Orionnnn/hub-stats huggingface dataset:
AdaSPEC: Selective Knowledge Distillation for Efficient Speculative
  Decoders
Authors: name: Yuezhou Hu, name: Jiaxin Guo, name: Xinyu Feng, name: Tuo Zhao
Published: 2025-10-22 17:13:00

Speculative Decoding (SD) accelerates large language model inference by
employing a small draft model to generate predictions, which are then verified
by a larger target model. The effectiveness of SD hinges on the alignment
between these models, which is typically enhanced by Knowledge Distillation
(KD). However, conventional KD methods aim to minimize the KL divergence
between the draft and target models across all tokens, a goal that is
misaligned with the true objective of SD, which is to maximize token acceptance
rate. Therefore, draft models often struggle to fully assimilate the target
model's knowledge due to capacity constraints, leading to suboptimal
performance. To address this challenge, we propose AdaSPEC, a novel method that
incorporates selective token filtering into the KD process. AdaSPEC utilizes a
reference model to identify and filter out difficult-to-fit tokens, enabling
the distillation of a draft model that better aligns with the target model on
simpler tokens. This approach improves the overall token acceptance rate
without compromising generation quality. We evaluate AdaSPEC across diverse
tasks, including arithmetic reasoning, instruction-following, coding, and
summarization, using model configurations of 31M/1.4B and 350M/2.7B parameters.
Our results demonstrate that AdaSPEC consistently outperforms the
state-of-the-art DistillSpec method, achieving higher acceptance rates across
all tasks (up to 15\%). The code is publicly available at
https://github.com/yuezhouhu/adaspec.
Human-Agent Collaborative Paper-to-Page Crafting for Under $0.1
Authors: name: Qianli Ma, name: Siyu Wang, name: Yilin Chen, name: Yinhao Tang, name: Yixiang Yang, name: Chang Guo, name: Bingjie Gao, name: Zhening Xing, name: Yanan Sun, name: Zhipeng Zhang
Published: 2025-10-22 13:53:57

In the quest for scientific progress, communicating research is as vital as
the discovery itself. Yet, researchers are often sidetracked by the manual,
repetitive chore of building project webpages to make their dense papers
accessible. While automation has tackled static slides and posters, the
dynamic, interactive nature of webpages has remained an unaddressed challenge.
To bridge this gap, we reframe the problem, arguing that the solution lies not
in a single command, but in a collaborative, hierarchical process. We introduce
AutoPage, a novel multi-agent system that embodies this philosophy.
AutoPage deconstructs paper-to-page creation into a coarse-to-fine pipeline
from narrative planning to multimodal content generation and interactive
rendering. To combat AI hallucination, dedicated "Checker" agents verify each
step against the source paper, while optional human checkpoints ensure the
final product aligns perfectly with the author's vision, transforming the
system from a mere tool into a powerful collaborative assistant. To rigorously
validate our approach, we also construct PageBench, the first
benchmark for this new task. Experiments show AutoPage not only generates
high-quality, visually appealing pages but does so with remarkable efficiency
in under 15 minutes for less than \0.1. Code and dataset will be released at
https://mqleet.github.io/AutoPage_ProjectPage/{Webpage}$.
Conan: Progressive Learning to Reason Like a Detective over Multi-Scale
  Visual Evidence
Authors: name: Kun Ouyang, name: Yuanxin Liu, name: Linli Yao, name: Yishuo Cai, name: Hao Zhou, name: Jie Zhou, name: Fandong Meng, name: Xu Sun
Published: 2025-10-23 12:11:46

Video reasoning, which requires multi-step deduction across frames, remains a
major challenge for multimodal large language models (MLLMs). While
reinforcement learning (RL)-based methods enhance reasoning capabilities, they
often rely on text-only chains that yield ungrounded or hallucinated
conclusions. Conversely, frame-retrieval approaches introduce visual grounding
but still struggle with inaccurate evidence localization. To address these
challenges, we present Conan, a framework for evidence-grounded multi-step
video reasoning. Conan identifies contextual and evidence frames, reasons over
cross-frame clues, and adaptively decides when to conclude or explore further.
To achieve this, we (1) construct Conan-91K, a large-scale dataset of
automatically generated reasoning traces that includes frame identification,
evidence reasoning, and action decision, and (2) design a multi-stage
progressive cold-start strategy combined with an
Identification-Reasoning-Action (AIR) RLVR training framework to jointly
enhance multi-step visual reasoning. Extensive experiments on six multi-step
reasoning benchmarks demonstrate that Conan surpasses the baseline
Qwen2.5-VL-7B-Instruct by an average of over 10% in accuracy, achieving
state-of-the-art performance. Furthermore, Conan generalizes effectively to
long-video understanding tasks, validating its strong scalability and
robustness.
ARGenSeg: Image Segmentation with Autoregressive Image Generation Model
Authors: name: Xiaolong Wang, name: Lixiang Ru, name: Ziyuan Huang, name: Kaixiang Ji, name: Dandan Zheng, name: Jingdong Chen, name: Jun Zhou
Published: 2025-10-23 17:58:26

We propose a novel AutoRegressive Generation-based paradigm for image
Segmentation (ARGenSeg), achieving multimodal understanding and pixel-level
perception within a unified framework. Prior works integrating image
segmentation into multimodal large language models (MLLMs) typically employ
either boundary points representation or dedicated segmentation heads. These
methods rely on discrete representations or semantic prompts fed into
task-specific decoders, which limits the ability of the MLLM to capture
fine-grained visual details. To address these challenges, we introduce a
segmentation framework for MLLM based on image generation, which naturally
produces dense masks for target objects. We leverage MLLM to output visual
tokens and detokenize them into images using an universal VQ-VAE, making the
segmentation fully dependent on the pixel-level understanding of the MLLM. To
reduce inference latency, we employ a next-scale-prediction strategy to
generate required visual tokens in parallel. Extensive experiments demonstrate
that our method surpasses prior state-of-the-art approaches on multiple
segmentation datasets with a remarkable boost in inference speed, while
maintaining strong understanding capabilities.
ImpossibleBench: Measuring LLMs' Propensity of Exploiting Test Cases
Authors: name: Ziqian Zhong, name: Aditi Raghunathan, name: Nicholas Carlini
Published: 2025-10-23 06:58:32

The tendency to find and exploit "shortcuts" to complete tasks poses
significant risks for reliable assessment and deployment of large language
models (LLMs). For example, an LLM agent with access to unit tests may delete
failing tests rather than fix the underlying bug. Such behavior undermines both
the validity of benchmark results and the reliability of real-world LLM coding
assistant deployments.
  To quantify, study, and mitigate such behavior, we introduce ImpossibleBench,
a benchmark framework that systematically measures LLM agents' propensity to
exploit test cases. ImpossibleBench creates "impossible" variants of tasks from
existing benchmarks like LiveCodeBench and SWE-bench by introducing direct
conflicts between the natural-language specification and the unit tests. We
measure an agent's "cheating rate" as its pass rate on these impossible tasks,
where any pass necessarily implies a specification-violating shortcut.
  As a practical framework, ImpossibleBench is not just an evaluation but a
versatile tool. We demonstrate its utility for: (1) studying model behaviors,
revealing more fine-grained details of cheating behaviors from simple test
modification to complex operator overloading; (2) context engineering, showing
how prompt, test access and feedback loop affect cheating rates; and (3)
developing monitoring tools, providing a testbed with verified deceptive
solutions. We hope ImpossibleBench serves as a useful framework for building
more robust and reliable LLM systems.
  Our implementation can be found at
https://github.com/safety-research/impossiblebench.
Loopholing Discrete Diffusion: Deterministic Bypass of the Sampling Wall
Authors: name: Mingyu Jo, name: Jaesik Yoon, name: Justin Deschenaux, name: Caglar Gulcehre, name: Sungjin Ahn
Published: 2025-10-22 07:08:47

Discrete diffusion models offer a promising alternative to autoregressive
generation through parallel decoding, but they suffer from a sampling wall:
once categorical sampling occurs, rich distributional information collapses
into one-hot vectors and cannot be propagated across steps, forcing subsequent
steps to operate with limited information. To mitigate this problem, we
introduce Loopholing, a novel and simple mechanism that preserves this
information via a deterministic latent pathway, leading to Loopholing Discrete
Diffusion Models (LDDMs). Trained efficiently with a self-conditioning
strategy, LDDMs achieve substantial gains-reducing generative perplexity by up
to 61% over prior baselines, closing (and in some cases surpassing) the gap
with autoregressive models, and producing more coherent text. Applied to
reasoning tasks, LDDMs also improve performance on arithmetic benchmarks such
as Countdown and Game of 24. These results also indicate that loopholing
mitigates idle steps and oscillations, providing a scalable path toward
high-quality non-autoregressive text generation.
Open-o3 Video: Grounded Video Reasoning with Explicit Spatio-Temporal
  Evidence
Authors: name: Jiahao Meng, name: Xiangtai Li, name: Haochen Wang, name: Yue Tan, name: Tao Zhang, name: Lingdong Kong, name: Yunhai Tong, name: Anran Wang, name: Zhiyang Teng, name: Yujing Wang, name: Zhuochen Wang
Published: 2025-10-23 14:05:56

Most video reasoning models only generate textual reasoning traces without
indicating when and where key evidence appears. Recent models such as OpenAI-o3
have sparked wide interest in evidence-centered reasoning for images, yet
extending this ability to videos is more challenging, as it requires joint
temporal tracking and spatial localization across dynamic scenes. We introduce
Open-o3 Video, a non-agent framework that integrates explicit spatio-temporal
evidence into video reasoning, and carefully collect training data and design
training strategies to address the aforementioned challenges. The model
highlights key timestamps, objects, and bounding boxes alongside its answers,
allowing reasoning to be grounded in concrete visual observations. To enable
this functionality, we first curate and build two high-quality datasets,
STGR-CoT-30k for SFT and STGR-RL-36k for RL, with carefully constructed
temporal and spatial annotations, since most existing datasets offer either
temporal spans for videos or spatial boxes on images, lacking unified
spatio-temporal supervision and reasoning traces. Then, we adopt a cold-start
reinforcement learning strategy with multiple specially designed rewards that
jointly encourage answer accuracy, temporal alignment, and spatial precision.
On V-STAR benchmark, Open-o3 Video achieves state-of-the-art performance,
raising mAM by 14.4% and mLGM by 24.2% on the Qwen2.5-VL baseline. Consistent
improvements are also observed on a broad range of video understanding
benchmarks, including VideoMME, WorldSense, VideoMMMU, and TVGBench. Beyond
accuracy, the reasoning traces produced by Open-o3 Video also provide valuable
signals for test-time scaling, enabling confidence-aware verification and
improving answer reliability.
AlphaFlow: Understanding and Improving MeanFlow Models
Authors: name: Huijie Zhang, name: Aliaksandr Siarohin, name: Willi Menapace, name: Michael Vasilkovsky, name: Sergey Tulyakov, name: Qing Qu, name: Ivan Skorokhodov
Published: 2025-10-23 17:45:06

MeanFlow has recently emerged as a powerful framework for few-step generative
modeling trained from scratch, but its success is not yet fully understood. In
this work, we show that the MeanFlow objective naturally decomposes into two
parts: trajectory flow matching and trajectory consistency. Through gradient
analysis, we find that these terms are strongly negatively correlated, causing
optimization conflict and slow convergence. Motivated by these insights, we
introduce alpha-Flow, a broad family of objectives that unifies trajectory
flow matching, Shortcut Model, and MeanFlow under one formulation. By adopting
a curriculum strategy that smoothly anneals from trajectory flow matching to
MeanFlow, alpha-Flow disentangles the conflicting objectives, and achieves
better convergence. When trained from scratch on class-conditional ImageNet-1K
256x256 with vanilla DiT backbones, alpha-Flow consistently outperforms
MeanFlow across scales and settings. Our largest alpha-Flow-XL/2+ model
achieves new state-of-the-art results using vanilla DiT backbones, with FID
scores of 2.58 (1-NFE) and 2.15 (2-NFE).
Every Question Has Its Own Value: Reinforcement Learning with Explicit
  Human Values
Authors: name: Dian Yu, name: Yulai Zhao, name: Kishan Panaganti, name: Linfeng Song, name: Haitao Mi, name: Dong Yu
Published: 2025-10-23 04:15:22

We propose Reinforcement Learning with Explicit Human Values (RLEV), a method
that aligns Large Language Model (LLM) optimization directly with quantifiable
human value signals. While Reinforcement Learning with Verifiable Rewards
(RLVR) effectively trains models in objective domains using binary correctness
rewards, it overlooks that not all tasks are equally significant. RLEV extends
this framework by incorporating human-defined value signals directly into the
reward function. Using exam-style data with explicit ground-truth value labels,
RLEV consistently outperforms correctness-only baselines across multiple RL
algorithms and model scales. Crucially, RLEV policies not only improve
value-weighted accuracy but also learn a value-sensitive termination policy:
concise for low-value prompts, thorough for high-value ones. We demonstrate
this behavior stems from value-weighted gradient amplification on
end-of-sequence tokens. Ablation studies confirm the gain is causally linked to
value alignment. RLEV remains robust under noisy value signals, such as
difficulty-based labels, demonstrating that optimizing for an explicit utility
function offers a practical path to aligning LLMs with human priorities.
LayerComposer: Interactive Personalized T2I via Spatially-Aware Layered
  Canvas
Authors: name: Guocheng Gordon Qian, name: Ruihang Zhang, name: Tsai-Shien Chen, name: Yusuf Dalva, name: Anujraaj Argo Goyal, name: Willi Menapace, name: Ivan Skorokhodov, name: Meng Dong, name: Arpit Sahni, name: Daniil Ostashev, name: Ju Hu, name: Sergey Tulyakov, name: Kuan-Chieh Jackson Wang
Published: 2025-10-23 17:59:55

Despite their impressive visual fidelity, existing personalized generative
models lack interactive control over spatial composition and scale poorly to
multiple subjects. To address these limitations, we present LayerComposer, an
interactive framework for personalized, multi-subject text-to-image generation.
Our approach introduces two main contributions: (1) a layered canvas, a novel
representation in which each subject is placed on a distinct layer, enabling
occlusion-free composition; and (2) a locking mechanism that preserves selected
layers with high fidelity while allowing the remaining layers to adapt flexibly
to the surrounding context. Similar to professional image-editing software, the
proposed layered canvas allows users to place, resize, or lock input subjects
through intuitive layer manipulation. Our versatile locking mechanism requires
no architectural changes, relying instead on inherent positional embeddings
combined with a new complementary data sampling strategy. Extensive experiments
demonstrate that LayerComposer achieves superior spatial control and identity
preservation compared to the state-of-the-art methods in multi-subject
personalized image generation.
HoloCine: Holistic Generation of Cinematic Multi-Shot Long Video
  Narratives
Authors: name: Yihao Meng, name: Hao Ouyang, name: Yue Yu, name: Qiuyu Wang, name: Wen Wang, name: Ka Leong Cheng, name: Hanlin Wang, name: Yixuan Li, name: Cheng Chen, name: Yanhong Zeng, name: Yujun Shen, name: Huamin Qu
Published: 2025-10-23 17:59:59

State-of-the-art text-to-video models excel at generating isolated clips but
fall short of creating the coherent, multi-shot narratives, which are the
essence of storytelling. We bridge this "narrative gap" with HoloCine, a model
that generates entire scenes holistically to ensure global consistency from the
first shot to the last. Our architecture achieves precise directorial control
through a Window Cross-Attention mechanism that localizes text prompts to
specific shots, while a Sparse Inter-Shot Self-Attention pattern (dense within
shots but sparse between them) ensures the efficiency required for minute-scale
generation. Beyond setting a new state-of-the-art in narrative coherence,
HoloCine develops remarkable emergent abilities: a persistent memory for
characters and scenes, and an intuitive grasp of cinematic techniques. Our work
marks a pivotal shift from clip synthesis towards automated filmmaking, making
end-to-end cinematic creation a tangible future. Our code is available at:
https://holo-cine.github.io/.
DeepWideSearch: Benchmarking Depth and Width in Agentic Information
  Seeking
Authors: name: Tian Lan, name: Bin Zhu, name: Qianghuai Jia, name: Junyang Ren, name: Haijun Li, name: Longyue Wang, name: Zhao Xu, name: Weihua Luo, name: Kaifu Zhang
Published: 2025-10-23 03:28:45

Current search agents fundamentally lack the ability to simultaneously
perform deep reasoning over multi-hop retrieval and
wide-scale information collection-a critical deficiency for real-world
applications like comprehensive market analysis and business development. To
bridge this gap, we introduce DeepWideSearch, the first benchmark explicitly
designed to evaluate agents to integrate depth and width in information
seeking. In DeepWideSearch, agents must process a large volume of data, each
requiring deep reasoning over multi-hop retrieval paths. Specifically, we
propose two methods to converse established datasets, resulting in a curated
collection of 220 questions spanning 15 diverse domains. Extensive experiments
demonstrate that even state-of-the-art agents achieve only 2.39% average
success rate on DeepWideSearch, highlighting the substantial challenge of
integrating depth and width search in information-seeking tasks. Furthermore,
our error analysis reveals four failure modes: lack of reflection, overreliance
on internal knowledge, insufficient retrieval, and context overflow-exposing
key limitations in current agent architectures. We publicly release
DeepWideSearch to catalyze future research on more capable and robust
information-seeking agents.
HSCodeComp: A Realistic and Expert-level Benchmark for Deep Search
  Agents in Hierarchical Rule Application
Authors: name: Yiqian Yang, name: Tian Lan, name: Qianghuai Jia, name: Li Zhu, name: Hui Jiang, name: Hang Zhu, name: Longyue Wang, name: Weihua Luo, name: Kaifu Zhang
Published: 2025-10-22 14:28:33

Effective deep search agents must not only access open-domain and
domain-specific knowledge but also apply complex rules-such as legal clauses,
medical manuals and tariff rules. These rules often feature vague boundaries
and implicit logic relationships, making precise application challenging for
agents. However, this critical capability is largely overlooked by current
agent benchmarks.
  To fill this gap, we introduce HSCodeComp, the first realistic, expert-level
e-commerce benchmark designed to evaluate deep search agents in hierarchical
rule application. In this task, the deep reasoning process of agents is guided
by these rules to predict 10-digit Harmonized System Code (HSCode) of products
with noisy but realistic descriptions. These codes, established by the World
Customs Organization, are vital for global supply chain efficiency. Built from
real-world data collected from large-scale e-commerce platforms, our proposed
HSCodeComp comprises 632 product entries spanning diverse product categories,
with these HSCodes annotated by several human experts.
  Extensive experimental results on several state-of-the-art LLMs, open-source,
and closed-source agents reveal a huge performance gap: best agent achieves
only 46.8% 10-digit accuracy, far below human experts at 95.0%. Besides,
detailed analysis demonstrates the challenges of hierarchical rule application,
and test-time scaling fails to improve performance further.
Text or Pixels? It Takes Half: On the Token Efficiency of Visual Text
  Inputs in Multimodal LLMs
Authors: name: Yanhong Li, name: Zixuan Lan, name: Jiawei Zhou
Published: 2025-10-21 04:07:20

Large language models (LLMs) and their multimodal variants can now process
visual inputs, including images of text. This raises an intriguing question:
can we compress textual inputs by feeding them as images to reduce token usage
while preserving performance? In this paper, we show that visual text
representations are a practical and surprisingly effective form of input
compression for decoder LLMs. We exploit the idea of rendering long text inputs
as a single image and provide it directly to the model. This leads to
dramatically reduced number of decoder tokens required, offering a new form of
input compression. Through experiments on two distinct benchmarks RULER
(long-context retrieval) and CNN/DailyMail (document summarization) we
demonstrate that this text-as-image method yields substantial token savings
(often nearly half) without degrading task performance.
Accelerating Vision Transformers with Adaptive Patch Sizes
Authors: name: Rohan Choudhury, name: JungEun Kim, name: Jinhyung Park, name: Eunho Yang, name: László A. Jeni, name: Kris M. Kitani
Published: 2025-10-20 20:37:11

Vision Transformers (ViTs) partition input images into uniformly sized
patches regardless of their content, resulting in long input sequence lengths
for high-resolution images. We present Adaptive Patch Transformers (APT), which
addresses this by using multiple different patch sizes within the same image.
APT reduces the total number of input tokens by allocating larger patch sizes
in more homogeneous areas and smaller patches in more complex ones. APT
achieves a drastic speedup in ViT inference and training, increasing throughput
by 40% on ViT-L and 50% on ViT-H while maintaining downstream performance, and
can be applied to a previously fine-tuned ViT, converging in as little as 1
epoch. It also significantly reduces training and inference time without loss
of performance in high-resolution dense visual tasks, achieving up to 30\%
faster training and inference in visual QA, object detection, and semantic
segmentation.