Orionnnn/hub-stats huggingface dataset:
BARREL: Boundary-Aware Reasoning for Factual and Reliable LRMs
Authors: name: Junxiao Yang, name: Jinzhe Tu, name: Haoran Liu, name: Xiaoce Wang, name: Chujie Zheng, name: Zhexin Zhang, name: Shiyao Cui, name: Caishun Chen, name: Tiantian He, name: Hongning Wang, name: Yew-Soon Ong, name: Minlie Huang
Published: 2025-05-18 07:27:34

Recent advances in Large Reasoning Models (LRMs) have shown impressive
capabilities in mathematical and logical reasoning. However, current LRMs
rarely admit ignorance or respond with "I don't know". Instead, they often
produce incorrect answers while showing undue confidence, raising concerns
about their factual reliability. In this work, we identify two pathological
reasoning patterns characterized by overthinking that contribute to the
overconfident and incorrect answers: last-minute guessing and second-thought
spiraling. To address these issues, we propose BARREL-a novel framework that
promotes concise and boundary-aware factual reasoning. Our experiments show
that BARREL-training increases the reliability of DeepSeek-R1-Distill-Llama-8B
from 39.33% to 61.48%, while still achieving accuracy comparable to models
finetuned on reasoning data generated by R1. These results demonstrate that our
pilot study is inspiring to build more reliable and factual System 2 LRMs.
PiFlow: Principle-aware Scientific Discovery with Multi-Agent
  Collaboration
Authors: name: Yingming Pu, name: Tao Lin, name: Hongyu Chen
Published: 2025-05-21 03:09:39

Large Language Model (LLM)-based multi-agent systems (MAS) demonstrate
remarkable potential for scientific discovery. Existing approaches, however,
often automate scientific discovery using predefined workflows that lack
rationality constraints. This often leads to aimless hypothesizing and a
failure to consistently link hypotheses with evidence, thereby hindering
systematic uncertainty reduction. Overcoming these limitations fundamentally
requires systematic uncertainty reduction. We introduce PiFlow, an
information-theoretical framework, treating automated scientific discovery as a
structured uncertainty reduction problem guided by principles (e.g., scientific
laws). In evaluations across three distinct scientific domains -- discovering
nanomaterial structures, bio-molecules, and superconductor candidates with
targeted properties -- our method significantly improves discovery efficiency,
reflected by a 73.55\% increase in the Area Under the Curve (AUC) of property
values versus exploration steps, and enhances solution quality by 94.06\%
compared to a vanilla agent system. Overall, PiFlow serves as a
Plug-and-Play method, establishing a novel paradigm shift in highly efficient
automated scientific discovery, paving the way for more robust and accelerated
AI-driven research. Code is publicly available at our
https://github.com/amair-lab/PiFlow{GitHub}.
How Should We Enhance the Safety of Large Reasoning Models: An Empirical
  Study
Authors: name: Zhexin Zhang, name: Xian Qi Loye, name: Victor Shea-Jay Huang, name: Junxiao Yang, name: Qi Zhu, name: Shiyao Cui, name: Fei Mi, name: Lifeng Shang, name: Yingkang Wang, name: Hongning Wang, name: Minlie Huang
Published: 2025-05-21 11:45:29

Large Reasoning Models (LRMs) have achieved remarkable success on
reasoning-intensive tasks such as mathematics and programming. However, their
enhanced reasoning capabilities do not necessarily translate to improved safety
performance-and in some cases, may even degrade it. This raises an important
research question: how can we enhance the safety of LRMs? In this paper, we
present a comprehensive empirical study on how to enhance the safety of LRMs
through Supervised Fine-Tuning (SFT). Our investigation begins with an
unexpected observation: directly distilling safe responses from DeepSeek-R1
fails to significantly enhance safety. We analyze this phenomenon and identify
three key failure patterns that contribute to it. We then demonstrate that
explicitly addressing these issues during the data distillation process can
lead to substantial safety improvements. Next, we explore whether a long and
complex reasoning process is necessary for achieving safety. Interestingly, we
find that simply using short or template-based reasoning process can attain
comparable safety performance-and are significantly easier for models to learn
than more intricate reasoning chains. These findings prompt a deeper reflection
on the role of reasoning in ensuring safety. Finally, we find that mixing math
reasoning data during safety fine-tuning is helpful to balance safety and
over-refusal. Overall, we hope our empirical study could provide a more
holistic picture on enhancing the safety of LRMs. The code and data used in our
experiments are released in https://github.com/thu-coai/LRM-Safety-Study.
Soft Thinking: Unlocking the Reasoning Potential of LLMs in Continuous
  Concept Space
Authors: name: Zhen Zhang, name: Xuehai He, name: Weixiang Yan, name: Ao Shen, name: Chenyang Zhao, name: Shuohang Wang, name: Yelong Shen, name: Xin Eric Wang
Published: 2025-05-21 17:29:15

Human cognition typically involves thinking through abstract, fluid concepts
rather than strictly using discrete linguistic tokens. Current reasoning
models, however, are constrained to reasoning within the boundaries of human
language, processing discrete token embeddings that represent fixed points in
the semantic space. This discrete constraint restricts the expressive power and
upper potential of such reasoning models, often causing incomplete exploration
of reasoning paths, as standard Chain-of-Thought (CoT) methods rely on sampling
one token per step. In this work, we introduce Soft Thinking, a training-free
method that emulates human-like "soft" reasoning by generating soft, abstract
concept tokens in a continuous concept space. These concept tokens are created
by the probability-weighted mixture of token embeddings, which form the
continuous concept space, enabling smooth transitions and richer
representations that transcend traditional discrete boundaries. In essence,
each generated concept token encapsulates multiple meanings from related
discrete tokens, implicitly exploring various reasoning paths to converge
effectively toward the correct answer. Empirical evaluations on diverse
mathematical and coding benchmarks consistently demonstrate the effectiveness
and efficiency of Soft Thinking, improving pass@1 accuracy by up to 2.48 points
while simultaneously reducing token usage by up to 22.4% compared to standard
CoT. Qualitative analysis further reveals that Soft Thinking outputs remain
highly interpretable and readable, highlighting the potential of Soft Thinking
to break the inherent bottleneck of discrete language-based reasoning. Code is
available at https://github.com/eric-ai-lab/Soft-Thinking.
Be Careful When Fine-tuning On Open-Source LLMs: Your Fine-tuning Data
  Could Be Secretly Stolen!
Authors: name: Zhexin Zhang, name: Yuhao Sun, name: Junxiao Yang, name: Shiyao Cui, name: Hongning Wang, name: Minlie Huang
Published: 2025-05-21 15:32:14

Fine-tuning on open-source Large Language Models (LLMs) with proprietary data
is now a standard practice for downstream developers to obtain task-specific
LLMs. Surprisingly, we reveal a new and concerning risk along with the
practice: the creator of the open-source LLMs can later extract the private
downstream fine-tuning data through simple backdoor training, only requiring
black-box access to the fine-tuned downstream model. Our comprehensive
experiments, across 4 popularly used open-source models with 3B to 32B
parameters and 2 downstream datasets, suggest that the extraction performance
can be strikingly high: in practical settings, as much as 76.3% downstream
fine-tuning data (queries) out of a total 5,000 samples can be perfectly
extracted, and the success rate can increase to 94.9% in more ideal settings.
We also explore a detection-based defense strategy but find it can be bypassed
with improved attack. Overall, we highlight the emergency of this newly
identified data breaching risk in fine-tuning, and we hope that more follow-up
research could push the progress of addressing this concerning risk. The code
and data used in our experiments are released at
https://github.com/thu-coai/Backdoor-Data-Extraction.
Efficient Agent Training for Computer Use
Authors: name: Yanheng He, name: Jiahe Jin, name: Pengfei Liu
Published: 2025-05-20 04:20:18

Scaling up high-quality trajectory data has long been a critical bottleneck
for developing human-like computer use agents. We introduce PC Agent-E, an
efficient agent training framework that significantly reduces reliance on
large-scale human demonstrations. Starting with just 312 human-annotated
computer use trajectories, we further improved data quality by synthesizing
diverse action decisions with Claude 3.7 Sonnet. Trained on these enriched
trajectories, our PC Agent-E model achieved a remarkable 141% relative
improvement, surpassing the strong Claude 3.7 Sonnet with extended thinking on
WindowsAgentArena-V2, an improved benchmark we also released. Furthermore, PC
Agent-E demonstrates strong generalizability to different operating systems on
OSWorld. Our findings suggest that strong computer use capabilities can be
stimulated from a small amount of high-quality trajectory data.
Scaling Law for Quantization-Aware Training
Authors: name: Mengzhao Chen, name: Chaoyi Zhang, name: Jing Liu, name: Yutao Zeng, name: Zeyue Xue, name: Zhiheng Liu, name: Yunshui Li, name: Jin Ma, name: Jie Huang, name: Xun Zhou, name: Ping Luo
Published: 2025-05-20 12:54:43

Large language models (LLMs) demand substantial computational and memory
resources, creating deployment challenges. Quantization-aware training (QAT)
addresses these challenges by reducing model precision while maintaining
performance. However, the scaling behavior of QAT, especially at 4-bit
precision (W4A4), is not well understood. Existing QAT scaling laws often
ignore key factors such as the number of training tokens and quantization
granularity, which limits their applicability. This paper proposes a unified
scaling law for QAT that models quantization error as a function of model size,
training data volume, and quantization group size. Through 268 QAT experiments,
we show that quantization error decreases as model size increases, but rises
with more training tokens and coarser quantization granularity. To identify the
sources of W4A4 quantization error, we decompose it into weight and activation
components. Both components follow the overall trend of W4A4 quantization
error, but with different sensitivities. Specifically, weight quantization
error increases more rapidly with more training tokens. Further analysis shows
that the activation quantization error in the FC2 layer, caused by outliers, is
the primary bottleneck of W4A4 QAT quantization error. By applying
mixed-precision quantization to address this bottleneck, we demonstrate that
weight and activation quantization errors can converge to similar levels.
Additionally, with more training data, weight quantization error eventually
exceeds activation quantization error, suggesting that reducing weight
quantization error is also important in such scenarios. These findings offer
key insights for improving QAT research and development.
Constructing a 3D Town from a Single Image
Authors: name: Kaizhi Zheng, name: Ruijian Zhang, name: Jing Gu, name: Jie Yang, name: Xin Eric Wang
Published: 2025-05-21 17:10:47

Acquiring detailed 3D scenes typically demands costly equipment, multi-view
data, or labor-intensive modeling. Therefore, a lightweight alternative,
generating complex 3D scenes from a single top-down image, plays an essential
role in real-world applications. While recent 3D generative models have
achieved remarkable results at the object level, their extension to full-scene
generation often leads to inconsistent geometry, layout hallucinations, and
low-quality meshes. In this work, we introduce 3DTown, a training-free
framework designed to synthesize realistic and coherent 3D scenes from a single
top-down view. Our method is grounded in two principles: region-based
generation to improve image-to-3D alignment and resolution, and spatial-aware
3D inpainting to ensure global scene coherence and high-quality geometry
generation. Specifically, we decompose the input image into overlapping regions
and generate each using a pretrained 3D object generator, followed by a masked
rectified flow inpainting process that fills in missing geometry while
maintaining structural continuity. This modular design allows us to overcome
resolution bottlenecks and preserve spatial structure without requiring 3D
supervision or fine-tuning. Extensive experiments across diverse scenes show
that 3DTown outperforms state-of-the-art baselines, including Trellis,
Hunyuan3D-2, and TripoSG, in terms of geometry quality, spatial coherence, and
texture fidelity. Our results demonstrate that high-quality 3D town generation
is achievable from a single image using a principled, training-free approach.
Deliberation on Priors: Trustworthy Reasoning of Large Language Models
  on Knowledge Graphs
Authors: name: Jie Ma, name: Ning Qu, name: Zhitao Gao, name: Rui Xing, name: Jun Liu, name: Hongbin Pei, name: Jiang Xie, name: Linyun Song, name: Pinghui Wang, name: Jing Tao, name: Zhou Su
Published: 2025-05-21 07:38:45

Knowledge graph-based retrieval-augmented generation seeks to mitigate
hallucinations in Large Language Models (LLMs) caused by insufficient or
outdated knowledge. However, existing methods often fail to fully exploit the
prior knowledge embedded in knowledge graphs (KGs), particularly their
structural information and explicit or implicit constraints. The former can
enhance the faithfulness of LLMs' reasoning, while the latter can improve the
reliability of response generation. Motivated by these, we propose a
trustworthy reasoning framework, termed Deliberation over Priors (DP), which
sufficiently utilizes the priors contained in KGs. Specifically, DP adopts a
progressive knowledge distillation strategy that integrates structural priors
into LLMs through a combination of supervised fine-tuning and Kahneman-Tversky
optimization, thereby improving the faithfulness of relation path generation.
Furthermore, our framework employs a reasoning-introspection strategy, which
guides LLMs to perform refined reasoning verification based on extracted
constraint priors, ensuring the reliability of response generation. Extensive
experiments on three benchmark datasets demonstrate that DP achieves new
state-of-the-art performance, especially a Hit@1 improvement of 13% on the
ComplexWebQuestions dataset, and generates highly trustworthy responses. We
also conduct various analyses to verify its flexibility and practicality. The
code is available at https://github.com/reml-group/Deliberation-on-Priors.
Web-Shepherd: Advancing PRMs for Reinforcing Web Agents
Authors: name: Hyungjoo Chae, name: Sunghwan Kim, name: Junhee Cho, name: Seungone Kim, name: Seungjun Moon, name: Gyeom Hwangbo, name: Dongha Lim, name: Minjin Kim, name: Yeonjun Hwang, name: Minju Gwak, name: Dongwook Choi, name: Minseok Kang, name: Gwanhoon Im, name: ByeongUng Cho, name: Hyojun Kim, name: Jun Hee Han, name: Taeyoon Kwon, name: Minju Kim, name: Beong-woo Kwak, name: Dongjin Kang, name: Jinyoung Yeo
Published: 2025-05-21 08:56:55

Web navigation is a unique domain that can automate many repetitive real-life
tasks and is challenging as it requires long-horizon sequential decision making
beyond typical multimodal large language model (MLLM) tasks. Yet, specialized
reward models for web navigation that can be utilized during both training and
test-time have been absent until now. Despite the importance of speed and
cost-effectiveness, prior works have utilized MLLMs as reward models, which
poses significant constraints for real-world deployment. To address this, in
this work, we propose the first process reward model (PRM) called Web-Shepherd
which could assess web navigation trajectories in a step-level. To achieve
this, we first construct the WebPRM Collection, a large-scale dataset with 40K
step-level preference pairs and annotated checklists spanning diverse domains
and difficulty levels. Next, we also introduce the WebRewardBench, the first
meta-evaluation benchmark for evaluating PRMs. In our experiments, we observe
that our Web-Shepherd achieves about 30 points better accuracy compared to
using GPT-4o on WebRewardBench. Furthermore, when testing on WebArena-lite by
using GPT-4o-mini as the policy and Web-Shepherd as the verifier, we achieve
10.9 points better performance, in 10 less cost compared to using GPT-4o-mini
as the verifier. Our model, dataset, and code are publicly available at LINK.
AutoMat: Enabling Automated Crystal Structure Reconstruction from
  Microscopy via Agentic Tool Use
Authors: name: Yaotian Yang, name: Yiwen Tang, name: Yizhe Chen, name: Xiao Chen, name: Jiangjie Qiu, name: Hao Xiong, name: Haoyu Yin, name: Zhiyao Luo, name: Yifei Zhang, name: Sijia Tao, name: Wentao Li, name: Qinghua Zhang, name: Yuqiang Li, name: Wanli Ouyang, name: Bin Zhao, name: Xiaonan Wang, name: Fei Wei
Published: 2025-05-19 03:04:50

Machine learning-based interatomic potentials and force fields depend
critically on accurate atomic structures, yet such data are scarce due to the
limited availability of experimentally resolved crystals. Although
atomic-resolution electron microscopy offers a potential source of structural
data, converting these images into simulation-ready formats remains
labor-intensive and error-prone, creating a bottleneck for model training and
validation. We introduce AutoMat, an end-to-end, agent-assisted pipeline that
automatically transforms scanning transmission electron microscopy (STEM)
images into atomic crystal structures and predicts their physical properties.
AutoMat combines pattern-adaptive denoising, physics-guided template retrieval,
symmetry-aware atomic reconstruction, fast relaxation and property prediction
via MatterSim, and coordinated orchestration across all stages. We propose the
first dedicated STEM2Mat-Bench for this task and evaluate performance using
lattice RMSD, formation energy MAE, and structure-matching success rate. By
orchestrating external tool calls, AutoMat enables a text-only LLM to
outperform vision-language models in this domain, achieving closed-loop
reasoning throughout the pipeline. In large-scale experiments over 450
structure samples, AutoMat substantially outperforms existing multimodal large
language models and tools. These results validate both AutoMat and
STEM2Mat-Bench, marking a key step toward bridging microscopy and atomistic
simulation in materials science.The code and dataset are publicly available at
https://github.com/yyt-2378/AutoMat and
https://huggingface.co/datasets/yaotianvector/STEM2Mat.
RLVR-World: Training World Models with Reinforcement Learning
Authors: name: Jialong Wu, name: Shaofeng Yin, name: Ningya Feng, name: Mingsheng Long
Published: 2025-05-20 05:02:53

World models predict state transitions in response to actions and are
increasingly developed across diverse modalities. However, standard training
objectives such as maximum likelihood estimation (MLE) often misalign with
task-specific goals of world models, i.e., transition prediction metrics like
accuracy or perceptual quality. In this paper, we present RLVR-World, a unified
framework that leverages reinforcement learning with verifiable rewards (RLVR)
to directly optimize world models for such metrics. Despite formulating world
modeling as autoregressive prediction of tokenized sequences, RLVR-World
evaluates metrics of decoded predictions as verifiable rewards. We demonstrate
substantial performance gains on both language- and video-based world models
across domains, including text games, web navigation, and robot manipulation.
Our work indicates that, beyond recent advances in reasoning language models,
RLVR offers a promising post-training paradigm for enhancing the utility of
generative models more broadly.
MMaDA: Multimodal Large Diffusion Language Models
Authors: name: Ling Yang, name: Ye Tian, name: Bowen Li, name: Xinchen Zhang, name: Ke Shen, name: Yunhai Tong, name: Mengdi Wang
Published: 2025-05-21 17:59:05

We introduce MMaDA, a novel class of multimodal diffusion foundation models
designed to achieve superior performance across diverse domains such as textual
reasoning, multimodal understanding, and text-to-image generation. The approach
is distinguished by three key innovations: (i) MMaDA adopts a unified diffusion
architecture with a shared probabilistic formulation and a modality-agnostic
design, eliminating the need for modality-specific components. This
architecture ensures seamless integration and processing across different data
types. (ii) We implement a mixed long chain-of-thought (CoT) fine-tuning
strategy that curates a unified CoT format across modalities. By aligning
reasoning processes between textual and visual domains, this strategy
facilitates cold-start training for the final reinforcement learning (RL)
stage, thereby enhancing the model's ability to handle complex tasks from the
outset. (iii) We propose UniGRPO, a unified policy-gradient-based RL algorithm
specifically tailored for diffusion foundation models. Utilizing diversified
reward modeling, UniGRPO unifies post-training across both reasoning and
generation tasks, ensuring consistent performance improvements. Experimental
results demonstrate that MMaDA-8B exhibits strong generalization capabilities
as a unified multimodal foundation model. It surpasses powerful models like
LLaMA-3-7B and Qwen2-7B in textual reasoning, outperforms Show-o and SEED-X in
multimodal understanding, and excels over SDXL and Janus in text-to-image
generation. These achievements highlight MMaDA's effectiveness in bridging the
gap between pretraining and post-training within unified diffusion
architectures, providing a comprehensive framework for future research and
development. We open-source our code and trained models at:
https://github.com/Gen-Verse/MMaDA
Streamline Without Sacrifice - Squeeze out Computation Redundancy in LMM
Authors: name: Penghao Wu, name: Lewei Lu, name: Ziwei Liu
Published: 2025-05-21 17:59:52

Large multimodal models excel in multimodal tasks but face significant
computational challenges due to excessive computation on visual tokens. Unlike
token reduction methods that focus on token-level redundancy, we identify and
study the computation-level redundancy on vision tokens to ensure no
information loss. Our key insight is that vision tokens from the pretrained
vision encoder do not necessarily require all the heavy operations (e.g.,
self-attention, FFNs) in decoder-only LMMs and could be processed more lightly
with proper designs. We designed a series of experiments to discover and
progressively squeeze out the vision-related computation redundancy. Based on
our findings, we propose ProxyV, a novel approach that utilizes proxy vision
tokens to alleviate the computational burden on original vision tokens. ProxyV
enhances efficiency without compromising performance and can even yield notable
performance gains in scenarios with more moderate efficiency improvements.
Furthermore, the flexibility of ProxyV is demonstrated through its combination
with token reduction methods to boost efficiency further. The code will be made
public at this https://github.com/penghao-wu/ProxyV URL.