Orionnnn/hub-stats huggingface dataset:
MobiAgent: A Systematic Framework for Customizable Mobile Agents
Authors: name: Cheng Zhang, name: Erhu Feng, name: Xi Zhao, name: Yisheng Zhao, name: Wangbo Gong, name: Jiahui Sun, name: Dong Du, name: Zhichao Hua, name: Yubin Xia, name: Haibo Chen
Published: 2025-08-30 15:24:47

With the rapid advancement of Vision-Language Models (VLMs), GUI-based mobile
agents have emerged as a key development direction for intelligent mobile
systems. However, existing agent models continue to face significant challenges
in real-world task execution, particularly in terms of accuracy and efficiency.
To address these limitations, we propose MobiAgent, a comprehensive mobile
agent system comprising three core components: the MobiMind-series agent
models, the AgentRR acceleration framework, and the MobiFlow benchmarking
suite. Furthermore, recognizing that the capabilities of current mobile agents
are still limited by the availability of high-quality data, we have developed
an AI-assisted agile data collection pipeline that significantly reduces the
cost of manual annotation. Compared to both general-purpose LLMs and
specialized GUI agent models, MobiAgent achieves state-of-the-art performance
in real-world mobile scenarios.
Fantastic Pretraining Optimizers and Where to Find Them
Authors: name: Kaiyue Wen, name: David Hall, name: Tengyu Ma, name: Percy Liang
Published: 2025-09-02 07:43:22

AdamW has long been the dominant optimizer in language model pretraining,
despite numerous claims that alternative optimizers offer 1.4 to 2x speedup. We
posit that two methodological shortcomings have obscured fair comparisons and
hindered practical adoption: (i) unequal hyperparameter tuning and (ii) limited
or misleading evaluation setups. To address these two issues, we conduct a
systematic study of ten deep learning optimizers across four model scales
(0.1B-1.2B parameters) and data-to-model ratios (1-8x the Chinchilla optimum).
We find that fair and informative comparisons require rigorous hyperparameter
tuning and evaluations across a range of model scales and data-to-model ratios,
performed at the end of training. First, optimal hyperparameters for one
optimizer may be suboptimal for another, making blind hyperparameter transfer
unfair. Second, the actual speedup of many proposed optimizers over well-tuned
baselines is lower than claimed and decreases with model size to only 1.1x for
1.2B parameter models. Thirdly, comparing intermediate checkpoints before
reaching the target training budgets can be misleading, as rankings between two
optimizers can flip during training due to learning rate decay. Through our
thorough investigation, we find that all the fastest optimizers such as Muon
and Soap, use matrices as preconditioners -- multiplying gradients with
matrices rather than entry-wise scalars. However, the speedup of matrix-based
optimizers is inversely proportional to model scale, decreasing from 1.4x over
AdamW for 0.1B parameter models to merely 1.1x for 1.2B parameter models.
Metis: Training Large Language Models with Advanced Low-Bit Quantization
Authors: name: Hengjie Cao, name: Mengyi Chen, name: Yifeng Yang, name: Ruijun Huang, name: Fang Dong, name: Jixian Zhou, name: Anrui Chen, name: Mingzhi Dong, name: Yujiang Wang, name: Jinlong Hou, name: Yuan Cheng, name: Fan Wu, name: Fan Yang, name: Tun Lu, name: Ning Gu, name: Li Shang
Published: 2025-08-30 08:09:08

This work identifies anisotropic parameter distributions as a fundamental
barrier to training large language models (LLMs) with low-bit quantization: a
few dominant singular values create wide numerical ranges that conflict with
the inherent bias of block-wise quantization. This bias disproportionately
preserves high-magnitude values while discarding smaller ones, causing training
instability and low model performance. This work introduces Metis, a training
framework that combines (i) spectral decomposition with random embedding to
efficiently disentangle dominant from long-tail components, compressing broad
distributions into quantization-friendly narrow ranges; (ii) adaptive learning
rates in the spectral domain to amplify underrepresented directions and better
capture diverse features critical for performance; and (iii) a dual-range
regularizer that jointly constrains numerical precision and parameter range
distribution, ensuring stable, unbiased low-bit training. With Metis, FP8
training surpasses FP32 baselines, and FP4 training achieves accuracy
comparable to FP32, paving the way for robust and scalable LLM training under
advanced low-bit quantization. The code implementation for Metis is available
at: https://github.com/typename-yyf/Metis-quantization.
SQL-of-Thought: Multi-agentic Text-to-SQL with Guided Error Correction
Authors: name: Saumya Chaturvedi, name: Aman Chadha, name: Laurent Bindschaedler
Published: 2025-08-30 18:27:12

Converting natural language queries into SQL queries is a crucial challenge
in both industry and academia, aiming to increase access to databases and
large-scale applications. This work examines how in-context learning and
chain-of-thought can be utilized to develop a robust solution for text-to-SQL
systems. We propose SQL-of-Thought: a multi-agent framework that decomposes the
Text2SQL task into schema linking, subproblem identification, query plan
generation, SQL generation, and a guided correction loop. Unlike prior systems
that rely only on execution-based static correction, we introduce
taxonomy-guided dynamic error modification informed by in-context learning.
SQL-of-Thought achieves state-of-the-art results on the Spider dataset and its
variants, combining guided error taxonomy with reasoning-based query planning.
AMBEDKAR-A Multi-level Bias Elimination through a Decoding Approach with
  Knowledge Augmentation for Robust Constitutional Alignment of Language Models
Authors: name: Snehasis Mukhopadhyay, name: Aryan Kasat, name: Shivam Dubey, name: Rahul Karthikeyan, name: Dhruv Sood, name: Vinija Jain, name: Aman Chadha, name: Amitava Das
Published: 2025-09-02 09:33:30

Large Language Models (LLMs) can inadvertently reflect societal biases
present in their training data, leading to harmful or prejudiced outputs. In
the Indian context, our empirical evaluations across a suite of models reveal
that biases around caste and religion are particularly salient. Yet, most
existing mitigation strategies are Western-centric and fail to address these
local nuances. We propose AMBEDKAR, a framework inspired by the egalitarian
vision of Dr B. R. Ambedkar, architect of the Indian Constitution, to guide LLM
outputs toward fairness, neutrality, and inclusion in line with Articles 14 to
17. Our approach introduces a Constitution-Aware Decoding Layer, guided by the
AI Constitution of India and applied only at inference time, without any
parameter updates to the base model. We incorporate a speculative decoding
algorithm that proactively reduces casteist and communal bias during
generation. This mitigation layer operates directly within the decoding
process, avoiding changes to model internals and lowering the computational and
infrastructural costs associated with retraining. We reinterpret speculative
decoding not merely as an efficiency tool but as a mechanism for fairness. In
this framework, a Small Language Model (SLM) acts as a potentially biased
generator, while a constitutionally guided Large Language Model (LLM) serves as
the verifier. Rather than accelerating generation, the LLM enforces bias-robust
trajectories in the SLM outputs. This inversion of roles gives rise to a
fairness-by-speculation paradigm. Our approach yields an absolute reduction of
bias up to 26.41 percent compared to baseline. Our source code, datasets, and
results are available at https://anonymous.4open.science/r/AMBEDKAR-983B/
Reasoning Vectors: Transferring Chain-of-Thought Capabilities via Task
  Arithmetic
Authors: name: Mohammad Zbeeb, name: Hasan Abed Al Kader Hammoud, name: Bernard Ghanem
Published: 2025-09-01 11:04:51

Large language models often require costly optimization, such as
reinforcement learning, to master complex reasoning tasks. This work
demonstrates that reasoning ability, once learned, can be extracted and
transferred between models as a compact task vector. We source two publicly
available, identically initialized Qwen2.5 models, one fine-tuned with
supervised fine-tuning (SFT) and the other with group relative policy
optimization (GRPO) on the same dataset. From these, we extract a reasoning
vector: v_{reason} = theta_{GRPO} - theta_{SFT}. We
hypothesize that this vector captures the reasoning capability instilled by
reinforcement learning while factoring out shared knowledge from the SFT
process. When added to compatible instruction-tuned models through simple
arithmetic, this vector consistently improves performance across diverse
reasoning benchmarks: GSM8K (+4.9%), HumanEval (+4.3%), SciQ (+1.7%), and
BigBenchHard (+12.3% for the 1.5B model). The performance improvements persist
under adversarial conditions. Conversely, subtracting the vector causes
significant performance degradation (-11.8% on GSM8K), demonstrating the
vector's strong contribution to the model's reasoning abilities. This work
shows how reasoning capabilities, typically developed through expensive
training, can be extracted from existing open-source models and reused through
simple tensor arithmetic, offering a practical way to enhance models by
recycling prior computational investments.
GenCompositor: Generative Video Compositing with Diffusion Transformer
Authors: name: Shuzhou Yang, name: Xiaoyu Li, name: Xiaodong Cun, name: Guangzhi Wang, name: Lingen Li, name: Ying Shan, name: Jian Zhang
Published: 2025-09-02 16:10:13

Video compositing combines live-action footage to create video production,
serving as a crucial technique in video creation and film production.
Traditional pipelines require intensive labor efforts and expert collaboration,
resulting in lengthy production cycles and high manpower costs. To address this
issue, we automate this process with generative models, called generative video
compositing. This new task strives to adaptively inject identity and motion
information of foreground video to the target video in an interactive manner,
allowing users to customize the size, motion trajectory, and other attributes
of the dynamic elements added in final video. Specifically, we designed a novel
Diffusion Transformer (DiT) pipeline based on its intrinsic properties. To
maintain consistency of the target video before and after editing, we revised a
light-weight DiT-based background preservation branch with masked token
injection. As to inherit dynamic elements from other sources, a DiT fusion
block is proposed using full self-attention, along with a simple yet effective
foreground augmentation for training. Besides, for fusing background and
foreground videos with different layouts based on user control, we developed a
novel position embedding, named Extended Rotary Position Embedding (ERoPE).
Finally, we curated a dataset comprising 61K sets of videos for our new task,
called VideoComp. This data includes complete dynamic elements and high-quality
target videos. Experiments demonstrate that our method effectively realizes
generative video compositing, outperforming existing possible solutions in
fidelity and consistency.
Baichuan-M2: Scaling Medical Capability with Large Verifier System
Authors: name: Baichuan-M2 Team, name: Chengfeng Dou, name: Chong Liu, name: Fan Yang, name: Fei Li, name: Jiyuan Jia, name: Mingyang Chen, name: Qiang Ju, name: Shuai Wang, name: Shunya Dang, name: Tianpeng Li, name: Xiangrong Zeng, name: Yijie Zhou, name: Chenzheng Zhu, name: Da Pan, name: Fei Deng, name: Guangwei Ai, name: Guosheng Dong, name: Hongda Zhang, name: Jinyang Tai, name: Jixiang Hong, name: Kai Lu, name: Linzhuang Sun, name: Peidong Guo, name: Qian Ma, name: Rihui Xin, name: Shihui Yang, name: Shusen Zhang, name: Yichuan Mo, name: Zheng Liang, name: Zhishou Zhang, name: Hengfu Cui, name: Zuyi Zhu, name: Xiaochuan Wang
Published: 2025-09-02 11:23:35

As large language models (LLMs) advance in conversational and reasoning
capabilities, their practical application in healthcare has become a critical
research focus. However, there is a notable gap between the performance of
medical LLMs on static benchmarks such as USMLE and their utility in real-world
clinical decision-making. This discrepancy arises because traditional exams
fail to capture the dynamic, interactive nature of medical consultations. To
address this challenge, we introduce a novel dynamic verification framework
that moves beyond static answer verifier, establishing a large-scale,
high-fidelity interactive reinforcement learning system. Our framework
comprises two key components: a Patient Simulator that creates realistic
clinical environments using de-identified medical records, and a Clinical
Rubrics Generator that dynamically produces multi-dimensional evaluation
metrics. Building on this foundation, we develop Baichuan-M2, a 32B-parameter
medical augmented reasoning model trained through a multi-stage reinforcement
learning strategy with an improved Group Relative Policy Optimization (GRPO)
algorithm. Evaluated on HealthBench, Baichuan-M2 outperforms all other
open-source models and most advanced closed-source counterparts, achieving a
score above 32 on the challenging HealthBench Hard benchmark-previously
exceeded only by GPT-5. Our work demonstrates that robust dynamic verifier
system is essential for aligning LLM capabilities with practical clinical
applications, establishing a new Pareto front in the performance-parameter
trade-off for medical AI deployment.
C-DiffDet+: Fusing Global Scene Context with Generative Denoising for
  High-Fidelity Object Detection
Authors: name: Abdellah Zakaria Sellam, name: Ilyes Benaissa, name: Salah Eddine Bekhouche, name: Abdenour Hadid, name: Vito Renó, name: Cosimo Distante
Published: 2025-08-30 18:06:06

Fine-grained object detection in challenging visual domains, such as vehicle
damage assessment, presents a formidable challenge even for human experts to
resolve reliably. While DiffusionDet has advanced the state-of-the-art through
conditional denoising diffusion, its performance remains limited by local
feature conditioning in context-dependent scenarios. We address this
fundamental limitation by introducing Context-Aware Fusion (CAF), which
leverages cross-attention mechanisms to integrate global scene context with
local proposal features directly. The global context is generated using a
separate dedicated encoder that captures comprehensive environmental
information, enabling each object proposal to attend to scene-level
understanding. Our framework significantly enhances the generative detection
paradigm by enabling each object proposal to attend to comprehensive
environmental information. Experimental results demonstrate an improvement over
state-of-the-art models on the CarDD benchmark, establishing new performance
benchmarks for context-aware object detection in fine-grained domains
VerlTool: Towards Holistic Agentic Reinforcement Learning with Tool Use
Authors: name: Dongfu Jiang, name: Yi Lu, name: Zhuofeng Li, name: Zhiheng Lyu, name: Ping Nie, name: Haozhe Wang, name: Alex Su, name: Hui Chen, name: Kai Zou, name: Chao Du, name: Tianyu Pang, name: Wenhu Chen
Published: 2025-09-01 01:45:18

Reinforcement Learning with Verifiable Rewards (RLVR) has demonstrated
success in enhancing LLM reasoning capabilities, but remains limited to
single-turn interactions without tool integration. While recent Agentic
Reinforcement Learning with Tool use (ARLT) approaches have emerged to address
multi-turn tool interactions, existing works develop task-specific codebases
that suffer from fragmentation, synchronous execution bottlenecks, and limited
extensibility across domains. These inefficiencies hinder broader community
adoption and algorithmic innovation. We introduce VerlTool, a unified and
modular framework that addresses these limitations through systematic design
principles. VerlTool provides four key contributions: (1) upstream alignment
with VeRL ensuring compatibility and simplified maintenance, (2) unified tool
management via standardized APIs supporting diverse modalities including code
execution, search, SQL databases, and vision processing, (3) asynchronous
rollout execution achieving near 2times speedup by eliminating
synchronization bottlenecks, and (4) comprehensive evaluation demonstrating
competitive performance across 6 ARLT domains. Our framework formalizes ARLT as
multi-turn trajectories with multi-modal observation tokens (text/image/video),
extending beyond single-turn RLVR paradigms. We train and evaluate models on
mathematical reasoning, knowledge QA, SQL generation, visual reasoning, web
search, and software engineering tasks, achieving results comparable to
specialized systems while providing unified training infrastructure. The
modular plugin architecture enables rapid tool integration requiring only
lightweight Python definitions, significantly reducing development overhead and
providing a scalable foundation for tool-augmented RL research. Our code is
open-sourced at https://github.com/TIGER-AI-Lab/verl-tool.
FlashAdventure: A Benchmark for GUI Agents Solving Full Story Arcs in
  Diverse Adventure Games
Authors: name: Jaewoo Ahn, name: Junseo Kim, name: Heeseung Yun, name: Jaehyeon Son, name: Dongmin Park, name: Jaewoong Cho, name: Gunhee Kim
Published: 2025-09-01 01:33:16

GUI agents powered by LLMs show promise in interacting with diverse digital
environments. Among these, video games offer a valuable testbed due to their
varied interfaces, with adventure games posing additional challenges through
complex, narrative-driven interactions. Existing game benchmarks, however, lack
diversity and rarely evaluate agents on completing entire storylines. To
address this, we introduce FlashAdventure, a benchmark of 34 Flash-based
adventure games designed to test full story arc completion and tackle the
observation-behavior gap: the challenge of remembering and acting on earlier
gameplay information. We also propose CUA-as-a-Judge, an automated gameplay
evaluator, and COAST, an agentic framework leveraging long-term clue memory to
better plan and solve sequential tasks. Experiments show current GUI agents
struggle with full story arcs, while COAST improves milestone completion by
bridging the observation-behavior gap. Nonetheless, a marked discrepancy
between humans and best-performing agents warrants continued research efforts
to narrow this divide.
Discrete Noise Inversion for Next-scale Autoregressive Text-based Image
  Editing
Authors: name: Quan Dao, name: Xiaoxiao He, name: Ligong Han, name: Ngan Hoai Nguyen, name: Amin Heyrani Nobar, name: Faez Ahmed, name: Han Zhang, name: Viet Anh Nguyen, name: Dimitris Metaxas
Published: 2025-09-02 06:01:52

Visual autoregressive models (VAR) have recently emerged as a promising class
of generative models, achieving performance comparable to diffusion models in
text-to-image generation tasks. While conditional generation has been widely
explored, the ability to perform prompt-guided image editing without additional
training is equally critical, as it supports numerous practical real-world
applications. This paper investigates the text-to-image editing capabilities of
VAR by introducing Visual AutoRegressive Inverse Noise (VARIN), the first noise
inversion-based editing technique designed explicitly for VAR models. VARIN
leverages a novel pseudo-inverse function for argmax sampling, named
Location-aware Argmax Inversion (LAI), to generate inverse Gumbel noises. These
inverse noises enable precise reconstruction of the source image and facilitate
targeted, controllable edits aligned with textual prompts. Extensive
experiments demonstrate that VARIN effectively modifies source images according
to specified prompts while significantly preserving the original background and
structural details, thus validating its efficacy as a practical editing
approach.
Attributes as Textual Genes: Leveraging LLMs as Genetic Algorithm
  Simulators for Conditional Synthetic Data Generation
Authors: name: Guangzeng Han, name: Weisi Liu, name: Xiaolei Huang
Published: 2025-09-02 07:35:20

Large Language Models (LLMs) excel at generating synthetic data, but ensuring
its quality and diversity remains challenging. We propose Genetic Prompt, a
novel framework that combines genetic algorithms with LLMs to augment synthetic
data generation. Our approach treats semantic text attributes as gene sequences
and leverages the LLM to simulate crossover and mutation operations. This
genetic process enhances data quality and diversity by creating novel attribute
combinations, yielding synthetic distributions closer to real-world data. To
optimize parent selection, we also integrate an active learning scheme that
expands the offspring search space. Our experiments on multiple NLP tasks
reveal several key findings: Genetic Prompt not only significantly outperforms
state-of-the-art baselines but also shows robust performance across various
generator model sizes and scales. Moreover, we demonstrate that fusing our
synthetic data with the original training set significantly boosts downstream
model performance, particularly for class-imbalanced scenarios. Our findings
validate that Genetic Prompt is an effective method for producing high-quality
synthetic data for a wide range of NLP applications.
LLaVA-Critic-R1: Your Critic Model is Secretly a Strong Policy Model
Authors: name: Xiyao Wang, name: Chunyuan Li, name: Jianwei Yang, name: Kai Zhang, name: Bo Liu, name: Tianyi Xiong, name: Furong Huang
Published: 2025-08-31 03:08:02

In vision-language modeling, critic models are typically trained to evaluate
outputs -- assigning scalar scores or pairwise preferences -- rather than to
generate responses. This separation from policy models, which produce the
responses, is so entrenched that critics are rarely considered for direct
policy use. In this work, we challenge this convention. We propose to
reorganize preference-labeled critic datasets into verifiable training signals
and perform reinforcement learning directly on a base generative model,
producing LLaVA-Critic-R1, a multimodal critic trained to optimize preference
judgments while retaining full generation ability. Surprisingly,
LLaVA-Critic-R1 emerges not only as a top-performing critic but also as a
competitive policy model -- matching or surpassing specialized reasoning VLMs
trained with in-domain data across 26 visual reasoning and understanding
benchmarks, with an average gain of +5.7% over its base model (Qwen-2.5-VL-7B).
Extending this approach to existing strong reasoning VLMs yields
LLaVA-Critic-R1+, which further advances policy performance without sacrificing
critic quality, achieving a SoTA performance of 71.9 on MMMU at the 7B scale.
Finally, we show that the enhanced critic ability benefits inference: applying
self-critique at test time yields an average +13.8% improvement on five
representative reasoning tasks without additional training. Our results reveal
that RL training on critic data can produce a unified model excelling at both
evaluation and generation, offering a simple path toward scalable,
self-improving multimodal systems.