Orionnnn/hub-stats huggingface dataset:
MetaEmbed: Scaling Multimodal Retrieval at Test-Time with Flexible Late
  Interaction
Authors: name: Zilin Xiao, name: Qi Ma, name: Mengting Gu, name: Chun-cheng Jason Chen, name: Xintao Chen, name: Vicente Ordonez, name: Vijai Mohan
Published: 2025-09-22 17:59:42

Universal multimodal embedding models have achieved great success in
capturing semantic relevance between queries and candidates. However, current
methods either condense queries and candidates into a single vector,
potentially limiting the expressiveness for fine-grained information, or
produce too many vectors that are prohibitively expensive for multi-vector
retrieval. In this work, we introduce MetaEmbed, a new framework for multimodal
retrieval that rethinks how multimodal embeddings are constructed and
interacted with at scale. During training, a fixed number of learnable Meta
Tokens are appended to the input sequence. At test-time, their last-layer
contextualized representations serve as compact yet expressive multi-vector
embeddings. Through the proposed Matryoshka Multi-Vector Retrieval training,
MetaEmbed learns to organize information by granularity across multiple
vectors. As a result, we enable test-time scaling in multimodal retrieval,
where users can balance retrieval quality against efficiency demands by
selecting the number of tokens used for indexing and retrieval interactions.
Extensive evaluations on the Massive Multimodal Embedding Benchmark (MMEB) and
the Visual Document Retrieval Benchmark (ViDoRe) confirm that MetaEmbed
achieves state-of-the-art retrieval performance while scaling robustly to
models with 32B parameters.
When Big Models Train Small Ones: Label-Free Model Parity Alignment for
  Efficient Visual Question Answering using Small VLMs
Authors: name: Abhirama Subramanyam Penamakuri, name: Navlika Singh, name: Piyush Arora, name: Anand Mishra
Published: 2025-09-20 11:12:23

Large Vision-Language Models (L-VLMs) have demonstrated remarkable
performance in various vision and language tasks, including visual question
answering (VQA). However, their high computational cost makes them impractical
for resource-constrained settings and inference-heavy applications. In
contrast, Small Vision-Language Models (S-VLMs) offer efficiency but suffer
from a significant performance gap compared to their larger counterparts. In
this work, we introduce the Model Parity Aligner (MPA), a novel framework
designed to systematically improve S-VLMs by leveraging unlabeled images and
effective knowledge transfer from L-VLMs. Instead of traditional knowledge
distillation methods that rely on labeled training data, MPA employs a
strategic parity-based approach that precisely identifies the knowledge
disparities between S-VLMs and L-VLMs, and optimizes training by targeting only
these disparities. We conduct extensive experiments on four diverse VQA
benchmarks, namely TextVQA, ST-VQA, ChartQA, and OKVQA, each of which requires
specialized reasoning capabilities such as text recognition, chart
interpretation, and commonsense and factual understanding. Our results
demonstrate that MPA consistently enhances the performance of S-VLMs on all
benchmarks, reducing the performance gap while maintaining computational
efficiency. We make our code publicly available.
AuditoryBench++: Can Language Models Understand Auditory Knowledge
  without Hearing?
Authors: name: Hyunjong Ok, name: Suho Yoo, name: Hyeonjun Kim, name: Jaeho Lee
Published: 2025-09-22 11:45:22

Even without directly hearing sounds, humans can effortlessly reason about
auditory properties, such as pitch, loudness, or sound-source associations,
drawing on auditory commonsense. In contrast, language models often lack this
capability, limiting their effectiveness in multimodal interactions. As an
initial step to address this gap, we present AuditoryBench++, a comprehensive
benchmark for evaluating auditory knowledge and reasoning in text-only
settings. The benchmark encompasses tasks that range from basic auditory
comparisons to contextually grounded reasoning, enabling fine-grained analysis
of how models process and integrate auditory concepts. In addition, we
introduce AIR-CoT, a novel auditory imagination reasoning method that generates
and integrates auditory information during inference through span detection
with special tokens and knowledge injection. Extensive experiments with recent
LLMs and Multimodal LLMs demonstrate that AIR-CoT generally outperforms both
the off-the-shelf models and those augmented with auditory knowledge. The
project page is available at https://auditorybenchpp.github.io.
StereoAdapter: Adapting Stereo Depth Estimation to Underwater Scenes
Authors: name: Zhengri Wu, name: Yiran Wang, name: Yu Wen, name: Zeyu Zhang, name: Biao Wu, name: Hao Tang
Published: 2025-09-19 20:57:03

Underwater stereo depth estimation provides accurate 3D geometry for robotics
tasks such as navigation, inspection, and mapping, offering metric depth from
low-cost passive cameras while avoiding the scale ambiguity of monocular
methods. However, existing approaches face two critical challenges: (i)
parameter-efficiently adapting large vision foundation encoders to the
underwater domain without extensive labeled data, and (ii) tightly fusing
globally coherent but scale-ambiguous monocular priors with locally metric yet
photometrically fragile stereo correspondences. To address these challenges, we
propose StereoAdapter, a parameter-efficient self-supervised framework that
integrates a LoRA-adapted monocular foundation encoder with a recurrent stereo
refinement module. We further introduce dynamic LoRA adaptation for efficient
rank selection and pre-training on the synthetic UW-StereoDepth-40K dataset to
enhance robustness under diverse underwater conditions. Comprehensive
evaluations on both simulated and real-world benchmarks show improvements of
6.11% on TartanAir and 5.12% on SQUID compared to state-of-the-art methods,
while real-world deployment with the BlueROV2 robot further demonstrates the
consistent robustness of our approach. Code:
https://github.com/AIGeeksGroup/StereoAdapter. Website:
https://aigeeksgroup.github.io/StereoAdapter.
LIMI: Less is More for Agency
Authors: name: Yang Xiao, name: Mohan Jiang, name: Jie Sun, name: Keyu Li, name: Jifan Lin, name: Yumin Zhuang, name: Ji Zeng, name: Shijie Xia, name: Qishuo Hua, name: Xuefeng Li, name: Xiaojie Cai, name: Tongyu Wang, name: Yue Zhang, name: Liming Liu, name: Xia Wu, name: Jinlong Hou, name: Yuan Cheng, name: Wenjie Li, name: Xiang Wang, name: Dequan Wang, name: Pengfei Liu
Published: 2025-09-22 10:59:32

We define Agency as the emergent capacity of AI systems to function as
autonomous agents actively discovering problems, formulating hypotheses, and
executing solutions through self-directed engagement with environments and
tools. This fundamental capability marks the dawn of the Age of AI Agency,
driven by a critical industry shift: the urgent need for AI systems that don't
just think, but work. While current AI excels at reasoning and generating
responses, industries demand autonomous agents that can execute tasks, operate
tools, and drive real-world outcomes. As agentic intelligence becomes the
defining characteristic separating cognitive systems from productive workers,
efficiently cultivating machine autonomy becomes paramount. Current approaches
assume that more data yields better agency, following traditional scaling laws
from language modeling. We fundamentally challenge this paradigm. LIMI (Less Is
More for Intelligent Agency) demonstrates that agency follows radically
different development principles. Through strategic focus on collaborative
software development and scientific research workflows, we show that
sophisticated agentic intelligence can emerge from minimal but strategically
curated demonstrations of autonomous behavior. Using only 78 carefully designed
training samples, LIMI achieves 73.5% on comprehensive agency benchmarks,
dramatically outperforming state-of-the-art models: Kimi-K2-Instruct (24.1%),
DeepSeek-V3.1 (11.9%), Qwen3-235B-A22B-Instruct (27.5%), and GLM-4.5 (45.1%).
Most strikingly, LIMI demonstrates 53.7% improvement over models trained on
10,000 samples-achieving superior agentic intelligence with 128 times fewer
samples. Our findings establish the Agency Efficiency Principle: machine
autonomy emerges not from data abundance but from strategic curation of
high-quality agentic demonstrations.
VideoFrom3D: 3D Scene Video Generation via Complementary Image and Video
  Diffusion Models
Authors: name: Geonung Kim, name: Janghyeok Han, name: Sunghyun Cho
Published: 2025-09-22 16:28:47

In this paper, we propose VideoFrom3D, a novel framework for synthesizing
high-quality 3D scene videos from coarse geometry, a camera trajectory, and a
reference image. Our approach streamlines the 3D graphic design workflow,
enabling flexible design exploration and rapid production of deliverables. A
straightforward approach to synthesizing a video from coarse geometry might
condition a video diffusion model on geometric structure. However, existing
video diffusion models struggle to generate high-fidelity results for complex
scenes due to the difficulty of jointly modeling visual quality, motion, and
temporal consistency. To address this, we propose a generative framework that
leverages the complementary strengths of image and video diffusion models.
Specifically, our framework consists of a Sparse Anchor-view Generation (SAG)
and a Geometry-guided Generative Inbetweening (GGI) module. The SAG module
generates high-quality, cross-view consistent anchor views using an image
diffusion model, aided by Sparse Appearance-guided Sampling. Building on these
anchor views, GGI module faithfully interpolates intermediate frames using a
video diffusion model, enhanced by flow-based camera control and structural
guidance. Notably, both modules operate without any paired dataset of 3D scene
models and natural images, which is extremely difficult to obtain.
Comprehensive experiments show that our method produces high-quality,
style-consistent scene videos under diverse and challenging scenarios,
outperforming simple and extended baselines.
ARE: Scaling Up Agent Environments and Evaluations
Authors: name: Pierre Andrews, name: Amine Benhalloum, name: Gerard Moreno-Torres Bertran, name: Matteo Bettini, name: Amar Budhiraja, name: Ricardo Silveira Cabral, name: Virginie Do, name: Romain Froger, name: Emilien Garreau, name: Jean-Baptiste Gaya, name: Hugo Laurençon, name: Maxime Lecanu, name: Kunal Malkan, name: Dheeraj Mekala, name: Pierre Ménard, name: Grégoire Mialon, name: Ulyana Piterbarg, name: Mikhail Plekhanov, name: Mathieu Rita, name: Andrey Rusakov, name: Thomas Scialom, name: Vladislav Vorotilov, name: Mengjue Wang, name: Ian Yu
Published: 2025-09-21 16:59:45

We introduce Meta Agents Research Environments (ARE), a research platform for
scalable creation of environments, integration of synthetic or real
applications, and execution of agentic orchestrations. ARE provides simple
abstractions to build complex and diverse environments, each with their own
rules, tools, content, and verifiers, helping to bridge the gap between model
development and real-world deployment. We also propose Gaia2, a benchmark built
in ARE and designed to measure general agent capabilities. Beyond search and
execution, Gaia2 requires agents to handle ambiguities and noise, adapt to
dynamic environments, collaborate with other agents, and operate under temporal
constraints. Unlike prior benchmarks, Gaia2 runs asynchronously, surfacing new
failure modes that are invisible in static settings. Our experiments show that
no system dominates across the intelligence spectrum: stronger reasoning often
comes at the cost of efficiency, and budget scaling curves plateau,
highlighting the need for new architectures and adaptive compute strategies.
Perhaps more importantly, ARE abstractions enable continuous extension of Gaia2
to other environments, empowering the community to rapidly create new
benchmarks tailored to their domains. In AI's second half, progress
increasingly depends on defining meaningful tasks and robust evaluations to
drive frontier capabilities forward.
SWE-Bench Pro: Can AI Agents Solve Long-Horizon Software Engineering
  Tasks?
Authors: name: Xiang Deng, name: Jeff Da, name: Edwin Pan, name: Yannis Yiming He, name: Charles Ide, name: Kanak Garg, name: Niklas Lauffer, name: Andrew Park, name: Nitin Pasari, name: Chetan Rane, name: Karmini Sampath, name: Maya Krishnan, name: Srivatsa Kundurthy, name: Sean Hendryx, name: Zifan Wang, name: Chen Bo Calvin Zhang, name: Noah Jacobson, name: Bing Liu, name: Brad Kenstler
Published: 2025-09-21 06:28:17

We introduce SWE-Bench Pro, a substantially more challenging benchmark that
builds upon the best practices of SWE-BENCH [25], but is explicitly designed to
capture realistic, complex, enterprise-level problems beyond the scope of
SWE-BENCH. SWE-BENCH PRO contains 1,865 problems sourced from a diverse set of
41 actively maintained repositories spanning business applications, B2B
services, and developer tools. The benchmark is partitioned into a public set
with open access to problems sourced from 11 repositories, a held-out set of 12
repositories and a commercial set of 18 proprietary repositories where we have
formal partnership agreements with early-stage startups. Problems in the
held-out and the commercial set are not publicly accessible, but we release
results on the commercial set. Our benchmark features long-horizon tasks that
may require hours to days for a professional software engineer to complete,
often involving patches across multiple files and substantial code
modifications. All tasks are human-verified and augmented with sufficient
context to ensure resolvability. In our evaluation of widely used coding
models, under a unified scaffold, we observe that their performance on
SWE-Bench PRO remains below 25% (Pass@1), with GPT-5 achieving the highest
score to date at 23.3%. To better understand these limitations, we cluster the
failure modes observed in the collected agent trajectories for a clearer
characterization of the error patterns exhibited by current models. Overall,
SWE-BENCH PRO provides a contamination-resistant testbed that more faithfully
captures the complexity and diversity of real-world software development,
advancing the pursuit of truly autonomous software engineering agents at a
professional level.
Mano Report
Authors: name: Tianyu Fu, name: Anyang Su, name: Chenxu Zhao, name: Hanning Wang, name: Minghui Wu, name: Zhe Yu, name: Fei Hu, name: Mingjia Shi, name: Wei Dong, name: Jiayao Wang, name: Yuyang Chen, name: Ruiyang Yu, name: Siran Peng, name: Menglin Li, name: Nan Huang, name: Haitian Wei, name: Jiawei Yu, name: Yi Xin, name: Xilin Zhao, name: Kai Gu, name: Ping Jiang, name: Sifan Zhou, name: Shuo Wang
Published: 2025-09-22 03:13:58

Graphical user interfaces (GUIs) are the primary medium for human-computer
interaction, yet automating GUI interactions remains challenging due to the
complexity of visual elements, dynamic environments, and the need for
multi-step reasoning. Existing methods based on vision-language models (VLMs)
often suffer from limited resolution, domain mismatch, and insufficient
sequential decisionmaking capability. To address these issues, we propose Mano,
a robust GUI agent built upon a multi-modal foundation model pre-trained on
extensive web and computer system data. Our approach integrates a novel
simulated environment for high-fidelity data generation, a three-stage training
pipeline (supervised fine-tuning, offline reinforcement learning, and online
reinforcement learning), and a verification module for error recovery. Mano
demonstrates state-of-the-art performance on multiple GUI benchmarks, including
Mind2Web and OSWorld, achieving significant improvements in success rate and
operational accuracy. Our work provides new insights into the effective
integration of reinforcement learning with VLMs for practical GUI agent
deployment, highlighting the importance of domain-specific data, iterative
training, and holistic reward design.
From Hugging Face to GitHub: Tracing License Drift in the Open-Source AI
  Ecosystem
Authors: name: James Jewitt, name: Hao Li, name: Bram Adams, name: Gopi Krishnan Rajbahadur, name: Ahmed E. Hassan
Published: 2025-09-11 21:46:20

Hidden license conflicts in the open-source AI ecosystem pose serious legal
and ethical risks, exposing organizations to potential litigation and users to
undisclosed risk. However, the field lacks a data-driven understanding of how
frequently these conflicts occur, where they originate, and which communities
are most affected. We present the first end-to-end audit of licenses for
datasets and models on Hugging Face, as well as their downstream integration
into open-source software applications, covering 364 thousand datasets, 1.6
million models, and 140 thousand GitHub projects. Our empirical analysis
reveals systemic non-compliance in which 35.5% of model-to-application
transitions eliminate restrictive license clauses by relicensing under
permissive terms. In addition, we prototype an extensible rule engine that
encodes almost 200 SPDX and model-specific clauses for detecting license
conflicts, which can solve 86.4% of license conflicts in software applications.
To support future research, we release our dataset and the prototype engine.
Our study highlights license compliance as a critical governance challenge in
open-source AI and provides both the data and tools necessary to enable
automated, AI-aware compliance at scale.
OmniInsert: Mask-Free Video Insertion of Any Reference via Diffusion
  Transformer Models
Authors: name: Jinshu Chen, name: Xinghui Li, name: Xu Bai, name: Tianxiang Ma, name: Pengze Zhang, name: Zhuowei Chen, name: Gen Li, name: Lijie Liu, name: Songtao Zhao, name: Bingchuan Li, name: Qian He
Published: 2025-09-22 11:35:55

Recent advances in video insertion based on diffusion models are impressive.
However, existing methods rely on complex control signals but struggle with
subject consistency, limiting their practical applicability. In this paper, we
focus on the task of Mask-free Video Insertion and aim to resolve three key
challenges: data scarcity, subject-scene equilibrium, and insertion
harmonization. To address the data scarcity, we propose a new data pipeline
InsertPipe, constructing diverse cross-pair data automatically. Building upon
our data pipeline, we develop OmniInsert, a novel unified framework for
mask-free video insertion from both single and multiple subject references.
Specifically, to maintain subject-scene equilibrium, we introduce a simple yet
effective Condition-Specific Feature Injection mechanism to distinctly inject
multi-source conditions and propose a novel Progressive Training strategy that
enables the model to balance feature injection from subjects and source video.
Meanwhile, we design the Subject-Focused Loss to improve the detailed
appearance of the subjects. To further enhance insertion harmonization, we
propose an Insertive Preference Optimization methodology to optimize the model
by simulating human preferences, and incorporate a Context-Aware Rephraser
module during reference to seamlessly integrate the subject into the original
scenes. To address the lack of a benchmark for the field, we introduce
InsertBench, a comprehensive benchmark comprising diverse scenes with
meticulously selected subjects. Evaluation on InsertBench indicates OmniInsert
outperforms state-of-the-art closed-source commercial solutions. The code will
be released.
Qwen3-Omni Technical Report
Authors: name: Jin Xu, name: Zhifang Guo, name: Hangrui Hu, name: Yunfei Chu, name: Xiong Wang, name: Jinzheng He, name: Yuxuan Wang, name: Xian Shi, name: Ting He, name: Xinfa Zhu, name: Yuanjun Lv, name: Yongqi Wang, name: Dake Guo, name: He Wang, name: Linhan Ma, name: Pei Zhang, name: Xinyu Zhang, name: Hongkun Hao, name: Zishan Guo, name: Baosong Yang, name: Bin Zhang, name: Ziyang Ma, name: Xipin Wei, name: Shuai Bai, name: Keqin Chen, name: Xuejing Liu, name: Peng Wang, name: Mingkun Yang, name: Dayiheng Liu, name: Xingzhang Ren, name: Bo Zheng, name: Rui Men, name: Fan Zhou, name: Bowen Yu, name: Jianxin Yang, name: Le Yu, name: Jingren Zhou, name: Junyang Lin
Published: 2025-09-22 13:26:24

We present Qwen3-Omni, a single multimodal model that, for the first time,
maintains state-of-the-art performance across text, image, audio, and video
without any degradation relative to single-modal counterparts. Qwen3-Omni
matches the performance of same-sized single-modal models within the Qwen
series and excels particularly on audio tasks. Across 36 audio and audio-visual
benchmarks, Qwen3-Omni achieves open-source SOTA on 32 benchmarks and overall
SOTA on 22, outperforming strong closed-source models such as Gemini-2.5-Pro,
Seed-ASR, and GPT-4o-Transcribe. Qwen3-Omni adopts a Thinker-Talker MoE
architecture that unifies perception and generation across text, images, audio,
and video, yielding fluent text and natural real-time speech. It supports text
interaction in 119 languages, speech understanding in 19 languages, and speech
generation in 10 languages. To reduce first-packet latency in streaming
synthesis, Talker autoregressively predicts discrete speech codecs using a
multi-codebook scheme. Leveraging the representational capacity of these
codebooks, we replace computationally intensive block-wise diffusion with a
lightweight causal ConvNet, enabling streaming from the first codec frame. In
cold-start settings, Qwen3-Omni achieves a theoretical end-to-end first-packet
latency of 234 ms. To further strengthen multimodal reasoning, we introduce a
Thinking model that explicitly reasons over inputs from any modality. Since the
research community currently lacks a general-purpose audio captioning model, we
fine-tuned Qwen3-Omni-30B-A3B to obtain Qwen3-Omni-30B-A3B-Captioner, which
produces detailed, low-hallucination captions for arbitrary audio inputs.
Qwen3-Omni-30B-A3B, Qwen3-Omni-30B-A3B-Thinking, and
Qwen3-Omni-30B-A3B-Captioner are publicly released under the Apache 2.0
license.