Orionnnn/hub-stats huggingface dataset:
Paper2Agent: Reimagining Research Papers As Interactive and Reliable AI
  Agents
Authors: name: Jiacheng Miao, name: Joe R. Davis, name: Jonathan K. Pritchard, name: James Zou
Published: 2025-09-08 17:28:42

We introduce Paper2Agent, an automated framework that converts research
papers into AI agents. Paper2Agent transforms research output from passive
artifacts into active systems that can accelerate downstream use, adoption, and
discovery. Conventional research papers require readers to invest substantial
effort to understand and adapt a paper's code, data, and methods to their own
work, creating barriers to dissemination and reuse. Paper2Agent addresses this
challenge by automatically converting a paper into an AI agent that acts as a
knowledgeable research assistant. It systematically analyzes the paper and the
associated codebase using multiple agents to construct a Model Context Protocol
(MCP) server, then iteratively generates and runs tests to refine and robustify
the resulting MCP. These paper MCPs can then be flexibly connected to a chat
agent (e.g. Claude Code) to carry out complex scientific queries through
natural language while invoking tools and workflows from the original paper. We
demonstrate Paper2Agent's effectiveness in creating reliable and capable paper
agents through in-depth case studies. Paper2Agent created an agent that
leverages AlphaGenome to interpret genomic variants and agents based on ScanPy
and TISSUE to carry out single-cell and spatial transcriptomics analyses. We
validate that these paper agents can reproduce the original paper's results and
can correctly carry out novel user queries. By turning static papers into
dynamic, interactive AI agents, Paper2Agent introduces a new paradigm for
knowledge dissemination and a foundation for the collaborative ecosystem of AI
co-scientists.
Reverse-Engineered Reasoning for Open-Ended Generation
Authors: name: Haozhe Wang, name: Haoran Que, name: Qixin Xu, name: Minghao Liu, name: Wangchunshu Zhou, name: Jiazhan Feng, name: Wanjun Zhong, name: Wei Ye, name: Tong Yang, name: Wenhao Huang, name: Ge Zhang, name: Fangzhen Lin
Published: 2025-09-07 18:07:58

While the ``deep reasoning'' paradigm has spurred significant advances in
verifiable domains like mathematics, its application to open-ended, creative
generation remains a critical challenge. The two dominant methods for
instilling reasoning -- reinforcement learning (RL) and instruction
distillation -- falter in this area; RL struggles with the absence of clear
reward signals and high-quality reward models, while distillation is
prohibitively expensive and capped by the teacher model's capabilities. To
overcome these limitations, we introduce REverse-Engineered Reasoning (REER), a
new paradigm that fundamentally shifts the approach. Instead of building a
reasoning process ``forwards'' through trial-and-error or imitation, REER works
``backwards'' from known-good solutions to computationally discover the latent,
step-by-step deep reasoning process that could have produced them. Using this
scalable, gradient-free approach, we curate and open-source DeepWriting-20K, a
large-scale dataset of 20,000 deep reasoning trajectories for open-ended tasks.
Our model, DeepWriter-8B, trained on this data, not only surpasses strong
open-source baselines but also achieves performance competitive with, and at
times superior to, leading proprietary models like GPT-4o and Claude 3.5.
Easier Painting Than Thinking: Can Text-to-Image Models Set the Stage,
  but Not Direct the Play?
Authors: name: Ouxiang Li, name: Yuan Wang, name: Xinting Hu, name: Huijuan Huang, name: Rui Chen, name: Jiarong Ou, name: Xin Tao, name: Pengfei Wan, name: Fuli Feng
Published: 2025-09-03 17:58:12

Text-to-image (T2I) generation aims to synthesize images from textual
prompts, which jointly specify what must be shown and imply what can be
inferred, thereby corresponding to two core capabilities: composition and
reasoning. However, with the emerging advances of T2I models in reasoning
beyond composition, existing benchmarks reveal clear limitations in providing
comprehensive evaluations across and within these capabilities. Meanwhile,
these advances also enable models to handle more complex prompts, whereas
current benchmarks remain limited to low scene density and simplified
one-to-one reasoning. To address these limitations, we propose T2I-CoReBench, a
comprehensive and complex benchmark that evaluates both composition and
reasoning capabilities of T2I models. To ensure comprehensiveness, we structure
composition around scene graph elements (instance, attribute, and relation) and
reasoning around the philosophical framework of inference (deductive,
inductive, and abductive), formulating a 12-dimensional evaluation taxonomy. To
increase complexity, driven by the inherent complexities of real-world
scenarios, we curate each prompt with high compositional density for
composition and multi-step inference for reasoning. We also pair each prompt
with a checklist that specifies individual yes/no questions to assess each
intended element independently to facilitate fine-grained and reliable
evaluation. In statistics, our benchmark comprises 1,080 challenging prompts
and around 13,500 checklist questions. Experiments across 27 current T2I models
reveal that their composition capability still remains limited in complex
high-density scenarios, while the reasoning capability lags even further behind
as a critical bottleneck, with all models struggling to infer implicit elements
from prompts. Our project page: https://t2i-corebench.github.io/.
Llama-GENBA-10B: A Trilingual Large Language Model for German, English
  and Bavarian
Authors: name: Michael Hoffmann, name: Jophin John, name: Stefan Schweter, name: Gokul Ramakrishnan, name: Hoi-Fong Mak, name: Alice Zhang, name: Dmitry Gaynullin, name: Nicolay J. Hammer
Published: 2025-09-06 10:12:52

We present Llama-GENBA-10B, a trilingual foundation model addressing
English-centric bias in large language models. Built on Llama 3.1-8B and scaled
to 10B parameters, Llama-GENBA-10B is continuously pretrained on 164B tokens
(82B English, 82B German, and 80M Bavarian), balancing resources while
preventing English dominance. Targeted at the German NLP community, the model
also promotes Bavarian as a low-resource language. Development tackled four
challenges: (1) curating a multilingual corpus despite Bavarian scarcity, (2)
creating a unified tokenizer for English, German, and Bavarian, (3) optimizing
architecture and language-ratio hyperparameters for cross-lingual transfer, and
(4) establishing the first standardized trilingual evaluation suite by
translating German benchmarks into Bavarian. Evaluations show that
Llama-GENBA-10B achieves strong cross-lingual performance, with the fine-tuned
variant surpassing Apertus-8B-2509 and gemma-2-9b in Bavarian and establishing
itself as the best model in its class for this language, while also
outperforming EuroLLM in English and matching its results in German. Training
on the Cerebras CS-2 demonstrated efficient large-scale multilingual
pretraining with documented energy use, offering a blueprint for inclusive
foundation models that integrate low-resource languages.
R^textbf{2AI}: Towards Resistant and Resilient AI in an
  Evolving World
Authors: name: Youbang Sun, name: Xiang Wang, name: Jie Fu, name: Chaochao Lu, name: Bowen Zhou
Published: 2025-09-08 15:13:23

In this position paper, we address the persistent gap between rapidly growing
AI capabilities and lagging safety progress. Existing paradigms divide into
``Make AI Safe'', which applies post-hoc alignment and guardrails but remains
brittle and reactive, and ``Make Safe AI'', which emphasizes intrinsic safety
but struggles to address unforeseen risks in open-ended environments. We
therefore propose safe-by-coevolution as a new formulation of the
``Make Safe AI'' paradigm, inspired by biological immunity, in which safety
becomes a dynamic, adversarial, and ongoing learning process. To operationalize
this vision, we introduce R^2AI -- Resistant and Resilient
AI -- as a practical framework that unites resistance against known threats
with resilience to unforeseen risks. R^2AI integrates fast
and slow safe models, adversarial simulation and verification through a
safety wind tunnel, and continual feedback loops that guide safety and
capability to coevolve. We argue that this framework offers a scalable and
proactive path to maintain continual safety in dynamic environments, addressing
both near-term vulnerabilities and long-term existential risks as AI advances
toward AGI and ASI.
Reinforcement Learning Foundations for Deep Research Systems: A Survey
Authors: name: Wenjun Li, name: Zhi Chen, name: Jingru Lin, name: Hannan Cao, name: Wei Han, name: Sheng Liang, name: Zhi Zhang, name: Kuicai Dong, name: Dexun Li, name: Chen Zhang, name: Yong Liu
Published: 2025-09-08 14:27:23

Deep research systems, agentic AI that solve complex, multi-step tasks by
coordinating reasoning, search across the open web and user files, and tool
use, are moving toward hierarchical deployments with a Planner, Coordinator,
and Executors. In practice, training entire stacks end-to-end remains
impractical, so most work trains a single planner connected to core tools such
as search, browsing, and code. While SFT imparts protocol fidelity, it suffers
from imitation and exposure biases and underuses environment feedback.
Preference alignment methods such as DPO are schema and proxy-dependent,
off-policy, and weak for long-horizon credit assignment and multi-objective
trade-offs. A further limitation of SFT and DPO is their reliance on human
defined decision points and subskills through schema design and labeled
comparisons. Reinforcement learning aligns with closed-loop, tool-interaction
research by optimizing trajectory-level policies, enabling exploration,
recovery behaviors, and principled credit assignment, and it reduces dependence
on such human priors and rater biases.
  This survey is, to our knowledge, the first dedicated to the RL foundations
of deep research systems. It systematizes work after DeepSeek-R1 along three
axes: (i) data synthesis and curation; (ii) RL methods for agentic research
covering stability, sample efficiency, long context handling, reward and credit
design, multi-objective optimization, and multimodal integration; and (iii)
agentic RL training systems and frameworks. We also cover agent architecture
and coordination, as well as evaluation and benchmarks, including recent QA,
VQA, long-form synthesis, and domain-grounded, tool-interaction tasks. We
distill recurring patterns, surface infrastructure bottlenecks, and offer
practical guidance for training robust, transparent deep research agents with
RL.
Revolutionizing Reinforcement Learning Framework for Diffusion Large
  Language Models
Authors: name: Yinjie Wang, name: Ling Yang, name: Bowen Li, name: Ye Tian, name: Ke Shen, name: Mengdi Wang
Published: 2025-09-08 17:58:06

We propose TraceRL, a trajectory-aware reinforcement learning framework for
diffusion language models (DLMs) that incorporates preferred inference
trajectory into post-training, and is applicable across different
architectures. Equipped with a diffusion-based value model that enhances
training stability, we demonstrate improved reasoning performance on complex
math and coding tasks. Besides, it can also be applied to adapt block-specific
models to larger blocks, which improves sampling flexibility. Employing
TraceRL, we derive a series of state-of-the-art diffusion language models,
namely TraDo. Although smaller than 7B-scale AR models, TraDo-4B-Instruct still
consistently outperforms them across complex math reasoning tasks.
TraDo-8B-Instruct achieves relative accuracy improvements of 6.1% over
Qwen2.5-7B-Instruct and 51.3% over Llama3.1-8B-Instruct on mathematical
reasoning benchmarks. Through curriculum learning, we also derive the first
long-CoT DLM, outperforming Qwen2.5-7B-Instruct on MATH500 with an 18.1%
relative accuracy gain. To facilitate reproducible research and practical
applications, we release a comprehensive open-source framework for building,
training, and deploying diffusion LLMs across diverse architectures. The
framework integrates accelerated KV-cache techniques and inference engines for
both inference and reinforcement learning, and includes implementations of
various supervised fine-tuning and RL methods for mathematics, coding, and
general tasks. Code and Models: https://github.com/Gen-Verse/dLLM-RL
Reinforced Visual Perception with Tools
Authors: name: Zetong Zhou, name: Dongping Chen, name: Zixian Ma, name: Zhihan Hu, name: Mingyang Fu, name: Sinan Wang, name: Yao Wan, name: Zhou Zhao, name: Ranjay Krishna
Published: 2025-09-01 17:57:49

Visual reasoning, a cornerstone of human intelligence, encompasses complex
perceptual and logical processes essential for solving diverse visual problems.
While advances in computer vision have produced powerful models for various
perceptual tasks, leveraging these for general visual reasoning remains
challenging. Prior work demonstrates that augmenting LLMs with vision models
via supervised finetuning improves performance, but faces key limitations such
as expensive data generation, reliance on careful data filtering, and poor
generalization. To address these issues, we propose ReVPT to enhance
multi-modal LLMs' abilities to reason about and use visual tools through
reinforcement learning. We introduce a novel RL algorithm based on GRPO,
designed to train models to reason with a suite of four visual tools. Through
extensive experiments, we show that our method achieves state-of-the-art
performance on several perception-heavy benchmarks, including SAT, CV-Bench,
BLINK and MMStar, significantly outperforming the supervised and text-based RL
finetuning baselines. Notably, Our ReVPT-3B and ReVPT-7B outperform the
instruct models by 9.03% and 9.44% on CV-Bench. Finally, we bring to the
community new insights on RL-based visual tool-usage through extensive
ablations. Our code is available at https://github.com/ls-kelvin/REVPT.
UniVerse-1: Unified Audio-Video Generation via Stitching of Experts
Authors: name: Duomin Wang, name: Wei Zuo, name: Aojie Li, name: Ling-Hao Chen, name: Xinyao Liao, name: Deyu Zhou, name: Zixin Yin, name: Xili Dai, name: Daxin Jiang, name: Gang Yu
Published: 2025-09-07 17:55:03

We introduce UniVerse-1, a unified, Veo-3-like model capable of
simultaneously generating coordinated audio and video. To enhance training
efficiency, we bypass training from scratch and instead employ a stitching of
experts (SoE) technique. This approach deeply fuses the corresponding blocks of
pre-trained video and music generation experts models, thereby fully leveraging
their foundational capabilities. To ensure accurate annotations and temporal
alignment for both ambient sounds and speech with video content, we developed
an online annotation pipeline that processes the required training data and
generates labels during training process. This strategy circumvents the
performance degradation often caused by misalignment text-based annotations.
Through the synergy of these techniques, our model, after being finetuned on
approximately 7,600 hours of audio-video data, produces results with
well-coordinated audio-visuals for ambient sounds generation and strong
alignment for speech generation. To systematically evaluate our proposed
method, we introduce Verse-Bench, a new benchmark dataset. In an effort to
advance research in audio-video generation and to close the performance gap
with state-of-the-art models such as Veo3, we make our model and code publicly
available. We hope this contribution will benefit the broader research
community. Project page: https://dorniwang.github.io/UniVerse-1/.
Set Block Decoding is a Language Model Inference Accelerator
Authors: name: Itai Gat, name: Heli Ben-Hamu, name: Marton Havasi, name: Daniel Haziza, name: Jeremy Reizenstein, name: Gabriel Synnaeve, name: David Lopez-Paz, name: Brian Karrer, name: Yaron Lipman
Published: 2025-09-04 13:02:39

Autoregressive next token prediction language models offer powerful
capabilities but face significant challenges in practical deployment due to the
high computational and memory costs of inference, particularly during the
decoding stage. We introduce Set Block Decoding (SBD), a simple and flexible
paradigm that accelerates generation by integrating standard next token
prediction (NTP) and masked token prediction (MATP) within a single
architecture. SBD allows the model to sample multiple, not necessarily
consecutive, future tokens in parallel, a key distinction from previous
acceleration methods. This flexibility allows the use of advanced solvers from
the discrete diffusion literature, offering significant speedups without
sacrificing accuracy. SBD requires no architectural changes or extra training
hyperparameters, maintains compatibility with exact KV-caching, and can be
implemented by fine-tuning existing next token prediction models. By
fine-tuning Llama-3.1 8B and Qwen-3 8B, we demonstrate that SBD enables a 3-5x
reduction in the number of forward passes required for generation while
achieving same performance as equivalent NTP training.
Bootstrapping Task Spaces for Self-Improvement
Authors: name: Minqi Jiang, name: Andrei Lupu, name: Yoram Bachrach
Published: 2025-09-04 18:01:00

Progress in many task domains emerges from repeated revisions to previous
solution attempts. Training agents that can reliably self-improve over such
sequences at inference-time is a natural target for reinforcement learning
(RL), yet the naive approach assumes a fixed maximum iteration depth, which can
be both costly and arbitrary. We present Exploratory Iteration (ExIt), a family
of autocurriculum RL methods that directly exploits the recurrent structure of
self-improvement tasks to train LLMs to perform multi-step self-improvement at
inference-time while only training on the most informative single-step
iterations. ExIt grows a task space by selectively sampling the most
informative intermediate, partial histories encountered during an episode for
continued iteration, treating these starting points as new self-iteration task
instances to train a self-improvement policy. ExIt can further pair with
explicit exploration mechanisms to sustain greater task diversity. Across
several domains, encompassing competition math, multi-turn tool-use, and
machine learning engineering, we demonstrate that ExIt strategies, starting
from either a single or many task instances, can produce policies exhibiting
strong inference-time self-improvement on held-out task instances, and the
ability to iterate towards higher performance over a step budget extending
beyond the average iteration depth encountered during training.
Symbolic Graphics Programming with Large Language Models
Authors: name: Yamei Chen, name: Haoquan Zhang, name: Yangyi Huang, name: Zeju Qiu, name: Kaipeng Zhang, name: Yandong Wen, name: Weiyang Liu
Published: 2025-09-05 16:10:53

Large language models (LLMs) excel at program synthesis, yet their ability to
produce symbolic graphics programs (SGPs) that render into precise visual
content remains underexplored. We study symbolic graphics programming, where
the goal is to generate an SGP from a natural-language description. This task
also serves as a lens into how LLMs understand the visual world by prompting
them to generate images rendered from SGPs. Among various SGPs, our paper
sticks to scalable vector graphics (SVGs). We begin by examining the extent to
which LLMs can generate SGPs. To this end, we introduce SGP-GenBench, a
comprehensive benchmark covering object fidelity, scene fidelity, and
compositionality (attribute binding, spatial relations, numeracy). On
SGP-GenBench, we discover that frontier proprietary models substantially
outperform open-source models, and performance correlates well with general
coding capabilities. Motivated by this gap, we aim to improve LLMs' ability to
generate SGPs. We propose a reinforcement learning (RL) with verifiable rewards
approach, where a format-validity gate ensures renderable SVG, and a
cross-modal reward aligns text and the rendered image via strong vision
encoders (e.g., SigLIP for text-image and DINO for image-image). Applied to
Qwen-2.5-7B, our method substantially improves SVG generation quality and
semantics, achieving performance on par with frontier systems. We further
analyze training dynamics, showing that RL induces (i) finer decomposition of
objects into controllable primitives and (ii) contextual details that improve
scene coherence. Our results demonstrate that symbolic graphics programming
offers a precise and interpretable lens on cross-modal grounding.
U-ARM : Ultra low-cost general teleoperation interface for robot
  manipulation
Authors: name: Yanwen Zou, name: Zhaoye Zhou, name: Chenyang Shi, name: Zewei Ye, name: Junda Huang, name: Yan Ding, name: Bo Zhao
Published: 2025-09-02 15:39:38

We propose U-Arm, a low-cost and rapidly adaptable leader-follower
teleoperation framework designed to interface with most of commercially
available robotic arms. Our system supports teleoperation through three
structurally distinct 3D-printed leader arms that share consistent control
logic, enabling seamless compatibility with diverse commercial robot
configurations. Compared with previous open-source leader-follower interfaces,
we further optimized both the mechanical design and servo selection, achieving
a bill of materials (BOM) cost of only \50.5 for the 6-DoF leader arm and
56.8 for the 7-DoF version. To enhance usability, we mitigate the common
challenge in controlling redundant degrees of freedom by %engineering methods
mechanical and control optimizations. Experimental results demonstrate that
U-Arm achieves 39\% higher data collection efficiency and comparable task
success rates across multiple manipulation scenarios compared with Joycon,
another low-cost teleoperation interface. We have open-sourced all CAD models
of three configs and also provided simulation support for validating
teleoperation workflows. We also open-sourced real-world manipulation data
collected with U-Arm. The project website is
https://github.com/MINT-SJTU/LeRobot-Anything-U-Arm.
LuxDiT: Lighting Estimation with Video Diffusion Transformer
Authors: name: Ruofan Liang, name: Kai He, name: Zan Gojcic, name: Igor Gilitschenski, name: Sanja Fidler, name: Nandita Vijaykumar, name: Zian Wang
Published: 2025-09-03 19:59:20

Estimating scene lighting from a single image or video remains a longstanding
challenge in computer vision and graphics. Learning-based approaches are
constrained by the scarcity of ground-truth HDR environment maps, which are
expensive to capture and limited in diversity. While recent generative models
offer strong priors for image synthesis, lighting estimation remains difficult
due to its reliance on indirect visual cues, the need to infer global
(non-local) context, and the recovery of high-dynamic-range outputs. We propose
LuxDiT, a novel data-driven approach that fine-tunes a video diffusion
transformer to generate HDR environment maps conditioned on visual input.
Trained on a large synthetic dataset with diverse lighting conditions, our
model learns to infer illumination from indirect visual cues and generalizes
effectively to real-world scenes. To improve semantic alignment between the
input and the predicted environment map, we introduce a low-rank adaptation
finetuning strategy using a collected dataset of HDR panoramas. Our method
produces accurate lighting predictions with realistic angular high-frequency
details, outperforming existing state-of-the-art techniques in both
quantitative and qualitative evaluations.