Orionnnn/hub-stats huggingface dataset:
RL-PLUS: Countering Capability Boundary Collapse of LLMs in
  Reinforcement Learning with Hybrid-policy Optimization
Authors: name: Yihong Dong, name: Xue Jiang, name: Yongding Tao, name: Huanyu Liu, name: Kechi Zhang, name: Lili Mou, name: Rongyu Cao, name: Yingwei Ma, name: Jue Chen, name: Binhua Li, name: Zhi Jin, name: Fei Huang, name: Yongbin Li, name: Ge Li
Published: 2025-07-31 23:55:29

Reinforcement Learning with Verifiable Reward (RLVR) has significantly
advanced the complex reasoning abilities of Large Language Models (LLMs).
However, it struggles to break through the inherent capability boundaries of
the base LLM, due to its essentially on-policy strategy coupled with LLM's
immense action space and sparse reward. Critically, RLVR can lead to the
capability boundary collapse, narrowing the LLM's problem-solving scope. To
address this problem, we propose RL-PLUS, a novel hybrid-policy optimization
approach for LLMs that synergizes internal exploitation with external data to
achieve stronger reasoning capabilities and surpass the boundaries of base
models. RL-PLUS integrates two core components, i.e., Multiple Importance
Sampling to address distributional mismatch from external data, and
Exploration-Based Advantage Function to guide the model towards high-value,
unexplored reasoning paths. We provide both theoretical analysis and extensive
experiments to demonstrate the superiority and generalizability of our
approach. Compared with existing RLVR methods, RL-PLUS achieves 1)
state-of-the-art performance on six math reasoning benchmarks; 2) superior
performance on six out-of-distribution reasoning tasks; 3) consistent and
significant gains across diverse model families, with average relative
improvements up to 69.2\%. Moreover, the analysis of Pass@k curves indicates
that RL-PLUS effectively resolves the capability boundary collapse problem.
SonicMaster: Towards Controllable All-in-One Music Restoration and
  Mastering
Authors: name: Jan Melechovsky, name: Ambuj Mehrish, name: Dorien Herremans
Published: 2025-08-05 13:49:04

Music recordings often suffer from audio quality issues such as excessive
reverberation, distortion, clipping, tonal imbalances, and a narrowed stereo
image, especially when created in non-professional settings without specialized
equipment or expertise. These problems are typically corrected using separate
specialized tools and manual adjustments. In this paper, we introduce
SonicMaster, the first unified generative model for music restoration and
mastering that addresses a broad spectrum of audio artifacts with text-based
control. SonicMaster is conditioned on natural language instructions to apply
targeted enhancements, or can operate in an automatic mode for general
restoration. To train this model, we construct the SonicMaster dataset, a large
dataset of paired degraded and high-quality tracks by simulating common
degradation types with nineteen degradation functions belonging to five
enhancements groups: equalization, dynamics, reverb, amplitude, and stereo. Our
approach leverages a flow-matching generative training paradigm to learn an
audio transformation that maps degraded inputs to their cleaned, mastered
versions guided by text prompts. Objective audio quality metrics demonstrate
that SonicMaster significantly improves sound quality across all artifact
categories. Furthermore, subjective listening tests confirm that listeners
prefer SonicMaster's enhanced outputs over the original degraded audio,
highlighting the effectiveness of our unified approach.
Efficient Agents: Building Effective Agents While Reducing Cost
Authors: name: Ningning Wang, name: Xavier Hu, name: Pai Liu, name: He Zhu, name: Yue Hou, name: Heyuan Huang, name: Shengyu Zhang, name: Jian Yang, name: Jiaheng Liu, name: Ge Zhang, name: Changwang Zhang, name: Jun Wang, name: Yuchen Eleanor Jiang, name: Wangchunshu Zhou
Published: 2025-07-24 17:56:51

The remarkable capabilities of Large Language Model (LLM)-driven agents have
enabled sophisticated systems to tackle complex, multi-step tasks, but their
escalating costs threaten scalability and accessibility. This work presents the
first systematic study of the efficiency-effectiveness trade-off in modern
agent systems, addressing the critical need for cost-effective designs without
sacrificing performance. We investigate three key questions: (1) How much
complexity do agentic tasks inherently require? (2) When do additional modules
yield diminishing returns? (3) How much efficiency can be gained through the
design of efficient agent frameworks? Through an empirical analysis on the GAIA
benchmark, we evaluate the impact of LLM backbone selection, agent framework
designs, and test-time scaling strategies. Using the cost-of-pass metric, we
quantify the efficiency-performance trade-off across these dimensions. Our
findings inform the development of Efficient Agents , a novel agent framework
that has an optimal complexity to task requirements. Efficient Agents retains
96.7% of the performance of OWL, one leading open-source agent framework, while
reducing operational costs from 0.398 to 0.228, resulting in a 28.4%
improvement in cost-of-pass. Our work provides actionable insights for
designing efficient, high-performing agent systems, advancing the accessibility
and sustainability of AI-driven solutions.
OpenMed NER: Open-Source, Domain-Adapted State-of-the-Art Transformers
  for Biomedical NER Across 12 Public Datasets
Authors: name: Maziyar Panahi
Published: 2025-08-03 07:33:28

Named-entity recognition (NER) is fundamental to extracting structured
information from the >80% of healthcare data that resides in unstructured
clinical notes and biomedical literature. Despite recent advances with large
language models, achieving state-of-the-art performance across diverse entity
types while maintaining computational efficiency remains a significant
challenge. We introduce OpenMed NER, a suite of open-source, domain-adapted
transformer models that combine lightweight domain-adaptive pre-training (DAPT)
with parameter-efficient Low-Rank Adaptation (LoRA). Our approach performs
cost-effective DAPT on a 350k-passage corpus compiled from ethically sourced,
publicly available research repositories and de-identified clinical notes
(PubMed, arXiv, and MIMIC-III) using DeBERTa-v3, PubMedBERT, and BioELECTRA
backbones. This is followed by task-specific fine-tuning with LoRA, which
updates less than 1.5% of model parameters. We evaluate our models on 12
established biomedical NER benchmarks spanning chemicals, diseases, genes, and
species. OpenMed NER achieves new state-of-the-art micro-F1 scores on 10 of
these 12 datasets, with substantial gains across diverse entity types. Our
models advance the state-of-the-art on foundational disease and chemical
benchmarks (e.g., BC5CDR-Disease, +2.70 pp), while delivering even larger
improvements of over 5.3 and 9.7 percentage points on more specialized gene and
clinical cell line corpora. This work demonstrates that strategically adapted
open-source models can surpass closed-source solutions. This performance is
achieved with remarkable efficiency: training completes in under 12 hours on a
single GPU with a low carbon footprint (< 1.2 kg CO2e), producing permissively
licensed, open-source checkpoints designed to help practitioners facilitate
compliance with emerging data protection and AI regulations, such as the EU AI
Act.
CoTox: Chain-of-Thought-Based Molecular Toxicity Reasoning and
  Prediction
Authors: name: Jueon Park, name: Yein Park, name: Minju Song, name: Soyon Park, name: Donghyeon Lee, name: Seungheun Baek, name: Jaewoo Kang
Published: 2025-08-05 07:04:44

Drug toxicity remains a major challenge in pharmaceutical development. Recent
machine learning models have improved in silico toxicity prediction, but their
reliance on annotated data and lack of interpretability limit their
applicability. This limits their ability to capture organ-specific toxicities
driven by complex biological mechanisms. Large language models (LLMs) offer a
promising alternative through step-by-step reasoning and integration of textual
data, yet prior approaches lack biological context and transparent rationale.
To address this issue, we propose CoTox, a novel framework that integrates LLM
with chain-of-thought (CoT) reasoning for multi-toxicity prediction. CoTox
combines chemical structure data, biological pathways, and gene ontology (GO)
terms to generate interpretable toxicity predictions through step-by-step
reasoning. Using GPT-4o, we show that CoTox outperforms both traditional
machine learning and deep learning model. We further examine its performance
across various LLMs to identify where CoTox is most effective. Additionally, we
find that representing chemical structures with IUPAC names, which are easier
for LLMs to understand than SMILES, enhances the model's reasoning ability and
improves predictive performance. To demonstrate its practical utility in drug
development, we simulate the treatment of relevant cell types with drug and
incorporated the resulting biological context into the CoTox framework. This
approach allow CoTox to generate toxicity predictions aligned with
physiological responses, as shown in case study. This result highlights the
potential of LLM-based frameworks to improve interpretability and support
early-stage drug safety assessment. The code and prompt used in this work are
available at https://github.com/dmis-lab/CoTox.
LaTCoder: Converting Webpage Design to Code with Layout-as-Thought
Authors: name: Yi Gui, name: Zhen Li, name: Zhongyi Zhang, name: Guohao Wang, name: Tianpeng Lv, name: Gaoyang Jiang, name: Yi Liu, name: Dongping Chen, name: Yao Wan, name: Hongyu Zhang, name: Wenbin Jiang, name: Xuanhua Shi, name: Hai Jin
Published: 2025-08-05 15:28:48

Converting webpage designs into code (design-to-code) plays a vital role in
User Interface (UI) development for front-end developers, bridging the gap
between visual design and functional implementation. While recent Multimodal
Large Language Models (MLLMs) have shown significant potential in
design-to-code tasks, they often fail to accurately preserve the layout during
code generation. To this end, we draw inspiration from the Chain-of-Thought
(CoT) reasoning in human cognition and propose LaTCoder, a novel approach that
enhances layout preservation in webpage design during code generation with
Layout-as-Thought (LaT). Specifically, we first introduce a simple yet
efficient algorithm to divide the webpage design into image blocks. Next, we
prompt MLLMs using a CoTbased approach to generate code for each block.
Finally, we apply two assembly strategies-absolute positioning and an
MLLM-based method-followed by dynamic selection to determine the optimal
output. We evaluate the effectiveness of LaTCoder using multiple backbone MLLMs
(i.e., DeepSeek-VL2, Gemini, and GPT-4o) on both a public benchmark and a newly
introduced, more challenging benchmark (CC-HARD) that features complex layouts.
The experimental results on automatic metrics demonstrate significant
improvements. Specifically, TreeBLEU scores increased by 66.67% and MAE
decreased by 38% when using DeepSeek-VL2, compared to direct prompting.
Moreover, the human preference evaluation results indicate that annotators
favor the webpages generated by LaTCoder in over 60% of cases, providing strong
evidence of the effectiveness of our method.
The Cow of Rembrandt - Analyzing Artistic Prompt Interpretation in
  Text-to-Image Models
Authors: name: Alfio Ferrara, name: Sergio Picascia, name: Elisabetta Rocchetti
Published: 2025-07-31 07:47:01

Text-to-image diffusion models have demonstrated remarkable capabilities in
generating artistic content by learning from billions of images, including
popular artworks. However, the fundamental question of how these models
internally represent concepts, such as content and style in paintings, remains
unexplored. Traditional computer vision assumes content and style are
orthogonal, but diffusion models receive no explicit guidance about this
distinction during training. In this work, we investigate how transformer-based
text-to-image diffusion models encode content and style concepts when
generating artworks. We leverage cross-attention heatmaps to attribute pixels
in generated images to specific prompt tokens, enabling us to isolate image
regions influenced by content-describing versus style-describing tokens. Our
findings reveal that diffusion models demonstrate varying degrees of
content-style separation depending on the specific artistic prompt and style
requested. In many cases, content tokens primarily influence object-related
regions while style tokens affect background and texture areas, suggesting an
emergent understanding of the content-style distinction. These insights
contribute to our understanding of how large-scale generative models internally
represent complex artistic concepts without explicit supervision. We share the
code and dataset, together with an exploratory tool for visualizing attention
maps at https://github.com/umilISLab/artistic-prompt-interpretation.
EVOC2RUST: A Skeleton-guided Framework for Project-Level C-to-Rust
  Translation
Authors: name: Chaofan Wang, name: Tingrui Yu, name: Jie Wang, name: Dong Chen, name: Wenrui Zhang, name: Yuling Shi, name: Xiaodong Gu, name: Beijun Shen
Published: 2025-08-06 10:31:23

Rust's compile-time safety guarantees make it ideal for safety-critical
systems, creating demand for translating legacy C codebases to Rust. While
various approaches have emerged for this task, they face inherent trade-offs:
rule-based solutions face challenges in meeting code safety and idiomaticity
requirements, while LLM-based solutions often fail to generate semantically
equivalent Rust code, due to the heavy dependencies of modules across the
entire codebase. Recent studies have revealed that both solutions are limited
to small-scale programs. In this paper, we propose EvoC2Rust, an automated
framework for converting entire C projects to equivalent Rust ones. EvoC2Rust
employs a skeleton-guided translation strategy for project-level translation.
The pipeline consists of three evolutionary stages: 1) it first decomposes the
C project into functional modules, employs a feature-mapping-enhanced LLM to
transform definitions and macros and generates type-checked function stubs,
which form a compilable Rust skeleton; 2) it then incrementally translates the
function, replacing the corresponding stub placeholder; 3) finally, it repairs
compilation errors by integrating LLM and static analysis. Through evolutionary
augmentation, EvoC2Rust combines the advantages of both rule-based and
LLM-based solutions. Our evaluation on open-source benchmarks and six
industrial projects demonstrates EvoC2Rust's superior performance in
project-level C-to-Rust translation. On average, it achieves 17.24% and 14.32%
improvements in syntax and semantic accuracy over the LLM-based approaches,
along with a 96.79% higher code safety rate than the rule-based tools. At the
module level, EvoC2Rust reaches 92.25% compilation and 89.53% test pass rates
on industrial projects, even for complex codebases and long functions.
Agent Lightning: Train ANY AI Agents with Reinforcement Learning
Authors: name: Xufang Luo, name: Yuge Zhang, name: Zhiyuan He, name: Zilong Wang, name: Siyun Zhao, name: Dongsheng Li, name: Luna K. Qiu, name: Yuqing Yang
Published: 2025-08-05 17:50:13

We present Agent Lightning, a flexible and extensible framework that enables
Reinforcement Learning (RL)-based training of Large Language Models (LLMs) for
any AI agent. Unlike existing methods that tightly couple RL training with
agent or rely on sequence concatenation with masking, Agent Lightning achieves
complete decoupling between agent execution and training, allowing seamless
integration with existing agents developed via diverse ways (e.g., using
frameworks like LangChain, OpenAI Agents SDK, AutoGen, and building from
scratch) with almost ZERO code modifications. By formulating agent execution as
Markov decision process, we define an unified data interface and propose a
hierarchical RL algorithm, LightningRL, which contains a credit assignment
module, allowing us to decompose trajectories generated by ANY agents into
training transition. This enables RL to handle complex interaction logic, such
as multi-agent scenarios and dynamic workflows. For the system design, we
introduce a Training-Agent Disaggregation architecture, and brings agent
observability frameworks into agent runtime, providing a standardized agent
finetuning interface. Experiments across text-to-SQL, retrieval-augmented
generation, and math tool-use tasks demonstrate stable, continuous
improvements, showcasing the framework's potential for real-world agent
training and deployment.
SEAgent: Self-Evolving Computer Use Agent with Autonomous Learning from
  Experience
Authors: name: Zeyi Sun, name: Ziyu Liu, name: Yuhang Zang, name: Yuhang Cao, name: Xiaoyi Dong, name: Tong Wu, name: Dahua Lin, name: Jiaqi Wang
Published: 2025-08-06 17:58:46

Repurposing large vision-language models (LVLMs) as computer use agents
(CUAs) has led to substantial breakthroughs, primarily driven by human-labeled
data. However, these models often struggle with novel and specialized software,
particularly in scenarios lacking human annotations. To address this challenge,
we propose SEAgent, an agentic self-evolving framework enabling CUAs to
autonomously evolve through interactions with unfamiliar software.
Specifically, SEAgent empowers computer-use agents to autonomously master novel
software environments via experiential learning, where agents explore new
software, learn through iterative trial-and-error, and progressively tackle
auto-generated tasks organized from simple to complex. To achieve this goal, we
design a World State Model for step-wise trajectory assessment, along with a
Curriculum Generator that generates increasingly diverse and challenging tasks.
The agent's policy is updated through experiential learning, comprised of
adversarial imitation of failure actions and Group Relative Policy Optimization
(GRPO) on successful ones. Furthermore, we introduce a specialist-to-generalist
training strategy that integrates individual experiential insights from
specialist agents, facilitating the development of a stronger generalist CUA
capable of continuous autonomous evolution. This unified agent ultimately
achieves performance surpassing ensembles of individual specialist agents on
their specialized software. We validate the effectiveness of SEAgent across
five novel software environments within OS-World. Our approach achieves a
significant improvement of 23.2% in success rate, from 11.3% to 34.5%, over a
competitive open-source CUA, i.e., UI-TARS.
VeriGUI: Verifiable Long-Chain GUI Dataset
Authors: name: Shunyu Liu, name: Minghao Liu, name: Huichi Zhou, name: Zhenyu Cui, name: Yang Zhou, name: Yuhao Zhou, name: Wendong Fan, name: Ge Zhang, name: Jiajun Shi, name: Weihao Xuan, name: Jiaxing Huang, name: Shuang Luo, name: Fang Wu, name: Heli Qi, name: Qingcheng Zeng, name: Ziqi Ren, name: Jialiang Gao, name: Jindi Lv, name: Junjie Wang, name: Aosong Feng, name: Heng Zhou, name: Wangchunshu Zhou, name: Zhenfei Yin, name: Wenlong Zhang, name: Guohao Li, name: Wenhao Yu, name: Irene Li, name: Lei Ma, name: Lei Bai, name: Qunshu Lin, name: Mingli Song, name: Dacheng Tao
Published: 2025-08-06 02:38:18

Recent studies have delved into constructing autonomous agents capable of
performing complex Graphical User Interface (GUI)-based computer tasks, with
the potential to revolutionize human-computer interaction. Despite encouraging
results, existing efforts mainly focus on short-term interactions and rely on
outcome-only verification, thereby limiting their scalability in real-world GUI
applications that demand long-horizon task decomposition and execution. In this
work, we introduce VeriGUI, a novel verifiable long-chain GUI dataset designed
to facilitate the development and evaluation of generalist GUI agents operating
in realistic computer environments. Our dataset emphasizes two critical
dimensions: (1) long-chain complexity, with tasks decomposed into a sequence of
interdependent subtasks spanning hundreds of steps, explicitly designed to
allow any subtask to serve as a valid starting point; and (2) subtask-level
verifiability, which enables diverse exploration strategies within each
subtask, while ensuring that each subtask-level goal remains verifiable and
consistent. The dataset consists of GUI task trajectories across both desktop
and web, annotated by human experts. Extensive experiments on VeriGUI using
various agents with different foundation models reveal significant performance
gaps in handling long-horizon tasks, highlighting the need for more robust
planning and decision-making capabilities in GUI agents.
Web-CogReasoner: Towards Knowledge-Induced Cognitive Reasoning for Web
  Agents
Authors: name: Yuhan Guo, name: Cong Guo, name: Aiwen Sun, name: Hongliang He, name: Xinyu Yang, name: Yue Lu, name: Yingji Zhang, name: Xuntao Guo, name: Dong Zhang, name: Jianzhuang Liu, name: Jiang Duan, name: Yijia Xiao, name: Liangjian Wen, name: Hai-Ming Xu, name: Yong Dai
Published: 2025-08-03 17:17:52

Multimodal large-scale models have significantly advanced the development of
web agents, enabling perception and interaction with digital environments akin
to human cognition. In this paper, we argue that web agents must first acquire
sufficient knowledge to effectively engage in cognitive reasoning. Therefore,
we decompose a web agent's capabilities into two essential stages: knowledge
content learning and cognitive processes. To formalize this, we propose
Web-CogKnowledge Framework, categorizing knowledge as Factual, Conceptual, and
Procedural. In this framework, knowledge content learning corresponds to the
agent's processes of Memorizing and Understanding, which rely on the first two
knowledge types, representing the "what" of learning. Conversely, cognitive
processes correspond to Exploring, grounded in Procedural knowledge, defining
the "how" of reasoning and action. To facilitate knowledge acquisition, we
construct the Web-CogDataset, a structured resource curated from 14 real-world
websites, designed to systematically instill core knowledge necessary for web
agent. This dataset serves as the agent's conceptual grounding-the "nouns" upon
which comprehension is built-as well as the basis for learning how to reason
and act. Building on this foundation, we operationalize these processes through
a novel knowledge-driven Chain-of-Thought (CoT) reasoning framework, developing
and training our proposed agent, the Web-CogReasoner. Extensive experimentation
reveals its significant superiority over existing models, especially in
generalizing to unseen tasks where structured knowledge is decisive. To enable
rigorous evaluation, we introduce the Web-CogBench, a comprehensive evaluation
suite designed to assess and compare agent performance across the delineated
knowledge domains and cognitive capabilities. Our code and data is open sourced
at https://github.com/Gnonymous/Web-CogReasoner