Orionnnn/hub-stats huggingface dataset:
AlphaApollo: Orchestrating Foundation Models and Professional Tools into
  a Self-Evolving System for Deep Agentic Reasoning
Authors: name: Zhanke Zhou, name: Chentao Cao, name: Xiao Feng, name: Xuan Li, name: Zongze Li, name: Xiangyu Lu, name: Jiangchao Yao, name: Weikai Huang, name: Linrui Xu, name: Tian Cheng, name: Guanyu Jiang, name: Yiming Zheng, name: Brando Miranda, name: Tongliang Liu, name: Sanmi Koyejo, name: Masashi Sugiyama, name: Bo Han
Published: 2025-10-05 15:42:24

We present AlphaApollo, a self-evolving agentic reasoning system that aims to
address two bottlenecks in foundation model (FM) reasoning-limited
model-intrinsic capacity and unreliable test-time iteration. AlphaApollo
orchestrates multiple models with professional tools to enable deliberate,
verifiable reasoning. It couples (i) a computation tool (Python with numerical
and symbolic libraries) and (ii) a retrieval tool (task-relevant external
information) to execute exact calculations and ground decisions. The system
further supports multi-round, multi-model solution evolution via a shared state
map that records candidates, executable checks, and feedback for iterative
refinement. In evaluations on AIME 2024/2025 across multiple models,
AlphaApollo delivers consistent gains: +5.15% Average@32 and +23.34% Pass@32
for Qwen2.5-14B-Instruct, and +8.91% Average@32 with +26.67% Pass@32 for
Llama-3.3-70B-Instruct. Tool-use analysis shows that more than 80% of tool
calls are successfully executed, with consistent outperformance of non-tool
baselines, thereby lifting the capability ceiling of FMs. More empirical
results and implementation details will be updated at
https://github.com/tmlr-group/AlphaApollo.
WristWorld: Generating Wrist-Views via 4D World Models for Robotic
  Manipulation
Authors: name: Zezhong Qian, name: Xiaowei Chi, name: Yuming Li, name: Shizun Wang, name: Zhiyuan Qin, name: Xiaozhu Ju, name: Sirui Han, name: Shanghang Zhang
Published: 2025-10-08 17:59:08

Wrist-view observations are crucial for VLA models as they capture
fine-grained hand-object interactions that directly enhance manipulation
performance. Yet large-scale datasets rarely include such recordings, resulting
in a substantial gap between abundant anchor views and scarce wrist views.
Existing world models cannot bridge this gap, as they require a wrist-view
first frame and thus fail to generate wrist-view videos from anchor views
alone. Amid this gap, recent visual geometry models such as VGGT emerge with
geometric and cross-view priors that make it possible to address extreme
viewpoint shifts. Inspired by these insights, we propose WristWorld, the first
4D world model that generates wrist-view videos solely from anchor views.
WristWorld operates in two stages: (i) Reconstruction, which extends VGGT and
incorporates our Spatial Projection Consistency (SPC) Loss to estimate
geometrically consistent wrist-view poses and 4D point clouds; (ii) Generation,
which employs our video generation model to synthesize temporally coherent
wrist-view videos from the reconstructed perspective. Experiments on Droid,
Calvin, and Franka Panda demonstrate state-of-the-art video generation with
superior spatial consistency, while also improving VLA performance, raising the
average task completion length on Calvin by 3.81% and closing 42.4% of the
anchor-wrist view gap.
Lumina-DiMOO: An Omni Diffusion Large Language Model for Multi-Modal
  Generation and Understanding
Authors: name: Yi Xin, name: Qi Qin, name: Siqi Luo, name: Kaiwen Zhu, name: Juncheng Yan, name: Yan Tai, name: Jiayi Lei, name: Yuewen Cao, name: Keqi Wang, name: Yibin Wang, name: Jinbin Bai, name: Qian Yu, name: Dengyang Jiang, name: Yuandong Pu, name: Haoxing Chen, name: Le Zhuo, name: Junjun He, name: Gen Luo, name: Tianbin Li, name: Ming Hu, name: Jin Ye, name: Shenglong Ye, name: Bo Zhang, name: Chang Xu, name: Wenhai Wang, name: Hongsheng Li, name: Guangtao Zhai, name: Tianfan Xue, name: Bin Fu, name: Xiaohong Liu, name: Yu Qiao, name: Yihao Liu
Published: 2025-10-07 17:59:20

We introduce Lumina-DiMOO, an open-source foundational model for seamless
multi-modal generation and understanding. Lumina-DiMOO sets itself apart from
prior unified models by utilizing a fully discrete diffusion modeling to handle
inputs and outputs across various modalities. This innovative approach allows
Lumina-DiMOO to achieve higher sampling efficiency compared to previous
autoregressive (AR) or hybrid AR-Diffusion paradigms and adeptly support a
broad spectrum of multi-modal tasks, including text-to-image generation,
image-to-image generation (e.g., image editing, subject-driven generation, and
image inpainting, etc.), as well as image understanding. Lumina-DiMOO achieves
state-of-the-art performance on multiple benchmarks, surpassing existing
open-source unified multi-modal models. To foster further advancements in
multi-modal and discrete diffusion model research, we release our code and
checkpoints to the community. Project Page:
https://synbol.github.io/Lumina-DiMOO.
MLE-Smith: Scaling MLE Tasks with Automated Multi-Agent Pipeline
Authors: name: Rushi Qiang, name: Yuchen Zhuang, name: Anikait Singh, name: Percy Liang, name: Chao Zhang, name: Sherry Yang, name: Bo Dai
Published: 2025-10-08 17:57:19

While Language Models (LMs) have made significant progress in automating
machine learning engineering (MLE), the acquisition of high-quality MLE
training data is significantly constrained. Current MLE benchmarks suffer from
low scalability and limited applicability because they rely on static, manually
curated tasks, demanding extensive time and manual effort to produce. We
introduce MLE-Smith, a fully automated multi-agent pipeline, to transform raw
datasets into competition-style MLE challenges through an efficient
generate-verify-execute paradigm for scaling MLE tasks with verifiable quality,
real-world usability, and rich diversity. The proposed multi-agent pipeline in
MLE-Smith drives structured task design and standardized refactoring, coupled
with a hybrid verification mechanism that enforces strict structural rules and
high-level semantic soundness. It further validates empirical solvability and
real-world fidelity through interactive execution. We apply MLE-Smith to 224 of
real-world datasets and generate 606 tasks spanning multiple categories,
objectives, and modalities, demonstrating that MLE-Smith can work effectively
across a wide range of real-world datasets. Evaluation on the generated tasks
shows that the performance of eight mainstream and cutting-edge LLMs on
MLE-Smith tasks is strongly correlated with their performance on carefully
human-designed tasks, highlighting the effectiveness of the MLE-Smith to
scaling up MLE tasks, while maintaining task quality.
TTRV: Test-Time Reinforcement Learning for Vision Language Models
Authors: name: Akshit Singh, name: Shyam Marjit, name: Wei Lin, name: Paul Gavrikov, name: Serena Yeung-Levy, name: Hilde Kuehne, name: Rogerio Feris, name: Sivan Doveh, name: James Glass, name: M. Jehanzeb Mirza
Published: 2025-10-08 09:10:31

Existing methods for extracting reward signals in Reinforcement Learning
typically rely on labeled data and dedicated training splits, a setup that
contrasts with how humans learn directly from their environment. In this work,
we propose TTRV to enhance vision language understanding by adapting the model
on the fly at inference time, without the need for any labeled data.
Concretely, we enhance the Group Relative Policy Optimization (GRPO) framework
by designing rewards based on the frequency of the base model's output, while
inferring on each test sample multiple times. Further, we also propose to
control the diversity of the model's output by simultaneously rewarding the
model for obtaining low entropy of the output empirical distribution. Our
approach delivers consistent gains across both object recognition and visual
question answering (VQA), with improvements of up to 52.4% and 29.8%,
respectively, and average boosts of 24.6% and 10.0% across 16
datasets.Remarkably, on image recognition, TTRV applied to InternVL 8B
surpasses GPT-4o by an average of 2.3% over 8 benchmarks, while remaining
highly competitive on VQA, demonstrating that test-time reinforcement learning
can match or exceed the strongest proprietary models. Finally, we find many
interesting properties of test-time RL for VLMs: for example, even in extremely
data-constrained scenarios, where adaptation is performed on a single randomly
chosen unlabeled test example, TTRV still yields non-trivial improvements of up
to 5.5% in recognition tasks.
U-Bench: A Comprehensive Understanding of U-Net through 100-Variant
  Benchmarking
Authors: name: Fenghe Tang, name: Chengqi Dong, name: Wenxin Ma, name: Zikang Xu, name: Heqin Zhu, name: Zihang Jiang, name: Rongsheng Wang, name: Yuhao Wang, name: Chenxu Wu, name: Shaohua Kevin Zhou
Published: 2025-10-08 14:06:17

Over the past decade, U-Net has been the dominant architecture in medical
image segmentation, leading to the development of thousands of U-shaped
variants. Despite its widespread adoption, there is still no comprehensive
benchmark to systematically evaluate their performance and utility, largely
because of insufficient statistical validation and limited consideration of
efficiency and generalization across diverse datasets. To bridge this gap, we
present U-Bench, the first large-scale, statistically rigorous benchmark that
evaluates 100 U-Net variants across 28 datasets and 10 imaging modalities. Our
contributions are threefold: (1) Comprehensive Evaluation: U-Bench evaluates
models along three key dimensions: statistical robustness, zero-shot
generalization, and computational efficiency. We introduce a novel metric,
U-Score, which jointly captures the performance-efficiency trade-off, offering
a deployment-oriented perspective on model progress. (2) Systematic Analysis
and Model Selection Guidance: We summarize key findings from the large-scale
evaluation and systematically analyze the impact of dataset characteristics and
architectural paradigms on model performance. Based on these insights, we
propose a model advisor agent to guide researchers in selecting the most
suitable models for specific datasets and tasks. (3) Public Availability: We
provide all code, models, protocols, and weights, enabling the community to
reproduce our results and extend the benchmark with future methods. In summary,
U-Bench not only exposes gaps in previous evaluations but also establishes a
foundation for fair, reproducible, and practically relevant benchmarking in the
next decade of U-Net-based segmentation models. The project can be accessed at:
https://fenghetan9.github.io/ubench. Code is available at:
https://github.com/FengheTan9/U-Bench.
Bridging Text and Video Generation: A Survey
Authors: name: Nilay Kumar, name: Priyansh Bhandari, name: G. Maragatham
Published: 2025-10-06 16:39:05

Text-to-video (T2V) generation technology holds potential to transform
multiple domains such as education, marketing, entertainment, and assistive
technologies for individuals with visual or reading comprehension challenges,
by creating coherent visual content from natural language prompts. From its
inception, the field has advanced from adversarial models to diffusion-based
models, yielding higher-fidelity, temporally consistent outputs. Yet challenges
persist, such as alignment, long-range coherence, and computational efficiency.
Addressing this evolving landscape, we present a comprehensive survey of
text-to-video generative models, tracing their development from early GANs and
VAEs to hybrid Diffusion-Transformer (DiT) architectures, detailing how these
models work, what limitations they addressed in their predecessors, and why
shifts toward new architectural paradigms were necessary to overcome challenges
in quality, coherence, and control. We provide a systematic account of the
datasets, which the surveyed text-to-video models were trained and evaluated
on, and, to support reproducibility and assess the accessibility of training
such models, we detail their training configurations, including their hardware
specifications, GPU counts, batch sizes, learning rates, optimizers, epochs,
and other key hyperparameters. Further, we outline the evaluation metrics
commonly used for evaluating such models and present their performance across
standard benchmarks, while also discussing the limitations of these metrics and
the emerging shift toward more holistic, perception-aligned evaluation
strategies. Finally, drawing from our analysis, we outline the current open
challenges and propose a few promising future directions, laying out a
perspective for future researchers to explore and build upon in advancing T2V
research and applications.
MATRIX: Mask Track Alignment for Interaction-aware Video Generation
Authors: name: Siyoon Jin, name: Seongchan Kim, name: Dahyun Chung, name: Jaeho Lee, name: Hyunwook Choi, name: Jisu Nam, name: Jiyoung Kim, name: Seungryong Kim
Published: 2025-10-08 17:57:38

Video DiTs have advanced video generation, yet they still struggle to model
multi-instance or subject-object interactions. This raises a key question: How
do these models internally represent interactions? To answer this, we curate
MATRIX-11K, a video dataset with interaction-aware captions and multi-instance
mask tracks. Using this dataset, we conduct a systematic analysis that
formalizes two perspectives of video DiTs: semantic grounding, via
video-to-text attention, which evaluates whether noun and verb tokens capture
instances and their relations; and semantic propagation, via video-to-video
attention, which assesses whether instance bindings persist across frames. We
find both effects concentrate in a small subset of interaction-dominant layers.
Motivated by this, we introduce MATRIX, a simple and effective regularization
that aligns attention in specific layers of video DiTs with multi-instance mask
tracks from the MATRIX-11K dataset, enhancing both grounding and propagation.
We further propose InterGenEval, an evaluation protocol for interaction-aware
video generation. In experiments, MATRIX improves both interaction fidelity and
semantic alignment while reducing drift and hallucination. Extensive ablations
validate our design choices. Codes and weights will be released.
Revisiting Long-context Modeling from Context Denoising Perspective
Authors: name: Zecheng Tang, name: Baibei Ji, name: Juntao Li, name: Lijun Wu, name: Haijia Gui, name: Min Zhang
Published: 2025-10-07 12:32:23

Long-context models (LCMs) have demonstrated great potential in processing
long sequences, facilitating many real-world applications. The success of LCMs
can be attributed to their ability to locate implicit critical information
within the context for further prediction. However, recent research reveals
that LCMs are often susceptible to contextual noise, i.e., irrelevant tokens,
that can mislead model attention. In this paper, we conduct a fine-grained
analysis of the context noise and propose an effective metric, the Integrated
Gradient (IG) score, to detect and quantify the noise information within the
context. Our findings reveal that even simple mitigation of detected context
noise can substantially boost the model's attention on critical tokens and
benefit subsequent predictions. Building on this insight, we propose Context
Denoising Training (CDT), a straightforward yet effective training strategy
that improves attention on critical tokens while reinforcing their influence on
model predictions. Extensive experiments across four tasks, under both context
window scaling and long-context alignment settings, demonstrate the superiority
of CDT. Notably, when trained with CDT, an open-source 8B model can achieve
performance (50.92) comparable to GPT-4o (51.00).
D^3QE: Learning Discrete Distribution Discrepancy-aware
  Quantization Error for Autoregressive-Generated Image Detection
Authors: name: Yanran Zhang, name: Bingyao Yu, name: Yu Zheng, name: Wenzhao Zheng, name: Yueqi Duan, name: Lei Chen, name: Jie Zhou, name: Jiwen Lu
Published: 2025-10-07 13:02:27

The emergence of visual autoregressive (AR) models has revolutionized image
generation while presenting new challenges for synthetic image detection.
Unlike previous GAN or diffusion-based methods, AR models generate images
through discrete token prediction, exhibiting both marked improvements in image
synthesis quality and unique characteristics in their vector-quantized
representations. In this paper, we propose to leverage Discrete Distribution
Discrepancy-aware Quantization Error (D^3QE) for autoregressive-generated
image detection that exploits the distinctive patterns and the frequency
distribution bias of the codebook existing in real and fake images. We
introduce a discrete distribution discrepancy-aware transformer that integrates
dynamic codebook frequency statistics into its attention mechanism, fusing
semantic features and quantization error latent. To evaluate our method, we
construct a comprehensive dataset termed ARForensics covering 7 mainstream
visual AR models. Experiments demonstrate superior detection accuracy and
strong generalization of D^3QE across different AR models, with robustness to
real-world perturbations. Code is available at
https://github.com/Zhangyr2022/D3QE{https://github.com/Zhangyr2022/D3QE}.
Why Low-Precision Transformer Training Fails: An Analysis on Flash
  Attention
Authors: name: Haiquan Qiu, name: Quanming Yao
Published: 2025-10-05 14:01:24

The pursuit of computational efficiency has driven the adoption of
low-precision formats for training transformer models. However, this progress
is often hindered by notorious training instabilities. This paper provides the
first mechanistic explanation for a long-standing and unresolved failure case
where training with flash attention in low-precision settings leads to
catastrophic loss explosions. Our in-depth analysis reveals that the failure is
not a random artifact but caused by two intertwined phenomena: the emergence of
similar low-rank representations within the attention mechanism and the
compounding effect of biased rounding errors inherent in low-precision
arithmetic. We demonstrate how these factors create a vicious cycle of error
accumulation that corrupts weight updates, ultimately derailing the training
dynamics. To validate our findings, we introduce a minimal modification to the
flash attention that mitigates the bias in rounding errors. This simple change
stabilizes the training process, confirming our analysis and offering a
practical solution to this persistent problem.
Cache-to-Cache: Direct Semantic Communication Between Large Language
  Models
Authors: name: Tianyu Fu, name: Zihan Min, name: Hanling Zhang, name: Jichao Yan, name: Guohao Dai, name: Wanli Ouyang, name: Yu Wang
Published: 2025-10-03 17:52:32

Multi-LLM systems harness the complementary strengths of diverse Large
Language Models, achieving performance and efficiency gains unattainable by a
single model. In existing designs, LLMs communicate through text, forcing
internal representations to be transformed into output token sequences. This
process both loses rich semantic information and incurs token-by-token
generation latency. Motivated by these limitations, we ask: Can LLMs
communicate beyond text? Oracle experiments show that enriching the KV-Cache
semantics can improve response quality without increasing cache size,
supporting KV-Cache as an effective medium for inter-model communication. Thus,
we propose Cache-to-Cache (C2C), a new paradigm for direct semantic
communication between LLMs. C2C uses a neural network to project and fuse the
source model's KV-cache with that of the target model to enable direct semantic
transfer. A learnable gating mechanism selects the target layers that benefit
from cache communication. Compared with text communication, C2C utilizes the
deep, specialized semantics from both models, while avoiding explicit
intermediate text generation. Experiments show that C2C achieves 8.5-10.5%
higher average accuracy than individual models. It further outperforms the text
communication paradigm by approximately 3.0-5.0%, while delivering an average
2.0x speedup in latency. Our code is available at
https://github.com/thu-nics/C2C.
Vibe Checker: Aligning Code Evaluation with Human Preference
Authors: name: Ming Zhong, name: Xiang Zhou, name: Ting-Yun Chang, name: Qingze Wang, name: Nan Xu, name: Xiance Si, name: Dan Garrette, name: Shyam Upadhyay, name: Jeremiah Liu, name: Jiawei Han, name: Benoit Schillings, name: Jiao Sun
Published: 2025-10-08 17:59:19

Large Language Models (LLMs) have catalyzed vibe coding, where users leverage
LLMs to generate and iteratively refine code through natural language
interactions until it passes their vibe check. Vibe check is tied to real-world
human preference and goes beyond functionality: the solution should feel right,
read cleanly, preserve intent, and remain correct. However, current code
evaluation remains anchored to pass@k and captures only functional correctness,
overlooking the non-functional instructions that users routinely apply. In this
paper, we hypothesize that instruction following is the missing piece
underlying vibe check that represents human preference in coding besides
functional correctness. To quantify models' code instruction following
capabilities with measurable signals, we present VeriCode, a taxonomy of 30
verifiable code instructions together with corresponding deterministic
verifiers. We use the taxonomy to augment established evaluation suites,
resulting in Vibe Checker, a testbed to assess both code instruction following
and functional correctness. Upon evaluating 31 leading LLMs, we show that even
the strongest models struggle to comply with multiple instructions and exhibit
clear functional regression. Most importantly, a composite score of functional
correctness and instruction following correlates the best with human
preference, with the latter emerging as the primary differentiator on
real-world programming tasks. Our work identifies core factors of the vibe
check, providing a concrete path for benchmarking and developing models that
better align with user preferences in coding.
Multi-Agent Tool-Integrated Policy Optimization
Authors: name: Zhanfeng Mo, name: Xingxuan Li, name: Yuntao Chen, name: Lidong Bing
Published: 2025-10-06 10:44:04

Large language models (LLMs) increasingly rely on multi-turn tool-integrated
planning for knowledge-intensive and complex reasoning tasks. Existing
implementations typically rely on a single agent, but they suffer from limited
context length and noisy tool responses. A natural solution is to adopt a
multi-agent framework with planner- and worker-agents to manage context.
However, no existing methods support effective reinforcement learning
post-training of tool-integrated multi-agent frameworks. To address this gap,
we propose Multi-Agent Tool-Integrated Policy Optimization (MATPO), which
enables distinct roles (planner and worker) to be trained within a single LLM
instance using role-specific prompts via reinforcement learning. MATPO is
derived from a principled credit assignment mechanism across planner and worker
rollouts. This design eliminates the need to deploy multiple LLMs, which would
be memory-intensive, while preserving the benefits of specialization.
Experiments on GAIA-text, WebWalkerQA, and FRAMES show that MATPO consistently
outperforms single-agent baselines by an average of 18.38% relative improvement
in performance and exhibits greater robustness to noisy tool outputs. Our
findings highlight the effectiveness of unifying multiple agent roles within a
single LLM and provide practical insights for stable and efficient multi-agent
RL training.