Orionnnn/hub-stats huggingface dataset:
Crosslingual Reasoning through Test-Time Scaling
Authors: name: Zheng-Xin Yong, name: M. Farid Adilazuarda, name: Jonibek Mansurov, name: Ruochen Zhang, name: Niklas Muennighoff, name: Carsten Eickhoff, name: Genta Indra Winata, name: Julia Kreutzer, name: Stephen H. Bach, name: Alham Fikri Aji
Published: 2025-05-08 16:50:06

Reasoning capabilities of large language models are primarily studied for
English, even when pretrained models are multilingual. In this work, we
investigate to what extent English reasoning finetuning with long
chain-of-thoughts (CoTs) can generalize across languages. First, we find that
scaling up inference compute for English-centric reasoning language models
(RLMs) improves multilingual mathematical reasoning across many languages
including low-resource languages, to an extent where they outperform models
twice their size. Second, we reveal that while English-centric RLM's CoTs are
naturally predominantly English, they consistently follow a quote-and-think
pattern to reason about quoted non-English inputs. Third, we discover an
effective strategy to control the language of long CoT reasoning, and we
observe that models reason better and more efficiently in high-resource
languages. Finally, we observe poor out-of-domain reasoning generalization, in
particular from STEM to cultural commonsense knowledge, even for English.
Overall, we demonstrate the potentials, study the mechanisms and outline the
limitations of crosslingual generalization of English reasoning test-time
scaling. We conclude that practitioners should let English-centric RLMs reason
in high-resource languages, while further work is needed to improve reasoning
in low-resource languages and out-of-domain contexts.
Flow-GRPO: Training Flow Matching Models via Online RL
Authors: name: Jie Liu, name: Gongye Liu, name: Jiajun Liang, name: Yangguang Li, name: Jiaheng Liu, name: Xintao Wang, name: Pengfei Wan, name: Di Zhang, name: Wanli Ouyang
Published: 2025-05-08 17:58:45

We propose Flow-GRPO, the first method integrating online reinforcement
learning (RL) into flow matching models. Our approach uses two key strategies:
(1) an ODE-to-SDE conversion that transforms a deterministic Ordinary
Differential Equation (ODE) into an equivalent Stochastic Differential Equation
(SDE) that matches the original model's marginal distribution at all timesteps,
enabling statistical sampling for RL exploration; and (2) a Denoising Reduction
strategy that reduces training denoising steps while retaining the original
inference timestep number, significantly improving sampling efficiency without
performance degradation. Empirically, Flow-GRPO is effective across multiple
text-to-image tasks. For complex compositions, RL-tuned SD3.5 generates nearly
perfect object counts, spatial relations, and fine-grained attributes, boosting
GenEval accuracy from 63% to 95%. In visual text rendering, its accuracy
improves from 59% to 92%, significantly enhancing text generation.
Flow-GRPO also achieves substantial gains in human preference alignment.
Notably, little to no reward hacking occurred, meaning rewards did not increase
at the cost of image quality or diversity, and both remained stable in our
experiments.
FG-CLIP: Fine-Grained Visual and Textual Alignment
Authors: name: Chunyu Xie, name: Bin Wang, name: Fanjing Kong, name: Jincheng Li, name: Dawei Liang, name: Gengshen Zhang, name: Dawei Leng, name: Yuhui Yin
Published: 2025-05-08 09:06:53

Contrastive Language-Image Pre-training (CLIP) excels in multimodal tasks
such as image-text retrieval and zero-shot classification but struggles with
fine-grained understanding due to its focus on coarse-grained short captions.
To address this, we propose Fine-Grained CLIP (FG-CLIP), which enhances
fine-grained understanding through three key innovations. First, we leverage
large multimodal models to generate 1.6 billion long caption-image pairs for
capturing global-level semantic details. Second, a high-quality dataset is
constructed with 12 million images and 40 million region-specific bounding
boxes aligned with detailed captions to ensure precise, context-rich
representations. Third, 10 million hard fine-grained negative samples are
incorporated to improve the model's ability to distinguish subtle semantic
differences. Corresponding training methods are meticulously designed for these
data. Extensive experiments demonstrate that FG-CLIP outperforms the original
CLIP and other state-of-the-art methods across various downstream tasks,
including fine-grained understanding, open-vocabulary object detection,
image-text retrieval, and general multimodal benchmarks. These results
highlight FG-CLIP's effectiveness in capturing fine-grained image details and
improving overall model performance. The related data, code, and models are
available at https://github.com/360CVGroup/FG-CLIP.
PlaceIt3D: Language-Guided Object Placement in Real 3D Scenes
Authors: name: Ahmed Abdelreheem, name: Filippo Aleotti, name: Jamie Watson, name: Zawar Qureshi, name: Abdelrahman Eldesokey, name: Peter Wonka, name: Gabriel Brostow, name: Sara Vicente, name: Guillermo Garcia-Hernando
Published: 2025-05-08 14:29:11

We introduce the novel task of Language-Guided Object Placement in Real 3D
Scenes. Our model is given a 3D scene's point cloud, a 3D asset, and a textual
prompt broadly describing where the 3D asset should be placed. The task here is
to find a valid placement for the 3D asset that respects the prompt. Compared
with other language-guided localization tasks in 3D scenes such as grounding,
this task has specific challenges: it is ambiguous because it has multiple
valid solutions, and it requires reasoning about 3D geometric relationships and
free space. We inaugurate this task by proposing a new benchmark and evaluation
protocol. We also introduce a new dataset for training 3D LLMs on this task, as
well as the first method to serve as a non-trivial baseline. We believe that
this challenging task and our new benchmark could become part of the suite of
benchmarks used to evaluate and compare generalist 3D LLM models.
StreamBridge: Turning Your Offline Video Large Language Model into a
  Proactive Streaming Assistant
Authors: name: Haibo Wang, name: Bo Feng, name: Zhengfeng Lai, name: Mingze Xu, name: Shiyu Li, name: Weifeng Ge, name: Afshin Dehghan, name: Meng Cao, name: Ping Huang
Published: 2025-05-08 17:57:40

We present StreamBridge, a simple yet effective framework that seamlessly
transforms offline Video-LLMs into streaming-capable models. It addresses two
fundamental challenges in adapting existing models into online scenarios: (1)
limited capability for multi-turn real-time understanding, and (2) lack of
proactive response mechanisms. Specifically, StreamBridge incorporates (1) a
memory buffer combined with a round-decayed compression strategy, supporting
long-context multi-turn interactions, and (2) a decoupled, lightweight
activation model that can be effortlessly integrated into existing Video-LLMs,
enabling continuous proactive responses. To further support StreamBridge, we
construct Stream-IT, a large-scale dataset tailored for streaming video
understanding, featuring interleaved video-text sequences and diverse
instruction formats. Extensive experiments show that StreamBridge significantly
improves the streaming understanding capabilities of offline Video-LLMs across
various tasks, outperforming even proprietary models such as GPT-4o and Gemini
1.5 Pro. Simultaneously, it achieves competitive or superior performance on
standard video understanding benchmarks.
BrowseComp-ZH: Benchmarking Web Browsing Ability of Large Language
  Models in Chinese
Authors: name: Peilin Zhou, name: Bruce Leon, name: Xiang Ying, name: Can Zhang, name: Yifan Shao, name: Qichen Ye, name: Dading Chong, name: Zhiling Jin, name: Chenxuan Xie, name: Meng Cao, name: Yuxin Gu, name: Sixin Hong, name: Jing Ren, name: Jian Chen, name: Chao Liu, name: Yining Hua
Published: 2025-04-27 17:32:43

As large language models (LLMs) evolve into tool-using agents, the ability to
browse the web in real-time has become a critical yardstick for measuring their
reasoning and retrieval competence. Existing benchmarks such as BrowseComp
concentrate on English and overlook the linguistic, infrastructural, and
censorship-related complexities of other major information ecosystems -- most
notably Chinese. To address this gap, we introduce BrowseComp-ZH, a
high-difficulty benchmark purpose-built to comprehensively evaluate LLM agents
on the Chinese web. BrowseComp-ZH consists of 289 multi-hop questions spanning
11 diverse domains. Each question is reverse-engineered from a short,
objective, and easily verifiable answer (e.g., a date, number, or proper noun).
A two-stage quality control protocol is applied to strive for high question
difficulty and answer uniqueness. We benchmark over 20 state-of-the-art
language models and agentic search systems on our proposed BrowseComp-ZH.
Despite their strong conversational and retrieval capabilities, most models
struggle severely: a large number achieve accuracy rates below 10%, and only a
handful exceed 20%. Even the best-performing system, OpenAI's DeepResearch,
reaches just 42.9%. These results demonstrate the considerable difficulty of
BrowseComp-ZH, where success demands not only effective retrieval strategies,
but also sophisticated reasoning and information reconciliation -- capabilities
that current models still struggle to master. Our dataset, construction
guidelines, and benchmark results have been publicly released at
https://github.com/PALIN2018/BrowseComp-ZH.
X-Reasoner: Towards Generalizable Reasoning Across Modalities and
  Domains
Authors: name: Qianchu Liu, name: Sheng Zhang, name: Guanghui Qin, name: Timothy Ossowski, name: Yu Gu, name: Ying Jin, name: Sid Kiblawi, name: Sam Preston, name: Mu Wei, name: Paul Vozila, name: Tristan Naumann, name: Hoifung Poon
Published: 2025-05-06 21:08:27

Recent proprietary models (e.g., o3) have begun to demonstrate strong
multimodal reasoning capabilities. Yet, most existing open-source research
concentrates on training text-only reasoning models, with evaluations limited
to mainly mathematical and general-domain tasks. Therefore, it remains unclear
how to effectively extend reasoning capabilities beyond text input and general
domains. This paper explores a fundamental research question: Is reasoning
generalizable across modalities and domains? Our findings support an
affirmative answer: General-domain text-based post-training can enable such
strong generalizable reasoning. Leveraging this finding, we introduce
X-Reasoner, a vision-language model post-trained solely on general-domain text
for generalizable reasoning, using a two-stage approach: an initial supervised
fine-tuning phase with distilled long chain-of-thoughts, followed by
reinforcement learning with verifiable rewards. Experiments show that
X-Reasoner successfully transfers reasoning capabilities to both multimodal and
out-of-domain settings, outperforming existing state-of-the-art models trained
with in-domain and multimodal data across various general and medical
benchmarks (Figure 1). Additionally, we find that X-Reasoner's performance in
specialized domains can be further enhanced through continued training on
domain-specific text-only data. Building upon this, we introduce
X-Reasoner-Med, a medical-specialized variant that achieves new state of the
art on numerous text-only and multimodal medical benchmarks.
Scalable Chain of Thoughts via Elastic Reasoning
Authors: name: Yuhui Xu, name: Hanze Dong, name: Lei Wang, name: Doyen Sahoo, name: Junnan Li, name: Caiming Xiong
Published: 2025-05-08 15:01:06

Large reasoning models (LRMs) have achieved remarkable progress on complex
tasks by generating extended chains of thought (CoT). However, their
uncontrolled output lengths pose significant challenges for real-world
deployment, where inference-time budgets on tokens, latency, or compute are
strictly constrained. We propose Elastic Reasoning, a novel framework for
scalable chain of thoughts that explicitly separates reasoning into two
phases--thinking and solution--with independently allocated budgets. At test
time, Elastic Reasoning prioritize that completeness of solution segments,
significantly improving reliability under tight resource constraints. To train
models that are robust to truncated thinking, we introduce a lightweight
budget-constrained rollout strategy, integrated into GRPO, which teaches the
model to reason adaptively when the thinking process is cut short and
generalizes effectively to unseen budget constraints without additional
training. Empirical results on mathematical (AIME, MATH500) and programming
(LiveCodeBench, Codeforces) benchmarks demonstrate that Elastic Reasoning
performs robustly under strict budget constraints, while incurring
significantly lower training cost than baseline methods. Remarkably, our
approach also produces more concise and efficient reasoning even in
unconstrained settings. Elastic Reasoning offers a principled and practical
solution to the pressing challenge of controllable reasoning at scale.
3D Scene Generation: A Survey
Authors: name: Beichen Wen, name: Haozhe Xie, name: Zhaoxi Chen, name: Fangzhou Hong, name: Ziwei Liu
Published: 2025-05-08 17:59:54

3D scene generation seeks to synthesize spatially structured, semantically
meaningful, and photorealistic environments for applications such as immersive
media, robotics, autonomous driving, and embodied AI. Early methods based on
procedural rules offered scalability but limited diversity. Recent advances in
deep generative models (e.g., GANs, diffusion models) and 3D representations
(e.g., NeRF, 3D Gaussians) have enabled the learning of real-world scene
distributions, improving fidelity, diversity, and view consistency. Recent
advances like diffusion models bridge 3D scene synthesis and photorealism by
reframing generation as image or video synthesis problems. This survey provides
a systematic overview of state-of-the-art approaches, organizing them into four
paradigms: procedural generation, neural 3D-based generation, image-based
generation, and video-based generation. We analyze their technical foundations,
trade-offs, and representative results, and review commonly used datasets,
evaluation protocols, and downstream applications. We conclude by discussing
key challenges in generation capacity, 3D representation, data and annotations,
and evaluation, and outline promising directions including higher fidelity,
physics-aware and interactive generation, and unified perception-generation
models. This review organizes recent advances in 3D scene generation and
highlights promising directions at the intersection of generative AI, 3D
vision, and embodied intelligence. To track ongoing developments, we maintain
an up-to-date project page:
https://github.com/hzxie/Awesome-3D-Scene-Generation.
Sentient Agent as a Judge: Evaluating Higher-Order Social Cognition in
  Large Language Models
Authors: name: Bang Zhang, name: Ruotian Ma, name: Qingxuan Jiang, name: Peisong Wang, name: Jiaqi Chen, name: Zheng Xie, name: Xingyu Chen, name: Yue Wang, name: Fanghua Ye, name: Jian Li, name: Yifan Yang, name: Zhaopeng Tu, name: Xiaolong Li
Published: 2025-05-01 19:06:10

Assessing how well a large language model (LLM) understands human, rather
than merely text, remains an open challenge. To bridge the gap, we introduce
Sentient Agent as a Judge (SAGE), an automated evaluation framework that
measures an LLM's higher-order social cognition. SAGE instantiates a Sentient
Agent that simulates human-like emotional changes and inner thoughts during
interaction, providing a more realistic evaluation of the tested model in
multi-turn conversations. At every turn, the agent reasons about (i) how its
emotion changes, (ii) how it feels, and (iii) how it should reply, yielding a
numerical emotion trajectory and interpretable inner thoughts. Experiments on
100 supportive-dialogue scenarios show that the final Sentient emotion score
correlates strongly with Barrett-Lennard Relationship Inventory (BLRI) ratings
and utterance-level empathy metrics, validating psychological fidelity. We also
build a public Sentient Leaderboard covering 18 commercial and open-source
models that uncovers substantial gaps (up to 4x) between frontier systems
(GPT-4o-Latest, Gemini2.5-Pro) and earlier baselines, gaps not reflected in
conventional leaderboards (e.g., Arena). SAGE thus provides a principled,
scalable and interpretable tool for tracking progress toward genuinely
empathetic and socially adept language agents.
On Path to Multimodal Generalist: General-Level and General-Bench
Authors: name: Hao Fei, name: Yuan Zhou, name: Juncheng Li, name: Xiangtai Li, name: Qingshan Xu, name: Bobo Li, name: Shengqiong Wu, name: Yaoting Wang, name: Junbao Zhou, name: Jiahao Meng, name: Qingyu Shi, name: Zhiyuan Zhou, name: Liangtao Shi, name: Minghe Gao, name: Daoan Zhang, name: Zhiqi Ge, name: Weiming Wu, name: Siliang Tang, name: Kaihang Pan, name: Yaobo Ye, name: Haobo Yuan, name: Tao Zhang, name: Tianjie Ju, name: Zixiang Meng, name: Shilin Xu, name: Liyu Jia, name: Wentao Hu, name: Meng Luo, name: Jiebo Luo, name: Tat-Seng Chua, name: Shuicheng Yan, name: Hanwang Zhang
Published: 2025-05-07 17:59:32

The Multimodal Large Language Model (MLLM) is currently experiencing rapid
growth, driven by the advanced capabilities of LLMs. Unlike earlier
specialists, existing MLLMs are evolving towards a Multimodal Generalist
paradigm. Initially limited to understanding multiple modalities, these models
have advanced to not only comprehend but also generate across modalities. Their
capabilities have expanded from coarse-grained to fine-grained multimodal
understanding and from supporting limited modalities to arbitrary ones. While
many benchmarks exist to assess MLLMs, a critical question arises: Can we
simply assume that higher performance across tasks indicates a stronger MLLM
capability, bringing us closer to human-level AI? We argue that the answer is
not as straightforward as it seems. This project introduces General-Level, an
evaluation framework that defines 5-scale levels of MLLM performance and
generality, offering a methodology to compare MLLMs and gauge the progress of
existing systems towards more robust multimodal generalists and, ultimately,
towards AGI. At the core of the framework is the concept of Synergy, which
measures whether models maintain consistent capabilities across comprehension
and generation, and across multiple modalities. To support this evaluation, we
present General-Bench, which encompasses a broader spectrum of skills,
modalities, formats, and capabilities, including over 700 tasks and 325,800
instances. The evaluation results that involve over 100 existing
state-of-the-art MLLMs uncover the capability rankings of generalists,
highlighting the challenges in reaching genuine AI. We expect this project to
pave the way for future research on next-generation multimodal foundation
models, providing a robust infrastructure to accelerate the realization of AGI.
Project page: https://generalist.top/
OpenVision: A Fully-Open, Cost-Effective Family of Advanced Vision
  Encoders for Multimodal Learning
Authors: name: Xianhang Li, name: Yanqing Liu, name: Haoqin Tu, name: Hongru Zhu, name: Cihang Xie
Published: 2025-05-07 17:48:35

OpenAI's CLIP, released in early 2021, have long been the go-to choice of
vision encoder for building multimodal foundation models. Although recent
alternatives such as SigLIP have begun to challenge this status quo, to our
knowledge none are fully open: their training data remains proprietary and/or
their training recipes are not released. This paper fills this gap with
OpenVision, a fully-open, cost-effective family of vision encoders that match
or surpass the performance of OpenAI's CLIP when integrated into multimodal
frameworks like LLaVA. OpenVision builds on existing works -- e.g., CLIPS for
training framework and Recap-DataComp-1B for training data -- while revealing
multiple key insights in enhancing encoder quality and showcasing practical
benefits in advancing multimodal models. By releasing vision encoders spanning
from 5.9M to 632.1M parameters, OpenVision offers practitioners a flexible
trade-off between capacity and efficiency in building multimodal models: larger
models deliver enhanced multimodal performance, while smaller versions enable
lightweight, edge-ready multimodal deployments.
COSMOS: Predictable and Cost-Effective Adaptation of LLMs
Authors: name: Jiayu Wang, name: Aws Albarghouthi, name: Frederic Sala
Published: 2025-04-30 02:06:26

Large language models (LLMs) achieve remarkable performance across numerous
tasks by using a diverse array of adaptation strategies. However, optimally
selecting a model and adaptation strategy under resource constraints is
challenging and often requires extensive experimentation. We investigate
whether it is possible to accurately predict both performance and cost without
expensive trials. We formalize the strategy selection problem for LLMs and
introduce COSMOS, a unified prediction framework that efficiently estimates
adaptation outcomes at minimal cost. We instantiate and study the capability of
our framework via a pair of powerful predictors: embedding-augmented
lightweight proxy models to predict fine-tuning performance, and low-sample
scaling laws to forecast retrieval-augmented in-context learning. Extensive
evaluation across eight representative benchmarks demonstrates that COSMOS
achieves high prediction accuracy while reducing computational costs by 92.72%
on average, and up to 98.71% in resource-intensive scenarios. Our results show
that efficient prediction of adaptation outcomes is not only feasible but can
substantially reduce the computational overhead of LLM deployment while
maintaining performance standards.
AutoLibra: Agent Metric Induction from Open-Ended Feedback
Authors: name: Hao Zhu, name: Phil Cuvin, name: Xinkai Yu, name: Charlotte Ka Yee Yan, name: Jason Zhang, name: Diyi Yang
Published: 2025-05-05 17:47:49

Agents are predominantly evaluated and optimized via task success metrics,
which are coarse, rely on manual design from experts, and fail to reward
intermediate emergent behaviors. We propose AutoLibra, a framework for agent
evaluation, that transforms open-ended human feedback, e.g., "If you find that
the button is disabled, don't click it again", or "This agent has too much
autonomy to decide what to do on its own", into metrics for evaluating
fine-grained behaviors in agent trajectories. AutoLibra accomplishes this by
grounding feedback to an agent's behavior, clustering similar positive and
negative behaviors, and creating concrete metrics with clear definitions and
concrete examples, which can be used for prompting LLM-as-a-Judge as
evaluators. We further propose two meta-metrics to evaluate the alignment of a
set of (induced) metrics with open feedback: "coverage" and "redundancy".
Through optimizing these meta-metrics, we experimentally demonstrate
AutoLibra's ability to induce more concrete agent evaluation metrics than the
ones proposed in previous agent evaluation benchmarks and discover new metrics
to analyze agents. We also present two applications of AutoLibra in agent
improvement: First, we show that AutoLibra-induced metrics serve as better
prompt-engineering targets than the task success rate on a wide range of text
game tasks, improving agent performance over baseline by a mean of 20%. Second,
we show that AutoLibra can iteratively select high-quality fine-tuning data for
web navigation agents. Our results suggest that AutoLibra is a powerful
task-agnostic tool for evaluating and improving language agents.