Orionnnn/hub-stats huggingface dataset:
InternSVG: Towards Unified SVG Tasks with Multimodal Large Language
  Models
Authors: name: Haomin Wang, name: Jinhui Yin, name: Qi Wei, name: Wenguang Zeng, name: Lixin Gu, name: Shenglong Ye, name: Zhangwei Gao, name: Yaohui Wang, name: Yanting Zhang, name: Yuanqi Li, name: Yanwen Guo, name: Wenhai Wang, name: Kai Chen, name: Yu Qiao, name: Hongjie Zhang
Published: 2025-10-13 12:38:04

General SVG modeling remains challenging due to fragmented datasets, limited
transferability of methods across tasks, and the difficulty of handling
structural complexity. In response, we leverage the strong transfer and
generalization capabilities of multimodal large language models (MLLMs) to
achieve unified modeling for SVG understanding, editing, and generation. We
present the InternSVG family, an integrated data-benchmark-model suite. At its
core is SAgoge, the largest and most comprehensive multimodal dataset for SVG
tasks, encompassing both static graphics and dynamic animations. It covers
icons, long-sequence illustrations, scientific diagrams, and dynamic
animations, supporting tasks of varied difficulty levels and providing deeper
hierarchies with richer attributes compared to previous datasets. Based on this
resource, we introduce SArena, a companion benchmark with comprehensive task
definitions and standardized evaluation that aligns with the domains and
difficulty spectrum covered by SAgoge. Building on these foundations, we
propose InternSVG, a unified MLLM for SVG understanding, editing, and
generation with SVG-specific special tokens, subword-based embedding
initialization, and a two-stage training strategy that progresses from short
static SVGs to long-sequence illustrations and complex animations. This unified
formulation induces positive transfer and improves overall performance.
Experiments on SArena and prior benchmark confirm that InternSVG achieves
substantial gains and consistently outperforms leading open and proprietary
counterparts.
Stable Video Infinity: Infinite-Length Video Generation with Error
  Recycling
Authors: name: Wuyang Li, name: Wentao Pan, name: Po-Chien Luan, name: Yang Gao, name: Alexandre Alahi
Published: 2025-10-10 09:45:46

We propose Stable Video Infinity (SVI) that is able to generate
infinite-length videos with high temporal consistency, plausible scene
transitions, and controllable streaming storylines. While existing long-video
methods attempt to mitigate accumulated errors via handcrafted anti-drifting
(e.g., modified noise scheduler, frame anchoring), they remain limited to
single-prompt extrapolation, producing homogeneous scenes with repetitive
motions. We identify that the fundamental challenge extends beyond error
accumulation to a critical discrepancy between the training assumption (seeing
clean data) and the test-time autoregressive reality (conditioning on
self-generated, error-prone outputs). To bridge this hypothesis gap, SVI
incorporates Error-Recycling Fine-Tuning, a new type of efficient training that
recycles the Diffusion Transformer (DiT)'s self-generated errors into
supervisory prompts, thereby encouraging DiT to actively identify and correct
its own errors. This is achieved by injecting, collecting, and banking errors
through closed-loop recycling, autoregressively learning from error-injected
feedback. Specifically, we (i) inject historical errors made by DiT to
intervene on clean inputs, simulating error-accumulated trajectories in flow
matching; (ii) efficiently approximate predictions with one-step bidirectional
integration and calculate errors with residuals; (iii) dynamically bank errors
into replay memory across discretized timesteps, which are resampled for new
input. SVI is able to scale videos from seconds to infinite durations with no
additional inference cost, while remaining compatible with diverse conditions
(e.g., audio, skeleton, and text streams). We evaluate SVI on three benchmarks,
including consistent, creative, and conditional settings, thoroughly verifying
its versatility and state-of-the-art role.
ACADREASON: Exploring the Limits of Reasoning Models with Academic
  Research Problems
Authors: name: Xin Gui, name: King Zhu, name: JinCheng Ren, name: Qianben Chen, name: Zekun Moore Wang, name: Yizhi LI, name: Xinpeng Liu, name: Xiaowan Li, name: Wenli Ren, name: Linyu Miao, name: Tianrui Qin, name: Ziqi Shu, name: He Zhu, name: Xiangru Tang, name: Dingfeng Shi, name: Jiaheng Liu, name: Yuchen Eleanor Jiang, name: Minghao Liu, name: Ge Zhang, name: Wangchunshu Zhou
Published: 2025-10-13 17:30:36

In recent years, the research focus of large language models (LLMs) and
agents has shifted increasingly from demonstrating novel capabilities to
complex reasoning and tackling challenging tasks. However, existing evaluations
focus mainly on math/code contests or general tasks, while existing
multi-domain academic benchmarks lack sufficient reasoning depth, leaving the
field without a rigorous benchmark for high-level reasoning. To fill this gap,
we introduce the Acadreason benchmark, designed to evaluate the ability of LLMs
and agents to acquire and reason over academic knowledge. It consists of 50
expert-annotated academic problems across five high-reasoning domains,
including computer science, economics, law, mathematics, and philosophy. All
questions are sourced from top-tier publications in recent years and undergo
rigorous annotation and quality control to ensure they are both challenging and
answerable. We conduct systematic evaluations of over 10 mainstream LLMs and
agents. The results show that most LLMs scored below 20 points, with even the
cutting-edge GPT-5 achieving only 16 points. While agents achieved higher
scores, none exceeded 40 points. This demonstrates the current capability gap
between LLMs and agents in super-intelligent academic research tasks and
highlights the challenges of Acadreason.
Spotlight on Token Perception for Multimodal Reinforcement Learning
Authors: name: Siyuan Huang, name: Xiaoye Qu, name: Yafu Li, name: Yun Luo, name: Zefeng He, name: Daizong Liu, name: Yu Cheng
Published: 2025-10-10 11:25:33

While Reinforcement Learning with Verifiable Rewards (RLVR) has advanced the
reasoning capabilities of Large Vision-Language Models (LVLMs), most existing
methods in multimodal reasoning neglect the critical role of visual perception
within the RLVR optimization process. In this paper, we undertake a pioneering
exploration of multimodal RLVR through the novel perspective of token
perception, which measures the visual dependency of each generated token. With
a granular analysis of Chain-of-Thought (CoT) processes, we uncover two key
insights: first, token perception in a rollout trajectory is sparsely
distributed, where only a small fraction of tokens have high visual dependency
for visually-grounded reasoning; second, different trajectories exhibit
significant divergence in their overall visual dependency. Based on these
observations, we propose Visually-Perceptive Policy Optimization (VPPO), a
novel policy gradient algorithm that explicitly leverages token perception to
refine the learning signal. Specifically, VPPO achieves this through a dual
mechanism: it reweights a trajectory's advantage by its overall visual
dependency, and focuses policy updates exclusively on perceptually pivotal
tokens. On a comprehensive suite of eight perception and reasoning benchmarks,
VPPO demonstrates substantial gains over leading open-source RL-tuned models,
with its effectiveness consistently validated across 7B and 32B model scales.
Our findings not only establish a new token-level perceptual perspective for
analyzing multimodal RLVR but also present a novel and effective optimization
strategy to significantly enhance the multimodal reasoning capabilities of
LVLMs.
VER: Vision Expert Transformer for Robot Learning via Foundation
  Distillation and Dynamic Routing
Authors: name: Yixiao Wang, name: Mingxiao Huo, name: Zhixuan Liang, name: Yushi Du, name: Lingfeng Sun, name: Haotian Lin, name: Jinghuan Shang, name: Chensheng Peng, name: Mohit Bansal, name: Mingyu Ding, name: Masayoshi Tomizuka
Published: 2025-10-06 18:00:43

Pretrained vision foundation models (VFMs) advance robotic learning via rich
visual representations, yet individual VFMs typically excel only in specific
domains, limiting generality across tasks. Distilling multiple VFMs into a
unified representation for policy can mitigate this limitation but often yields
inflexible task-specific feature selection and requires costly full re-training
to incorporate robot-domain knowledge. We propose VER, a Vision Expert
transformer for Robot learning. During pretraining, VER distills multiple VFMs
into a vision expert library. It then fine-tunes only a lightweight routing
network (fewer than 0.4% of parameters) to dynamically select task-relevant
experts from the pretrained library for downstream robot tasks. We further
introduce Patchwise Expert Routing with Curriculum Top-K Annealing to improve
both flexibility and precision of dynamic expert selection. Moreover, VER
supports parameter-efficient finetuning for scalable expert utilization and
adaptive robot-domain knowledge integration. Across 17 diverse robotic tasks
and multiple policy heads, VER achieves state-of-the-art performance. We find
that VER reduces large-norm outliers in task-irrelevant regions (e.g.,
background) and concentrates on task-critical regions. Visualizations and codes
can be found in https://yixiaowang7.github.io/ver_page/.
HUME: Measuring the Human-Model Performance Gap in Text Embedding Task
Authors: name: Adnan El Assadi, name: Isaac Chung, name: Roman Solomatin, name: Niklas Muennighoff, name: Kenneth Enevoldsen
Published: 2025-10-11 06:56:53

Comparing human and model performance offers a valuable perspective for
understanding the strengths and limitations of embedding models, highlighting
where they succeed and where they fail to capture meaning and nuance. However,
such comparisons are rarely made, as human performance on embedding tasks is
difficult to measure. To fill this gap, we introduce HUME: Human Evaluation
Framework for Text Embeddings. While frameworks like MTEB provide broad model
evaluation, they lack reliable estimates of human performance, limiting the
interpretability of model scores. We measure human performance across 16 MTEB
datasets spanning reranking, classification, clustering, and semantic textual
similarity across linguistically diverse high- and low-resource languages.
Humans achieve an average performance of 77.6% compared to 80.1% for the best
embedding model, although variation is substantial: models reach near-ceiling
performance on some datasets while struggling on others, suggesting dataset
issues and revealing shortcomings in low-resource languages. We provide human
performance baselines, insight into task difficulty patterns, and an extensible
evaluation framework that enables a more meaningful interpretation of the model
and informs the development of both models and benchmarks. Our code, dataset,
and leaderboard are publicly available at
https://github.com/embeddings-benchmark/mteb.
Don't Just Fine-tune the Agent, Tune the Environment
Authors: name: Siyuan Lu, name: Zechuan Wang, name: Hongxuan Zhang, name: Qintong Wu, name: Leilei Gan, name: Chenyi Zhuang, name: Jinjie Gu, name: Tao Lin
Published: 2025-10-11 12:35:15

Large Language Model (LLM) agents show great promise for complex, multi-turn
tool-use tasks, but their development is often hampered by the extreme scarcity
of high-quality training data. Supervised fine-tuning (SFT) on synthetic data
leads to overfitting, whereas standard reinforcement learning (RL) struggles
with a critical cold-start problem and training instability. To address these
challenges, we introduce Environment Tuning, a novel training
paradigm that enables agents to learn complex behaviors directly from problem
instances without relying on pre-collected expert trajectories.
Environment Tuning orchestrates this learning process through a
structured curriculum, actionable environment augmentation that provides
corrective feedback, and fine-grained progress rewards to ensure stable and
efficient exploration. Using only 400 problem instances from Berkeley
Function-Calling Leaderboard (BFCL) benchmark, our method not only achieves
competitive in-distribution performance against strong baselines but also
demonstrates superior out-of-distribution generalization, overcoming the
performance collapse common to SFT-based approaches. Our work presents a
paradigm shift from supervised fine-tuning on static trajectories to dynamic,
environment-based exploration, paving the way for training more robust and
data-efficient agents.
High-Fidelity Simulated Data Generation for Real-World Zero-Shot Robotic
  Manipulation Learning with Gaussian Splatting
Authors: name: Haoyu Zhao, name: Cheng Zeng, name: Linghao Zhuang, name: Yaxi Zhao, name: Shengke Xue, name: Hao Wang, name: Xingyue Zhao, name: Zhongyu Li, name: Kehan Li, name: Siteng Huang, name: Mingxiu Chen, name: Xin Li, name: Deli Zhao, name: Hua Zou
Published: 2025-10-12 14:42:07

The scalability of robotic learning is fundamentally bottlenecked by the
significant cost and labor of real-world data collection. While simulated data
offers a scalable alternative, it often fails to generalize to the real world
due to significant gaps in visual appearance, physical properties, and object
interactions. To address this, we propose RoboSimGS, a novel Real2Sim2Real
framework that converts multi-view real-world images into scalable,
high-fidelity, and physically interactive simulation environments for robotic
manipulation. Our approach reconstructs scenes using a hybrid representation:
3D Gaussian Splatting (3DGS) captures the photorealistic appearance of the
environment, while mesh primitives for interactive objects ensure accurate
physics simulation. Crucially, we pioneer the use of a Multi-modal Large
Language Model (MLLM) to automate the creation of physically plausible,
articulated assets. The MLLM analyzes visual data to infer not only physical
properties (e.g., density, stiffness) but also complex kinematic structures
(e.g., hinges, sliding rails) of objects. We demonstrate that policies trained
entirely on data generated by RoboSimGS achieve successful zero-shot
sim-to-real transfer across a diverse set of real-world manipulation tasks.
Furthermore, data from RoboSimGS significantly enhances the performance and
generalization capabilities of SOTA methods. Our results validate RoboSimGS as
a powerful and scalable solution for bridging the sim-to-real gap.
The Hidden DNA of LLM-Generated JavaScript: Structural Patterns Enable
  High-Accuracy Authorship Attribution
Authors: name: Norbert Tihanyi, name: Bilel Cherif, name: Richard A. Dubniczky, name: Mohamed Amine Ferrag, name: Tamás Bisztray
Published: 2025-10-12 07:51:03

In this paper, we present the first large-scale study exploring whether
JavaScript code generated by Large Language Models (LLMs) can reveal which
model produced it, enabling reliable authorship attribution and model
fingerprinting. With the rapid rise of AI-generated code, attribution is
playing a critical role in detecting vulnerabilities, flagging malicious
content, and ensuring accountability. While AI-vs-human detection usually
treats AI as a single category we show that individual LLMs leave unique
stylistic signatures, even among models belonging to the same family or
parameter size. To this end, we introduce LLM-NodeJS, a dataset of 50,000
Node.js back-end programs from 20 large language models. Each has four
transformed variants, yielding 250,000 unique JavaScript samples and two
additional representations (JSIR and AST) for diverse research applications.
Using this dataset, we benchmark traditional machine learning classifiers
against fine-tuned Transformer encoders and introduce CodeT5-JSA, a custom
architecture derived from the 770M-parameter CodeT5 model with its decoder
removed and a modified classification head. It achieves 95.8% accuracy on
five-class attribution, 94.6% on ten-class, and 88.5% on twenty-class tasks,
surpassing other tested models such as BERT, CodeBERT, and Longformer. We
demonstrate that classifiers capture deeper stylistic regularities in program
dataflow and structure, rather than relying on surface-level features. As a
result, attribution remains effective even after mangling, comment removal, and
heavy code transformations. To support open science and reproducibility, we
release the LLM-NodeJS dataset, Google Colab training scripts, and all related
materials on GitHub: https://github.com/LLM-NodeJS-dataset.
BrowserAgent: Building Web Agents with Human-Inspired Web Browsing
  Actions
Authors: name: Zhengbo Zhang, name: Zhiheng Lyu, name: Junhao Gong, name: Hongzhu Yi, name: Xinming Wang, name: Yuxuan Zhou, name: Jiabing Yang, name: Ping Nie, name: Yan Huang, name: Wenhu Chen
Published: 2025-10-12 15:43:37

Efficiently solving real-world problems with LLMs increasingly hinges on
their ability to interact with dynamic web environments and autonomously
acquire external information. While recent research like Search-R1 and
WebDancer demonstrates strong performance in solving web tasks, they heavily
rely on additional tools to convert the interactive web environment into static
text content. This is in contrast to human browsing behaviors, which involve
diverse interactions with the browser, such as scrolling, clicking, and typing.
In this paper, we propose BrowserAgent, a more interactive agent that solves
complex tasks through human-inspired browser actions. BrowserAgent operates
directly on raw web pages via Playwright through a set of predefined browser
actions. We adopt a two-stage training (Supervised Fine-Tuning (SFT) and
Rejection Fine-Tuning (RFT)) to improve the model's generalization abilities.
Despite using significantly less training data than Search-R1, BrowserAgent
achieves more competitive results across different Open-QA tasks. Additionally,
we introduce an explicit memory mechanism to store key conclusions across
steps, further enhancing the model's reasoning capabilities for long-horizon
tasks. Notably, BrowserAgent-7B can achieve around 20\% improvement over
Search-R1 on multi-hop QA tasks like HotpotQA, 2Wiki, and Bamboogle. These
results indicate that BrowserAgent can serve as a more advanced framework for
more interactive and scalable web agents.
Building a Foundational Guardrail for General Agentic Systems via
  Synthetic Data
Authors: name: Yue Huang, name: Hang Hua, name: Yujun Zhou, name: Pengcheng Jing, name: Manish Nagireddy, name: Inkit Padhi, name: Greta Dolcetti, name: Zhangchen Xu, name: Subhajit Chaudhury, name: Ambrish Rawat, name: Liubov Nedoshivina, name: Pin-Yu Chen, name: Prasanna Sattigeri, name: Xiangliang Zhang
Published: 2025-10-10 18:42:32

While LLM agents can plan multi-step tasks, intervening at the planning
stage-before any action is executed-is often the safest way to prevent harm,
since certain risks can lead to severe consequences once carried out. However,
existing guardrails mostly operate post-execution, which is difficult to scale
and leaves little room for controllable supervision at the plan level. To
address this challenge, we highlight three critical gaps in current research:
data gap, model gap, and evaluation gap. To close the data gap, we introduce
AuraGen, a controllable engine that (i) synthesizes benign trajectories, (ii)
injects category-labeled risks with calibrated difficulty, and (iii) filters
outputs via an automated reward model, producing large and reliable corpora for
pre-execution safety. To close the guardian model gap, we propose a
foundational guardrail Safiron, combining a cross-planner adapter with a
compact guardian model. The adapter unifies different input formats, while
Safiron flags risky cases, assigns risk types, and generates rationales;
trained in two stages with a broadly explored data recipe, Safiron achieves
robust transfer across settings. To close the evaluation gap, we release
Pre-Exec Bench, a realistic benchmark covering diverse tools and branching
trajectories, which measures detection, fine-grained categorization,
explanation, and cross-planner generalization in human-verified scenarios.
Extensive experiments demonstrate consistent gains of the proposed guardrail
over strong baselines on Pre-Exec Bench, and ablations further distill
actionable practices, providing a practical template for safer agentic systems.
Are Large Reasoning Models Interruptible?
Authors: name: Tsung-Han Wu, name: Mihran Miroyan, name: David M. Chan, name: Trevor Darrell, name: Narges Norouzi, name: Joseph E. Gonzalez
Published: 2025-10-13 17:59:35

Large Reasoning Models (LRMs) excel at complex reasoning but are
traditionally evaluated in static, "frozen world" settings: model responses are
assumed to be instantaneous, and the context of a request is presumed to be
immutable over the duration of the response. While generally true for
short-term tasks, the "frozen world" assumption breaks down in modern reasoning
tasks such as assistive programming, where models may take hours to think
through problems and code may change dramatically from the time the model
starts thinking to the model's final output. In this work, we challenge the
frozen world assumption and evaluate LRM robustness under two realistic dynamic
scenarios: interruptions, which test the quality of the model's partial outputs
on a limited budget, and dynamic context, which tests model adaptation to
in-flight changes. Across mathematics and programming benchmarks that require
long-form reasoning, static evaluations consistently overestimate robustness:
even state-of-the-art LRMs, which achieve high accuracy in static settings, can
fail unpredictably when interrupted or exposed to changing context, with
performance dropping by up to 60% when updates are introduced late in the
reasoning process. Our analysis further reveals several novel failure modes,
including reasoning leakage, where models fold the reasoning into their final
answer when interrupted; panic, where under time pressure models abandon
reasoning entirely and return incorrect answers; and self-doubt, where
performance degrades while incorporating updated information.
RePro: Training Language Models to Faithfully Recycle the Web for
  Pretraining
Authors: name: Zichun Yu, name: Chenyan Xiong
Published: 2025-10-12 16:08:38

High-quality pretraining data is the fossil fuel of large language models
(LLMs), yet its reserves are running low for frontier models. In this paper, we
introduce RePro, a novel web recycling method that trains a relatively small LM
with reinforcement learning to generate effective and faithful rephrasings of
pretraining data. Specifically, we design one quality reward and three
faithfulness rewards, optimizing the LM rephraser to convert organic data into
high-quality rephrasings while maintaining its core semantics and structure. In
our experiment, we train a 4B rephraser to recycle 72B tokens sampled from
DCLM-RefinedWeb. Pretraining results on 400M and 1.4B models demonstrate that
RePro delivers 4.7%-14.0% relative accuracy gains over organic-only baseline on
22 downstream tasks. RePro also outperforms ReWire, the state-of-the-art web
recycling method that prompts a 70B rephraser, as well as the organic baseline
with a 4x larger data pool. Experiments with different amounts of recycled data
highlight that RePro improves organic data efficiency by 2-3x. Individual and
distributional analyses validate that RePro preserves more critical information
and faithfully reflects the characteristics of organic data compared to
prompting-based methods. Together, these results show that RePro provides an
efficient and controllable path to effectively harness the fossil fuel of LLM
pretraining. We open-source our code, rephraser, and recycled data at
https://github.com/cxcscmu/RePro.