Orionnnn/hub-stats huggingface dataset:
One Patch to Caption Them All: A Unified Zero-Shot Captioning Framework
Authors: name: Lorenzo Bianchi, name: Giacomo Pacini, name: Fabio Carrara, name: Nicola Messina, name: Giuseppe Amato, name: Fabrizio Falchi
Published: 2025-10-03 11:05:56

Zero-shot captioners are recently proposed models that utilize common-space
vision-language representations to caption images without relying on paired
image-text data. To caption an image, they proceed by textually decoding a
text-aligned image feature, but they limit their scope to global
representations and whole-image captions. We present , a
unified framework for zero-shot captioning that shifts from an image-centric to
a patch-centric paradigm, enabling the captioning of arbitrary regions without
the need of region-level supervision. Instead of relying on global image
representations, we treat individual patches as atomic captioning units and
aggregate them to describe arbitrary regions, from single patches to
non-contiguous areas and entire images. We analyze the key ingredients that
enable current latent captioners to work in our novel proposed framework.
Experiments demonstrate that backbones producing meaningful, dense visual
features, such as DINO, are key to achieving state-of-the-art performance in
multiple region-based captioning tasks. Compared to other baselines and
state-of-the-art competitors, our models achieve better performance on
zero-shot dense, region-set, and a newly introduced trace captioning task,
highlighting the effectiveness of patch-wise semantic representations for
scalable caption generation. Project page at https://paciosoft.com/Patch-ioner/ .
Better Together: Leveraging Unpaired Multimodal Data for Stronger
  Unimodal Models
Authors: name: Sharut Gupta, name: Shobhita Sundaram, name: Chenyu Wang, name: Stefanie Jegelka, name: Phillip Isola
Published: 2025-10-09 17:32:23

Traditional multimodal learners find unified representations for tasks like
visual question answering, but rely heavily on paired datasets. However, an
overlooked yet potentially powerful question is: can one leverage auxiliary
unpaired multimodal data to directly enhance representation learning in a
target modality? We introduce UML: Unpaired Multimodal Learner, a
modality-agnostic training paradigm in which a single model alternately
processes inputs from different modalities while sharing parameters across
them. This design exploits the assumption that different modalities are
projections of a shared underlying reality, allowing the model to benefit from
cross-modal structure without requiring explicit pairs. Theoretically, under
linear data-generating assumptions, we show that unpaired auxiliary data can
yield representations strictly more informative about the data-generating
process than unimodal training. Empirically, we show that using unpaired data
from auxiliary modalities -- such as text, audio, or images -- consistently
improves downstream performance across diverse unimodal targets such as image
and audio. Our project page: https://unpaired-multimodal.github.io/
DISCO: Diversifying Sample Condensation for Efficient Model Evaluation
Authors: name: Alexander Rubinstein, name: Benjamin Raible, name: Martin Gubri, name: Seong Joon Oh
Published: 2025-10-09 08:53:59

Evaluating modern machine learning models has become prohibitively expensive.
Benchmarks such as LMMs-Eval and HELM demand thousands of GPU hours per model.
Costly evaluation reduces inclusivity, slows the cycle of innovation, and
worsens environmental impact. The typical approach follows two steps. First,
select an anchor subset of data. Second, train a mapping from the accuracy on
this subset to the final test result. The drawback is that anchor selection
depends on clustering, which can be complex and sensitive to design choices. We
argue that promoting diversity among samples is not essential; what matters is
to select samples that maximise diversity in model responses. Our
method, Diversifying Sample Condensation (DISCO), selects the top-k
samples with the greatest model disagreements. This uses greedy, sample-wise
statistics rather than global clustering. The approach is conceptually simpler.
From a theoretical view, inter-model disagreement provides an
information-theoretically optimal rule for such greedy selection.
DISCO shows empirical gains over prior methods, achieving
state-of-the-art results in performance prediction across MMLU, Hellaswag,
Winogrande, and ARC. Code is available here:
https://github.com/arubique/disco-public.
ACE: Attribution-Controlled Knowledge Editing for Multi-hop Factual
  Recall
Authors: name: Jiayu Yang, name: Yuxuan Fan, name: Songning Lai, name: Shengen Wu, name: Jiaqi Tang, name: Chun Kang, name: Zhijiang Guo, name: Yutao Yue
Published: 2025-10-09 07:46:08

Large Language Models (LLMs) require efficient knowledge editing (KE) to
update factual information, yet existing methods exhibit significant
performance decay in multi-hop factual recall. This failure is particularly
acute when edits involve intermediate implicit subjects within reasoning
chains. Through causal analysis, we reveal that this limitation stems from an
oversight of how chained knowledge is dynamically represented and utilized at
the neuron level. We discover that during multi hop reasoning, implicit
subjects function as query neurons, which sequentially activate corresponding
value neurons across transformer layers to accumulate information toward the
final answer, a dynamic prior KE work has overlooked. Guided by this insight,
we propose ACE: Attribution-Controlled Knowledge Editing for Multi-hop Factual
Recall, a framework that leverages neuron-level attribution to identify and
edit these critical query-value (Q-V) pathways. ACE provides a mechanistically
grounded solution for multi-hop KE, empirically outperforming state-of-the-art
methods by 9.44% on GPT-J and 37.46% on Qwen3-8B. Our analysis further reveals
more fine-grained activation patterns in Qwen3 and demonstrates that the
semantic interpretability of value neurons is orchestrated by query-driven
accumulation. These findings establish a new pathway for advancing KE
capabilities based on the principled understanding of internal reasoning
mechanisms.
Speculative Jacobi-Denoising Decoding for Accelerating Autoregressive
  Text-to-image Generation
Authors: name: Yao Teng, name: Fuyun Wang, name: Xian Liu, name: Zhekai Chen, name: Han Shi, name: Yu Wang, name: Zhenguo Li, name: Weiyang Liu, name: Difan Zou, name: Xihui Liu
Published: 2025-10-10 04:30:45

As a new paradigm of visual content generation, autoregressive text-to-image
models suffer from slow inference due to their sequential token-by-token
decoding process, often requiring thousands of model forward passes to generate
a single image. To address this inefficiency, we propose Speculative
Jacobi-Denoising Decoding (SJD2), a framework that incorporates the denoising
process into Jacobi iterations to enable parallel token generation in
autoregressive models. Our method introduces a next-clean-token prediction
paradigm that enables the pre-trained autoregressive models to accept
noise-perturbed token embeddings and predict the next clean tokens through
low-cost fine-tuning. This denoising paradigm guides the model towards more
stable Jacobi trajectories. During inference, our method initializes token
sequences with Gaussian noise and performs iterative
next-clean-token-prediction in the embedding space. We employ a probabilistic
criterion to verify and accept multiple tokens in parallel, and refine the
unaccepted tokens for the next iteration with the denoising trajectory.
Experiments show that our method can accelerate generation by reducing model
forward passes while maintaining the visual quality of generated images.
AutoPR: Let's Automate Your Academic Promotion!
Authors: name: Qiguang Chen, name: Zheng Yan, name: Mingda Yang, name: Libo Qin, name: Yixin Yuan, name: Hanjing Li, name: Jinhao Liu, name: Yiyan Ji, name: Dengyun Peng, name: Jiannan Guan, name: Mengkang Hu, name: Yantao Du, name: Wanxiang Che
Published: 2025-10-10 17:08:36

As the volume of peer-reviewed research surges, scholars increasingly rely on
social platforms for discovery, while authors invest considerable effort in
promoting their work to ensure visibility and citations. To streamline this
process and reduce the reliance on human effort, we introduce Automatic
Promotion (AutoPR), a novel task that transforms research papers into accurate,
engaging, and timely public content. To enable rigorous evaluation, we release
PRBench, a multimodal benchmark that links 512 peer-reviewed articles to
high-quality promotional posts, assessing systems along three axes: Fidelity
(accuracy and tone), Engagement (audience targeting and appeal), and Alignment
(timing and channel optimization). We also introduce PRAgent, a multi-agent
framework that automates AutoPR in three stages: content extraction with
multimodal preparation, collaborative synthesis for polished outputs, and
platform-specific adaptation to optimize norms, tone, and tagging for maximum
reach. When compared to direct LLM pipelines on PRBench, PRAgent demonstrates
substantial improvements, including a 604% increase in total watch time, a 438%
rise in likes, and at least a 2.9x boost in overall engagement. Ablation
studies show that platform modeling and targeted promotion contribute the most
to these gains. Our results position AutoPR as a tractable, measurable research
problem and provide a roadmap for scalable, impactful automated scholarly
communication.
ARES: Multimodal Adaptive Reasoning via Difficulty-Aware Token-Level
  Entropy Shaping
Authors: name: Shuang Chen, name: Yue Guo, name: Yimeng Ye, name: Shijue Huang, name: Wenbo Hu, name: Haoxi Li, name: Manyuan Zhang, name: Jiayu Chen, name: Song Guo, name: Nanyun Peng
Published: 2025-10-09 17:03:28

Recent advances in multimodal large reasoning models (MLRMs) have
substantially improved their ability to solve complex textual and visual tasks.
However, these models tend to overthink on simple problems, producing
unnecessarily lengthy reasoning traces, while under-exploring on challenging
ones, leading to missed solutions. To address this imbalance, we propose ARES,
a unified open-source framework for adaptive reasoning that dynamically
allocates exploration effort based on task difficulty. Our approach is
motivated by two key empirical findings: (i) while single-token entropy is
noisy, high window-entropy (HWE) tokens (token-level entropies averaged under a
sliding window) can reliably capture reasoning-critical moments; and (ii)
reducing HWE usage benefits easy problems, while increasing it is essential for
solving hard ones. Building on these insights, ARES introduces a two-stage
training pipeline. In the Adaptive Cold-Start stage, we curate multimodal and
textual data paired with reasoning traces of length proportional to problem
difficulty, equipping the model with initial difficulty awareness. In the
second stage, we develop Adaptive Entropy Policy Optimization (AEPO), which
uses HWE tokens as exploration triggers to decide when to explore, and a
hierarchical entropy reward with dynamic KL control to decide how much to
explore. Extensive experiments demonstrate that ARES achieves superior
performance and reasoning efficiency across diverse mathematical, logical, and
multimodal benchmarks, while closing the gap to leading commercial systems
under significantly lower inference costs.
GTAlign: Game-Theoretic Alignment of LLM Assistants for Mutual Welfare
Authors: name: Siqi Zhu, name: David Zhang, name: Pedro Cisneros-Velarde, name: Jiaxuan You
Published: 2025-10-10 00:05:14

Large Language Models (LLMs) have achieved remarkable progress in reasoning,
yet sometimes produce responses that are suboptimal for users in tasks such as
writing, information seeking, or providing practical guidance. Conventional
alignment practices typically assume that maximizing model reward also
maximizes user welfare, but this assumption frequently fails in practice:
models may over-clarify or generate overly verbose reasoning when users prefer
concise answers. Such behaviors resemble the prisoner's dilemma, where
individually rational choices lead to socially suboptimal outcomes. The
fundamental challenge is the lack of a principled decision making mechanism
that mutually benefits both the LLM and the user. We propose Game-Theoretic
Alignment (GTAlign), an alignment framework that integrates game-theoretic
decision making into both reasoning and training. During reasoning, the model
explicitly treats user-LLM interaction as a strategic game: it constructs
payoff matrices within its reasoning chain to estimate welfare for both itself
and the user, and then selects actions that are mutually beneficial. During
training, we introduce a mutual welfare reward that reinforces cooperative
responses, aligning model behavior with socially efficient outcomes. In
addition, we introduce an inference technique that leverages game-theoretic
reasoning to dynamically adapt LLM's response when pricing policies of LLM
service change. Extensive experiments demonstrate that GTAlign substantially
improves reasoning efficiency, answer quality, and mutual welfare compared to
baselines across diverse tasks. The code is available at
https://github.com/ulab-uiuc/GTAlign .
MRMR: A Realistic and Expert-Level Multidisciplinary Benchmark for
  Reasoning-Intensive Multimodal Retrieval
Authors: name: Siyue Zhang, name: Yuan Gao, name: Xiao Zhou, name: Yilun Zhao, name: Tingyu Song, name: Arman Cohan, name: Anh Tuan Luu, name: Chen Zhao
Published: 2025-10-10 16:14:56

We introduce MRMR, the first expert-level multidisciplinary multimodal
retrieval benchmark requiring intensive reasoning. MRMR contains 1,502 queries
spanning 23 domains, with positive documents carefully verified by human
experts. Compared to prior benchmarks, MRMR introduces three key advancements.
First, it challenges retrieval systems across diverse areas of expertise,
enabling fine-grained model comparison across domains. Second, queries are
reasoning-intensive, with images requiring deeper interpretation such as
diagnosing microscopic slides. We further introduce Contradiction Retrieval, a
novel task requiring models to identify conflicting concepts. Finally, queries
and documents are constructed as image-text interleaved sequences. Unlike
earlier benchmarks restricted to single images or unimodal documents, MRMR
offers a realistic setting with multi-image queries and mixed-modality corpus
documents. We conduct an extensive evaluation of 4 categories of multimodal
retrieval systems and 14 frontier models on MRMR. The text embedding model
Qwen3-Embedding with LLM-generated image captions achieves the highest
performance, highlighting substantial room for improving multimodal retrieval
models. Although latest multimodal models such as Ops-MM-Embedding perform
competitively on expert-domain queries, they fall short on reasoning-intensive
tasks. We believe that MRMR paves the way for advancing multimodal retrieval in
more realistic and challenging scenarios.
Understanding DeepResearch via Reports
Authors: name: Tianyu Fan, name: Xinyao Niu, name: Yuxiang Zheng, name: Fengji Zhang, name: Chengen Huang, name: Bei Chen, name: Junyang Lin, name: Chao Huang
Published: 2025-10-09 07:03:43

DeepResearch agents represent a transformative AI paradigm, conducting
expert-level research through sophisticated reasoning and multi-tool
integration. However, evaluating these systems remains critically challenging
due to open-ended research scenarios and existing benchmarks that focus on
isolated capabilities rather than holistic performance. Unlike traditional LLM
tasks, DeepResearch systems must synthesize diverse sources, generate insights,
and present coherent findings, which are capabilities that resist simple
verification. To address this gap, we introduce DeepResearch-ReportEval, a
comprehensive framework designed to assess DeepResearch systems through their
most representative outputs: research reports. Our approach systematically
measures three dimensions: quality, redundancy, and factuality, using an
innovative LLM-as-a-Judge methodology achieving strong expert concordance. We
contribute a standardized benchmark of 100 curated queries spanning 12
real-world categories, enabling systematic capability comparison. Our
evaluation of four leading commercial systems reveals distinct design
philosophies and performance trade-offs, establishing foundational insights as
DeepResearch evolves from information assistants toward intelligent research
partners. Source code and data are available at:
https://github.com/HKUDS/DeepResearch-Eval.
StatEval: A Comprehensive Benchmark for Large Language Models in
  Statistics
Authors: name: Yuchen Lu, name: Run Yang, name: Yichen Zhang, name: Shuguang Yu, name: Runpeng Dai, name: Ziwei Wang, name: Jiayi Xiang, name: Wenxin E, name: Siran Gao, name: Xinyao Ruan, name: Yirui Huang, name: Chenjing Xi, name: Haibo Hu, name: Yueming Fu, name: Qinglan Yu, name: Xiaobing Wei, name: Jiani Gu, name: Rui Sun, name: Jiaxuan Jia, name: Fan Zhou
Published: 2025-10-10 16:28:43

Large language models (LLMs) have demonstrated remarkable advances in
mathematical and logical reasoning, yet statistics, as a distinct and
integrative discipline, remains underexplored in benchmarking efforts. To
address this gap, we introduce StatEval, the first comprehensive
benchmark dedicated to statistics, spanning both breadth and depth across
difficulty levels. StatEval consists of 13,817 foundational problems covering
undergraduate and graduate curricula, together with 2374 research-level proof
tasks extracted from leading journals. To construct the benchmark, we design a
scalable multi-agent pipeline with human-in-the-loop validation that automates
large-scale problem extraction, rewriting, and quality control, while ensuring
academic rigor. We further propose a robust evaluation framework tailored to
both computational and proof-based tasks, enabling fine-grained assessment of
reasoning ability. Experimental results reveal that while closed-source models
such as GPT5-mini achieve below 57\% on research-level problems, with
open-source models performing significantly lower. These findings highlight the
unique challenges of statistical reasoning and the limitations of current LLMs.
We expect StatEval to serve as a rigorous benchmark for advancing statistical
intelligence in large language models. All data and code are available on our
web platform: https://stateval.github.io/.
A Goal Without a Plan Is Just a Wish: Efficient and Effective Global
  Planner Training for Long-Horizon Agent Tasks
Authors: name: Shuzheng Si, name: Haozhe Zhao, name: Kangyang Luo, name: Gang Chen, name: Fanchao Qi, name: Minjia Zhang, name: Baobao Chang, name: Maosong Sun
Published: 2025-10-07 06:10:53

Agents based on large language models (LLMs) struggle with brainless
trial-and-error and generating hallucinatory actions due to a lack of global
planning in long-horizon tasks. In this paper, we introduce a plan-and-execute
framework and propose EAGLET, an efficient and effective planner training
method to enhance the executor agent's planning abilities without human effort.
Specifically, we train a plug-and-play global planner through a two-step
process: we first synthesize high-quality plans from an advanced LLM using our
proposed homologous consensus filtering strategy, and apply fine-tuning as a
cold start. Moreover, we further improve the planner with a rule-based
reinforcement learning stage using a novel executor capability gain reward,
ensuring it can handle task instructions of varying difficulty. Experiments on
three long-horizon agent tasks show that executor agents equipped with our
planner outperform existing methods, achieving new state-of-the-art
performance. Meanwhile, EAGLET reduces training costs by 8x compared to
RL-based baselines, and it does not require manual effort or extra training
data, offering an efficient and effective solution.
Mitigating Overthinking through Reasoning Shaping
Authors: name: Feifan Song, name: Shaohang Wei, name: Bofei Gao, name: Yejie Wang, name: Wen Luo, name: Wei Li, name: Linli Yao, name: Weimin Xiong, name: Liang Chen, name: Tianyu Liu, name: Houfeng Wang
Published: 2025-10-10 16:49:03

Large reasoning models (LRMs) boosted by Reinforcement Learning from Verifier
Reward (RLVR) have shown great power in problem solving, yet they often cause
overthinking: excessive, meandering reasoning that inflates computational cost.
Prior designs of penalization in RLVR manage to reduce token consumption while
often harming model performance, which arises from the oversimplicity of
token-level supervision. In this paper, we argue that the granularity of
supervision plays a crucial role in balancing efficiency and accuracy, and
propose Group Relative Segment Penalization (GRSP), a step-level method to
regularize reasoning. Since preliminary analyses show that reasoning segments
are strongly correlated with token consumption and model performance, we design
a length-aware weighting mechanism across segment clusters. Extensive
experiments demonstrate that GRSP achieves superior token efficiency without
heavily compromising accuracy, especially the advantages with harder problems.
Moreover, GRSP stabilizes RL training and scales effectively across model
sizes.
Webscale-RL: Automated Data Pipeline for Scaling RL Data to Pretraining
  Levels
Authors: name: Zhepeng Cen, name: Haolin Chen, name: Shiyu Wang, name: Zuxin Liu, name: Zhiwei Liu, name: Ding Zhao, name: Silvio Savarese, name: Caiming Xiong, name: Huan Wang, name: Weiran Yao
Published: 2025-10-07 22:30:59

Large Language Models (LLMs) have achieved remarkable success through
imitation learning on vast text corpora, but this paradigm creates a
training-generation gap and limits robust reasoning. Reinforcement learning
(RL) offers a more data-efficient solution capable of bridging this gap, yet
its application has been constrained by a critical data bottleneck: existing RL
datasets are orders of magnitude smaller and less diverse than web-scale
pre-training corpora. To address this, we introduce the Webscale-RL pipeline, a
scalable data engine that systematically converts large-scale pre-training
documents into millions of diverse, verifiable question-answer pairs for RL.
Using this pipeline, we construct the Webscale-RL dataset, containing 1.2
million examples across more than 9 domains. Our experiments show that the
model trained on this dataset significantly outperforms continual pretraining
and strong data refinement baselines across a suite of benchmarks. Notably, RL
training with our dataset proves substantially more efficient, achieving the
performance of continual pre-training with up to 100times fewer tokens. Our
work presents a viable path toward scaling RL to pre-training levels, enabling
more capable and efficient language models.