Orionnnn/hub-stats huggingface dataset:
MOD-X: A Modular Open Decentralized eXchange Framework proposal for
  Heterogeneous Interoperable Artificial Agents
Authors: name: Georgios Ioannides, name: Christos Constantinou, name: Vinija Jain, name: Aman Chadha, name: Aaron Elkins
Published: 2025-07-06 12:46:57

As Artificial Intelligence systems evolve from monolithic models to
ecosystems of specialized agents, the need for standardized communication
protocols becomes increasingly critical. This paper introduces MOD-X (Modular
Open Decentralized eXchange), a novel architectural framework proposal for
agent interoperability that addresses key limitations of existing protocols.
Unlike current approaches, MOD-X proposes a layered architecture with a
Universal Message Bus, thorough state management, translation capabilities, and
blockchain-based security mechanisms. We present MOD-X's architecture, compare
it with existing protocols, and demonstrate its application through a worked
example how it enables integration between heterogeneous specialist agents
(agents with different architectures, vendors, capabilities, and knowledge
representations--including rule-based systems, neural networks, symbolic
reasoning engines, and legacy software with agent wrappers). MOD-X's key
innovations include a publish-subscribe communication model, semantic
capability discovery, and dynamic workflow orchestration--providing a framework
that bridges theoretical formalism with practical implementation. This
architecture addresses the growing need for truly decentralized, interoperable
agent ecosystems that can scale effectively without the need for central
coordination.
DreamVLA: A Vision-Language-Action Model Dreamed with Comprehensive
  World Knowledge
Authors: name: Wenyao Zhang, name: Hongsi Liu, name: Zekun Qi, name: Yunnan Wang, name: XinQiang Yu, name: Jiazhao Zhang, name: Runpei Dong, name: Jiawei He, name: He Wang, name: Zhizheng Zhang, name: Li Yi, name: Wenjun Zeng, name: Xin Jin
Published: 2025-07-06 16:14:29

Recent advances in vision-language-action (VLA) models have shown promise in
integrating image generation with action prediction to improve generalization
and reasoning in robot manipulation. However, existing methods are limited to
challenging image-based forecasting, which suffers from redundant information
and lacks comprehensive and critical world knowledge, including dynamic,
spatial and semantic information. To address these limitations, we propose
DreamVLA, a novel VLA framework that integrates comprehensive world knowledge
forecasting to enable inverse dynamics modeling, thereby establishing a
perception-prediction-action loop for manipulation tasks. Specifically,
DreamVLA introduces a dynamic-region-guided world knowledge prediction,
integrated with the spatial and semantic cues, which provide compact yet
comprehensive representations for action planning. This design aligns with how
humans interact with the world by first forming abstract multimodal reasoning
chains before acting. To mitigate interference among the dynamic, spatial and
semantic information during training, we adopt a block-wise structured
attention mechanism that masks their mutual attention, preventing information
leakage and keeping each representation clean and disentangled. Moreover, to
model the conditional distribution over future actions, we employ a
diffusion-based transformer that disentangles action representations from
shared latent features. Extensive experiments on both real-world and simulation
environments demonstrate that DreamVLA achieves 76.7% success rate on real
robot tasks and 4.44 average length on the CALVIN ABC-D benchmarks.
Evaluating LLMs on Real-World Forecasting Against Human Superforecasters
Authors: name: Janna Lu
Published: 2025-07-06 22:26:59

Large language models (LLMs) have demonstrated remarkable capabilities across
diverse tasks, but their ability to forecast future events remains
understudied. A year ago, large language models struggle to come close to the
accuracy of a human crowd. I evaluate state-of-the-art LLMs on 464 forecasting
questions from Metaculus, comparing their performance against human
superforecasters. Frontier models achieve Brier scores that ostensibly surpass
the human crowd but still significantly underperform a group of
superforecasters.
Beyond Simple Edits: X-Planner for Complex Instruction-Based Image
  Editing
Authors: name: Chun-Hsiao Yeh, name: Yilin Wang, name: Nanxuan Zhao, name: Richard Zhang, name: Yuheng Li, name: Yi Ma, name: Krishna Kumar Singh
Published: 2025-07-07 17:59:56

Recent diffusion-based image editing methods have significantly advanced
text-guided tasks but often struggle to interpret complex, indirect
instructions. Moreover, current models frequently suffer from poor identity
preservation, unintended edits, or rely heavily on manual masks. To address
these challenges, we introduce X-Planner, a Multimodal Large Language Model
(MLLM)-based planning system that effectively bridges user intent with editing
model capabilities. X-Planner employs chain-of-thought reasoning to
systematically decompose complex instructions into simpler, clear
sub-instructions. For each sub-instruction, X-Planner automatically generates
precise edit types and segmentation masks, eliminating manual intervention and
ensuring localized, identity-preserving edits. Additionally, we propose a novel
automated pipeline for generating large-scale data to train X-Planner which
achieves state-of-the-art results on both existing benchmarks and our newly
introduced complex editing benchmark.
ArtifactsBench: Bridging the Visual-Interactive Gap in LLM Code
  Generation Evaluation
Authors: name: Chenchen Zhang, name: Yuhang Li, name: Can Xu, name: Jiaheng Liu, name: Ao Liu, name: Shihui Hu, name: Dengpeng Wu, name: Guanhua Huang, name: Kejiao Li, name: Qi Yi, name: Ruibin Xiong, name: Haotian Zhu, name: Yuanxing Zhang, name: Yuhao Jiang, name: Yue Zhang, name: Zenan Xu, name: Bohui Zhai, name: Guoxiang He, name: Hebin Li, name: Jie Zhao, name: Le Zhang, name: Lingyun Tan, name: Pengyu Guo, name: Xianshu Pang, name: Yang Ruan, name: Zhifeng Zhang, name: Zhonghu Wang, name: Ziyan Xu, name: Zuopu Yin, name: Wiggin Zhou, name: Chayse Zhou, name: Fengzong Lian
Published: 2025-07-07 12:53:00

The generative capabilities of Large Language Models (LLMs) are rapidly
expanding from static code to dynamic, interactive visual artifacts. This
progress is bottlenecked by a critical evaluation gap: established benchmarks
focus on algorithmic correctness and are blind to the visual fidelity and
interactive integrity that define modern user experiences. To bridge this gap,
we introduce ArtifactsBench, a new benchmark and paradigm for the automated,
multimodal evaluation of visual code generation. Our framework programmatically
renders each generated artifact and captures its dynamic behavior through
temporal screenshots. This visual evidence, alongside the source code, is then
assessed by a Multimodal LLM (MLLM)-as-Judge, which is rigorously guided by a
fine-grained, per-task checklist to ensure holistic and reproducible scoring.
We construct a new benchmark of 1,825 diverse tasks and evaluate over 30
leading LLMs. Our automated evaluation achieves a striking 94.4% ranking
consistency with WebDev Arena, the gold-standard for human preference in web
development, and over 90% pairwise agreement with human experts. This
establishes ArtifactsBench as the first framework to reliably automate the
assessment of human-perceived quality at scale. Our analysis provides a
high-resolution map of the current SOTA, revealing that generalist models often
outperform domain-specific ones. We open-source ArtifactsBench, including the
benchmark, evaluation harness, and baseline results at
https://artifactsbenchmark.github.io/, to provide the community with a scalable
and accurate tool to accelerate the development of user-centric generative
models.
Disambiguation-Centric Finetuning Makes Enterprise Tool-Calling LLMs
  More Realistic and Less Risky
Authors: name: Ashutosh Hathidara, name: Julien Yu, name: Sebastian Schreiber
Published: 2025-07-04 06:49:02

Large language models (LLMs) are increasingly tasked with invoking enterprise
APIs, yet they routinely falter when near-duplicate tools vie for the same user
intent or when required arguments are left underspecified. We introduce
DiaFORGE (Dialogue Framework for Organic Response Generation & Evaluation), a
disambiguation-centric, three-stage pipeline that (i) synthesizes
persona-driven, multi-turn dialogues in which the assistant must distinguish
among highly similar tools, (ii) performs supervised fine-tuning of open-source
models with reasoning traces across 3B - 70B parameters, and (iii) evaluates
real-world readiness via a dynamic suite that redeploys each model in a live
agentic loop and reports end-to-end goal completion alongside conventional
static metrics. On our dynamic benchmark DiaBENCH, models trained with DiaFORGE
raise tool-invocation success by 27 pp over GPT-4o and by 49 pp over
Claude-3.5-Sonnet, both under optimized prompting. To spur further research, we
release an open corpus of 5000 production-grade enterprise API specifications
paired with rigorously validated, disambiguation-focused dialogues, offering a
practical blueprint for building reliable, enterprise-ready tool-calling
agents.
OmniDraft: A Cross-vocabulary, Online Adaptive Drafter for On-device
  Speculative Decoding
Authors: name: Ramchalam Kinattinkara Ramakrishnan, name: Zhaocong Yuan, name: Shaojie Zhuo, name: Chen Feng, name: Yicheng Lin, name: Chenzheng Su, name: Xiaopeng Zhang
Published: 2025-07-03 14:20:41

Speculative decoding generally dictates having a small, efficient draft model
that is either pretrained or distilled offline to a particular target model
series, for instance, Llama or Qwen models. However, within online deployment
settings, there are two major challenges: 1) usage of a target model that is
incompatible with the draft model; 2) expectation of latency improvements over
usage and time. In this work, we propose OmniDraft, a unified framework that
enables a single draft model to operate with any target model and adapt
dynamically to user data. We introduce an online n-gram cache with hybrid
distillation fine-tuning to address the cross-vocabulary mismatch across draft
and target models; and further improve decoding speed by leveraging adaptive
drafting techniques. OmniDraft is particularly suitable for on-device LLM
applications where model cost, efficiency and user customization are the major
points of contention. This further highlights the need to tackle the above
challenges and motivates the ``one drafter for all'' paradigm. We
showcase the proficiency of the OmniDraft framework by performing online
learning on math reasoning, coding and text generation tasks. Notably,
OmniDraft enables a single Llama-68M model to pair with various target models
including Vicuna-7B, Qwen2-7B and Llama3-8B models for speculative decoding;
and additionally provides up to 1.5-2x speedup.
VLM2Vec-V2: Advancing Multimodal Embedding for Videos, Images, and
  Visual Documents
Authors: name: Rui Meng, name: Ziyan Jiang, name: Ye Liu, name: Mingyi Su, name: Xinyi Yang, name: Yuepeng Fu, name: Can Qin, name: Zeyuan Chen, name: Ran Xu, name: Caiming Xiong, name: Yingbo Zhou, name: Wenhu Chen, name: Semih Yavuz
Published: 2025-07-07 00:51:57

Multimodal embedding models have been crucial in enabling various downstream
tasks such as semantic similarity, information retrieval, and clustering over
different modalities. However, existing multimodal embeddings like VLM2Vec,
E5-V, GME are predominantly focused on natural images, with limited support for
other visual forms such as videos and visual documents. This restricts their
applicability in real-world scenarios, including AI agents, multi-modal search
and recommendation, and retrieval-augmented generation (RAG). To close this
gap, we propose VLM2Vec-V2, a unified framework for learning embeddings across
diverse visual forms. First, we introduce MMEB-V2, a comprehensive benchmark
that extends MMEB with five new task types: visual document retrieval, video
retrieval, temporal grounding, video classification and video question
answering - spanning text, image, video, and visual document inputs. Next, we
train VLM2Vec-V2, a general-purpose embedding model that supports text, image,
video, and visual document inputs. Extensive experiments show that VLM2Vec-V2
achieves strong performance not only on the newly introduced video and document
retrieval tasks, but also improves over prior baselines on the original image
benchmarks. Through extensive evaluation, our study offers insights into the
generalizability of various multimodal embedding models and highlights
effective strategies for unified embedding learning, laying the groundwork for
more scalable and adaptable representation learning in both research and
real-world settings.
Pre-Trained Policy Discriminators are General Reward Models
Authors: name: Shihan Dou, name: Shichun Liu, name: Yuming Yang, name: Yicheng Zou, name: Yunhua Zhou, name: Shuhao Xing, name: Chenhao Huang, name: Qiming Ge, name: Demin Song, name: Haijun Lv, name: Songyang Gao, name: Chengqi Lv, name: Enyu Zhou, name: Honglin Guo, name: Zhiheng Xi, name: Wenwei Zhang, name: Qipeng Guo, name: Qi Zhang, name: Xipeng Qiu, name: Xuanjing Huang, name: Tao Gui, name: Kai Chen
Published: 2025-07-07 16:56:31

We offer a novel perspective on reward modeling by formulating it as a policy
discriminator, which quantifies the difference between two policies to generate
a reward signal, guiding the training policy towards a target policy with
desired behaviors. Based on this conceptual insight, we propose a scalable
pre-training method named Policy Discriminative Learning (POLAR), which trains
a reward model (RM) to discern identical policies and discriminate different
ones. Unlike traditional reward modeling methods relying on absolute
preferences, POLAR captures the relative difference between one policy and an
arbitrary target policy, which is a scalable, high-level optimization objective
suitable for modeling generic ranking relationships. Leveraging the POLAR
pre-training paradigm, we present a series of RMs with parameter scales from
1.8B to 7B. Empirical results show that POLAR substantially outperforms
traditional non-pre-trained methods, significantly enhancing RM performance.
For instance, POLAR-7B could improve preference accuracy from 54.8% to 81.0% on
STEM tasks and from 57.9% to 85.5% on creative writing tasks compared to SOTA
baselines. POLAR also shows robust generalization capabilities in RLHF using
Reinforcement Fine-tuning (RFT), providing reliable reward signals and markedly
enhancing policy performance--improving LLaMa3.1-8B from an average of 47.36%
to 56.33% and Qwen2.5-32B from 64.49% to 70.47% on 20 benchmarks. Moreover,
scaling experiments reveal a clear power-law relationship between computation
and performance, supported by linear correlation coefficients approaching 0.99.
The impressive performance, strong generalization, and scaling properties
suggest that POLAR is a promising direction for developing general and strong
reward models.
4DSloMo: 4D Reconstruction for High Speed Scene with Asynchronous
  Capture
Authors: name: Yutian Chen, name: Shi Guo, name: Tianshuo Yang, name: Lihe Ding, name: Xiuyuan Yu, name: Jinwei Gu, name: Tianfan Xue
Published: 2025-07-07 16:18:35

Reconstructing fast-dynamic scenes from multi-view videos is crucial for
high-speed motion analysis and realistic 4D reconstruction. However, the
majority of 4D capture systems are limited to frame rates below 30 FPS (frames
per second), and a direct 4D reconstruction of high-speed motion from low FPS
input may lead to undesirable results. In this work, we propose a high-speed 4D
capturing system only using low FPS cameras, through novel capturing and
processing modules. On the capturing side, we propose an asynchronous capture
scheme that increases the effective frame rate by staggering the start times of
cameras. By grouping cameras and leveraging a base frame rate of 25 FPS, our
method achieves an equivalent frame rate of 100-200 FPS without requiring
specialized high-speed cameras. On processing side, we also propose a novel
generative model to fix artifacts caused by 4D sparse-view reconstruction, as
asynchrony reduces the number of viewpoints at each timestamp. Specifically, we
propose to train a video-diffusion-based artifact-fix model for sparse 4D
reconstruction, which refines missing details, maintains temporal consistency,
and improves overall reconstruction quality. Experimental results demonstrate
that our method significantly enhances high-speed 4D reconstruction compared to
synchronous capture.
PresentAgent: Multimodal Agent for Presentation Video Generation
Authors: name: Jingwei Shi, name: Zeyu Zhang, name: Biao Wu, name: Yanjie Liang, name: Meng Fang, name: Ling Chen, name: Yang Zhao
Published: 2025-07-05 13:24:15

We present PresentAgent, a multimodal agent that transforms long-form
documents into narrated presentation videos. While existing approaches are
limited to generating static slides or text summaries, our method advances
beyond these limitations by producing fully synchronized visual and spoken
content that closely mimics human-style presentations. To achieve this
integration, PresentAgent employs a modular pipeline that systematically
segments the input document, plans and renders slide-style visual frames,
generates contextual spoken narration with large language models and
Text-to-Speech models, and seamlessly composes the final video with precise
audio-visual alignment. Given the complexity of evaluating such multimodal
outputs, we introduce PresentEval, a unified assessment framework powered by
Vision-Language Models that comprehensively scores videos across three critical
dimensions: content fidelity, visual clarity, and audience comprehension
through prompt-based evaluation. Our experimental validation on a curated
dataset of 30 document-presentation pairs demonstrates that PresentAgent
approaches human-level quality across all evaluation metrics. These results
highlight the significant potential of controllable multimodal agents in
transforming static textual materials into dynamic, effective, and accessible
presentation formats. Code will be available at
https://github.com/AIGeeksGroup/PresentAgent.
BMMR: A Large-Scale Bilingual Multimodal Multi-Discipline Reasoning
  Dataset
Authors: name: Zhiheng Xi, name: Guanyu Li, name: Yutao Fan, name: Honglin Guo, name: Yufang Liu, name: Xiaoran Fan, name: Jiaqi Liu, name: Jingchao Ding, name: Wangmeng Zuo, name: Zhenfei Yin, name: Lei Bai, name: Tao Ji, name: Tao Gui, name: Qi Zhang, name: Xuanjing Huang
Published: 2025-07-04 11:20:09

In this paper, we introduce BMMR, a large-scale bilingual, multimodal,
multi-disciplinary reasoning dataset for the community to develop and evaluate
large multimodal models (LMMs). BMMR comprises 110k college-level questions
spanning 300 UNESCO-defined subjects, spanning diverse formats-multiple-choice,
fill-in-the-blank, and open-ended QA-and sourced from both print and digital
media such as books, exams, and quizzes. All data are curated and filtered via
a human-in-the-loop and scalable framework, and each instance is paired with a
high-quality reasoning path. The dataset is organized into two parts: BMMR-Eval
that comprises 20,458 high-quality instances to comprehensively assess LMMs'
knowledge and reasoning across multiple disciplines in both Chinese and
English; and BMMR-Train that contains 88,991 instances to support further
research and development, extending the current focus on mathematical reasoning
to diverse disciplines and domains. In addition, we propose the process-based
multi-discipline verifier (i.e., BMMR-Verifier) for accurate and fine-grained
evaluation of reasoning paths. Extensive experiments on 24 models reveal that
(i) even SOTA models (e.g., o3 and Gemini-2.5-Pro) leave substantial headroom
on BMMR-Eval; (ii) reasoning models exhibit discipline bias and outperform LMMs
only on specific subjects; (iii) open-source models still trail their
proprietary counterparts; and (iv) fine-tuning on BMMR-Train narrows this gap.
Additionally, we conduct reasoning-chain analyses using BMMR-Verifier and other
in-depth studies, uncovering the challenges LMMs currently face in
multidisciplinary reasoning. We will release the data, and we hope our work can
offer insights and contributions to the community.
RefineX: Learning to Refine Pre-training Data at Scale from
  Expert-Guided Programs
Authors: name: Baolong Bi, name: Shenghua Liu, name: Xingzhang Ren, name: Dayiheng Liu, name: Junyang Lin, name: Yiwei Wang, name: Lingrui Mei, name: Junfeng Fang, name: Jiafeng Guo, name: Xueqi Cheng
Published: 2025-07-04 02:19:58

The foundational capabilities of large language models (LLMs) are deeply
influenced by the quality of their pre-training corpora. However, enhancing
data quality at scale remains a significant challenge, primarily due to the
trade-off between refinement effectiveness and processing efficiency. While
rule-based filtering remains the dominant paradigm, it typically operates at
the document level and lacks the granularity needed to refine specific content
within documents. Inspired by emerging work such as ProX, we propose
RefineX, a novel framework for large-scale, surgical refinement of
pre-training data through programmatic editing tasks. RefineX enables efficient
and fine-grained data refinement while reliably preserving the diversity and
naturalness of raw text. The core strength of RefineX lies in distilling
high-quality, expert-guided end-to-end refinement results into minimal
edit-based deletion programs. This high-precision distillation pipeline is used
to train an efficient and reliable refine model that can systematically improve
every instance in the corpus at scale. We evaluate RefineX across from-scratch
pre-training at multiple model scales and find that it consistently outperforms
models trained on raw, filtered, or alternatively refined data across diverse
downstream tasks. On the 750M model, RefineX yields 2.6%-7.2% average gains on
lighteval tasks, and achieves comparable performance using significantly fewer
training tokens. Further analysis shows that RefineX reliably enhances text
quality with both high efficiency and precision, outperforming prior approaches
such as end-to-end generation and Prox-C. These results position RefineX as a
scalable, effective, and reliable solution for optimizing pre-training data in
modern LLM pipelines.