Orionnnn/hub-stats huggingface dataset:
FinTrust: A Comprehensive Benchmark of Trustworthiness Evaluation in
  Finance Domain
Authors: name: Tiansheng Hu, name: Tongyan Hu, name: Liuyang Bai, name: Yilun Zhao, name: Arman Cohan, name: Chen Zhao
Published: 2025-10-17 01:45:49

Recent LLMs have demonstrated promising ability in solving finance related
problems. However, applying LLMs in real-world finance application remains
challenging due to its high risk and high stakes property. This paper
introduces FinTrust, a comprehensive benchmark specifically designed for
evaluating the trustworthiness of LLMs in finance applications. Our benchmark
focuses on a wide range of alignment issues based on practical context and
features fine-grained tasks for each dimension of trustworthiness evaluation.
We assess eleven LLMs on FinTrust and find that proprietary models like o4-mini
outperforms in most tasks such as safety while open-source models like
DeepSeek-V3 have advantage in specific areas like industry-level fairness. For
challenging task like fiduciary alignment and disclosure, all LLMs fall short,
showing a significant gap in legal awareness. We believe that FinTrust can be a
valuable benchmark for LLMs' trustworthiness evaluation in finance domain.
Paper2Web: Let's Make Your Paper Alive!
Authors: name: Yuhang Chen, name: Tianpeng Lv, name: Siyi Zhang, name: Yixiang Yin, name: Yao Wan, name: Philip S. Yu, name: Dongping Chen
Published: 2025-10-17 17:35:58

Academic project websites can more effectively disseminate research when they
clearly present core content and enable intuitive navigation and interaction.
However, current approaches such as direct Large Language Model (LLM)
generation, templates, or direct HTML conversion struggle to produce
layout-aware, interactive sites, and a comprehensive evaluation suite for this
task has been lacking. In this paper, we introduce Paper2Web, a benchmark
dataset and multi-dimensional evaluation framework for assessing academic
webpage generation. It incorporates rule-based metrics like Connectivity,
Completeness and human-verified LLM-as-a-Judge (covering interactivity,
aesthetics, and informativeness), and PaperQuiz, which measures paper-level
knowledge retention. We further present PWAgent, an autonomous pipeline that
converts scientific papers into interactive and multimedia-rich academic
homepages. The agent iteratively refines both content and layout through MCP
tools that enhance emphasis, balance, and presentation quality. Our experiments
show that PWAgent consistently outperforms end-to-end baselines like
template-based webpages and arXiv/alphaXiv versions by a large margin while
maintaining low cost, achieving the Pareto-front in academic webpage
generation.
Explore to Evolve: Scaling Evolved Aggregation Logic via Proactive
  Online Exploration for Deep Research Agents
Authors: name: Rui Wang, name: Ce Zhang, name: Jun-Yu Ma, name: Jianshu Zhang, name: Hongru Wang, name: Yi Chen, name: Boyang Xue, name: Tianqing Fang, name: Zhisong Zhang, name: Hongming Zhang, name: Haitao Mi, name: Dong Yu, name: Kam-Fai Wong
Published: 2025-10-16 08:37:42

Deep research web agents not only retrieve information from diverse sources
such as web environments, files, and multimodal inputs, but more importantly,
they need to rigorously analyze and aggregate knowledge for insightful
research. However, existing open-source deep research agents predominantly
focus on enhancing information-seeking capabilities of web agents to locate
specific information, while overlooking the essential need for information
aggregation, which would limit their ability to support in-depth research. We
propose an Explore to Evolve paradigm to scalably construct verifiable training
data for web agents. Begins with proactive online exploration, an agent sources
grounded information by exploring the real web. Using the collected evidence,
the agent then self-evolves an aggregation program by selecting, composing, and
refining operations from 12 high-level logical types to synthesize a verifiable
QA pair. This evolution from high-level guidance to concrete operations allowed
us to scalably produce WebAggregatorQA, a dataset of 10K samples across 50K
websites and 11 domains. Based on an open-source agent framework, SmolAgents,
we collect supervised fine-tuning trajectories to develop a series of
foundation models, WebAggregator. WebAggregator-8B matches the performance of
GPT-4.1, while the 32B variant surpasses GPT-4.1 by more than 10% on GAIA-text
and closely approaches Claude-3.7-sonnet. Moreover, given the limited
availability of benchmarks that evaluate web agents' information aggregation
abilities, we construct a human-annotated evaluation split of WebAggregatorQA
as a challenging test set. On this benchmark, Claude-3.7-sonnet only achieves
28%, and GPT-4.1 scores 25.8%. Even when agents manage to retrieve all
references, they still struggle on WebAggregatorQA, highlighting the need to
strengthen the information aggregation capabilities of web agent foundations.
Imaginarium: Vision-guided High-Quality 3D Scene Layout Generation
Authors: name: Xiaoming Zhu, name: Xu Huang, name: Qinghongbing Xie, name: Zhi Deng, name: Junsheng Yu, name: Yirui Guan, name: Zhongyuan Liu, name: Lin Zhu, name: Qijun Zhao, name: Ligang Liu, name: Long Zeng
Published: 2025-10-17 11:48:08

Generating artistic and coherent 3D scene layouts is crucial in digital
content creation. Traditional optimization-based methods are often constrained
by cumbersome manual rules, while deep generative models face challenges in
producing content with richness and diversity. Furthermore, approaches that
utilize large language models frequently lack robustness and fail to accurately
capture complex spatial relationships. To address these challenges, this paper
presents a novel vision-guided 3D layout generation system. We first construct
a high-quality asset library containing 2,037 scene assets and 147 3D scene
layouts. Subsequently, we employ an image generation model to expand prompt
representations into images, fine-tuning it to align with our asset library. We
then develop a robust image parsing module to recover the 3D layout of scenes
based on visual semantics and geometric information. Finally, we optimize the
scene layout using scene graphs and overall visual semantics to ensure logical
coherence and alignment with the images. Extensive user testing demonstrates
that our algorithm significantly outperforms existing methods in terms of
layout richness and quality. The code and dataset will be available at
https://github.com/HiHiAllen/Imaginarium.
Skyfall-GS: Synthesizing Immersive 3D Urban Scenes from Satellite
  Imagery
Authors: name: Jie-Ying Lee, name: Yi-Ruei Liu, name: Shr-Ruei Tsai, name: Wei-Cheng Chang, name: Chung-Ho Wu, name: Jiewen Chan, name: Zhenjun Zhao, name: Chieh Hubert Lin, name: Yu-Lun Liu
Published: 2025-10-17 17:59:51

Synthesizing large-scale, explorable, and geometrically accurate 3D urban
scenes is a challenging yet valuable task in providing immersive and embodied
applications. The challenges lie in the lack of large-scale and high-quality
real-world 3D scans for training generalizable generative models. In this
paper, we take an alternative route to create large-scale 3D scenes by
synergizing the readily available satellite imagery that supplies realistic
coarse geometry and the open-domain diffusion model for creating high-quality
close-up appearances. We propose Skyfall-GS, the first city-block
scale 3D scene creation framework without costly 3D annotations, also featuring
real-time, immersive 3D exploration. We tailor a curriculum-driven iterative
refinement strategy to progressively enhance geometric completeness and
photorealistic textures. Extensive experiments demonstrate that Skyfall-GS
provides improved cross-view consistent geometry and more realistic textures
compared to state-of-the-art approaches. Project page:
https://skyfall-gs.jayinnn.dev/
MorphoBench: A Benchmark with Difficulty Adaptive to Model Reasoning
Authors: name: Xukai Wang, name: Xuanbo Liu, name: Mingrui Chen, name: Haitian Zhong, name: Xuanlin Yang, name: Bohan Zeng, name: Jinbo Hu, name: Hao Liang, name: Junbo Niu, name: Xuchen Li, name: Ruitao Wu, name: Ruichuan An, name: Yang Shi, name: Liu Liu, name: Xu-Yao Zhang, name: Qiang Liu, name: Zhouchen Lin, name: Wentao Zhang, name: Bin Dong
Published: 2025-10-16 03:30:56

With the advancement of powerful large-scale reasoning models, effectively
evaluating the reasoning capabilities of these models has become increasingly
important. However, existing benchmarks designed to assess the reasoning
abilities of large models tend to be limited in scope and lack the flexibility
to adapt their difficulty according to the evolving reasoning capacities of the
models. To address this, we propose MorphoBench, a benchmark that incorporates
multidisciplinary questions to evaluate the reasoning capabilities of large
models and can adjust and update question difficulty based on the reasoning
abilities of advanced models. Specifically, we curate the benchmark by
selecting and collecting complex reasoning questions from existing benchmarks
and sources such as Olympiad-level competitions. Additionally, MorphoBench
adaptively modifies the analytical challenge of questions by leveraging key
statements generated during the model's reasoning process. Furthermore, it
includes questions generated using simulation software, enabling dynamic
adjustment of benchmark difficulty with minimal resource consumption. We have
gathered over 1,300 test questions and iteratively adjusted the difficulty of
MorphoBench based on the reasoning capabilities of models such as o3 and GPT-5.
MorphoBench enhances the comprehensiveness and validity of model reasoning
evaluation, providing reliable guidance for improving both the reasoning
abilities and scientific robustness of large models. The code has been released
in https://github.com/OpenDCAI/MorphoBench.
LightsOut: Diffusion-based Outpainting for Enhanced Lens Flare Removal
Authors: name: Shr-Ruei Tsai, name: Wei-Cheng Chang, name: Jie-Ying Lee, name: Chih-Hai Su, name: Yu-Lun Liu
Published: 2025-10-17 17:59:50

Lens flare significantly degrades image quality, impacting critical computer
vision tasks like object detection and autonomous driving. Recent Single Image
Flare Removal (SIFR) methods perform poorly when off-frame light sources are
incomplete or absent. We propose LightsOut, a diffusion-based outpainting
framework tailored to enhance SIFR by reconstructing off-frame light sources.
Our method leverages a multitask regression module and LoRA fine-tuned
diffusion model to ensure realistic and physically consistent outpainting
results. Comprehensive experiments demonstrate LightsOut consistently boosts
the performance of existing SIFR methods across challenging scenarios without
additional retraining, serving as a universally applicable plug-and-play
preprocessing solution. Project page: https://ray-1026.github.io/lightsout/
BLIP3o-NEXT: Next Frontier of Native Image Generation
Authors: name: Jiuhai Chen, name: Le Xue, name: Zhiyang Xu, name: Xichen Pan, name: Shusheng Yang, name: Can Qin, name: An Yan, name: Honglu Zhou, name: Zeyuan Chen, name: Lifu Huang, name: Tianyi Zhou, name: Junnan Li, name: Silvio Savarese, name: Caiming Xiong, name: Ran Xu
Published: 2025-10-17 17:50:58

We present BLIP3o-NEXT, a fully open-source foundation model in the BLIP3
series that advances the next frontier of native image generation. BLIP3o-NEXT
unifies text-to-image generation and image editing within a single
architecture, demonstrating strong image generation and image editing
capabilities. In developing the state-of-the-art native image generation model,
we identify four key insights: (1) Most architectural choices yield comparable
performance; an architecture can be deemed effective provided it scales
efficiently and supports fast inference; (2) The successful application of
reinforcement learning can further push the frontier of native image
generation; (3) Image editing still remains a challenging task, yet instruction
following and the consistency between generated and reference images can be
significantly enhanced through post-training and data engine; (4) Data quality
and scale continue to be decisive factors that determine the upper bound of
model performance. Building upon these insights, BLIP3o-NEXT leverages an
Autoregressive + Diffusion architecture in which an autoregressive model first
generates discrete image tokens conditioned on multimodal inputs, whose hidden
states are then used as conditioning signals for a diffusion model to generate
high-fidelity images. This architecture integrates the reasoning strength and
instruction following of autoregressive models with the fine-detail rendering
ability of diffusion models, achieving a new level of coherence and realism.
Extensive evaluations of various text-to-image and image-editing benchmarks
show that BLIP3o-NEXT achieves superior performance over existing models.
OmniVinci: Enhancing Architecture and Data for Omni-Modal Understanding
  LLM
Authors: name: Hanrong Ye, name: Chao-Han Huck Yang, name: Arushi Goel, name: Wei Huang, name: Ligeng Zhu, name: Yuanhang Su, name: Sean Lin, name: An-Chieh Cheng, name: Zhen Wan, name: Jinchuan Tian, name: Yuming Lou, name: Dong Yang, name: Zhijian Liu, name: Yukang Chen, name: Ambrish Dantrey, name: Ehsan Jahangiri, name: Sreyan Ghosh, name: Daguang Xu, name: Ehsan Hosseini-Asl, name: Danial Mohseni Taheri, name: Vidya Murali, name: Sifei Liu, name: Jason Lu, name: Oluwatobi Olabiyi, name: Frank Wang, name: Rafael Valle, name: Bryan Catanzaro, name: Andrew Tao, name: Song Han, name: Jan Kautz, name: Hongxu Yin, name: Pavlo Molchanov
Published: 2025-10-17 17:59:59

Advancing machine intelligence requires developing the ability to perceive
across multiple modalities, much as humans sense the world. We introduce
OmniVinci, an initiative to build a strong, open-source, omni-modal LLM. We
carefully study the design choices across model architecture and data curation.
For model architecture, we present three key innovations: (i) OmniAlignNet for
strengthening alignment between vision and audio embeddings in a shared
omni-modal latent space; (ii) Temporal Embedding Grouping for capturing
relative temporal alignment between vision and audio signals; and (iii)
Constrained Rotary Time Embedding for encoding absolute temporal information in
omni-modal embeddings. We introduce a curation and synthesis pipeline that
generates 24M single-modal and omni-modal conversations. We find that
modalities reinforce one another in both perception and reasoning. Our model,
OmniVinci, outperforms Qwen2.5-Omni with +19.05 on DailyOmni (cross-modal
understanding), +1.7 on MMAR (audio), and +3.9 on Video-MME (vision), while
using just 0.2T training tokens - a 6 times reduction compared to
Qwen2.5-Omni's 1.2T. We finally demonstrate omni-modal advantages in downstream
applications spanning robotics, medical AI, and smart factory.
Budget-aware Test-time Scaling via Discriminative Verification
Authors: name: Kyle Montgomery, name: Sijun Tan, name: Yuqi Chen, name: Siyuan Zhuang, name: Tianjun Zhang, name: Raluca Ada Popa, name: Chenguang Wang
Published: 2025-10-16 17:30:02

Test-time scaling is a powerful strategy for boosting the performance of
large language models on complex reasoning tasks. While state-of-the-art
approaches often employ generative verifiers to select the best solution from a
pool of candidates, this method incurs prohibitive computational costs,
limiting its practicality. In this work, we shift the focus to a more
budget-aware paradigm: discriminative verification. We conduct a thorough
empirical analysis and demonstrate that while discriminative verifiers may
underperform in isolation, combining them with self-consistency in a hybrid
approach creates a powerful and efficient test-time scaling mechanism. Notably,
under a fixed compute budget, this hybrid approach surpasses state-of-the-art
generative verification by a significant margin: achieving up to 15.3\% higher
accuracy on AIME2025. Our findings establish that for practical, real-world
applications, budget-aware scaling with discriminative verifiers is not only a
"free" upgrade over self-consistency, but also a more effective and efficient
alternative to costly generative techniques. Code is available at
https://github.com/wang-research-lab/verification.
LLMs Can Get "Brain Rot"!
Authors: name: Shuo Xing, name: Junyuan Hong, name: Yifan Wang, name: Runjin Chen, name: Zhenyu Zhang, name: Ananth Grama, name: Zhengzhong Tu, name: Zhangyang Wang
Published: 2025-10-15 13:28:49

We propose and test the LLM Brain Rot Hypothesis: continual exposure to junk
web text induces lasting cognitive decline in large language models (LLMs). To
causally isolate data quality, we run controlled experiments on real Twitter/X
corpora, constructing junk and reversely controlled datasets via two orthogonal
operationalizations: M1 (engagement degree) and M2 (semantic quality), with
matched token scale and training operations across conditions. Contrary to the
control group, continual pre-training of 4 LLMs on the junk dataset causes
non-trivial declines (Hedges' g>0.3) on reasoning, long-context
understanding, safety, and inflating "dark traits" (e.g., psychopathy,
narcissism). The gradual mixtures of junk and control datasets also yield
dose-response cognition decay: for example, under M1, ARC-Challenge with Chain
Of Thoughts drops 74.9 rightarrow 57.2 and RULER-CWE 84.4 rightarrow 52.3
as junk ratio rises from 0% to 100%.
  Error forensics reveal several key insights. First, we identify
thought-skipping as the primary lesion: models increasingly truncate or skip
reasoning chains, explaining most of the error growth. Second, partial but
incomplete healing is observed: scaling instruction tuning and clean data
pre-training improve the declined cognition yet cannot restore baseline
capability, suggesting persistent representational drift rather than format
mismatch. Finally, we discover that the popularity, a non-semantic metric, of a
tweet is a better indicator of the Brain Rot effect than the length in M1.
Together, the results provide significant, multi-perspective evidence that data
quality is a causal driver of LLM capability decay, reframing curation for
continual pretraining as a training-time safety problem and motivating
routine "cognitive health checks" for deployed LLMs.
Predicting Task Performance with Context-aware Scaling Laws
Authors: name: Kyle Montgomery, name: David Park, name: Jianhong Tu, name: Michael Bendersky, name: Beliz Gunel, name: Dawn Song, name: Chenguang Wang
Published: 2025-10-16 17:35:18

Scaling laws have transformed our understanding of large language models by
linking upstream metrics like cross-entropy loss to design factors such as
model size, training data, and compute. However, these conventional laws fail
to capture downstream task performance, where context plays a critical role. In
this work, we propose a straightforward, interpretable framework that jointly
models downstream performance as a function of the training compute and the
provided context. We empirically validate our framework by fitting it on the
observed downstream performance of extended-context variants of Llama-2-7B and
Llama-2-13B across 65,500 unique instances spanning three tasks: arithmetic
reasoning, common sense reasoning, and machine translation. Our results
demonstrate that our framework accurately models in-distribution downstream
performance, generalizes across three orders of magnitude in training compute,
and reliably extrapolates performance as the amount of context increases. These
findings offer valuable insights into the interplay between training compute
and context utilization, providing guidance for designing more efficient
long-context LLMs for diverse downstream tasks. Our code is available at
https://github.com/wang-research-lab/context-scaling.
LiveResearchBench: A Live Benchmark for User-Centric Deep Research in
  the Wild
Authors: name: Jiayu Wang, name: Yifei Ming, name: Riya Dulepet, name: Qinglin Chen, name: Austin Xu, name: Zixuan Ke, name: Frederic Sala, name: Aws Albarghouthi, name: Caiming Xiong, name: Shafiq Joty
Published: 2025-10-16 02:49:16

Deep research -- producing comprehensive, citation-grounded reports by
searching and synthesizing information from hundreds of live web sources --
marks an important frontier for agentic systems. To rigorously evaluate this
ability, four principles are essential: tasks should be (1) user-centric,
reflecting realistic information needs, (2) dynamic, requiring up-to-date
information beyond parametric knowledge, (3) unambiguous, ensuring consistent
interpretation across users, and (4) multi-faceted and search-intensive,
requiring search over numerous web sources and in-depth analysis. Existing
benchmarks fall short of these principles, often focusing on narrow domains or
posing ambiguous questions that hinder fair comparison. Guided by these
principles, we introduce LiveResearchBench, a benchmark of 100 expert-curated
tasks spanning daily life, enterprise, and academia, each requiring extensive,
dynamic, real-time web search and synthesis. Built with over 1,500 hours of
human labor, LiveResearchBench provides a rigorous basis for systematic
evaluation. To evaluate citation-grounded long-form reports, we introduce
DeepEval, a comprehensive suite covering both content- and report-level
quality, including coverage, presentation, citation accuracy and association,
consistency and depth of analysis. DeepEval integrates four complementary
evaluation protocols, each designed to ensure stable assessment and high
agreement with human judgments. Using LiveResearchBench and DeepEval, we
conduct a comprehensive evaluation of 17 frontier deep research systems,
including single-agent web search, single-agent deep research, and multi-agent
systems. Our analysis reveals current strengths, recurring failure modes, and
key system components needed to advance reliable, insightful deep research.
Unlocking Out-of-Distribution Generalization in Transformers via
  Recursive Latent Space Reasoning
Authors: name: Awni Altabaa, name: Siyu Chen, name: John Lafferty, name: Zhuoran Yang
Published: 2025-10-15 21:03:59

Systematic, compositional generalization beyond the training distribution
remains a core challenge in machine learning -- and a critical bottleneck for
the emergent reasoning abilities of modern language models. This work
investigates out-of-distribution (OOD) generalization in Transformer networks
using a GSM8K-style modular arithmetic on computational graphs task as a
testbed. We introduce and explore a set of four architectural mechanisms aimed
at enhancing OOD generalization: (i) input-adaptive recurrence; (ii)
algorithmic supervision; (iii) anchored latent representations via a discrete
bottleneck; and (iv) an explicit error-correction mechanism. Collectively,
these mechanisms yield an architectural approach for native and scalable latent
space reasoning in Transformer networks with robust algorithmic generalization
capabilities. We complement these empirical results with a detailed mechanistic
interpretability analysis that reveals how these mechanisms give rise to robust
OOD generalization abilities.