Orionnnn/hub-stats huggingface dataset:
Gaperon: A Peppered English-French Generative Language Model Suite
Authors: name: Nathan Godey, name: Wissam Antoun, name: Rian Touchent, name: Rachel Bawden, name: Éric de la Clergerie, name: Benoît Sagot, name: Djamé Seddah
Published: 2025-10-29 17:59:39

We release Gaperon, a fully open suite of French-English-coding language
models designed to advance transparency and reproducibility in large-scale
model training. The Gaperon family includes 1.5B, 8B, and 24B parameter models
trained on 2-4 trillion tokens, released with all elements of the training
pipeline: French and English datasets filtered with a neural quality
classifier, an efficient data curation and training framework, and hundreds of
intermediate checkpoints. Through this work, we study how data filtering and
contamination interact to shape both benchmark and generative performance. We
find that filtering for linguistic quality enhances text fluency and coherence
but yields subpar benchmark results, and that late deliberate contamination --
continuing training on data mixes that include test sets -- recovers
competitive scores while only reasonably harming generation quality. We discuss
how usual neural filtering can unintentionally amplify benchmark leakage. To
support further research, we also introduce harmless data poisoning during
pretraining, providing a realistic testbed for safety studies. By openly
releasing all models, datasets, code, and checkpoints, Gaperon establishes a
reproducible foundation for exploring the trade-offs between data curation,
evaluation, safety, and openness in multilingual language model development.
ChronoPlay: A Framework for Modeling Dual Dynamics and Authenticity in
  Game RAG Benchmarks
Authors: name: Liyang He, name: Yuren Zhang, name: Ziwei Zhu, name: Zhenghui Li, name: Shiwei Tong
Published: 2025-10-21 09:28:13

Retrieval Augmented Generation (RAG) systems are increasingly vital in
dynamic domains like online gaming, yet the lack of a dedicated benchmark has
impeded standardized evaluation in this area. The core difficulty lies in Dual
Dynamics: the constant interplay between game content updates and the shifting
focus of the player community. Furthermore, the necessity of automating such a
benchmark introduces a critical requirement for player-centric authenticity to
ensure generated questions are realistic. To address this integrated challenge,
we introduce ChronoPlay, a novel framework for the automated and continuous
generation of game RAG benchmarks. ChronoPlay utilizes a dual-dynamic update
mechanism to track both forms of change, and a dual-source synthesis engine
that draws from official sources and player community to ensure both factual
correctness and authentic query patterns. We instantiate our framework on three
distinct games to create the first dynamic RAG benchmark for the gaming domain,
offering new insights into model performance under these complex and realistic
conditions. Code is avaliable at: https://github.com/hly1998/ChronoPlay.
Evolving Diagnostic Agents in a Virtual Clinical Environment
Authors: name: Pengcheng Qiu, name: Chaoyi Wu, name: Junwei Liu, name: Qiaoyu Zheng, name: Yusheng Liao, name: Haowen Wang, name: Yun Yue, name: Qianrui Fan, name: Shuai Zhen, name: Jian Wang, name: Jinjie Gu, name: Yanfeng Wang, name: Ya Zhang, name: Weidi Xie
Published: 2025-10-28 17:19:47

In this paper, we present a framework for training large language models
(LLMs) as diagnostic agents with reinforcement learning, enabling them to
manage multi-turn diagnostic processes, adaptively select examinations, and
commit to final diagnoses. Unlike instruction-tuned models trained on static
case summaries, our method acquires diagnostic strategies through interactive
exploration and outcome-based feedback. Our contributions are fourfold: (i) We
present DiagGym, a diagnostics world model trained with electronic health
records that emits examination outcomes conditioned on patient history and
recommended examination, serving as a virtual clinical environment for
realistic diagnosis training and evaluation; (ii) We train DiagAgent via
end-to-end, multi-turn reinforcement learning to learn diagnostic policies that
optimize both information yield and diagnostic accuracy; (iii) We introduce
DiagBench, a diagnostic benchmark comprising 750 cases with physician-validated
examination recommendations and 99 cases annotated with 973 physician-written
rubrics on diagnosis process; (iv) we demonstrate superior performance across
diverse diagnostic settings. DiagAgent significantly outperforms 10
state-of-the-art LLMs, including DeepSeek-v3 and GPT-4o, as well as two
prompt-engineered agents. In single-turn settings, DiagAgent achieves 9.34%
higher diagnostic accuracy and 44.03% improvement in examination recommendation
hit ratio. In end-to-end settings, it delivers 15.12% increase in diagnostic
accuracy and 23.09% boost in examination recommendation F1 score. In
rubric-based evaluation, it surpasses the next-best model, Claude-sonnet-4, by
7.1% in weighted rubric score. These findings indicate that learning policies
in interactive clinical environments confers dynamic and clinically meaningful
diagnostic management abilities unattainable through passive training alone.
Multimodal Spatial Reasoning in the Large Model Era: A Survey and
  Benchmarks
Authors: name: Xu Zheng, name: Zihao Dongfang, name: Lutao Jiang, name: Boyuan Zheng, name: Yulong Guo, name: Zhenquan Zhang, name: Giuliano Albanese, name: Runyi Yang, name: Mengjiao Ma, name: Zixin Zhang, name: Chenfei Liao, name: Dingcheng Zhen, name: Yuanhuiyi Lyu, name: Yuqian Fu, name: Bin Ren, name: Linfeng Zhang, name: Danda Pani Paudel, name: Nicu Sebe, name: Luc Van Gool, name: Xuming Hu
Published: 2025-10-29 17:55:43

Humans possess spatial reasoning abilities that enable them to understand
spaces through multimodal observations, such as vision and sound. Large
multimodal reasoning models extend these abilities by learning to perceive and
reason, showing promising performance across diverse spatial tasks. However,
systematic reviews and publicly available benchmarks for these models remain
limited. In this survey, we provide a comprehensive review of multimodal
spatial reasoning tasks with large models, categorizing recent progress in
multimodal large language models (MLLMs) and introducing open benchmarks for
evaluation. We begin by outlining general spatial reasoning, focusing on
post-training techniques, explainability, and architecture. Beyond classical 2D
tasks, we examine spatial relationship reasoning, scene and layout
understanding, as well as visual question answering and grounding in 3D space.
We also review advances in embodied AI, including vision-language navigation
and action models. Additionally, we consider emerging modalities such as audio
and egocentric video, which contribute to novel spatial understanding through
new sensors. We believe this survey establishes a solid foundation and offers
insights into the growing field of multimodal spatial reasoning. Updated
information about this survey, codes and implementation of the open benchmarks
can be found at https://github.com/zhengxuJosh/Awesome-Spatial-Reasoning.
FAPO: Flawed-Aware Policy Optimization for Efficient and Reliable
  Reasoning
Authors: name: Yuyang Ding, name: Chi Zhang, name: Juntao Li, name: Haibin Lin, name: Xin Liu, name: Min Zhang
Published: 2025-10-26 05:49:38

Reinforcement learning with verifiable rewards (RLVR) has emerged as a
promising paradigm for enhancing the reasoning capabilities of large language
models (LLMs). In this context, models explore reasoning trajectories and
exploit rollouts with correct answers as positive signals for policy
optimization. However, these rollouts might involve flawed patterns such as
answer-guessing and jump-in-reasoning. Such flawed-positive rollouts are
rewarded identically to fully correct ones, causing policy models to
internalize these unreliable reasoning patterns. In this work, we first conduct
a systematic study of flawed-positive rollouts in RL and find that they enable
rapid capability gains during the early optimization stage, while constraining
reasoning capability later by reinforcing unreliable patterns. Building on
these insights, we propose Flawed-Aware Policy Optimization (FAPO), which
presents a parameter-free reward penalty for flawed-positive rollouts, enabling
the policy to leverage them as useful shortcuts in the warm-up stage, securing
stable early gains, while gradually shifting optimization toward reliable
reasoning in the later refinement stage. To accurately and comprehensively
detect flawed-positive rollouts, we introduce a generative reward model (GenRM)
with a process-level reward that precisely localizes reasoning errors.
Experiments show that FAPO is effective in broad domains, improving outcome
correctness, process reliability, and training stability without increasing the
token budget.
PairUni: Pairwise Training for Unified Multimodal Language Models
Authors: name: Jiani Zheng, name: Zhiyang Teng, name: Xiangtai Li, name: Anran Wang, name: Yu Tian, name: Kunpeng Qiu, name: Ye Tian, name: Haochen Wang, name: Zhuochen Wang
Published: 2025-10-29 16:47:02

Unified vision-language models (UVLMs) must perform both understanding and
generation within a single architecture, but these tasks rely on heterogeneous
data and supervision, making it difficult to balance them during reinforcement
learning (RL). We propose PairUni, a unified framework that reorganizes data
into understanding-generation (UG) pairs and aligns optimization accordingly.
We first use GPT-o3 to augment single-task data, generating captions for
understanding samples and question-answer (QA) pairs for generation samples,
forming aligned pairs from the same instance. Additionally, for each generation
sample, we retrieve a semantically related understanding example to form a
retrieved pair, linking different but related data points. These paired
structures expose cross-task semantic correspondences and support consistent
policy learning. To leverage this structure, we present Pair-GPRO, a pair-aware
variant based on Group Relative Policy Optimization. It assigns a similarity
score to each pair to modulate the advantage, strengthening learning from
well-aligned examples and reducing task interference. We curate a high-quality
dataset of 16K UG pairs named PairUG for RL fine-tuning and evaluate PairUni on
the powerful Janus-Pro UVLMs. Our approach achieves balanced improvements on
various UVLMs, outperforming strong UVLM RL baselines. Code:
https://github.com/Haochen-Wang409/PairUni{github.com/Haochen-Wang409/PairUni}
Reasoning-Aware GRPO using Process Mining
Authors: name: Taekhyun Park, name: Yongjae Lee, name: Hyerim Bae
Published: 2025-10-29 01:07:45

Reinforcement learning (RL)-based post-training has been crucial for enabling
multi-step reasoning in large reasoning models (LRMs), yet current reward
schemes are typically outcome-centric. We propose PM4GRPO, a reasoning-aware
Group Relative Policy Optimization (GRPO) that augments standard answer/format
rewards with signals over the reasoning procedure. To this end, process mining
techniques are utilized to compute a scalar conformance reward that measures
how closely a policy model's reasoning aligns with the pretrained teacher
model. The empirical results on five benchmarks demonstrate that PM4GRPO
significantly outperforms existing methodologies for GRPO-based post-training.
These results highlight that leveraging process mining for reasoning-aware GRPO
effectively enhances the reasoning capabilities of policy models.
SeeingEye: Agentic Information Flow Unlocks Multimodal Reasoning In
  Text-only LLMs
Authors: name: Weijia Zhang, name: Zijia Liu, name: Haoru Li, name: Haoqi Chen, name: Jiaxuan You
Published: 2025-10-29 01:57:11

Recent advances in text-only large language models (LLMs), such as
DeepSeek-R1, demonstrate remarkable reasoning ability. However, these models
remain fragile or entirely incapable when extended to multi-modal tasks.
Existing approaches largely rely on single-form captions, which lack diversity
and often fail to adapt across different types of Visual Question Answering
(VQA) benchmarks. As a result, they provide no principled or efficient channel
for transmitting fine-grained visual information. We introduce Seeing Eye, a
modular framework that unlocks multimodal reasoning in text-only LLMs through
an agent-based small VLM translator. This translator acts as a perception
agent: it can invoke specialized tools (e.g., OCR and crop) and iteratively
distill multimodal inputs into structured intermediate representations (SIRs)
tailored to the question. These SIRs are then passed to the text-only LLM,
which serves as a reasoning agent. Crucially, the translator and reasoner
engage in multi-round feedback and interaction, enabling the extraction of
targeted visual details and yielding more confident answers. Experiments on
knowledge-intensive VQA benchmarks, including MMMU and MIA-Bench, demonstrate
that Seeing Eye not only reduces inference cost but also surpasses much larger
end-to-end VLMs. For example, an instantiation combining a 3B-parameter vision
translator with an 8B-parameter language reasoner outperforms a monolithic 32B
VLM on challenging knowledge-based questions. Our results highlight that
decoupling perception from reasoning via agent information flow offers a
scalable and plug-and-play pathway to multimodal reasoning, allowing strong
text-only LLMs to fully leverage their reasoning capabilities. Code is
available at: https://github.com/ulab-uiuc/SeeingEye
JanusCoder: Towards a Foundational Visual-Programmatic Interface for
  Code Intelligence
Authors: name: Qiushi Sun, name: Jingyang Gong, name: Yang Liu, name: Qiaosheng Chen, name: Lei Li, name: Kai Chen, name: Qipeng Guo, name: Ben Kao, name: Fei Yuan
Published: 2025-10-27 17:13:49

The scope of neural code intelligence is rapidly expanding beyond text-based
source code to encompass the rich visual outputs that programs generate. This
visual dimension is critical for advanced applications like flexible content
generation and precise, program-driven editing of visualizations. However,
progress has been impeded by the scarcity of high-quality multimodal code data,
a bottleneck stemming from challenges in synthesis and quality assessment. To
address these challenges, we make contributions from both a data and modeling
perspective. We first introduce a complete synthesis toolkit that leverages
reciprocal synergies between data modalities to efficiently produce a
large-scale, high-quality corpus spanning from standard charts to complex
interactive web UIs and code-driven animations. Leveraging this toolkit, we
construct JanusCode-800K, the largest multimodal code corpus to date. This
powers the training of our models, JanusCoder and JanusCoderV, which establish
a visual-programmatic interface for generating code from textual instructions,
visual inputs, or a combination of both. Our unified model is a departure from
existing approaches that build specialized models for isolated tasks. Extensive
experiments on both text-centric and vision-centric coding tasks demonstrate
the superior performance of the JanusCoder series, with our 7B to 14B scale
models approaching or even exceeding the performance of commercial models.
Furthermore, extensive analysis provides key insights into harmonizing
programmatic logic with its visual expression. Our code and checkpoints will
are available at https://github.com/InternLM/JanusCoder.
MASPRM: Multi-Agent System Process Reward Model
Authors: name: Milad Yazdani, name: Mahdi Mostajabdaveh, name: Zirui Zhou, name: Ying Xiong
Published: 2025-10-28 00:48:20

Practical deployment of Multi-Agent Systems (MAS) demands strong test-time
performance, motivating methods that guide inference-time search and
selectively spend compute to improve quality. We present the Multi-Agent System
Process Reward Model (MASPRM). It assigns per-action, per-agent values to
partial inter-agent transcripts and acts as an inference-time controller.
MASPRM is trained from multi-agent Monte Carlo Tree Search (MCTS) rollouts
without requiring step-level human annotations, by propagating returns to local
targets. At inference, MASPRM guides step-level beam search and MCTS, focusing
computation on promising branches and pruning early. On GSM8K and MATH,
MASPRM-guided decoding with an outcome reward model (ORM) applied to the final
answer, improves exact match (EM) over a single straight-through MAS pass by
+30.7 and +22.9 points, respectively. A MASPRM trained on GSM8K transfers
zero-shot to MATH without retraining, adding 8.4 EM points at the same
budget. MASPRM is a plug-in value model that estimates per-agent progress and
complements verifier-style decoders, enabling more reliable, compute-aware
multi-agent reasoning. Code: https://github.com/milad1378yz/MASPRM
Video-Thinker: Sparking "Thinking with Videos" via Reinforcement
  Learning
Authors: name: Shijian Wang, name: Jiarui Jin, name: Xingjian Wang, name: Linxin Song, name: Runhao Fu, name: Hecheng Wang, name: Zongyuan Ge, name: Yuan Lu, name: Xuelian Cheng
Published: 2025-10-27 16:10:45

Recent advances in image reasoning methods, particularly "Thinking with
Images", have demonstrated remarkable success in Multimodal Large Language
Models (MLLMs); however, this dynamic reasoning paradigm has not yet been
extended to video reasoning tasks. In this paper, we propose Video-Thinker,
which empowers MLLMs to think with videos by autonomously leveraging their
intrinsic "grounding" and "captioning" capabilities to generate reasoning clues
throughout the inference process. To spark this capability, we construct
Video-Thinker-10K, a curated dataset featuring autonomous tool usage within
chain-of-thought reasoning sequences. Our training strategy begins with
Supervised Fine-Tuning (SFT) to learn the reasoning format, followed by Group
Relative Policy Optimization (GRPO) to strengthen this reasoning capability.
Through this approach, Video-Thinker enables MLLMs to autonomously navigate
grounding and captioning tasks for video reasoning, eliminating the need for
constructing and calling external tools. Extensive experiments demonstrate that
Video-Thinker achieves significant performance gains on both in-domain tasks
and challenging out-of-domain video reasoning benchmarks, including
Video-Holmes, CG-Bench-Reasoning, and VRBench. Our Video-Thinker-7B
substantially outperforms existing baselines such as Video-R1 and establishes
state-of-the-art performance among 7B-sized MLLMs.
ReForm: Reflective Autoformalization with Prospective Bounded Sequence
  Optimization
Authors: name: Guoxin Chen, name: Jing Wu, name: Xinjie Chen, name: Wayne Xin Zhao, name: Ruihua Song, name: Chengxi Li, name: Kai Fan, name: Dayiheng Liu, name: Minpeng Liao
Published: 2025-10-28 16:22:54

Autoformalization, which translates natural language mathematics into
machine-verifiable formal statements, is critical for using formal mathematical
reasoning to solve math problems stated in natural language. While Large
Language Models can generate syntactically correct formal statements, they
often fail to preserve the original problem's semantic intent. This limitation
arises from the LLM approaches' treating autoformalization as a simplistic
translation task which lacks mechanisms for self-reflection and iterative
refinement that human experts naturally employ. To address these issues, we
propose ReForm, a Reflective Autoformalization method that tightly integrates
semantic consistency evaluation into the autoformalization process. This
enables the model to iteratively generate formal statements, assess its
semantic fidelity, and self-correct identified errors through progressive
refinement. To effectively train this reflective model, we introduce
Prospective Bounded Sequence Optimization (PBSO), which employs different
rewards at different sequence positions to ensure that the model develops both
accurate autoformalization and correct semantic validations, preventing
superficial critiques that would undermine the purpose of reflection. Extensive
experiments across four autoformalization benchmarks demonstrate that ReForm
achieves an average improvement of 17.2 percentage points over the strongest
baselines. To further ensure evaluation reliability, we introduce
ConsistencyCheck, a benchmark of 859 expert-annotated items that not only
validates LLMs as judges but also reveals that autoformalization is inherently
difficult: even human experts produce semantic errors in up to 38.5% of cases.
TheraMind: A Strategic and Adaptive Agent for Longitudinal Psychological
  Counseling
Authors: name: He Hu, name: Yucheng Zhou, name: Chiyuan Ma, name: Qianning Wang, name: Zheng Zhang, name: Fei Ma, name: Laizhong Cui, name: Qi Tian
Published: 2025-10-29 17:54:20

Large language models (LLMs) in psychological counseling have attracted
increasing attention. However, existing approaches often lack emotional
understanding, adaptive strategies, and the use of therapeutic methods across
multiple sessions with long-term memory, leaving them far from real clinical
practice. To address these critical gaps, we introduce TheraMind, a strategic
and adaptive agent for longitudinal psychological counseling. The cornerstone
of TheraMind is a novel dual-loop architecture that decouples the complex
counseling process into an Intra-Session Loop for tactical dialogue management
and a Cross-Session Loop for strategic therapeutic planning. The Intra-Session
Loop perceives the patient's emotional state to dynamically select response
strategies while leveraging cross-session memory to ensure continuity.
Crucially, the Cross-Session Loop empowers the agent with long-term
adaptability by evaluating the efficacy of the applied therapy after each
session and adjusting the method for subsequent interactions. We validate our
approach in a high-fidelity simulation environment grounded in real clinical
cases. Extensive evaluations show that TheraMind outperforms other methods,
especially on multi-session metrics like Coherence, Flexibility, and
Therapeutic Attunement, validating the effectiveness of its dual-loop design in
emulating strategic, adaptive, and longitudinal therapeutic behavior. The code
is publicly available at https://0mwwm0.github.io/TheraMind/.