Orionnnn/hub-stats huggingface dataset:
Unimedvl: Unifying Medical Multimodal Understanding And Generation
  Through Observation-Knowledge-Analysis
Authors: name: Junzhi Ning, name: Wei Li, name: Cheng Tang, name: Jiashi Lin, name: Chenglong Ma, name: Chaoyang Zhang, name: Jiyao Liu, name: Ying Chen, name: Shujian Gao, name: Lihao Liu, name: Yuandong Pu, name: Huihui Xu, name: Chenhui Gou, name: Ziyan Huang, name: Yi Xin, name: Qi Qin, name: Zhongying Deng, name: Diping Song, name: Bin Fu, name: Guang Yang, name: Yuanfeng Ji, name: Tianbin Li, name: Yanzhou Su, name: Jin Ye, name: Shixiang Tang, name: Ming Hu, name: Junjun He
Published: 2025-10-17 14:54:58

Medical diagnostic applications require models that can process multimodal
medical inputs (images, patient histories, lab results) and generate diverse
outputs including both textual reports and visual content (annotations,
segmentation masks, and images). Despite this need, existing medical AI systems
disrupt this unified process: medical image understanding models interpret
images but cannot generate visual outputs, while medical image generation
models synthesize images but cannot provide textual explanations. This leads to
gaps in data representation, feature integration, and task-level multimodal
capabilities. To this end, we propose a multi-level framework that draws
inspiration from diagnostic workflows through the
Observation-Knowledge-Analysis (OKA) paradigm. Specifically, at the observation
level, we construct UniMed-5M, a dataset comprising over 5.6M samples that
reformat diverse unimodal data into multimodal pairs for foundational
observation. At the knowledge level, we propose Progressive Curriculum Learning
that systematically introduces medical multimodal knowledge. At the analysis
level, we introduce UniMedVL, the first medical unified multimodal model for
the simultaneous analysis of image understanding and generation tasks within a
single architecture. UniMedVL achieves superior performance on five medical
image understanding benchmarks, while matching specialized models in generation
quality across eight medical imaging modalities. Crucially, our unified
architecture enables bidirectional knowledge sharing: generation tasks enhance
visual understanding features, demonstrating that integrating traditionally
separate capabilities within a single medical framework unlocks improvements
across diverse medical vision-language tasks. Code is available at
https://github.com/uni-medical/UniMedVL.
Think with 3D: Geometric Imagination Grounded Spatial Reasoning from
  Limited Views
Authors: name: Zhangquan Chen, name: Manyuan Zhang, name: Xinlei Yu, name: Xufang Luo, name: Mingze Sun, name: Zihao Pan, name: Yan Feng, name: Peng Pei, name: Xunliang Cai, name: Ruqi Huang
Published: 2025-10-21 13:36:58

Though recent advances in vision-language models (VLMs) have achieved
remarkable progress across a wide range of multimodal tasks, understanding 3D
spatial relationships from limited views remains a significant challenge.
Previous reasoning methods typically rely on pure text (e.g., topological
cognitive maps) or on 2D visual cues. However, their limited representational
capacity hinders performance in specific tasks that require 3D spatial
imagination. To address this limitation, we propose 3DThinker, a framework that
can effectively exploits the rich geometric information embedded within images
while reasoning, like humans do. Our framework is the first to enable 3D
mentaling during reasoning without any 3D prior input, and it does not rely on
explicitly labeled 3D data for training. Specifically, our training consists of
two stages. First, we perform supervised training to align the 3D latent
generated by VLM while reasoning with that of a 3D foundation model (e.g.,
VGGT). Then, we optimize the entire reasoning trajectory solely based on
outcome signals, thereby refining the underlying 3D mentaling. Extensive
experiments across multiple benchmarks show that 3DThinker consistently
outperforms strong baselines and offers a new perspective toward unifying 3D
representations into multimodal reasoning. Our code will be available at
https://github.com/zhangquanchen/3DThinker.
GAS: Improving Discretization of Diffusion ODEs via Generalized
  Adversarial Solver
Authors: name: Aleksandr Oganov, name: Ilya Bykov, name: Eva Neudachina, name: Mishan Aliev, name: Alexander Tolmachev, name: Alexander Sidorov, name: Aleksandr Zuev, name: Andrey Okhotin, name: Denis Rakitin, name: Aibek Alanov
Published: 2025-10-20 16:14:38

While diffusion models achieve state-of-the-art generation quality, they
still suffer from computationally expensive sampling. Recent works address this
issue with gradient-based optimization methods that distill a few-step ODE
diffusion solver from the full sampling process, reducing the number of
function evaluations from dozens to just a few. However, these approaches often
rely on intricate training techniques and do not explicitly focus on preserving
fine-grained details. In this paper, we introduce the Generalized Solver: a
simple parameterization of the ODE sampler that does not require additional
training tricks and improves quality over existing approaches. We further
combine the original distillation loss with adversarial training, which
mitigates artifacts and enhances detail fidelity. We call the resulting method
the Generalized Adversarial Solver and demonstrate its superior performance
compared to existing solver training methods under similar resource
constraints. Code is available at https://github.com/3145tttt/GAS.
DeepSeek-OCR: Contexts Optical Compression
Authors: name: Haoran Wei, name: Yaofeng Sun, name: Yukun Li
Published: 2025-10-21 02:41:44

We present DeepSeek-OCR as an initial investigation into the feasibility of
compressing long contexts via optical 2D mapping. DeepSeek-OCR consists of two
components: DeepEncoder and DeepSeek3B-MoE-A570M as the decoder. Specifically,
DeepEncoder serves as the core engine, designed to maintain low activations
under high-resolution input while achieving high compression ratios to ensure
an optimal and manageable number of vision tokens. Experiments show that when
the number of text tokens is within 10 times that of vision tokens (i.e., a
compression ratio < 10x), the model can achieve decoding (OCR) precision of
97%. Even at a compression ratio of 20x, the OCR accuracy still remains at
about 60%. This shows considerable promise for research areas such as
historical long-context compression and memory forgetting mechanisms in LLMs.
Beyond this, DeepSeek-OCR also demonstrates high practical value. On
OmniDocBench, it surpasses GOT-OCR2.0 (256 tokens/page) using only 100 vision
tokens, and outperforms MinerU2.0 (6000+ tokens per page on average) while
utilizing fewer than 800 vision tokens. In production, DeepSeek-OCR can
generate training data for LLMs/VLMs at a scale of 200k+ pages per day (a
single A100-40G). Codes and model weights are publicly accessible at
http://github.com/deepseek-ai/DeepSeek-OCR.
Is Multilingual LLM Watermarking Truly Multilingual? A Simple
  Back-Translation Solution
Authors: name: Asim Mohamed, name: Martin Gubri
Published: 2025-10-20 18:51:20

Multilingual watermarking aims to make large language model (LLM) outputs
traceable across languages, yet current methods still fall short. Despite
claims of cross-lingual robustness, they are evaluated only on high-resource
languages. We show that existing multilingual watermarking methods are not
truly multilingual: they fail to remain robust under translation attacks in
medium- and low-resource languages. We trace this failure to semantic
clustering, which fails when the tokenizer vocabulary contains too few
full-word tokens for a given language. To address this, we introduce STEAM, a
back-translation-based detection method that restores watermark strength lost
through translation. STEAM is compatible with any watermarking method, robust
across different tokenizers and languages, non-invasive, and easily extendable
to new languages. With average gains of +0.19 AUC and +40%p TPR@1% on 17
languages, STEAM provides a simple and robust path toward fairer watermarking
across diverse languages.
PokeeResearch: Effective Deep Research via Reinforcement Learning from
  AI Feedback and Robust Reasoning Scaffold
Authors: name: Yi Wan, name: Jiuqi Wang, name: Liam Li, name: Jinsong Liu, name: Ruihao Zhu, name: Zheqing Zhu
Published: 2025-10-17 17:53:06

Tool-augmented large language models (LLMs) are emerging as deep research
agents, systems that decompose complex queries, retrieve external evidence, and
synthesize grounded responses. Yet current agents remain limited by shallow
retrieval, weak alignment metrics, and brittle tool-use behavior. We introduce
PokeeResearch-7B, a 7B-parameter deep research agent built under a unified
reinforcement learning framework for robustness, alignment, and scalability.
PokeeResearch-7B is trained by an annotation-free Reinforcement Learning from
AI Feedback (RLAIF) framework to optimize policies using LLM-based reward
signals that capture factual accuracy, citation faithfulness, and instruction
adherence. A chain-of-thought-driven multi-call reasoning scaffold further
enhances robustness through self-verification and adaptive recovery from tool
failures. Among 10 popular deep research benchmarks, PokeeResearch-7B achieves
state-of-the-art performance among 7B-scale deep research agents. This
highlights that careful reinforcement learning and reasoning design can produce
efficient, resilient, and research-grade AI agents. The model and inference
code is open-sourced under MIT license at
https://github.com/Pokee-AI/PokeeResearchOSS.
Extracting alignment data in open models
Authors: name: Federico Barbero, name: Xiangming Gu, name: Christopher A. Choquette-Choo, name: Chawin Sitawarin, name: Matthew Jagielski, name: Itay Yona, name: Petar Veličković, name: Ilia Shumailov, name: Jamie Hayes
Published: 2025-10-21 12:06:00

In this work, we show that it is possible to extract significant amounts of
alignment training data from a post-trained model -- useful to steer the model
to improve certain capabilities such as long-context reasoning, safety,
instruction following, and maths. While the majority of related work on
memorisation has focused on measuring success of training data extraction
through string matching, we argue that embedding models are better suited for
our specific goals. Distances measured through a high quality embedding model
can identify semantic similarities between strings that a different metric such
as edit distance will struggle to capture. In fact, in our investigation,
approximate string matching would have severely undercounted (by a conservative
estimate of 10times) the amount of data that can be extracted due to trivial
artifacts that deflate the metric. Interestingly, we find that models readily
regurgitate training data that was used in post-training phases such as SFT or
RL. We show that this data can be then used to train a base model, recovering a
meaningful amount of the original performance. We believe our work exposes a
possibly overlooked risk towards extracting alignment data. Finally, our work
opens up an interesting discussion on the downstream effects of distillation
practices: since models seem to be regurgitating aspects of their training set,
distillation can therefore be thought of as indirectly training on the model's
original dataset.
PRISMM-Bench: A Benchmark of Peer-Review Grounded Multimodal
  Inconsistencies
Authors: name: Lukas Selch, name: Yufang Hou, name: M. Jehanzeb Mirza, name: Sivan Doveh, name: James Glass, name: Rogerio Feris, name: Wei Lin
Published: 2025-10-18 13:46:26

Large Multimodal Models (LMMs) are increasingly applied to scientific
research, yet it remains unclear whether they can reliably understand and
reason over the multimodal complexity of papers. A central challenge lies in
detecting and resolving inconsistencies across text, figures, tables, and
equations, issues that are often subtle, domain-specific, and ultimately
undermine clarity, reproducibility, and trust. Existing benchmarks overlook
this issue, either isolating single modalities or relying on synthetic errors
that fail to capture real-world complexity. We introduce PRISMM-Bench
(Peer-Review-sourced Inconsistency Set for Multimodal Models), the first
benchmark grounded in real reviewer-flagged inconsistencies in scientific
papers. Through a multi-stage pipeline of review mining, LLM-assisted filtering
and human verification, we curate 262 inconsistencies from 242 papers. Based on
this set, we design three tasks, namely inconsistency identification, remedy
and pair matching, which assess a model's capacity to detect, correct, and
reason over inconsistencies across different modalities. Furthermore, to
address the notorious problem of choice-only shortcuts in multiple-choice
evaluation, where models exploit answer patterns without truly understanding
the question, we further introduce structured JSON-based answer representations
that minimize linguistic biases by reducing reliance on superficial stylistic
cues. We benchmark 21 leading LMMs, including large open-weight models
(GLM-4.5V 106B, InternVL3 78B) and proprietary models (Gemini 2.5 Pro, GPT-5
with high reasoning). Results reveal strikingly low performance (26.1-54.2%),
underscoring the challenge of multimodal scientific reasoning and motivating
progress towards trustworthy scientific assistants.
AlphaQuanter: An End-to-End Tool-Orchestrated Agentic Reinforcement
  Learning Framework for Stock Trading
Authors: name: Zheye Deng, name: Jiashu Wang
Published: 2025-10-16 03:30:22

While Large Language Model (LLM) agents show promise in automated trading,
they still face critical limitations. Prominent multi-agent frameworks often
suffer from inefficiency, produce inconsistent signals, and lack the end-to-end
optimization required to learn a coherent strategy from market feedback. To
address this, we introduce AlphaQuanter, a single-agent framework that uses
reinforcement learning (RL) to learn a dynamic policy over a transparent,
tool-augmented decision workflow, which empowers a single agent to autonomously
orchestrate tools and proactively acquire information on demand, establishing a
transparent and auditable reasoning process. Extensive experiments demonstrate
that AlphaQuanter achieves state-of-the-art performance on key financial
metrics. Moreover, its interpretable reasoning reveals sophisticated
strategies, offering novel and valuable insights for human traders. Our code
for data acquisition and agent training is publicly available at:
https://github.com/AlphaQuanter/AlphaQuanter
EvoSyn: Generalizable Evolutionary Data Synthesis for Verifiable
  Learning
Authors: name: He Du, name: Bowen Li, name: Aijun Yang, name: Siyang He, name: Qipeng Guo, name: Dacheng Tao
Published: 2025-10-20 11:56:35

Reliable verifiable data has become a key driver of capability gains in
modern language models, enabling stable reinforcement learning with verifiable
rewards and effective distillation that transfers competence across math,
coding, and agentic tasks. Yet constructing generalizable synthetic verifiable
data remains difficult due to hallucination-prone generation, and weak or
trivial verification artifacts that fail to separate strong from weak
solutions. Existing approaches often rely on task-specific heuristics or
post-hoc filters that do not transfer across domains and lack a principled,
universal evaluator of verifiability. In this work, we introduce an
evolutionary, task-agnostic, strategy-guided, executably-checkable data
synthesis framework that, from minimal seed supervision, jointly synthesizes
problems, diverse candidate solutions, and verification artifacts, and
iteratively discovers strategies via a consistency-based evaluator that
enforces agreement between human-annotated and strategy-induced checks. This
pipeline upgrades filtering into principled synthesis: it reliably assembles
coherent, verifiable training instances and generalizes without domain-specific
rules. Our experiments demonstrate the effectiveness of the proposed approach
under both RLVR and model distillation training paradigms. The results show
that training with our synthesized data yields significant improvements on both
the LiveCodeBench and AgentBench-OS tasks, highlighting the robust
generalization of our framework.
Mono4DGS-HDR: High Dynamic Range 4D Gaussian Splatting from
  Alternating-exposure Monocular Videos
Authors: name: Jinfeng Liu, name: Lingtong Kong, name: Mi Zhou, name: Jinwen Chen, name: Dan Xu
Published: 2025-10-21 10:14:33

We introduce Mono4DGS-HDR, the first system for reconstructing renderable 4D
high dynamic range (HDR) scenes from unposed monocular low dynamic range (LDR)
videos captured with alternating exposures. To tackle such a challenging
problem, we present a unified framework with two-stage optimization approach
based on Gaussian Splatting. The first stage learns a video HDR Gaussian
representation in orthographic camera coordinate space, eliminating the need
for camera poses and enabling robust initial HDR video reconstruction. The
second stage transforms video Gaussians into world space and jointly refines
the world Gaussians with camera poses. Furthermore, we propose a temporal
luminance regularization strategy to enhance the temporal consistency of the
HDR appearance. Since our task has not been studied before, we construct a new
evaluation benchmark using publicly available datasets for HDR video
reconstruction. Extensive experiments demonstrate that Mono4DGS-HDR
significantly outperforms alternative solutions adapted from state-of-the-art
methods in both rendering quality and speed.
DSI-Bench: A Benchmark for Dynamic Spatial Intelligence
Authors: name: Ziang Zhang, name: Zehan Wang, name: Guanghao Zhang, name: Weilong Dai, name: Yan Xia, name: Ziang Yan, name: Minjie Hong, name: Zhou Zhao
Published: 2025-10-21 17:59:36

Reasoning about dynamic spatial relationships is essential, as both observers
and objects often move simultaneously. Although vision-language models (VLMs)
and visual expertise models excel in 2D tasks and static scenarios, their
ability to fully understand dynamic 3D scenarios remains limited. We introduce
Dynamic Spatial Intelligence and propose DSI-Bench, a benchmark with nearly
1,000 dynamic videos and over 1,700 manually annotated questions covering nine
decoupled motion patterns of observers and objects. Spatially and temporally
symmetric designs reduce biases and enable systematic evaluation of models'
reasoning about self-motion and object motion. Our evaluation of 14 VLMs and
expert models reveals key limitations: models often conflate observer and
object motion, exhibit semantic biases, and fail to accurately infer relative
relationships in dynamic scenarios. Our DSI-Bench provides valuable findings
and insights about the future development of general and expertise models with
dynamic spatial intelligence.
Unleashing Scientific Reasoning for Bio-experimental Protocol Generation
  via Structured Component-based Reward Mechanism
Authors: name: Haoran Sun, name: Yankai Jiang, name: Zhenyu Tang, name: Yaning Pan, name: Shuang Gu, name: Zekai Lin, name: Lilong Wang, name: Wenjie Lou, name: Lei Liu, name: Lei Bai, name: Xiaosong Wang
Published: 2025-10-17 12:47:50

The foundation of reproducible science lies in protocols that are precise,
logically ordered, and executable. The autonomous generation of these protocols
through natural language queries could greatly improve the efficiency of the
reproduction process. However, current leading large language models (LLMs)
often generate incomplete or inconsistent protocols, limiting their utility. To
address this limitation, we first introduce SciRecipe, a large-scale dataset of
over 12K structured protocols spanning 27 biological subfields and encompassing
both comprehension and problem-solving tasks. To further improve protocol
generation, we propose the "Sketch-and-Fill" paradigm, which separates
analysis, structuring, and expression to ensure each step is explicit and
verifiable. Complementing this, the structured component-based reward mechanism
evaluates step granularity, action order, and semantic fidelity, aligning model
optimization with experimental reliability. Building on these components, we
develop Thoth, trained through a staged Knowledge-to-Action process that
progresses from knowledge acquisition to operational reasoning and ultimately
to robust, executable protocol generation. Across multiple benchmarks, Thoth
consistently surpasses both proprietary and open-source LLMs, achieving
significant improvements in step alignment, logical sequencing, and semantic
accuracy. Our approach paves the way for reliable scientific assistants that
bridge knowledge with experimental execution. All data, code, and models will
be released publicly.
ssToken: Self-modulated and Semantic-aware Token Selection for LLM
  Fine-tuning
Authors: name: Xiaohan Qin, name: Xiaoxing Wang, name: Ning Liao, name: Cancheng Zhang, name: Xiangdong Zhang, name: Mingquan Feng, name: Jingzhi Wang, name: Junchi Yan
Published: 2025-10-21 03:21:04

Data quality plays a critical role in enhancing supervised fine-tuning (SFT)
for large language models (LLMs), and token-level data selection has emerged as
a promising direction for its fine-grained nature. Despite their strong
empirical performance, existing token-level selection methods share two key
limitations: (1) requiring training or accessing an additional reference model,
and (2) relying solely on loss information for token selection, which cannot
well preserve semantically important tokens that are not favored by loss-based
metrics. To address these challenges, we propose ssToken, a Self-modulated and
Semantic-aware Token Selection approach. ssToken leverages readily accessible
history models to compute the per-token loss difference with the current model,
which serves as a self-modulated signal that enables the model to adaptively
select tokens along its optimization trajectory, rather than relying on excess
loss from an offline-trained reference model as in prior works. We further
introduce a semantic-aware, attention-based token importance estimation metric,
orthogonal to loss-based selection and providing complementary semantic
information for more effective filtering. Extensive experiments across
different model families and scales demonstrate that both self-modulated
selection and semantic-aware selection alone outperform full-data fine-tuning,
while their integration--ssToken--achieves synergistic gains and further
surpasses prior token-level selection methods, delivering performance
improvements while maintaining training efficiency.