Orionnnn/hub-stats huggingface dataset:
EAGER: Entropy-Aware GEneRation for Adaptive Inference-Time Scaling
Authors: name: Daniel Scalena, name: Leonidas Zotos, name: Elisabetta Fersini, name: Malvina Nissim, name: Ahmet Üstün
Published: 2025-10-13 09:04:28

With the rise of reasoning language models and test-time scaling methods as a
paradigm for improving model performance, substantial computation is often
required to generate multiple candidate sequences from the same prompt. This
enables exploration of different reasoning paths toward the correct solution,
however, allocates the same compute budget for each prompt. Grounded on the
assumption that different prompts carry different degrees of complexity, and
thus different computation needs, we propose EAGer, a training-free generation
method that leverages model uncertainty through token-wise entropy distribution
to reduce redundant computation and concurrently improve overall performance.
EAGer allows branching to multiple reasoning paths only in the presence of
high-entropy tokens, and then reallocates the saved compute budget to the
instances where exploration of alternative paths is most needed. We find that
across multiple open-source models on complex reasoning benchmarks such as AIME
2025, EAGer can reallocate the budget without accessing target labels,
achieving the best efficiency-performance trade-off in terms of reasoning
length and Pass@k. When target labels are accessible, EAGer generates up to 65%
fewer tokens (hence saving compute) and achieves up to 37% improvement in
Pass@k compared to the Full Parallel Sampling.
MATH-Beyond: A Benchmark for RL to Expand Beyond the Base Model
Authors: name: Prasanna Mayilvahanan, name: Ricardo Dominguez-Olmedo, name: Thaddäus Wiedemer, name: Wieland Brendel
Published: 2025-10-13 17:30:54

With the advent of DeepSeek-R1, a new wave of reinforcement learning (RL)
methods has emerged that seem to unlock stronger mathematical reasoning.
However, a closer look at the open-source ecosystem reveals a critical
limitation: with sufficiently many draws (e.g., pass@1024), many
existing base models already solve nearly all questions on widely used math
benchmarks such as MATH-500 and AIME 2024. This suggests that the RL
fine-tuning methods prevalent in the LLM reasoning literature largely sharpen
existing solution modes rather than discovering entirely new ones. Such
sharpening stands in contrast to the broader promise of RL: to foster
exploration and to acquire new skills. To move beyond this plateau, we
introduce MATH-Beyond (MATH-B), a benchmark deliberately constructed to defeat
common open-source models of up to 8B parameters even under large sampling
budgets. Improving performance on our benchmark via RL requires methods that
learn to reason in ways that go beyond base model capabilities in repeated
sampling. Since the problems are drawn from subsets of DAPO-Math-17K and
DeepScaleR datasets, they remain topically equivalent to standard high-school
math. Validating our premise, RL fine-tuned models such as
Nemotron-Research-Reasoning-Qwen-1.5B and DeepScaleR-1.5B-Preview perform
poorly on MATH-B at pass@1024, showing how existing approaches fall
short on tackling harder instances. We hope MATH-B will catalyze
exploration-driven RL approaches that elicit deeper reasoning capabilities. We
release MATH-B at https://huggingface.co/datasets/brendel-group/MATH-Beyond.
Hierarchical Frequency Tagging Probe (HFTP): A Unified Approach to
  Investigate Syntactic Structure Representations in Large Language Models and
  the Human Brain
Authors: name: Jingmin An, name: Yilong Song, name: Ruolin Yang, name: Nai Ding, name: Lingxi Lu, name: Yuxuan Wang, name: Wei Wang, name: Chu Zhuang, name: Qian Wang, name: Fang Fang
Published: 2025-10-15 08:04:49

Large Language Models (LLMs) demonstrate human-level or even superior
language abilities, effectively modeling syntactic structures, yet the specific
computational modules responsible remain unclear. A key question is whether LLM
behavioral capabilities stem from mechanisms akin to those in the human brain.
To address these questions, we introduce the Hierarchical Frequency Tagging
Probe (HFTP), a tool that utilizes frequency-domain analysis to identify
neuron-wise components of LLMs (e.g., individual Multilayer Perceptron (MLP)
neurons) and cortical regions (via intracranial recordings) encoding syntactic
structures. Our results show that models such as GPT-2, Gemma, Gemma 2, Llama
2, Llama 3.1, and GLM-4 process syntax in analogous layers, while the human
brain relies on distinct cortical regions for different syntactic levels.
Representational similarity analysis reveals a stronger alignment between LLM
representations and the left hemisphere of the brain (dominant in language
processing). Notably, upgraded models exhibit divergent trends: Gemma 2 shows
greater brain similarity than Gemma, while Llama 3.1 shows less alignment with
the brain compared to Llama 2. These findings offer new insights into the
interpretability of LLM behavioral improvements, raising questions about
whether these advancements are driven by human-like or non-human-like
mechanisms, and establish HFTP as a valuable tool bridging computational
linguistics and cognitive neuroscience. This project is available at
https://github.com/LilTiger/HFTP.
Reasoning in Space via Grounding in the World
Authors: name: Yiming Chen, name: Zekun Qi, name: Wenyao Zhang, name: Xin Jin, name: Li Zhang, name: Peidong Liu
Published: 2025-10-15 17:58:08

In this paper, we claim that 3D visual grounding is the cornerstone of
spatial reasoning and introduce the Grounded-Spatial Reasoner (GS-Reasoner) to
explore the effective spatial representations that bridge the gap between them.
Existing 3D LLMs suffer from the absence of a unified 3D representation capable
of jointly capturing semantic and geometric information. This deficiency is
manifested either in poor performance on grounding or in an excessive reliance
on external modules, ultimately hindering the seamless integration of grounding
and spatial reasoning. To address this, we propose a simple yet effective
dual-path pooling mechanism that tightly aligns geometric features with both
semantic and positional cues, constructing a unified image patch-based 3D
representation that encapsulates all essential information without increasing
the number of input tokens. Leveraging this holistic representation,
GS-Reasoner is the first 3D LLM that achieves autoregressive grounding entirely
without external modules while delivering performance comparable to
state-of-the-art models, establishing a unified and self-contained framework
for 3D spatial reasoning. To further bridge grounding and spatial reasoning, we
introduce the Grounded Chain-of-Thought (GCoT) dataset. This dataset is
meticulously curated to include both 3D bounding box annotations for objects
referenced in reasoning questions and step-by-step reasoning paths that
integrate grounding as a core component of the problem-solving process.
Extensive experiments demonstrate that GS-Reasoner achieves impressive results
on 3D visual grounding, which in turn significantly enhances its spatial
reasoning capabilities, leading to state-of-the-art performance.
Point Prompting: Counterfactual Tracking with Video Diffusion Models
Authors: name: Ayush Shrivastava, name: Sanyam Mehta, name: Daniel Geng, name: Andrew Owens
Published: 2025-10-13 17:59:46

Trackers and video generators solve closely related problems: the former
analyze motion, while the latter synthesize it. We show that this connection
enables pretrained video diffusion models to perform zero-shot point tracking
by simply prompting them to visually mark points as they move over time. We
place a distinctively colored marker at the query point, then regenerate the
rest of the video from an intermediate noise level. This propagates the marker
across frames, tracing the point's trajectory. To ensure that the marker
remains visible in this counterfactual generation, despite such markers being
unlikely in natural videos, we use the unedited initial frame as a negative
prompt. Through experiments with multiple image-conditioned video diffusion
models, we find that these "emergent" tracks outperform those of prior
zero-shot methods and persist through occlusions, often obtaining performance
that is competitive with specialized self-supervised models.
Stronger Together: On-Policy Reinforcement Learning for Collaborative
  LLMs
Authors: name: Yujie Zhao, name: Lanxiang Hu, name: Yang Wang, name: Minmin Hou, name: Hao Zhang, name: Ke Ding, name: Jishen Zhao
Published: 2025-10-13 06:55:09

Multi-agent systems (MAS) and reinforcement learning (RL) are widely used to
enhance the agentic capabilities of large language models (LLMs). MAS improves
task performance through role-based orchestration, while RL uses environmental
rewards to learn stronger policies, such as GRPO-style optimization. However,
applying on-policy RL to MAS remains underexplored and presents unique
challenges. Algorithmically, standard GRPO grouping assumptions break down
because prompts vary by role and by turn. System-wise, the training stack must
support MAS-workflow rollouts and on-policy updates for both single-policy and
multi-policy models.
  We propose AT-GRPO, which includes (i) an agent- and turn-wise grouped RL
algorithm tailored to MAS and (ii) a training system that supports both single-
and multi-policy regimes. Across game, planning, coding, and math tasks,
AT-GRPO delivers substantial gains. On long-horizon planning, it increases
accuracy from a 14.0 to 47.0 percent single-agent RL baseline to 96.0 to 99.5
percent. It also improves reasoning performance, with average gains of 3.87 to
7.62 percent on coding tasks and 9.0 to 17.93 percent on math. Code and
environments are available at: https://github.com/pettingllms-ai/PettingLLMs.
Universal Image Restoration Pre-training via Masked Degradation
  Classification
Authors: name: JiaKui Hu, name: Zhengjian Yao, name: Lujia Jin, name: Yinghao Chen, name: Yanye Lu
Published: 2025-10-15 08:30:15

This study introduces a Masked Degradation Classification Pre-Training method
(MaskDCPT), designed to facilitate the classification of degradation types in
input images, leading to comprehensive image restoration pre-training. Unlike
conventional pre-training methods, MaskDCPT uses the degradation type of the
image as an extremely weak supervision, while simultaneously leveraging the
image reconstruction to enhance performance and robustness. MaskDCPT includes
an encoder and two decoders: the encoder extracts features from the masked
low-quality input image. The classification decoder uses these features to
identify the degradation type, whereas the reconstruction decoder aims to
reconstruct a corresponding high-quality image. This design allows the
pre-training to benefit from both masked image modeling and contrastive
learning, resulting in a generalized representation suited for restoration
tasks. Benefit from the straightforward yet potent MaskDCPT, the pre-trained
encoder can be used to address universal image restoration and achieve
outstanding performance. Implementing MaskDCPT significantly improves
performance for both convolution neural networks (CNNs) and Transformers, with
a minimum increase in PSNR of 3.77 dB in the 5D all-in-one restoration task and
a 34.8% reduction in PIQE compared to baseline in real-world degradation
scenarios. It also emergences strong generalization to previously unseen
degradation types and levels. In addition, we curate and release the UIR-2.5M
dataset, which includes 2.5 million paired restoration samples across 19
degradation types and over 200 degradation levels, incorporating both synthetic
and real-world data. The dataset, source code, and models are available at
https://github.com/MILab-PKU/MaskDCPT.
X-VLA: Soft-Prompted Transformer as Scalable Cross-Embodiment
  Vision-Language-Action Model
Authors: name: Jinliang Zheng, name: Jianxiong Li, name: Zhihao Wang, name: Dongxiu Liu, name: Xirui Kang, name: Yuchun Feng, name: Yinan Zheng, name: Jiayin Zou, name: Yilun Chen, name: Jia Zeng, name: Ya-Qin Zhang, name: Jiangmiao Pang, name: Jingjing Liu, name: Tai Wang, name: Xianyuan Zhan
Published: 2025-10-11 16:20:17

Successful generalist Vision-Language-Action (VLA) models rely on effective
training across diverse robotic platforms with large-scale, cross-embodiment,
heterogeneous datasets. To facilitate and leverage the heterogeneity in rich,
diverse robotic data sources, we propose a novel Soft Prompt approach with
minimally added parameters, by infusing prompt learning concepts into
cross-embodiment robot learning and introducing separate sets of learnable
embeddings for each distinct data source. These embeddings serve as
embodiment-specific prompts, which in unity empower VLA models with effective
exploitation of varying cross-embodiment features. Our new X-VLA, a neat
flow-matching-based VLA architecture, relies exclusively on soft-prompted
standard Transformer encoders, enjoying both scalability and simplicity.
Evaluated across 6 simulations as well as 3 real-world robots, our 0.9B
instantiation-X-VLA-0.9B simultaneously achieves SOTA performance over a sweep
of benchmarks, demonstrating superior results on a wide axes of capabilities,
from flexible dexterity to quick adaptation across embodiments, environments,
and tasks. Website: https://thu-air-dream.github.io/X-VLA/
FG-CLIP 2: A Bilingual Fine-grained Vision-Language Alignment Model
Authors: name: Chunyu Xie, name: Bin Wang, name: Fanjing Kong, name: Jincheng Li, name: Dawei Liang, name: Ji Ao, name: Dawei Leng, name: Yuhui Yin
Published: 2025-10-13 02:32:07

Fine-grained vision-language understanding requires precise alignment between
visual content and linguistic descriptions, a capability that remains limited
in current models, particularly in non-English settings. While models like CLIP
perform well on global alignment, they often struggle to capture fine-grained
details in object attributes, spatial relations, and linguistic expressions,
with limited support for bilingual comprehension. To address these challenges,
we introduce FG-CLIP 2, a bilingual vision-language model designed to advance
fine-grained alignment for both English and Chinese. Our approach leverages
rich fine-grained supervision, including region-text matching and long-caption
modeling, alongside multiple discriminative objectives. We further introduce
the Textual Intra-modal Contrastive (TIC) loss to better distinguish
semantically similar captions. Trained on a carefully curated mixture of
large-scale English and Chinese data, FG-CLIP 2 achieves powerful bilingual
performance. To enable rigorous evaluation, we present a new benchmark for
Chinese multimodal understanding, featuring long-caption retrieval and bounding
box classification. Extensive experiments on 29 datasets across 8 tasks show
that FG-CLIP 2 outperforms existing methods, achieving state-of-the-art results
in both languages. We release the model, code, and benchmark to facilitate
future research on bilingual fine-grained alignment.
Attention Illuminates LLM Reasoning: The Preplan-and-Anchor Rhythm
  Enables Fine-Grained Policy Optimization
Authors: name: Yang Li, name: Zhichen Dong, name: Yuhan Sun, name: Weixun Wang, name: Shaopan Xiong, name: Yijia Luo, name: Jiashun Liu, name: Han Lu, name: Jiamang Wang, name: Wenbo Su, name: Bo Zheng, name: Junchi Yan
Published: 2025-10-15 13:49:51

The reasoning pattern of Large language models (LLMs) remains opaque, and
Reinforcement learning (RL) typically applies uniform credit across an entire
generation, blurring the distinction between pivotal and routine steps. This
work positions attention as a privileged substrate that renders the internal
logic of LLMs legible, not merely as a byproduct of computation, but as a
mechanistic blueprint of reasoning itself. We first distinguish attention heads
between locally and globally focused information processing and reveal that
locally focused heads produce a sawtooth pattern near the diagonal indicating
phrasal chunks, while globally focused heads expose tokens that exert broad
downstream influence over future tokens. We formalize these with two metrics:
1) Windowed Average Attention Distance, which measures the extent of backward
attention within a clipped window; 2) Future Attention Influence, which
quantifies a token's global importance as the average attention it receives
from subsequent tokens. Taken together, these signals reveal a recurring
preplan-and-anchor mechanism, where the model first performs a long-range
contextual reference to generate an introductory token, which is immediately
followed by or coincides with a semantic anchor token that organizes subsequent
reasoning. Leveraging these insights, we introduce three novel RL strategies
that dynamically perform targeted credit assignment to critical nodes (preplan
tokens, anchor tokens, and their temporal coupling) and show consistent
performance gains across various reasoning tasks. By aligning optimization with
the model's intrinsic reasoning rhythm, we aim to transform opaque optimization
into an actionable structure-aware process, hoping to offer a potential step
toward more transparent and effective optimization of LLM reasoning.
Uni-MMMU: A Massive Multi-discipline Multimodal Unified Benchmark
Authors: name: Kai Zou, name: Ziqi Huang, name: Yuhao Dong, name: Shulin Tian, name: Dian Zheng, name: Hongbo Liu, name: Jingwen He, name: Bin Liu, name: Yu Qiao, name: Ziwei Liu
Published: 2025-10-15 17:10:35

Unified multimodal models aim to jointly enable visual understanding and
generation, yet current benchmarks rarely examine their true integration.
Existing evaluations either treat the two abilities in isolation or overlook
tasks that inherently couple them. To address this gap, we present Uni-MMMU, a
comprehensive and discipline-aware benchmark that systematically unfolds the
bidirectional synergy between generation and understanding across eight
reasoning-centric domains, including science, coding, mathematics, and puzzles.
Each task is bidirectionally coupled, demanding models to (i) leverage
conceptual understanding to guide precise visual synthesis, or (ii) utilize
generation as a cognitive scaffold for analytical reasoning. Uni-MMMU
incorporates verifiable intermediate reasoning steps, unique ground truths, and
a reproducible scoring protocol for both textual and visual outputs. Through
extensive evaluation of state-of-the-art unified, generation-only, and
understanding-only models, we reveal substantial performance disparities and
cross-modal dependencies, offering new insights into when and how these
abilities reinforce one another, and establishing a reliable foundation for
advancing unified models.
Bee: A High-Quality Corpus and Full-Stack Suite to Unlock Advanced Fully
  Open MLLMs
Authors: name: Yi Zhang, name: Bolin Ni, name: Xin-Sheng Chen, name: Heng-Rui Zhang, name: Yongming Rao, name: Houwen Peng, name: Qinglin Lu, name: Han Hu, name: Meng-Hao Guo, name: Shi-Min Hu
Published: 2025-10-15 17:52:59

Fully open multimodal large language models (MLLMs) currently lag behind
proprietary counterparts, primarily due to a significant gap in data quality
for supervised fine-tuning (SFT). Existing open-source datasets are often
plagued by widespread noise and a critical deficit in complex reasoning data,
such as Chain-of-Thought (CoT), which hinders the development of advanced model
capabilities. Addressing these challenges, our work makes three primary
contributions. First, we introduce Honey-Data-15M, a new SFT dataset comprising
approximately 15 million QA pairs, processed through multiple cleaning
techniques and enhanced with a novel dual-level (short and long) CoT enrichment
strategy. Second, we introduce HoneyPipe, the data curation pipeline, and its
underlying framework DataStudio, providing the community with a transparent and
adaptable methodology for data curation that moves beyond static dataset
releases. Finally, to validate our dataset and pipeline, we train Bee-8B, an 8B
model on Honey-Data-15M. Experiments show that Bee-8B establishes a new
state-of-the-art (SOTA) for fully open MLLMs, achieving performance that is
competitive with, and in some cases surpasses, recent semi-open models such as
InternVL3.5-8B. Our work delivers to the community a suite of foundational
resources, including: the Honey-Data-15M corpus; the full-stack suite
comprising HoneyPipe and DataStudio; training recipes; an evaluation harness;
and the model weights. This effort demonstrates that a principled focus on data
quality is a key pathway to developing fully open MLLMs that are highly
competitive with their semi-open counterparts.
LIBERO-Plus: In-depth Robustness Analysis of Vision-Language-Action
  Models
Authors: name: Senyu Fei, name: Siyin Wang, name: Junhao Shi, name: Zihao Dai, name: Jikun Cai, name: Pengfang Qian, name: Li Ji, name: Xinzhe He, name: Shiduo Zhang, name: Zhaoye Fei, name: Jinlan Fu, name: Jingjing Gong, name: Xipeng Qiu
Published: 2025-10-15 14:51:36

Visual-Language-Action (VLA) models report impressive success rates on
robotic manipulation benchmarks, yet these results may mask fundamental
weaknesses in robustness. We perform a systematic vulnerability analysis by
introducing controlled perturbations across seven dimensions: objects layout,
camera viewpoints, robot initial states, language instructions, light
conditions, background textures and sensor noise. We comprehensively analyzed
multiple state-of-the-art models and revealed consistent brittleness beneath
apparent competence. Our analysis exposes critical weaknesses: models exhibit
extreme sensitivity to perturbation factors, including camera viewpoints and
robot initial states, with performance dropping from 95% to below 30% under
modest perturbations. Surprisingly, models are largely insensitive to language
variations, with further experiments revealing that models tend to ignore
language instructions completely. Our findings challenge the assumption that
high benchmark scores equate to true competency and highlight the need for
evaluation practices that assess reliability under realistic variation.
CVD-STORM: Cross-View Video Diffusion with Spatial-Temporal
  Reconstruction Model for Autonomous Driving
Authors: name: Tianrui Zhang, name: Yichen Liu, name: Zilin Guo, name: Yuxin Guo, name: Jingcheng Ni, name: Chenjing Ding, name: Dan Xu, name: Lewei Lu, name: Zehuan Wu
Published: 2025-10-09 08:41:58

Generative models have been widely applied to world modeling for environment
simulation and future state prediction. With advancements in autonomous
driving, there is a growing demand not only for high-fidelity video generation
under various controls, but also for producing diverse and meaningful
information such as depth estimation. To address this, we propose CVD-STORM, a
cross-view video diffusion model utilizing a spatial-temporal reconstruction
Variational Autoencoder (VAE) that generates long-term, multi-view videos with
4D reconstruction capabilities under various control inputs. Our approach first
fine-tunes the VAE with an auxiliary 4D reconstruction task, enhancing its
ability to encode 3D structures and temporal dynamics. Subsequently, we
integrate this VAE into the video diffusion process to significantly improve
generation quality. Experimental results demonstrate that our model achieves
substantial improvements in both FID and FVD metrics. Additionally, the
jointly-trained Gaussian Splatting Decoder effectively reconstructs dynamic
scenes, providing valuable geometric information for comprehensive scene
understanding.