Orionnnn/hub-stats huggingface dataset:
FinePhys: Fine-grained Human Action Generation by Explicitly
  Incorporating Physical Laws for Effective Skeletal Guidance
Authors: name: Dian Shao, name: Mingfei Shi, name: Shengda Xu, name: Haodong Chen, name: Yongle Huang, name: Binglu Wang
Published: 2025-05-19 17:58:11

Despite significant advances in video generation, synthesizing physically
plausible human actions remains a persistent challenge, particularly in
modeling fine-grained semantics and complex temporal dynamics. For instance,
generating gymnastics routines such as "switch leap with 0.5 turn" poses
substantial difficulties for current methods, often yielding unsatisfactory
results. To bridge this gap, we propose FinePhys, a Fine-grained human action
generation framework that incorporates Physics to obtain effective skeletal
guidance. Specifically, FinePhys first estimates 2D poses in an online manner
and then performs 2D-to-3D dimension lifting via in-context learning. To
mitigate the instability and limited interpretability of purely data-driven 3D
poses, we further introduce a physics-based motion re-estimation module
governed by Euler-Lagrange equations, calculating joint accelerations via
bidirectional temporal updating. The physically predicted 3D poses are then
fused with data-driven ones, offering multi-scale 2D heatmap guidance for the
diffusion process. Evaluated on three fine-grained action subsets from FineGym
(FX-JUMP, FX-TURN, and FX-SALTO), FinePhys significantly outperforms
competitive baselines. Comprehensive qualitative results further demonstrate
FinePhys's ability to generate more natural and plausible fine-grained human
actions.
MM-PRM: Enhancing Multimodal Mathematical Reasoning with Scalable
  Step-Level Supervision
Authors: name: Lingxiao Du, name: Fanqing Meng, name: Zongkai Liu, name: Zhixiang Zhou, name: Ping Luo, name: Qiaosheng Zhang, name: Wenqi Shao
Published: 2025-05-19 17:55:08

While Multimodal Large Language Models (MLLMs) have achieved impressive
progress in vision-language understanding, they still struggle with complex
multi-step reasoning, often producing logically inconsistent or partially
correct solutions. A key limitation lies in the lack of fine-grained
supervision over intermediate reasoning steps. To address this, we propose
MM-PRM, a process reward model trained within a fully automated, scalable
framework. We first build MM-Policy, a strong multimodal model trained on
diverse mathematical reasoning data. Then, we construct MM-K12, a curated
dataset of 10,000 multimodal math problems with verifiable answers, which
serves as seed data. Leveraging a Monte Carlo Tree Search (MCTS)-based
pipeline, we generate over 700k step-level annotations without human labeling.
The resulting PRM is used to score candidate reasoning paths in the Best-of-N
inference setup and achieves significant improvements across both in-domain
(MM-K12 test set) and out-of-domain (OlympiadBench, MathVista, etc.)
benchmarks. Further analysis confirms the effectiveness of soft labels, smaller
learning rates, and path diversity in optimizing PRM performance. MM-PRM
demonstrates that process supervision is a powerful tool for enhancing the
logical robustness of multimodal reasoning systems. We release all our codes
and data at https://github.com/ModalMinds/MM-PRM.
Delta Attention: Fast and Accurate Sparse Attention Inference by Delta
  Correction
Authors: name: Jeffrey Willette, name: Heejun Lee, name: Sung Ju Hwang
Published: 2025-05-16 13:48:33

The attention mechanism of a transformer has a quadratic complexity, leading
to high inference costs and latency for long sequences. However, attention
matrices are mostly sparse, which implies that many entries may be omitted from
computation for efficient inference. Sparse attention inference methods aim to
reduce this computational burden; however, they also come with a troublesome
performance degradation. We discover that one reason for this degradation is
that the sparse calculation induces a distributional shift in the attention
outputs. The distributional shift causes decoding-time queries to fail to align
well with the appropriate keys from the prefill stage, leading to a drop in
performance. We propose a simple, novel, and effective procedure for correcting
this distributional shift, bringing the distribution of sparse attention
outputs closer to that of quadratic attention. Our method can be applied on top
of any sparse attention method, and results in an average 36%pt performance
increase, recovering 88% of quadratic attention accuracy on the 131K RULER
benchmark when applied on top of sliding window attention with sink tokens
while only adding a small overhead. Our method can maintain approximately 98.5%
sparsity over full quadratic attention, making our model 32 times faster than
Flash Attention 2 when processing 1M token prefills.
CPGD: Toward Stable Rule-based Reinforcement Learning for Language
  Models
Authors: name: Zongkai Liu, name: Fanqing Meng, name: Lingxiao Du, name: Zhixiang Zhou, name: Chao Yu, name: Wenqi Shao, name: Qiaosheng Zhang
Published: 2025-05-18 17:44:53

Recent advances in rule-based reinforcement learning (RL) have significantly
improved the reasoning capability of language models (LMs) with rule-based
rewards. However, existing RL methods -- such as GRPO, REINFORCE++, and RLOO --
often suffer from training instability, where large policy updates and improper
clipping can lead to training collapse. To address this issue, we propose
Clipped Policy Gradient Optimization with Policy Drift (CPGD), a novel
algorithm designed to stabilize policy learning in LMs. CPGD introduces a
policy drift constraint based on KL divergence to dynamically regularize policy
updates, and leverages a clip mechanism on the logarithm of the ratio to
prevent excessive policy updates. We provide theoretical justification for CPGD
and demonstrate through empirical analysis that it mitigates the instability
observed in prior approaches. Furthermore, we show that CPGD significantly
improves performance while maintaining training stability. Our implementation
balances theoretical rigor with practical usability, offering a robust
alternative for RL in the post-training of LMs. We release our code at
https://github.com/ModalMinds/MM-EUREKA.
Neuro-Symbolic Query Compiler
Authors: name: Yuyao Zhang, name: Zhicheng Dou, name: Xiaoxi Li, name: Jiajie Jin, name: Yongkang Wu, name: Zhonghua Li, name: Qi Ye, name: Ji-Rong Wen
Published: 2025-05-17 09:36:03

Precise recognition of search intent in Retrieval-Augmented Generation (RAG)
systems remains a challenging goal, especially under resource constraints and
for complex queries with nested structures and dependencies. This paper
presents QCompiler, a neuro-symbolic framework inspired by linguistic grammar
rules and compiler design, to bridge this gap. It theoretically designs a
minimal yet sufficient Backus-Naur Form (BNF) grammar G[q] to formalize
complex queries. Unlike previous methods, this grammar maintains completeness
while minimizing redundancy. Based on this, QCompiler includes a Query
Expression Translator, a Lexical Syntax Parser, and a Recursive Descent
Processor to compile queries into Abstract Syntax Trees (ASTs) for execution.
The atomicity of the sub-queries in the leaf nodes ensures more precise
document retrieval and response generation, significantly improving the RAG
system's ability to address complex queries.
Thinkless: LLM Learns When to Think
Authors: name: Gongfan Fang, name: Xinyin Ma, name: Xinchao Wang
Published: 2025-05-19 17:24:16

Reasoning Language Models, capable of extended chain-of-thought reasoning,
have demonstrated remarkable performance on tasks requiring complex logical
inference. However, applying elaborate reasoning for all queries often results
in substantial computational inefficiencies, particularly when many problems
admit straightforward solutions. This motivates an open question: Can LLMs
learn when to think? To answer this, we propose Thinkless, a learnable
framework that empowers an LLM to adaptively select between short-form and
long-form reasoning, based on both task complexity and the model's ability.
Thinkless is trained under a reinforcement learning paradigm and employs two
control tokens, <short> for concise responses and <think> for detailed
reasoning. At the core of our method is a Decoupled Group Relative Policy
Optimization (DeGRPO) algorithm, which decomposes the learning objective of
hybrid reasoning into two components: (1) a control token loss that governs the
selection of the reasoning mode, and (2) a response loss that improves the
accuracy of the generated answers. This decoupled formulation enables
fine-grained control over the contributions of each objective, stabilizing
training and effectively preventing collapse observed in vanilla GRPO.
Empirically, on several benchmarks such as Minerva Algebra, MATH-500, and
GSM8K, Thinkless is able to reduce the usage of long-chain thinking by 50% -
90%, significantly improving the efficiency of Reasoning Language Models. The
code is available at https://github.com/VainF/Thinkless
AdaCoT: Pareto-Optimal Adaptive Chain-of-Thought Triggering via
  Reinforcement Learning
Authors: name: Chenwei Lou, name: Zewei Sun, name: Xinnian Liang, name: Meng Qu, name: Wei Shen, name: Wenqi Wang, name: Yuntao Li, name: Qingping Yang, name: Shuangzhi Wu
Published: 2025-05-17 08:27:00

Large Language Models (LLMs) have demonstrated remarkable capabilities but
often face challenges with tasks requiring sophisticated reasoning. While
Chain-of-Thought (CoT) prompting significantly enhances reasoning, it
indiscriminately generates lengthy reasoning steps for all queries, leading to
substantial computational costs and inefficiency, especially for simpler
inputs. To address this critical issue, we introduce AdaCoT (Adaptive
Chain-of-Thought), a novel framework enabling LLMs to adaptively decide when to
invoke CoT. AdaCoT framed adaptive reasoning as a Pareto optimization problem
that seeks to balance model performance with the costs associated with CoT
invocation (both frequency and computational overhead). We propose a
reinforcement learning (RL) based method, specifically utilizing Proximal
Policy Optimization (PPO), to dynamically control the CoT triggering decision
boundary by adjusting penalty coefficients, thereby allowing the model to
determine CoT necessity based on implicit query complexity. A key technical
contribution is Selective Loss Masking (SLM), designed to counteract decision
boundary collapse during multi-stage RL training, ensuring robust and stable
adaptive triggering. Experimental results demonstrate that AdaCoT successfully
navigates the Pareto frontier, achieving substantial reductions in CoT usage
for queries not requiring elaborate reasoning. For instance, on our production
traffic testset, AdaCoT reduced CoT triggering rates to as low as 3.18\% and
decreased average response tokens by 69.06%, while maintaining high performance
on complex tasks.
Accelerate TarFlow Sampling with GS-Jacobi Iteration
Authors: name: Ben Liu, name: Zhen Qin
Published: 2025-05-19 08:35:44

Image generation models have achieved widespread applications. As an
instance, the TarFlow model combines the transformer architecture with
Normalizing Flow models, achieving state-of-the-art results on multiple
benchmarks. However, due to the causal form of attention requiring sequential
computation, TarFlow's sampling process is extremely slow. In this paper, we
demonstrate that through a series of optimization strategies, TarFlow sampling
can be greatly accelerated by using the Gauss-Seidel-Jacobi (abbreviated as
GS-Jacobi) iteration method. Specifically, we find that blocks in the TarFlow
model have varying importance: a small number of blocks play a major role in
image generation tasks, while other blocks contribute relatively little; some
blocks are sensitive to initial values and prone to numerical overflow, while
others are relatively robust. Based on these two characteristics, we propose
the Convergence Ranking Metric (CRM) and the Initial Guessing Metric (IGM): CRM
is used to identify whether a TarFlow block is "simple" (converges in few
iterations) or "tough" (requires more iterations); IGM is used to evaluate
whether the initial value of the iteration is good. Experiments on four TarFlow
models demonstrate that GS-Jacobi sampling can significantly enhance sampling
efficiency while maintaining the quality of generated images (measured by FID),
achieving speed-ups of 4.53x in Img128cond, 5.32x in AFHQ, 2.96x in
Img64uncond, and 2.51x in Img64cond without degrading FID scores or sample
quality. Code and checkpoints are accessible on
https://github.com/encoreus/GS-Jacobi_for_TarFlow
Hybrid 3D-4D Gaussian Splatting for Fast Dynamic Scene Representation
Authors: name: Seungjun Oh, name: Younggeun Lee, name: Hyejin Jeon, name: Eunbyung Park
Published: 2025-05-19 14:59:58

Recent advancements in dynamic 3D scene reconstruction have shown promising
results, enabling high-fidelity 3D novel view synthesis with improved temporal
consistency. Among these, 4D Gaussian Splatting (4DGS) has emerged as an
appealing approach due to its ability to model high-fidelity spatial and
temporal variations. However, existing methods suffer from substantial
computational and memory overhead due to the redundant allocation of 4D
Gaussians to static regions, which can also degrade image quality. In this
work, we introduce hybrid 3D-4D Gaussian Splatting (3D-4DGS), a novel framework
that adaptively represents static regions with 3D Gaussians while reserving 4D
Gaussians for dynamic elements. Our method begins with a fully 4D Gaussian
representation and iteratively converts temporally invariant Gaussians into 3D,
significantly reducing the number of parameters and improving computational
efficiency. Meanwhile, dynamic Gaussians retain their full 4D representation,
capturing complex motions with high fidelity. Our approach achieves
significantly faster training times compared to baseline 4D Gaussian Splatting
methods while maintaining or improving the visual quality.
VisionReasoner: Unified Visual Perception and Reasoning via
  Reinforcement Learning
Authors: name: Yuqi Liu, name: Tianyuan Qu, name: Zhisheng Zhong, name: Bohao Peng, name: Shu Liu, name: Bei Yu, name: Jiaya Jia
Published: 2025-05-17 16:51:47

Large vision-language models exhibit inherent capabilities to handle diverse
visual perception tasks. In this paper, we introduce VisionReasoner, a unified
framework capable of reasoning and solving multiple visual perception tasks
within a shared model. Specifically, by designing novel multi-object cognitive
learning strategies and systematic task reformulation, VisionReasoner enhances
its reasoning capabilities to analyze visual inputs, and addresses diverse
perception tasks in a unified framework. The model generates a structured
reasoning process before delivering the desired outputs responding to user
queries. To rigorously assess unified visual perception capabilities, we
evaluate VisionReasoner on ten diverse tasks spanning three critical domains:
detection, segmentation, and counting. Experimental results show that
VisionReasoner achieves superior performance as a unified model, outperforming
Qwen2.5VL by relative margins of 29.1% on COCO (detection), 22.1% on ReasonSeg
(segmentation), and 15.3% on CountBench (counting).
AdaptThink: Reasoning Models Can Learn When to Think
Authors: name: Jiajie Zhang, name: Nianyi Lin, name: Lei Hou, name: Ling Feng, name: Juanzi Li
Published: 2025-05-19 17:50:52

Recently, large reasoning models have achieved impressive performance on
various tasks by employing human-like deep thinking. However, the lengthy
thinking process substantially increases inference overhead, making efficiency
a critical bottleneck. In this work, we first demonstrate that NoThinking,
which prompts the reasoning model to skip thinking and directly generate the
final solution, is a better choice for relatively simple tasks in terms of both
performance and efficiency. Motivated by this, we propose AdaptThink, a novel
RL algorithm to teach reasoning models to choose the optimal thinking mode
adaptively based on problem difficulty. Specifically, AdaptThink features two
core components: (1) a constrained optimization objective that encourages the
model to choose NoThinking while maintaining the overall performance; (2) an
importance sampling strategy that balances Thinking and NoThinking samples
during on-policy training, thereby enabling cold start and allowing the model
to explore and exploit both thinking modes throughout the training process. Our
experiments indicate that AdaptThink significantly reduces the inference costs
while further enhancing performance. Notably, on three math datasets,
AdaptThink reduces the average response length of DeepSeek-R1-Distill-Qwen-1.5B
by 53% and improves its accuracy by 2.4%, highlighting the promise of adaptive
thinking-mode selection for optimizing the balance between reasoning quality
and efficiency. Our codes and models are available at
https://github.com/THU-KEG/AdaptThink.
Unifying Segment Anything in Microscopy with Multimodal Large Language
  Model
Authors: name: Manyu Li, name: Ruian He, name: Zixian Zhang, name: Weimin Tan, name: Bo Yan
Published: 2025-05-16 00:55:56

Accurate segmentation of regions of interest in biomedical images holds
substantial value in image analysis. Although several foundation models for
biomedical segmentation have currently achieved excellent performance on
certain datasets, they typically demonstrate sub-optimal performance on unseen
domain data. We owe the deficiency to lack of vision-language knowledge before
segmentation. Multimodal Large Language Models (MLLMs) bring outstanding
understanding and reasoning capabilities to multimodal tasks, which inspires us
to leverage MLLMs to inject Vision-Language Knowledge (VLK), thereby enabling
vision models to demonstrate superior generalization capabilities on
cross-domain datasets. In this paper, we propose using MLLMs to guide SAM in
learning microscopy crose-domain data, unifying Segment Anything in Microscopy,
named uLLSAM. Specifically, we propose the Vision-Language Semantic Alignment
(VLSA) module, which injects VLK into Segment Anything Model (SAM). We find
that after SAM receives global VLK prompts, its performance improves
significantly, but there are deficiencies in boundary contour perception.
Therefore, we further propose Semantic Boundary Regularization (SBR) to prompt
SAM. Our method achieves performance improvements of 7.71% in Dice and 12.10%
in SA across 9 in-domain microscopy datasets, achieving state-of-the-art
performance. Our method also demonstrates improvements of 6.79% in Dice and
10.08% in SA across 10 out-ofdomain datasets, exhibiting strong generalization
capabilities. Code is available at https://github.com/ieellee/uLLSAM.
InstanceGen: Image Generation with Instance-level Instructions
Authors: name: Etai Sella, name: Yanir Kleiman, name: Hadar Averbuch-Elor
Published: 2025-05-08 22:31:23

Despite rapid advancements in the capabilities of generative models,
pretrained text-to-image models still struggle in capturing the semantics
conveyed by complex prompts that compound multiple objects and instance-level
attributes. Consequently, we are witnessing growing interests in integrating
additional structural constraints, typically in the form of coarse bounding
boxes, to better guide the generation process in such challenging cases. In
this work, we take the idea of structural guidance a step further by making the
observation that contemporary image generation models can directly provide a
plausible fine-grained structural initialization. We propose a technique that
couples this image-based structural guidance with LLM-based instance-level
instructions, yielding output images that adhere to all parts of the text
prompt, including object counts, instance-level attributes, and spatial
relations between instances.
GIE-Bench: Towards Grounded Evaluation for Text-Guided Image Editing
Authors: name: Yusu Qian, name: Jiasen Lu, name: Tsu-Jui Fu, name: Xinze Wang, name: Chen Chen, name: Yinfei Yang, name: Wenze Hu, name: Zhe Gan
Published: 2025-05-16 17:55:54

Editing images using natural language instructions has become a natural and
expressive way to modify visual content; yet, evaluating the performance of
such models remains challenging. Existing evaluation approaches often rely on
image-text similarity metrics like CLIP, which lack precision. In this work, we
introduce a new benchmark designed to evaluate text-guided image editing models
in a more grounded manner, along two critical dimensions: (i) functional
correctness, assessed via automatically generated multiple-choice questions
that verify whether the intended change was successfully applied; and (ii)
image content preservation, which ensures that non-targeted regions of the
image remain visually consistent using an object-aware masking technique and
preservation scoring. The benchmark includes over 1000 high-quality editing
examples across 20 diverse content categories, each annotated with detailed
editing instructions, evaluation questions, and spatial object masks. We
conduct a large-scale study comparing GPT-Image-1, the latest flagship in the
text-guided image editing space, against several state-of-the-art editing
models, and validate our automatic metrics against human ratings. Results show
that GPT-Image-1 leads in instruction-following accuracy, but often
over-modifies irrelevant image regions, highlighting a key trade-off in the
current model behavior. GIE-Bench provides a scalable, reproducible framework
for advancing more accurate evaluation of text-guided image editing.
Improving Assembly Code Performance with Large Language Models via
  Reinforcement Learning
Authors: name: Anjiang Wei, name: Tarun Suresh, name: Huanmi Tan, name: Yinglun Xu, name: Gagandeep Singh, name: Ke Wang, name: Alex Aiken
Published: 2025-05-16 17:40:45

Large language models (LLMs) have demonstrated strong performance across a
wide range of programming tasks, yet their potential for code optimization
remains underexplored. This work investigates whether LLMs can optimize the
performance of assembly code, where fine-grained control over execution enables
improvements that are difficult to express in high-level languages. We present
a reinforcement learning framework that trains LLMs using Proximal Policy
Optimization (PPO), guided by a reward function that considers both functional
correctness, validated through test cases, and execution performance relative
to the industry-standard compiler gcc -O3. To support this study, we introduce
a benchmark of 8,072 real-world programs. Our model, Qwen2.5-Coder-7B-PPO,
achieves 96.0% test pass rates and an average speedup of 1.47x over the gcc -O3
baseline, outperforming all 20 other models evaluated, including
Claude-3.7-sonnet. These results indicate that reinforcement learning can
unlock the potential of LLMs to serve as effective optimizers for assembly code
performance.