Orionnnn/hub-stats huggingface dataset:
Think in Games: Learning to Reason in Games via Reinforcement Learning
  with Large Language Models
Authors: name: Yi Liao, name: Yu Gu, name: Yuan Sui, name: Zining Zhu, name: Yifan Lu, name: Guohua Tang, name: Zhongqian Sun, name: Wei Yang
Published: 2025-08-29 07:13:39

Large language models (LLMs) excel at complex reasoning tasks such as
mathematics and coding, yet they frequently struggle with simple interactive
tasks that young children perform effortlessly. This discrepancy highlights a
critical gap between declarative knowledge (knowing about something) and
procedural knowledge (knowing how to do something). Although traditional
reinforcement learning (RL) agents can acquire procedural knowledge through
environmental interaction, they often operate as black boxes and require
substantial training data. In contrast, LLMs possess extensive world knowledge
and reasoning capabilities, but are unable to effectively convert this static
knowledge into dynamic decision-making in interactive settings. To address this
challenge, we propose Think in Games (TiG), a novel framework that empowers
LLMs to develop procedural understanding through direct interaction with game
environments, while retaining their inherent reasoning and explanatory
abilities. Specifically, TiG reformulates RL-based decision-making as a
language modeling task: LLMs generate language-guided policies, which are
refined iteratively through online reinforcement learning based on
environmental feedback. Our experimental results show that TiG successfully
bridges the gap between declarative and procedural knowledge, achieving
competitive performance with dramatically lower data and computational demands
compared to conventional RL methods. Moreover, TiG provides step-by-step
natural language explanations for its decisions, greatly improving transparency
and interpretability in complex interactive tasks.
TiKMiX: Take Data Influence into Dynamic Mixture for Language Model
  Pre-training
Authors: name: Yifan Wang, name: Binbin Liu, name: Fengze Liu, name: Yuanfan Guo, name: Jiyao Deng, name: Xuecheng Wu, name: Weidong Zhou, name: Xiaohuan Zhou, name: Taifeng Wang
Published: 2025-08-25 05:18:32

The data mixture used in the pre-training of a language model is a
cornerstone of its final performance. However, a static mixing strategy is
suboptimal, as the model's learning preferences for various data domains shift
dynamically throughout training. Crucially, observing these evolving
preferences in a computationally efficient manner remains a significant
challenge. To address this, we propose TiKMiX, a method that dynamically
adjusts the data mixture according to the model's evolving preferences. TiKMiX
introduces Group Influence, an efficient metric for evaluating the impact of
data domains on the model. This metric enables the formulation of the data
mixing problem as a search for an optimal, influence-maximizing distribution.
We solve this via two approaches: TiKMiX-D for direct optimization, and
TiKMiX-M, which uses a regression model to predict a superior mixture. We
trained models with different numbers of parameters, on up to 1 trillion
tokens. TiKMiX-D exceeds the performance of state-of-the-art methods like
REGMIX while using just 20% of the computational resources. TiKMiX-M leads to
an average performance gain of 2% across 9 downstream benchmarks. Our
experiments reveal that a model's data preferences evolve with training
progress and scale, and we demonstrate that dynamically adjusting the data
mixture based on Group Influence, a direct measure of these preferences,
significantly improves performance by mitigating the underdigestion of data
seen with static ratios.
AHELM: A Holistic Evaluation of Audio-Language Models
Authors: name: Tony Lee, name: Haoqin Tu, name: Chi Heem Wong, name: Zijun Wang, name: Siwei Yang, name: Yifan Mai, name: Yuyin Zhou, name: Cihang Xie, name: Percy Liang
Published: 2025-08-29 07:40:39

Evaluations of audio-language models (ALMs) -- multimodal models that take
interleaved audio and text as input and output text -- are hindered by the lack
of standardized benchmarks; most benchmarks measure only one or two
capabilities and omit evaluative aspects such as fairness or safety.
Furthermore, comparison across models is difficult as separate evaluations test
a limited number of models and use different prompting methods and inference
parameters. To address these shortfalls, we introduce AHELM, a benchmark that
aggregates various datasets -- including 2 new synthetic audio-text datasets
called PARADE, which evaluates the ALMs on avoiding stereotypes, and
CoRe-Bench, which measures reasoning over conversational audio through
inferential multi-turn question answering -- to holistically measure the
performance of ALMs across 10 aspects we have identified as important to the
development and usage of ALMs: audio perception, knowledge, reasoning, emotion
detection, bias, fairness, multilinguality, robustness, toxicity, and safety.
We also standardize the prompts, inference parameters, and evaluation metrics
to ensure equitable comparisons across models. We test 14 open-weight and
closed-API ALMs from 3 developers and 3 additional simple baseline systems each
consisting of an automatic speech recognizer and a language model. Our results
show that while Gemini 2.5 Pro ranks top in 5 out of 10 aspects, it exhibits
group unfairness (p=0.01) on ASR tasks whereas most of the other models do
not. We also find that the baseline systems perform reasonably well on AHELM,
with one ranking 5th overall despite having only speech-to-text capabilities.
For transparency, all raw prompts, model generations, and outputs are available
on our website at https://crfm.stanford.edu/helm/audio/v1.0.0. AHELM is
intended to be a living benchmark and new datasets and models will be added
over time.
TalkVid: A Large-Scale Diversified Dataset for Audio-Driven Talking Head
  Synthesis
Authors: name: Shunian Chen, name: Hejin Huang, name: Yexin Liu, name: Zihan Ye, name: Pengcheng Chen, name: Chenghao Zhu, name: Michael Guan, name: Rongsheng Wang, name: Junying Chen, name: Guanbin Li, name: Ser-Nam Lim, name: Harry Yang, name: Benyou Wang
Published: 2025-08-19 08:31:15

Audio-driven talking head synthesis has achieved remarkable photorealism, yet
state-of-the-art (SOTA) models exhibit a critical failure: they lack
generalization to the full spectrum of human diversity in ethnicity, language,
and age groups. We argue that this generalization gap is a direct symptom of
limitations in existing training data, which lack the necessary scale, quality,
and diversity. To address this challenge, we introduce TalkVid, a new
large-scale, high-quality, and diverse dataset containing 1244 hours of video
from 7729 unique speakers. TalkVid is curated through a principled, multi-stage
automated pipeline that rigorously filters for motion stability, aesthetic
quality, and facial detail, and is validated against human judgments to ensure
its reliability. Furthermore, we construct and release TalkVid-Bench, a
stratified evaluation set of 500 clips meticulously balanced across key
demographic and linguistic axes. Our experiments demonstrate that a model
trained on TalkVid outperforms counterparts trained on previous datasets,
exhibiting superior cross-dataset generalization. Crucially, our analysis on
TalkVid-Bench reveals performance disparities across subgroups that are
obscured by traditional aggregate metrics, underscoring its necessity for
future research. Code and data can be found in
https://github.com/FreedomIntelligence/TalkVid
UItron: Foundational GUI Agent with Advanced Perception and Planning
Authors: name: Zhixiong Zeng, name: Jing Huang, name: Liming Zheng, name: Wenkang Han, name: Yufeng Zhong, name: Lei Chen, name: Longrong Yang, name: Yingjie Chu, name: Yuzhi He, name: Lin Ma
Published: 2025-08-29 16:40:57

GUI agent aims to enable automated operations on Mobile/PC devices, which is
an important task toward achieving artificial general intelligence. The rapid
advancement of VLMs accelerates the development of GUI agents, owing to their
powerful capabilities in visual understanding and task planning. However,
building a GUI agent remains a challenging task due to the scarcity of
operation trajectories, the availability of interactive infrastructure, and the
limitation of initial capabilities in foundation models. In this work, we
introduce UItron, an open-source foundational model for automatic GUI agents,
featuring advanced GUI perception, grounding, and planning capabilities. UItron
highlights the necessity of systemic data engineering and interactive
infrastructure as foundational components for advancing GUI agent development.
It not only systematically studies a series of data engineering strategies to
enhance training effects, but also establishes an interactive environment
connecting both Mobile and PC devices. In training, UItron adopts supervised
finetuning over perception and planning tasks in various GUI scenarios, and
then develop a curriculum reinforcement learning framework to enable complex
reasoning and exploration for online environments. As a result, UItron achieves
superior performance in benchmarks of GUI perception, grounding, and planning.
In particular, UItron highlights the interaction proficiency with top-tier
Chinese mobile APPs, as we identified a general lack of Chinese capabilities
even in state-of-the-art solutions. To this end, we manually collect over one
million steps of operation trajectories across the top 100 most popular apps,
and build the offline and online agent evaluation environments. Experimental
results demonstrate that UItron achieves significant progress in Chinese app
scenarios, propelling GUI agents one step closer to real-world application.
Morae: Proactively Pausing UI Agents for User Choices
Authors: name: Yi-Hao Peng, name: Dingzeyu Li, name: Jeffrey P. Bigham, name: Amy Pavel
Published: 2025-08-29 09:39:00

User interface (UI) agents promise to make inaccessible or complex UIs easier
to access for blind and low-vision (BLV) users. However, current UI agents
typically perform tasks end-to-end without involving users in critical choices
or making them aware of important contextual information, thus reducing user
agency. For example, in our field study, a BLV participant asked to buy the
cheapest available sparkling water, and the agent automatically chose one from
several equally priced options, without mentioning alternative products with
different flavors or better ratings. To address this problem, we introduce
Morae, a UI agent that automatically identifies decision points during task
execution and pauses so that users can make choices. Morae uses large
multimodal models to interpret user queries alongside UI code and screenshots,
and prompt users for clarification when there is a choice to be made. In a
study over real-world web tasks with BLV participants, Morae helped users
complete more tasks and select options that better matched their preferences,
as compared to baseline agents, including OpenAI Operator. More broadly, this
work exemplifies a mixed-initiative approach in which users benefit from the
automation of UI agents while being able to express their preferences.
A.S.E: A Repository-Level Benchmark for Evaluating Security in
  AI-Generated Code
Authors: name: Keke Lian, name: Bin Wang, name: Lei Zhang, name: Libo Chen, name: Junjie Wang, name: Ziming Zhao, name: Yujiu Yang, name: Haotong Duan, name: Haoran Zhao, name: Shuang Liao, name: Mingda Guo, name: Jiazheng Quan, name: Yilu Zhong, name: Chenhao He, name: Zichuan Chen, name: Jie Wu, name: Haoling Li, name: Zhaoxuan Li, name: Jiongchi Yu, name: Hui Li, name: Dong Zhang
Published: 2025-08-25 15:11:11

The increasing adoption of large language models (LLMs) in software
engineering necessitates rigorous security evaluation of their generated code.
However, existing benchmarks often lack relevance to real-world AI programming
scenarios, making them inadequate for assessing the practical security risks
associated with AI-generated code in production environments. To address this
gap, we introduce A.S.E (AI Code Generation Security Evaluation), a
repository-level evaluation benchmark designed to closely mirror real-world AI
programming tasks, offering a comprehensive and reliable framework for
assessing the security of AI-generated code. Our evaluation of leading LLMs on
A.S.E reveals several key findings. In particular, current LLMs still struggle
with secure coding. The complexity in repository-level scenarios presents
challenges for LLMs that typically perform well on snippet-level tasks.
Morever, a larger reasoning budget does not necessarily lead to better code
generation. These observations offer valuable insights into the current state
of AI code generation, assisting developers in selecting the most appropriate
models for practical tasks, while laying the foundation for refining LLMs to
generate secure and efficient code in real-world applications.
R-4B: Incentivizing General-Purpose Auto-Thinking Capability in MLLMs
  via Bi-Mode Annealing and Reinforce Learning
Authors: name: Jie Jiang, name: Qi Yang, name: Bolin Ni, name: Shiming Xiang, name: Han Hu, name: Houwen Peng
Published: 2025-08-28 17:48:19

Multimodal Large Language Models (MLLMs) equipped with step-by-step thinking
capabilities have demonstrated remarkable performance on complex reasoning
problems. However, this thinking process is redundant for simple problems
solvable without complex reasoning. To address this inefficiency, we propose
R-4B, an auto-thinking MLLM, which can adaptively decide when to think based on
problem complexity. The central idea of R-4B is to empower the model with both
thinking and non-thinking capabilities using bi-mode annealing, and apply
Bi-mode Policy Optimization~(BPO) to improve the model's accuracy in
determining whether to activate the thinking process. Specifically, we first
train the model on a carefully curated dataset spanning various topics, which
contains samples from both thinking and non-thinking modes. Then it undergoes a
second phase of training under an improved GRPO framework, where the policy
model is forced to generate responses from both modes for each input query.
Experimental results show that R-4B achieves state-of-the-art performance
across 25 challenging benchmarks. It outperforms Qwen2.5-VL-7B in most tasks
and achieves performance comparable to larger models such as
Kimi-VL-A3B-Thinking-2506 (16B) on reasoning-intensive benchmarks with lower
computational cost.
EmbodiedOneVision: Interleaved Vision-Text-Action Pretraining for
  General Robot Control
Authors: name: Delin Qu, name: Haoming Song, name: Qizhi Chen, name: Zhaoqing Chen, name: Xianqiang Gao, name: Xinyi Ye, name: Qi Lv, name: Modi Shi, name: Guanghui Ren, name: Cheng Ruan, name: Maoqing Yao, name: Haoran Yang, name: Jiacheng Bao, name: Bin Zhao, name: Dong Wang
Published: 2025-08-28 17:26:15

The human ability to seamlessly perform multimodal reasoning and physical
interaction in the open world is a core goal for general-purpose embodied
intelligent systems. Recent vision-language-action (VLA) models, which are
co-trained on large-scale robot and visual-text data, have demonstrated notable
progress in general robot control. However, they still fail to achieve
human-level flexibility in interleaved reasoning and interaction. In this work,
introduce EO-Robotics, consists of EO-1 model and EO-Data1.5M dataset. EO-1 is
a unified embodied foundation model that achieves superior performance in
multimodal embodied reasoning and robot control through interleaved
vision-text-action pre-training. The development of EO-1 is based on two key
pillars: (i) a unified architecture that processes multimodal inputs
indiscriminately (image, text, video, and action), and (ii) a massive,
high-quality multimodal embodied reasoning dataset, EO-Data1.5M, which contains
over 1.5 million samples with emphasis on interleaved vision-text-action
comprehension. EO-1 is trained through synergies between auto-regressive
decoding and flow matching denoising on EO-Data1.5M, enabling seamless robot
action generation and multimodal embodied reasoning. Extensive experiments
demonstrate the effectiveness of interleaved vision-text-action learning for
open-world understanding and generalization, validated through a variety of
long-horizon, dexterous manipulation tasks across multiple embodiments. This
paper details the architecture of EO-1, the data construction strategy of
EO-Data1.5M, and the training methodology, offering valuable insights for
developing advanced embodied foundation models.
Social-MAE: A Transformer-Based Multimodal Autoencoder for Face and
  Voice
Authors: name: Hugo Bohy, name: Minh Tran, name: Kevin El Haddad, name: Thierry Dutoit, name: Mohammad Soleymani
Published: 2025-08-24 19:49:48

Human social behaviors are inherently multimodal necessitating the
development of powerful audiovisual models for their perception. In this paper,
we present Social-MAE, our pre-trained audiovisual Masked Autoencoder based on
an extended version of Contrastive Audio-Visual Masked Auto-Encoder (CAV-MAE),
which is pre-trained on audiovisual social data. Specifically, we modify
CAV-MAE to receive a larger number of frames as input and pre-train it on a
large dataset of human social interaction (VoxCeleb2) in a self-supervised
manner. We demonstrate the effectiveness of this model by finetuning and
evaluating the model on different social and affective downstream tasks,
namely, emotion recognition, laughter detection and apparent personality
estimation. The model achieves state-of-the-art results on multimodal emotion
recognition and laughter recognition and competitive results for apparent
personality estimation, demonstrating the effectiveness of in-domain
self-supervised pre-training. Code and model weight are available here
https://github.com/HuBohy/SocialMAE.
OneReward: Unified Mask-Guided Image Generation via Multi-Task Human
  Preference Learning
Authors: name: Yuan Gong, name: Xionghui Wang, name: Jie Wu, name: Shiyin Wang, name: Yitong Wang, name: Xinglong Wu
Published: 2025-08-28 17:59:46

In this paper, we introduce OneReward, a unified reinforcement learning
framework that enhances the model's generative capabilities across multiple
tasks under different evaluation criteria using only One Reward model.
By employing a single vision-language model (VLM) as the generative reward
model, which can distinguish the winner and loser for a given task and a given
evaluation criterion, it can be effectively applied to multi-task generation
models, particularly in contexts with varied data and diverse task objectives.
We utilize OneReward for mask-guided image generation, which can be further
divided into several sub-tasks such as image fill, image extend, object
removal, and text rendering, involving a binary mask as the edit area. Although
these domain-specific tasks share same conditioning paradigm, they differ
significantly in underlying data distributions and evaluation metrics. Existing
methods often rely on task-specific supervised fine-tuning (SFT), which limits
generalization and training efficiency. Building on OneReward, we develop
Seedream 3.0 Fill, a mask-guided generation model trained via multi-task
reinforcement learning directly on a pre-trained base model, eliminating the
need for task-specific SFT. Experimental results demonstrate that our unified
edit model consistently outperforms both commercial and open-source
competitors, such as Ideogram, Adobe Photoshop, and FLUX Fill [Pro], across
multiple evaluation dimensions. Code and model are available at:
https://one-reward.github.io
Persuasion Dynamics in LLMs: Investigating Robustness and Adaptability
  in Knowledge and Safety with DuET-PD
Authors: name: Bryan Chen Zhengyu Tan, name: Daniel Wai Kit Chin, name: Zhengyuan Liu, name: Nancy F. Chen, name: Roy Ka-Wei Lee
Published: 2025-08-24 17:08:37

Large Language Models (LLMs) can struggle to balance gullibility to
misinformation and resistance to valid corrections in persuasive dialogues, a
critical challenge for reliable deployment. We introduce DuET-PD (Dual
Evaluation for Trust in Persuasive Dialogues), a framework evaluating
multi-turn stance-change dynamics across dual dimensions: persuasion type
(corrective/misleading) and domain (knowledge via MMLU-Pro, and safety via
SALAD-Bench). We find that even a state-of-the-art model like GPT-4o achieves
only 27.32% accuracy in MMLU-Pro under sustained misleading persuasions.
Moreover, results reveal a concerning trend of increasing sycophancy in newer
open-source models. To address this, we introduce Holistic DPO, a training
approach balancing positive and negative persuasion examples. Unlike prompting
or resist-only training, Holistic DPO enhances both robustness to
misinformation and receptiveness to corrections, improving
Llama-3.1-8B-Instruct's accuracy under misleading persuasion in safety contexts
from 4.21% to 76.54%. These contributions offer a pathway to developing more
reliable and adaptable LLMs for multi-turn dialogue. Code is available at
https://github.com/Social-AI-Studio/DuET-PD.
Multi-View 3D Point Tracking
Authors: name: Frano Rajič, name: Haofei Xu, name: Marko Mihajlovic, name: Siyuan Li, name: Irem Demir, name: Emircan Gündoğdu, name: Lei Ke, name: Sergey Prokudin, name: Marc Pollefeys, name: Siyu Tang
Published: 2025-08-28 17:58:20

We introduce the first data-driven multi-view 3D point tracker, designed to
track arbitrary points in dynamic scenes using multiple camera views. Unlike
existing monocular trackers, which struggle with depth ambiguities and
occlusion, or prior multi-camera methods that require over 20 cameras and
tedious per-sequence optimization, our feed-forward model directly predicts 3D
correspondences using a practical number of cameras (e.g., four), enabling
robust and accurate online tracking. Given known camera poses and either
sensor-based or estimated multi-view depth, our tracker fuses multi-view
features into a unified point cloud and applies k-nearest-neighbors correlation
alongside a transformer-based update to reliably estimate long-range 3D
correspondences, even under occlusion. We train on 5K synthetic multi-view
Kubric sequences and evaluate on two real-world benchmarks: Panoptic Studio and
DexYCB, achieving median trajectory errors of 3.1 cm and 2.0 cm, respectively.
Our method generalizes well to diverse camera setups of 1-8 views with varying
vantage points and video lengths of 24-150 frames. By releasing our tracker
alongside training and evaluation datasets, we aim to set a new standard for
multi-view 3D tracking research and provide a practical tool for real-world
applications. Project page available at https://ethz-vlg.github.io/mvtracker.
Turning the Spell Around: Lightweight Alignment Amplification via
  Rank-One Safety Injection
Authors: name: Harethah Abu Shairah, name: Hasan Abed Al Kader Hammoud, name: George Turkiyyah, name: Bernard Ghanem
Published: 2025-08-28 13:22:33

Safety alignment in Large Language Models (LLMs) often involves mediating
internal representations to refuse harmful requests. Recent research has
demonstrated that these safety mechanisms can be bypassed by ablating or
removing specific representational directions within the model. In this paper,
we propose the opposite approach: Rank-One Safety Injection (ROSI), a white-box
method that amplifies a model's safety alignment by permanently steering its
activations toward the refusal-mediating subspace. ROSI operates as a simple,
fine-tuning-free rank-one weight modification applied to all residual stream
write matrices. The required safety direction can be computed from a small set
of harmful and harmless instruction pairs. We show that ROSI consistently
increases safety refusal rates - as evaluated by Llama Guard 3 - while
preserving the utility of the model on standard benchmarks such as MMLU,
HellaSwag, and Arc. Furthermore, we show that ROSI can also re-align
'uncensored' models by amplifying their own latent safety directions,
demonstrating its utility as an effective last-mile safety procedure. Our
results suggest that targeted, interpretable weight steering is a cheap and
potent mechanism to improve LLM safety, complementing more resource-intensive
fine-tuning paradigms.