Orionnnn/hub-stats huggingface dataset:
LangSplatV2: High-dimensional 3D Language Gaussian Splatting with 450+ FPS
Authors: name: Wanhua Li, name: Yujie Zhao, name: Minghan Qin, name: Yang Liu, name: Yuanhao Cai, name: Chuang Gan, name: Hanspeter Pfister
Published: 2025-07-09 00:19:58

In this paper, we introduce LangSplatV2, which achieves high-dimensional feature splatting at 476.2 FPS and 3D open-vocabulary text querying at 384.6 FPS for high-resolution images, providing a 42 times speedup and a 47 times boost over LangSplat respectively, along with improved query accuracy. LangSplat employs Gaussian Splatting to embed 2D CLIP language features into 3D, significantly enhancing speed and learning a precise 3D language field with SAM semantics. Such advancements in 3D language fields are crucial for applications that require language interaction within complex scenes. However, LangSplat does not yet achieve real-time inference performance (8.2 FPS), even with advanced A100 GPUs, severely limiting its broader application. In this paper, we first conduct a detailed time analysis of LangSplat, identifying the heavyweight decoder as the primary speed bottleneck. Our solution, LangSplatV2 assumes that each Gaussian acts as a sparse code within a global dictionary, leading to the learning of a 3D sparse coefficient field that entirely eliminates the need for a heavyweight decoder. By leveraging this sparsity, we further propose an efficient sparse coefficient splatting method with CUDA optimization, rendering high-dimensional feature maps at high quality while incurring only the time cost of splatting an ultra-low-dimensional feature. Our experimental results demonstrate that LangSplatV2 not only achieves better or competitive query accuracy but is also significantly faster. Codes and demos are available at our project page: https://langsplat-v2.github.io.
Machine Bullshit: Characterizing the Emergent Disregard for Truth in
  Large Language Models
Authors: name: Kaiqu Liang, name: Haimin Hu, name: Xuandong Zhao, name: Dawn Song, name: Thomas L. Griffiths, name: Jaime Fernández Fisac
Published: 2025-07-10 07:11:57

Bullshit, as conceptualized by philosopher Harry Frankfurt, refers to
statements made without regard to their truth value. While previous work has
explored large language model (LLM) hallucination and sycophancy, we propose
machine bullshit as an overarching conceptual framework that can allow
researchers to characterize the broader phenomenon of emergent loss of
truthfulness in LLMs and shed light on its underlying mechanisms. We introduce
the Bullshit Index, a novel metric quantifying LLMs' indifference to truth, and
propose a complementary taxonomy analyzing four qualitative forms of bullshit:
empty rhetoric, paltering, weasel words, and unverified claims. We conduct
empirical evaluations on the Marketplace dataset, the Political Neutrality
dataset, and our new BullshitEval benchmark (2,400 scenarios spanning 100 AI
assistants) explicitly designed to evaluate machine bullshit. Our results
demonstrate that model fine-tuning with reinforcement learning from human
feedback (RLHF) significantly exacerbates bullshit and inference-time
chain-of-thought (CoT) prompting notably amplify specific bullshit forms,
particularly empty rhetoric and paltering. We also observe prevalent machine
bullshit in political contexts, with weasel words as the dominant strategy. Our
findings highlight systematic challenges in AI alignment and provide new
insights toward more truthful LLM behavior.
Geometry Forcing: Marrying Video Diffusion and 3D Representation for
  Consistent World Modeling
Authors: name: Haoyu Wu, name: Diankun Wu, name: Tianyu He, name: Junliang Guo, name: Yang Ye, name: Yueqi Duan, name: Jiang Bian
Published: 2025-07-10 17:55:08

Videos inherently represent 2D projections of a dynamic 3D world. However,
our analysis suggests that video diffusion models trained solely on raw video
data often fail to capture meaningful geometric-aware structure in their
learned representations. To bridge this gap between video diffusion models and
the underlying 3D nature of the physical world, we propose Geometry Forcing, a
simple yet effective method that encourages video diffusion models to
internalize latent 3D representations. Our key insight is to guide the model's
intermediate representations toward geometry-aware structure by aligning them
with features from a pretrained geometric foundation model. To this end, we
introduce two complementary alignment objectives: Angular Alignment, which
enforces directional consistency via cosine similarity, and Scale Alignment,
which preserves scale-related information by regressing unnormalized geometric
features from normalized diffusion representation. We evaluate Geometry Forcing
on both camera view-conditioned and action-conditioned video generation tasks.
Experimental results demonstrate that our method substantially improves visual
quality and 3D consistency over the baseline methods. Project page:
https://GeometryForcing.github.io.
A Survey on Long-Video Storytelling Generation: Architectures,
  Consistency, and Cinematic Quality
Authors: name: Mohamed Elmoghany, name: Ryan Rossi, name: Seunghyun Yoon, name: Subhojyoti Mukherjee, name: Eslam Bakr, name: Puneet Mathur, name: Gang Wu, name: Viet Dac Lai, name: Nedim Lipka, name: Ruiyi Zhang, name: Varun Manjunatha, name: Chien Nguyen, name: Daksh Dangi, name: Abel Salinas, name: Mohammad Taesiri, name: Hongjie Chen, name: Xiaolei Huang, name: Joe Barrow, name: Nesreen Ahmed, name: Hoda Eldardiry, name: Namyong Park, name: Yu Wang, name: Jaemin Cho, name: Anh Totti Nguyen, name: Zhengzhong Tu, name: Thien Nguyen, name: Dinesh Manocha, name: Mohamed Elhoseiny, name: Franck Dernoncourt
Published: 2025-07-09 18:20:33

Despite the significant progress that has been made in video generative
models, existing state-of-the-art methods can only produce videos lasting 5-16
seconds, often labeled "long-form videos". Furthermore, videos exceeding 16
seconds struggle to maintain consistent character appearances and scene layouts
throughout the narrative. In particular, multi-subject long videos still fail
to preserve character consistency and motion coherence. While some methods can
generate videos up to 150 seconds long, they often suffer from frame redundancy
and low temporal diversity. Recent work has attempted to produce long-form
videos featuring multiple characters, narrative coherence, and high-fidelity
detail. We comprehensively studied 32 papers on video generation to identify
key architectural components and training strategies that consistently yield
these qualities. We also construct a comprehensive novel taxonomy of existing
methods and present comparative tables that categorize papers by their
architectural designs and performance characteristics.
PyVision: Agentic Vision with Dynamic Tooling
Authors: name: Shitian Zhao, name: Haoquan Zhang, name: Shaoheng Lin, name: Ming Li, name: Qilong Wu, name: Kaipeng Zhang, name: Chen Wei
Published: 2025-07-10 17:59:55

LLMs are increasingly deployed as agents, systems capable of planning,
reasoning, and dynamically calling external tools. However, in visual
reasoning, prior approaches largely remain limited by predefined workflows and
static toolsets. In this report, we present PyVision, an interactive,
multi-turn framework that enables MLLMs to autonomously generate, execute, and
refine Python-based tools tailored to the task at hand, unlocking flexible and
interpretable problem-solving. We develop a taxonomy of the tools created by
PyVision and analyze their usage across a diverse set of benchmarks.
Quantitatively, PyVision achieves consistent performance gains, boosting
GPT-4.1 by +7.8% on V* and Claude-4.0-Sonnet by +31.1% on VLMsAreBlind-mini.
These results point to a broader shift: dynamic tooling allows models not just
to use tools, but to invent them, advancing toward more agentic visual
reasoning.
Traceable Evidence Enhanced Visual Grounded Reasoning: Evaluation and
  Methodology
Authors: name: Haochen Wang, name: Xiangtai Li, name: Zilong Huang, name: Anran Wang, name: Jiacong Wang, name: Tao Zhang, name: Jiani Zheng, name: Sule Bai, name: Zijian Kang, name: Jiashi Feng, name: Zhuochen Wang, name: Zhaoxiang Zhang
Published: 2025-07-10 17:59:58

Models like OpenAI-o3 pioneer visual grounded reasoning by dynamically
referencing visual regions, just like human "thinking with images". However, no
benchmark exists to evaluate these capabilities holistically. To bridge this
gap, we propose TreeBench (Traceable Evidence Evaluation Benchmark), a
diagnostic benchmark built on three principles: (1) focused visual perception
of subtle targets in complex scenes, (2) traceable evidence via bounding box
evaluation, and (3) second-order reasoning to test object interactions and
spatial hierarchies beyond simple object localization. Prioritizing images with
dense objects, we initially sample 1K high-quality images from SA-1B, and
incorporate eight LMM experts to manually annotate questions, candidate
options, and answers for each image. After three stages of quality control,
TreeBench consists of 405 challenging visual question-answering pairs, even the
most advanced models struggle with this benchmark, where none of them reach 60%
accuracy, e.g., OpenAI-o3 scores only 54.87. Furthermore, we introduce TreeVGR
(Traceable Evidence Enhanced Visual Grounded Reasoning), a training paradigm to
supervise localization and reasoning jointly with reinforcement learning,
enabling accurate localizations and explainable reasoning pathways. Initialized
from Qwen2.5-VL-7B, it improves V* Bench (+16.8), MME-RealWorld (+12.6), and
TreeBench (+13.4), proving traceability is key to advancing vision-grounded
reasoning. The code is available at https://github.com/Haochen-Wang409/TreeVGR.
Scaling RL to Long Videos
Authors: name: Yukang Chen, name: Wei Huang, name: Baifeng Shi, name: Qinghao Hu, name: Hanrong Ye, name: Ligeng Zhu, name: Zhijian Liu, name: Pavlo Molchanov, name: Jan Kautz, name: Xiaojuan Qi, name: Sifei Liu, name: Hongxu Yin, name: Yao Lu, name: Song Han
Published: 2025-07-10 17:47:40

We introduce a full-stack framework that scales up reasoning in
vision-language models (VLMs) to long videos, leveraging reinforcement
learning. We address the unique challenges of long video reasoning by
integrating three critical components: (1) a large-scale dataset,
LongVideo-Reason, comprising 52K long video QA pairs with high-quality
reasoning annotations across diverse domains such as sports, games, and vlogs;
(2) a two-stage training pipeline that extends VLMs with chain-of-thought
supervised fine-tuning (CoT-SFT) and reinforcement learning (RL); and (3) a
training infrastructure for long video RL, named Multi-modal Reinforcement
Sequence Parallelism (MR-SP), which incorporates sequence parallelism and a
vLLM-based engine tailored for long video, using cached video embeddings for
efficient rollout and prefilling. In experiments, LongVILA-R1-7B achieves
strong performance on long video QA benchmarks such as VideoMME. It also
outperforms Video-R1-7B and even matches Gemini-1.5-Pro across temporal
reasoning, goal and purpose reasoning, spatial reasoning, and plot reasoning on
our LongVideo-Reason-eval benchmark. Notably, our MR-SP system achieves up to
2.1x speedup on long video RL training. LongVILA-R1 demonstrates consistent
performance gains as the number of input video frames scales. LongVILA-R1 marks
a firm step towards long video reasoning in VLMs. In addition, we release our
training system for public availability that supports RL training on various
modalities (video, text, and audio), various models (VILA and Qwen series), and
even image and video generation models. On a single A100 node (8 GPUs), it
supports RL training on hour-long videos (e.g., 3,600 frames / around 256k
tokens).
Skywork-R1V3 Technical Report
Authors: name: Wei Shen, name: Jiangbo Pei, name: Yi Peng, name: Xuchen Song, name: Yang Liu, name: Jian Peng, name: Haofeng Sun, name: Yunzhuo Hao, name: Peiyu Wang, name: Yahui Zhou
Published: 2025-07-08 16:47:16

We introduce Skywork-R1V3, an advanced, open-source vision-language model
(VLM) that pioneers a new approach to visual reasoning. Its key innovation lies
in effectively transferring reasoning skills from text-only Large Language
Models (LLMs) to visual tasks. The strong performance of Skywork-R1V3 primarily
stems from our elaborate post-training RL framework, which effectively
activates and enhances the model's reasoning ability, without the need for
additional continue pre-training. Through this framework, we further uncover
the fundamental role of the connector module in achieving robust cross-modal
alignment for multimodal reasoning models. In addition, we introduce a unique
indicator of reasoning capability, the entropy of critical reasoning tokens,
which has proven highly effective for checkpoint selection during RL training.
Skywork-R1V3 achieves state-of-the-art results on MMMU, significantly improving
from 64.3% to 76.0%. This performance matches entry-level human capabilities.
Remarkably, our RL-powered post-training approach enables even the 38B
parameter model to rival top closed-source VLMs. The implementation
successfully transfers mathematical reasoning to other subject-related
reasoning tasks. We also include an analysis of curriculum learning and
reinforcement finetuning strategies, along with a broader discussion on
multimodal reasoning. Skywork-R1V3 represents a significant leap in multimodal
reasoning, showcasing RL as a powerful engine for advancing open-source VLM
capabilities.
MIRIX: Multi-Agent Memory System for LLM-Based Agents
Authors: name: Yu Wang, name: Xi Chen
Published: 2025-07-10 17:40:11

Although memory capabilities of AI agents are gaining increasing attention,
existing solutions remain fundamentally limited. Most rely on flat, narrowly
scoped memory components, constraining their ability to personalize, abstract,
and reliably recall user-specific information over time. To this end, we
introduce MIRIX, a modular, multi-agent memory system that redefines the future
of AI memory by solving the field's most critical challenge: enabling language
models to truly remember. Unlike prior approaches, MIRIX transcends text to
embrace rich visual and multimodal experiences, making memory genuinely useful
in real-world scenarios. MIRIX consists of six distinct, carefully structured
memory types: Core, Episodic, Semantic, Procedural, Resource Memory, and
Knowledge Vault, coupled with a multi-agent framework that dynamically controls
and coordinates updates and retrieval. This design enables agents to persist,
reason over, and accurately retrieve diverse, long-term user data at scale. We
validate MIRIX in two demanding settings. First, on ScreenshotVQA, a
challenging multimodal benchmark comprising nearly 20,000 high-resolution
computer screenshots per sequence, requiring deep contextual understanding and
where no existing memory systems can be applied, MIRIX achieves 35% higher
accuracy than the RAG baseline while reducing storage requirements by 99.9%.
Second, on LOCOMO, a long-form conversation benchmark with single-modal textual
input, MIRIX attains state-of-the-art performance of 85.4%, far surpassing
existing baselines. These results show that MIRIX sets a new performance
standard for memory-augmented LLM agents. To allow users to experience our
memory system, we provide a packaged application powered by MIRIX. It monitors
the screen in real time, builds a personalized memory base, and offers
intuitive visualization and secure local storage to ensure privacy.
RabakBench: Scaling Human Annotations to Construct Localized
  Multilingual Safety Benchmarks for Low-Resource Languages
Authors: name: Gabriel Chua, name: Leanne Tan, name: Ziyu Ge, name: Roy Ka-Wei Lee
Published: 2025-07-08 13:37:25

Large language models (LLMs) and their safety classifiers often perform
poorly on low-resource languages due to limited training data and evaluation
benchmarks. This paper introduces RabakBench, a new multilingual safety
benchmark localized to Singapore's unique linguistic context, covering
Singlish, Chinese, Malay, and Tamil. RabakBench is constructed through a
scalable three-stage pipeline: (i) Generate - adversarial example generation by
augmenting real Singlish web content with LLM-driven red teaming; (ii) Label -
semi-automated multi-label safety annotation using majority-voted LLM labelers
aligned with human judgments; and (iii) Translate - high-fidelity translation
preserving linguistic nuance and toxicity across languages. The final dataset
comprises over 5,000 safety-labeled examples across four languages and six
fine-grained safety categories with severity levels. Evaluations of 11 popular
open-source and closed-source guardrail classifiers reveal significant
performance degradation. RabakBench not only enables robust safety evaluation
in Southeast Asian multilingual settings but also offers a reproducible
framework for building localized safety datasets in low-resource environments.
The benchmark dataset, including the human-verified translations, and
evaluation code are publicly available.
FlexOlmo: Open Language Models for Flexible Data Use
Authors: name: Weijia Shi, name: Akshita Bhagia, name: Kevin Farhat, name: Niklas Muennighoff, name: Pete Walsh, name: Jacob Morrison, name: Dustin Schwenk, name: Shayne Longpre, name: Jake Poznanski, name: Allyson Ettinger, name: Daogao Liu, name: Margaret Li, name: Dirk Groeneveld, name: Mike Lewis, name: Wen-tau Yih, name: Luca Soldaini, name: Kyle Lo, name: Noah A. Smith, name: Luke Zettlemoyer, name: Pang Wei Koh, name: Hannaneh Hajishirzi, name: Ali Farhadi, name: Sewon Min
Published: 2025-07-09 16:54:21

We introduce FlexOlmo, a new class of language models (LMs) that supports (1)
distributed training without data sharing, where different model parameters are
independently trained on closed datasets, and (2) data-flexible inference,
where these parameters along with their associated data can be flexibly
included or excluded from model inferences with no further training. FlexOlmo
employs a mixture-of-experts (MoE) architecture where each expert is trained
independently on closed datasets and later integrated through a new
domain-informed routing without any joint training. FlexOlmo is trained on
FlexMix, a corpus we curate comprising publicly available datasets alongside
seven domain-specific sets, representing realistic approximations of closed
sets. We evaluate models with up to 37 billion parameters (20 billion active)
on 31 diverse downstream tasks. We show that a general expert trained on public
data can be effectively combined with independently trained experts from other
data owners, leading to an average 41% relative improvement while allowing
users to opt out of certain data based on data licensing or permission
requirements. Our approach also outperforms prior model merging methods by
10.1% on average and surpasses the standard MoE trained without data
restrictions using the same training FLOPs. Altogether, this research presents
a solution for both data owners and researchers in regulated industries with
sensitive or protected data. FlexOlmo enables benefiting from closed data while
respecting data owners' preferences by keeping their data local and supporting
fine-grained control of data access during inference.
Decoder-Hybrid-Decoder Architecture for Efficient Reasoning with Long
  Generation
Authors: name: Liliang Ren, name: Congcong Chen, name: Haoran Xu, name: Young Jin Kim, name: Adam Atkinson, name: Zheng Zhan, name: Jiankai Sun, name: Baolin Peng, name: Liyuan Liu, name: Shuohang Wang, name: Hao Cheng, name: Jianfeng Gao, name: Weizhu Chen, name: Yelong Shen
Published: 2025-07-09 07:27:00

Recent advances in language modeling have demonstrated the effectiveness of
State Space Models (SSMs) for efficient sequence modeling. While hybrid
architectures such as Samba and the decoder-decoder architecture, YOCO, have
shown promising performance gains over Transformers, prior works have not
investigated the efficiency potential of representation sharing between SSM
layers. In this paper, we introduce the Gated Memory Unit (GMU), a simple yet
effective mechanism for efficient memory sharing across layers. We apply it to
create SambaY, a decoder-hybrid-decoder architecture that incorporates GMUs in
the cross-decoder to share memory readout states from a Samba-based
self-decoder. SambaY significantly enhances decoding efficiency, preserves
linear pre-filling time complexity, and boosts long-context performance, all
while eliminating the need for explicit positional encoding. Through extensive
scaling experiments, we demonstrate that our model exhibits a significantly
lower irreducible loss compared to a strong YOCO baseline, indicating superior
performance scalability under large-scale compute regimes. Our largest model
enhanced with Differential Attention, Phi4-mini-Flash-Reasoning, achieves
significantly better performance than Phi4-mini-Reasoning on reasoning tasks
such as Math500, AIME24/25, and GPQA Diamond without any reinforcement
learning, while delivering up to 10x higher decoding throughput on 2K-length
prompts with 32K generation length under the vLLM inference framework. We
release our training codebase on open-source data at
https://github.com/microsoft/ArchScale.
PERK: Long-Context Reasoning as Parameter-Efficient Test-Time Learning
Authors: name: Zeming Chen, name: Angelika Romanou, name: Gail Weiss, name: Antoine Bosselut
Published: 2025-07-08 21:38:45

Long-context reasoning requires accurately identifying relevant information
in extensive, noisy input contexts. Previous research shows that using
test-time learning to encode context directly into model parameters can
effectively enable reasoning over noisy information. However, meta-learning
methods for enabling test-time learning are prohibitively memory-intensive,
preventing their application to long context settings. In this work, we propose
PERK (Parameter Efficient Reasoning over Knowledge), a scalable approach for
learning to encode long input contexts using gradient updates to a lightweight
model adapter at test time. Specifically, PERK employs two nested optimization
loops in a meta-training phase. The inner loop rapidly encodes contexts into a
low-rank adapter (LoRA) that serves as a parameter-efficient memory module for
the base model. Concurrently, the outer loop learns to use the updated adapter
to accurately recall and reason over relevant information from the encoded long
context. Our evaluations on several long-context reasoning tasks show that PERK
significantly outperforms the standard prompt-based long-context baseline,
achieving average absolute performance gains of up to 90% for smaller models
(GPT-2) and up to 27% for our largest evaluated model, Qwen-2.5-0.5B. In
general, PERK is more robust to reasoning complexity, length extrapolation, and
the locations of relevant information in contexts. Finally, we show that while
PERK is memory-intensive during training, it scales more efficiently at
inference time than prompt-based long-context inference.