
answering reduces accuracy slightly, STAR-Bench induces far larger drops
(-31.5\% temporal, -35.2\% spatial), evidencing its focus on linguistically
hard-to-describe cues. Evaluating 19 models reveals substantial gaps compared
with humans and a capability hierarchy: closed-source models are bottlenecked
by fine-grained perception, while open-source models lag across perception,
knowledge, and reasoning. Our STAR-Bench provides critical insights and a clear
path forward for developing future models with a more robust understanding of
the physical world.
ATLAS: Adaptive Transfer Scaling Laws for Multilingual Pretraining,
  Finetuning, and Decoding the Curse of Multilinguality
Authors: name: Shayne Longpre, name: Sneha Kudugunta, name: Niklas Muennighoff, name: I-Hung Hsu, name: Isaac Caswell, name: Alex Pentland, name: Sercan Arik, name: Chen-Yu Lee, name: Sayna Ebrahimi
Published: 2025-10-24 21:45:22

Scaling laws research has focused overwhelmingly on English -- yet the most
prominent AI models explicitly serve billions of international users. In this
work, we undertake the largest multilingual scaling laws study to date,
totaling 774 multilingual training experiments, spanning 10M-8B model
parameters, 400+ training languages and 48 evaluation languages. We introduce
the Adaptive Transfer Scaling Law (ATLAS) for both monolingual and multilingual
pretraining, which outperforms existing scaling laws' out-of-sample
generalization often by more than 0.3 R^2. Our analyses of the experiments shed
light on multilingual learning dynamics, transfer properties between languages,
and the curse of multilinguality. First, we derive a cross-lingual transfer
matrix, empirically measuring mutual benefit scores between 38 x 38=1444
language pairs. Second, we derive a language-agnostic scaling law that reveals
how to optimally scale model size and data when adding languages without
sacrificing performance. Third, we identify the computational crossover points
for when to pretrain from scratch versus finetune from multilingual
checkpoints. We hope these findings provide the scientific foundation for
democratizing scaling laws across languages, and enable practitioners to
efficiently scale models -- beyond English-first AI.
Group Relative Attention Guidance for Image Editing
Authors: name: Xuanpu Zhang, name: Xuesong Niu, name: Ruidong Chen, name: Dan Song, name: Jianhao Zeng, name: Penghui Du, name: Haoxiang Cao, name: Kai Wu, name: An-an Liu
Published: 2025-10-28 17:22:44

Recently, image editing based on Diffusion-in-Transformer models has
undergone rapid development. However, existing editing methods often lack
effective control over the degree of editing, limiting their ability to achieve
more customized results. To address this limitation, we investigate the
MM-Attention mechanism within the DiT model and observe that the Query and Key
tokens share a bias vector that is only layer-dependent. We interpret this bias
as representing the model's inherent editing behavior, while the delta between
each token and its corresponding bias encodes the content-specific editing
signals. Based on this insight, we propose Group Relative Attention Guidance, a
simple yet effective method that reweights the delta values of different tokens
to modulate the focus of the model on the input image relative to the editing
instruction, enabling continuous and fine-grained control over editing
intensity without any tuning. Extensive experiments conducted on existing image
editing frameworks demonstrate that GRAG can be integrated with as few as four
lines of code, consistently enhancing editing quality. Moreover, compared to
the commonly used Classifier-Free Guidance, GRAG achieves smoother and more
precise control over the degree of editing. Our code will be released at
https://github.com/little-misfit/GRAG-Image-Editing.
InteractComp: Evaluating Search Agents With Ambiguous Queries
Authors: name: Mingyi Deng, name: Lijun Huang, name: Yani Fan, name: Jiayi Zhang, name: Fashen Ren, name: Jinyi Bai, name: Fuzhen Yang, name: Dayi Miao, name: Zhaoyang Yu, name: Yifan Wu, name: Yanfei Zhang, name: Fengwei Teng, name: Yingjia Wan, name: Song Hu, name: Yude Li, name: Xin Jin, name: Conghao Hu, name: Haoyu Li, name: Qirui Fu, name: Tai Zhong, name: Xinyu Wang, name: Xiangru Tang, name: Nan Tang, name: Chenglin Wu, name: Yuyu Luo
Published: 2025-10-28 17:35:54

Language agents have demonstrated remarkable potential in web search and
information retrieval. However, these search agents assume user queries are
complete and unambiguous, an assumption that diverges from reality where users
begin with incomplete queries requiring clarification through interaction. Yet
most agents lack interactive mechanisms during the search process, and existing
benchmarks cannot assess this capability. To address this gap, we introduce
InteractComp, a benchmark designed to evaluate whether search agents can
recognize query ambiguity and actively interact to resolve it during search.
Following the principle of easy to verify, interact to disambiguate, we
construct 210 expert-curated questions across 9 domains through a
target-distractor methodology that creates genuine ambiguity resolvable only
through interaction. Evaluation of 17 models reveals striking failure: the best
model achieves only 13.73% accuracy despite 71.50% with complete context,
exposing systematic overconfidence rather than reasoning deficits. Forced
interaction produces dramatic gains, demonstrating latent capability current
strategies fail to engage. Longitudinal analysis shows interaction capabilities
stagnated over 15 months while search performance improved seven-fold,
revealing a critical blind spot. This stagnation, coupled with the immediate
feedback inherent to search tasks, makes InteractComp a valuable resource for
both evaluating and training interaction capabilities in search agents. The
code is available at https://github.com/FoundationAgents/InteractComp.
VisCoder2: Building Multi-Language Visualization Coding Agents
Authors: name: Yuansheng Ni, name: Songcheng Cai, name: Xiangchao Chen, name: Jiarong Liang, name: Zhiheng Lyu, name: Jiaqi Deng, name: Kai Zou, name: Ping Nie, name: Fei Yuan, name: Xiang Yue, name: Wenhu Chen
Published: 2025-10-24 18:03:57

Large language models (LLMs) have recently enabled coding agents capable of
generating, executing, and revising visualization code. However, existing
models often fail in practical workflows due to limited language coverage,
unreliable execution, and lack of iterative correction mechanisms. Progress has
been constrained by narrow datasets and benchmarks that emphasize single-round
generation and single-language tasks. To address these challenges, we introduce
three complementary resources for advancing visualization coding agents.
VisCode-Multi-679K is a large-scale, supervised dataset containing 679K
validated and executable visualization samples with multi-turn correction
dialogues across 12 programming languages. VisPlotBench is a benchmark for
systematic evaluation, featuring executable tasks, rendered outputs, and
protocols for both initial generation and multi-round self-debug. Finally, we
present VisCoder2, a family of multi-language visualization models trained on
VisCode-Multi-679K. Experiments show that VisCoder2 significantly outperforms
strong open-source baselines and approaches the performance of proprietary
models like GPT-4.1, with further gains from iterative self-debug, reaching
82.4% overall execution pass rate at the 32B scale, particularly in symbolic or
compiler-dependent languages.
Routing Matters in MoE: Scaling Diffusion Transformers with Explicit
  Routing Guidance
Authors: name: Yujie Wei, name: Shiwei Zhang, name: Hangjie Yuan, name: Yujin Han, name: Zhekai Chen, name: Jiayu Wang, name: Difan Zou, name: Xihui Liu, name: Yingya Zhang, name: Yu Liu, name: Hongming Shan
Published: 2025-10-28 17:59:02

Mixture-of-Experts (MoE) has emerged as a powerful paradigm for scaling model
capacity while preserving computational efficiency. Despite its notable success
in large language models (LLMs), existing attempts to apply MoE to Diffusion
Transformers (DiTs) have yielded limited gains. We attribute this gap to
fundamental differences between language and visual tokens. Language tokens are
semantically dense with pronounced inter-token variation, while visual tokens
exhibit spatial redundancy and functional heterogeneity, hindering expert
specialization in vision MoE. To this end, we present ProMoE, an MoE framework
featuring a two-step router with explicit routing guidance that promotes expert
specialization. Specifically, this guidance encourages the router to partition
image tokens into conditional and unconditional sets via conditional routing
according to their functional roles, and refine the assignments of conditional
image tokens through prototypical routing with learnable prototypes based on
semantic content. Moreover, the similarity-based expert allocation in latent
space enabled by prototypical routing offers a natural mechanism for
incorporating explicit semantic guidance, and we validate that such guidance is
crucial for vision MoE. Building on this, we propose a routing contrastive loss
that explicitly enhances the prototypical routing process, promoting
intra-expert coherence and inter-expert diversity. Extensive experiments on
ImageNet benchmark demonstrate that ProMoE surpasses state-of-the-art methods
under both Rectified Flow and DDPM training objectives. Code and models will be
made publicly available.
Game-TARS: Pretrained Foundation Models for Scalable Generalist
  Multimodal Game Agents
Authors: name: Zihao Wang, name: Xujing Li, name: Yining Ye, name: Junjie Fang, name: Haoming Wang, name: Longxiang Liu, name: Shihao Liang, name: Junting Lu, name: Zhiyong Wu, name: Jiazhan Feng, name: Wanjun Zhong, name: Zili Li, name: Yu Wang, name: Yu Miao, name: Bo Zhou, name: Yuanfan Li, name: Hao Wang, name: Zhongkai Zhao, name: Faming Wu, name: Zhengxuan Jiang, name: Weihao Tan, name: Heyuan Yao, name: Shi Yan, name: Xiangyang Li, name: Yitao Liang, name: Yujia Qin, name: Guang Shi
Published: 2025-10-27 17:43:51

We present Game-TARS, a generalist game agent trained with a unified,
scalable action space anchored to human-aligned native keyboard-mouse inputs.
Unlike API- or GUI-based approaches, this paradigm enables large-scale
continual pre-training across heterogeneous domains, including OS, web, and
simulation games. Game-TARS is pre-trained on over 500B tokens with diverse
trajectories and multimodal data. Key techniques include a decaying continual
loss to reduce causal confusion and an efficient Sparse-Thinking strategy that
balances reasoning depth and inference cost. Experiments show that Game-TARS
achieves about 2 times the success rate over the previous sota model on
open-world Minecraft tasks, is close to the generality of fresh humans in
unseen web 3d games, and outperforms GPT-5, Gemini-2.5-Pro, and Claude-4-Sonnet
in FPS benchmarks. Scaling results on training-time and test-time confirm that
the unified action space sustains improvements when scaled to cross-game and
multimodal data. Our results demonstrate that simple, scalable action
representations combined with large-scale pre-training provide a promising path
toward generalist agents with broad computer-use abilities.
FunReason-MT Technical Report: Overcoming the Complexity Barrier in
  Multi-Turn Function Calling
Authors: name: Zengzhuang Xu, name: Bingguang Hao, name: Zechuan Wang, name: Yuntao Wen, name: Maolin Wang, name: Yang Liu, name: Long Chen, name: Dong Wang, name: Yicheng Chen, name: Cunyin Peng, name: Chenyi Zhuang, name: Jinjie Gu, name: Leilei Gan, name: Xiangyu Zhao, name: Shi Gu
Published: 2025-10-28 17:15:26

Function calling (FC) empowers large language models (LLMs) and autonomous
agents to interface with external tools, a critical capability for solving
complex, real-world problems. As this ability becomes increasingly central to
advanced AI systems, the need for high-quality, multi-turn training data to
develop and refine it cannot be overstated. Existing data synthesis methods,
such as random environment sampling or multi-agent role-playing, are not
powerful enough to generate high-quality data in real-world environments.
Practical challenges come in three folds: targeted model training, isolation of
tool architecture, and multi-turn logical dependency. To address these
structural deficiencies, we present FunReason-MT, a novel data synthesis
framework for real-world multi-turn tool use. FunReason-MT resolves the
complexity barrier in multi-turn FC data by employing 1) Environment-API Graph
Interactions to gather varied high-quality trajectories, 2) Advanced Tool-Query
Synthesis to simplify hard query construction, and 3) Guided Iterative Chain
for sophisticated CoT generation. Evaluations on Berkeley Function-Calling
Leaderboard (BFCLv3) demonstrate the power of our framework: a 4B model built
upon FunReason-MT generated data achieves state-of-the-art performance among
comparable-sized models, outperforming most close-source models. Further
performance improvements on BFCLv4 confirm that FunReason-MT provides a
reliable and robust source for agentic learning.
Repurposing Synthetic Data for Fine-grained Search Agent Supervision
Authors: name: Yida Zhao, name: Kuan Li, name: Xixi Wu, name: Liwen Zhang, name: Dingchu Zhang, name: Baixuan Li, name: Maojia Song, name: Zhuo Chen, name: Chenxi Wang, name: Xinyu Wang, name: Kewei Tu, name: Pengjun Xie, name: Jingren Zhou, name: Yong Jiang
Published: 2025-10-28 17:50:40

LLM-based search agents are increasingly trained on entity-centric synthetic
data to solve complex, knowledge-intensive tasks. However, prevailing training
methods like Group Relative Policy Optimization (GRPO) discard this rich entity
information, relying instead on sparse, outcome-based rewards. This critical
limitation renders them unable to distinguish informative "near-miss"
samples-those with substantially correct reasoning but a flawed final
answer-from complete failures, thus discarding valuable learning signals. We
address this by leveraging the very entities discarded during training. Our
empirical analysis reveals a strong positive correlation between the number of
ground-truth entities identified during an agent's reasoning process and final
answer accuracy. Building on this insight, we introduce Entity-aware Group
Relative Policy Optimization (E-GRPO), a novel framework that formulates a
dense entity-aware reward function. E-GRPO assigns partial rewards to incorrect
samples proportional to their entity match rate, enabling the model to
effectively learn from these "near-misses". Experiments on diverse
question-answering (QA) and deep research benchmarks show that E-GRPO
consistently and significantly outperforms the GRPO baseline. Furthermore, our
analysis reveals that E-GRPO not only achieves superior accuracy but also
induces more efficient reasoning policies that require fewer tool calls,
demonstrating a more effective and sample-efficient approach to aligning search
agents.
AgentFrontier: Expanding the Capability Frontier of LLM Agents with
  ZPD-Guided Data Synthesis
Authors: name: Xuanzhong Chen, name: Zile Qiao, name: Guoxin Chen, name: Liangcai Su, name: Zhen Zhang, name: Xinyu Wang, name: Pengjun Xie, name: Fei Huang, name: Jingren Zhou, name: Yong Jiang
Published: 2025-10-28 17:50:47

Training large language model agents on tasks at the frontier of their
capabilities is key to unlocking advanced reasoning. We introduce a data
synthesis approach inspired by the educational theory of the Zone of Proximal
Development (ZPD), which defines this frontier as tasks an LLM cannot solve
alone but can master with guidance. To operationalize this, we present the
AgentFrontier Engine, an automated pipeline that synthesizes high-quality,
multidisciplinary data situated precisely within the LLM's ZPD. This engine
supports both continued pre-training with knowledge-intensive data and targeted
post-training on complex reasoning tasks. From the same framework, we derive
the ZPD Exam, a dynamic and automated benchmark designed to evaluate agent
capabilities on these frontier tasks. We train AgentFrontier-30B-A3B model on
our synthesized data, which achieves state-of-the-art results on demanding
benchmarks like Humanity's Last Exam, even surpassing some leading proprietary
agents. Our work demonstrates that a ZPD-guided approach to data synthesis
offers a scalable and effective path toward building more capable LLM agents.
WebLeaper: Empowering Efficiency and Efficacy in WebAgent via Enabling
  Info-Rich Seeking
Authors: name: Zhengwei Tao, name: Haiyang Shen, name: Baixuan Li, name: Wenbiao Yin, name: Jialong Wu, name: Kuan Li, name: Zhongwang Zhang, name: Huifeng Yin, name: Rui Ye, name: Liwen Zhang, name: Xinyu Wang, name: Pengjun Xie, name: Jingren Zhou, name: Yong Jiang
Published: 2025-10-28 17:51:42

Large Language Model (LLM)-based agents have emerged as a transformative
approach for open-ended problem solving, with information seeking (IS) being a
core capability that enables autonomous reasoning and decision-making. While
prior research has largely focused on improving retrieval depth, we observe
that current IS agents often suffer from low search efficiency, which in turn
constrains overall performance. A key factor underlying this inefficiency is
the sparsity of target entities in training tasks, which limits opportunities
for agents to learn and generalize efficient search behaviors. To address these
challenges, we propose WebLeaper, a framework for constructing high-coverage IS
tasks and generating efficient solution trajectories. We formulate IS as a
tree-structured reasoning problem, enabling a substantially larger set of
target entities to be embedded within a constrained context. Leveraging curated
Wikipedia tables, we propose three variants for synthesizing IS tasks, Basic,
Union, and Reverse-Union, to systematically increase both IS efficiency and
efficacy. Finally, we curate training trajectories by retaining only those that
are simultaneously accurate and efficient, ensuring that the model is optimized
for both correctness and search performance. Extensive experiments on both
basic and comprehensive settings, conducted on five IS benchmarks, BrowserComp,
GAIA, xbench-DeepSearch, WideSearch, and Seal-0, demonstrate that our method
consistently achieves improvements in both effectiveness and efficiency over
strong baselines.
RoboOmni: Proactive Robot Manipulation in Omni-modal Context
Authors: name: Siyin Wang, name: Jinlan Fu, name: Feihong Liu, name: Xinzhe He, name: Huangxuan Wu, name: Junhao Shi, name: Kexin Huang, name: Zhaoye Fei, name: Jingjing Gong, name: Zuxuan Wu, name: Yugang Jiang, name: See-Kiong Ng, name: Tat-Seng Chua, name: Xipeng Qiu
Published: 2025-10-27 18:49:03

Recent advances in Multimodal Large Language Models (MLLMs) have driven rapid
progress in Vision-Language-Action (VLA) models for robotic manipulation.
Although effective in many scenarios, current approaches largely rely on
explicit instructions, whereas in real-world interactions, humans rarely issue
instructions directly. Effective collaboration requires robots to infer user
intentions proactively. In this work, we introduce cross-modal contextual
instructions, a new setting where intent is derived from spoken dialogue,
environmental sounds, and visual cues rather than explicit commands. To address
this new setting, we present RoboOmni, a Perceiver-Thinker-Talker-Executor
framework based on end-to-end omni-modal LLMs that unifies intention
recognition, interaction confirmation, and action execution. RoboOmni fuses
auditory and visual signals spatiotemporally for robust intention recognition,
while supporting direct speech interaction. To address the absence of training
data for proactive intention recognition in robotic manipulation, we build
OmniAction, comprising 140k episodes, 5k+ speakers, 2.4k event sounds, 640
backgrounds, and six contextual instruction types. Experiments in simulation
and real-world settings show that RoboOmni surpasses text- and ASR-based
baselines in success rate, inference speed, intention recognition, and
proactive assistance.
Latent Sketchpad: Sketching Visual Thoughts to Elicit Multimodal
  Reasoning in MLLMs
Authors: name: Huanyu Zhang, name: Wenshan Wu, name: Chengzu Li, name: Ning Shang, name: Yan Xia, name: Yangyu Huang, name: Yifan Zhang, name: Li Dong, name: Zhang Zhang, name: Liang Wang, name: Tieniu Tan, name: Furu Wei
Published: 2025-10-28 15:26:20

While Multimodal Large Language Models (MLLMs) excel at visual understanding,
they often struggle in complex scenarios that require visual planning and
imagination. Inspired by how humans use sketching as a form of visual thinking
to develop and communicate ideas, we introduce Latent Sketchpad, a framework
that equips MLLMs with an internal visual scratchpad. The internal visual
representations of MLLMs have traditionally been confined to perceptual
understanding. We repurpose them to support generative visual thought without
compromising reasoning ability. Building on frontier MLLMs, our approach
integrates visual generation directly into their native autoregressive
reasoning process. It allows the model to interleave textual reasoning with the
generation of visual latents. These latents guide the internal thought process
and can be translated into sketch images for interpretability. To realize this,
we introduce two components: a Context-Aware Vision Head autoregressively
produces visual representations, and a pretrained Sketch Decoder renders these
into human-interpretable images. We evaluate the framework on our new dataset
MazePlanning. Experiments across various MLLMs show that Latent Sketchpad
delivers comparable or even superior reasoning performance to their backbone.
It further generalizes across distinct frontier MLLMs, including Gemma3 and
Qwen2.5-VL. By extending model's textual reasoning to visual thinking, our
framework opens new opportunities for richer human-computer interaction and
broader applications. More details and resources are available on our project
page: https://latent-sketchpad.github.io/.