Orionnnn/hub-stats huggingface dataset:
Beyond Reasoning Gains: Mitigating General Capabilities Forgetting in
  Large Reasoning Models
Authors: name: Hoang Phan, name: Xianjun Yang, name: Kevin Yao, name: Jingyu Zhang, name: Shengjie Bi, name: Xiaocheng Tang, name: Madian Khabsa, name: Lijuan Liu, name: Deren Lei
Published: 2025-10-24 19:08:48

Reinforcement learning with verifiable rewards (RLVR) has delivered
impressive gains in mathematical and multimodal reasoning and has become a
standard post-training paradigm for contemporary language and vision-language
models. However, the RLVR recipe introduces a significant risk of capability
regression, where models forget foundational skills after prolonged training
without employing regularization strategies. We empirically confirm this
concern, observing that open-source reasoning models suffer performance
degradation on core capabilities such as perception and faithfulness. While
imposing regularization terms like KL divergence can help prevent deviation
from the base model, these terms are calculated on the current task, thus they
do not guarantee broader knowledge. Meanwhile, commonly used experience replay
across heterogeneous domains makes it nontrivial to decide how much training
focus each objective should receive. To address this, we propose RECAP-a replay
strategy with dynamic objective reweighting for general knowledge preservation.
Our reweighting mechanism adapts in an online manner using short-horizon
signals of convergence and instability, shifting the post-training focus away
from saturated objectives and toward underperforming or volatile ones. Our
method is end-to-end and readily applicable to existing RLVR pipelines without
training additional models or heavy tuning. Extensive experiments on benchmarks
based on Qwen2.5-VL-3B and Qwen2.5-VL-7B demonstrate the effectiveness of our
method, which not only preserves general capabilities but also improves
reasoning by enabling more flexible trade-offs among in-task rewards.
Latent Chain-of-Thought for Visual Reasoning
Authors: name: Guohao Sun, name: Hang Hua, name: Jian Wang, name: Jiebo Luo, name: Sohail Dianat, name: Majid Rabbani, name: Raghuveer Rao, name: Zhiqiang Tao
Published: 2025-10-27 23:10:06

Chain-of-thought (CoT) reasoning is critical for improving the
interpretability and reliability of Large Vision-Language Models (LVLMs).
However, existing training algorithms such as SFT, PPO, and GRPO may not
generalize well across unseen reasoning tasks and heavily rely on a biased
reward model. To address this challenge, we reformulate reasoning in LVLMs as
posterior inference and propose a scalable training algorithm based on
amortized variational inference. By leveraging diversity-seeking reinforcement
learning algorithms, we introduce a novel sparse reward function for
token-level learning signals that encourage diverse, high-likelihood latent
CoT, overcoming deterministic sampling limitations and avoiding reward hacking.
Additionally, we implement a Bayesian inference-scaling strategy that replaces
costly Best-of-N and Beam Search with a marginal likelihood to efficiently rank
optimal rationales and answers. We empirically demonstrate that the proposed
method enhances the state-of-the-art LVLMs on seven reasoning benchmarks, in
terms of effectiveness, generalization, and interpretability.
MMPersuade: A Dataset and Evaluation Framework for Multimodal Persuasion
Authors: name: Haoyi Qiu, name: Yilun Zhou, name: Pranav Narayanan Venkit, name: Kung-Hsiang Huang, name: Jiaxin Zhang, name: Nanyun Peng, name: Chien-Sheng Wu
Published: 2025-10-26 17:39:21

As Large Vision-Language Models (LVLMs) are increasingly deployed in domains
such as shopping, health, and news, they are exposed to pervasive persuasive
content. A critical question is how these models function as persuadees-how and
why they can be influenced by persuasive multimodal inputs. Understanding both
their susceptibility to persuasion and the effectiveness of different
persuasive strategies is crucial, as overly persuadable models may adopt
misleading beliefs, override user preferences, or generate unethical or unsafe
outputs when exposed to manipulative messages. We introduce MMPersuade, a
unified framework for systematically studying multimodal persuasion dynamics in
LVLMs. MMPersuade contributes (i) a comprehensive multimodal dataset that pairs
images and videos with established persuasion principles across commercial,
subjective and behavioral, and adversarial contexts, and (ii) an evaluation
framework that quantifies both persuasion effectiveness and model
susceptibility via third-party agreement scoring and self-estimated token
probabilities on conversation histories. Our study of six leading LVLMs as
persuadees yields three key insights: (i) multimodal inputs substantially
increase persuasion effectiveness-and model susceptibility-compared to text
alone, especially in misinformation scenarios; (ii) stated prior preferences
decrease susceptibility, yet multimodal information maintains its persuasive
advantage; and (iii) different strategies vary in effectiveness across
contexts, with reciprocity being most potent in commercial and subjective
contexts, and credibility and logic prevailing in adversarial contexts. By
jointly analyzing persuasion effectiveness and susceptibility, MMPersuade
provides a principled foundation for developing models that are robust,
preference-consistent, and ethically aligned when engaging with persuasive
multimodal content.
GRPO-Guard: Mitigating Implicit Over-Optimization in Flow Matching via
  Regulated Clipping
Authors: name: Jing Wang, name: Jiajun Liang, name: Jie Liu, name: Henglin Liu, name: Gongye Liu, name: Jun Zheng, name: Wanyuan Pang, name: Ao Ma, name: Zhenyu Xie, name: Xintao Wang, name: Meng Wang, name: Pengfei Wan, name: Xiaodan Liang
Published: 2025-10-25 14:51:17

Recently, GRPO-based reinforcement learning has shown remarkable progress in
optimizing flow-matching models, effectively improving their alignment with
task-specific rewards. Within these frameworks, the policy update relies on
importance-ratio clipping to constrain overconfident positive and negative
gradients. However, in practice, we observe a systematic shift in the
importance-ratio distribution-its mean falls below 1 and its variance differs
substantially across timesteps. This left-shifted and inconsistent distribution
prevents positive-advantage samples from entering the clipped region, causing
the mechanism to fail in constraining overconfident positive updates. As a
result, the policy model inevitably enters an implicit over-optimization
stage-while the proxy reward continues to increase, essential metrics such as
image quality and text-prompt alignment deteriorate sharply, ultimately making
the learned policy impractical for real-world use. To address this issue, we
introduce GRPO-Guard, a simple yet effective enhancement to existing GRPO
frameworks. Our method incorporates ratio normalization, which restores a
balanced and step-consistent importance ratio, ensuring that PPO clipping
properly constrains harmful updates across denoising timesteps. In addition, a
gradient reweighting strategy equalizes policy gradients over noise conditions,
preventing excessive updates from particular timestep regions. Together, these
designs act as a regulated clipping mechanism, stabilizing optimization and
substantially mitigating implicit over-optimization without relying on heavy KL
regularization. Extensive experiments on multiple diffusion backbones (e.g.,
SD3.5M, Flux.1-dev) and diverse proxy tasks demonstrate that GRPO-Guard
significantly reduces over-optimization while maintaining or even improving
generation quality.
SAO-Instruct: Free-form Audio Editing using Natural Language
  Instructions
Authors: name: Michael Ungersböck, name: Florian Grötschla, name: Luca A. Lanzendörfer, name: June Young Yi, name: Changho Choi, name: Roger Wattenhofer
Published: 2025-10-26 18:57:16

Generative models have made significant progress in synthesizing
high-fidelity audio from short textual descriptions. However, editing existing
audio using natural language has remained largely underexplored. Current
approaches either require the complete description of the edited audio or are
constrained to predefined edit instructions that lack flexibility. In this
work, we introduce SAO-Instruct, a model based on Stable Audio Open capable of
editing audio clips using any free-form natural language instruction. To train
our model, we create a dataset of audio editing triplets (input audio, edit
instruction, output audio) using Prompt-to-Prompt, DDPM inversion, and a manual
editing pipeline. Although partially trained on synthetic data, our model
generalizes well to real in-the-wild audio clips and unseen edit instructions.
We demonstrate that SAO-Instruct achieves competitive performance on objective
metrics and outperforms other audio editing approaches in a subjective
listening study. To encourage future research, we release our code and model
weights.
SPICE: Self-Play In Corpus Environments Improves Reasoning
Authors: name: Bo Liu, name: Chuanyang Jin, name: Seungone Kim, name: Weizhe Yuan, name: Wenting Zhao, name: Ilia Kulikov, name: Xian Li, name: Sainbayar Sukhbaatar, name: Jack Lanchantin, name: Jason Weston
Published: 2025-10-28 17:46:16

Self-improving systems require environmental interaction for continuous
adaptation. We introduce SPICE (Self-Play In Corpus Environments), a
reinforcement learning framework where a single model acts in two roles: a
Challenger that mines documents from a large corpus to generate diverse
reasoning tasks, and a Reasoner that solves them. Through adversarial dynamics,
the Challenger creates an automatic curriculum at the frontier of the
Reasoner's capability, while corpus grounding provides the rich,
near-inexhaustible external signal necessary for sustained improvement. Unlike
existing ungrounded self-play methods that offer more limited benefits, SPICE
achieves consistent gains across mathematical (+8.9%) and general reasoning
(+9.8%) benchmarks on multiple model families. Our analysis reveals how
document grounding is a key ingredient in SPICE to continuously generate its
own increasingly challenging goals and achieve them, enabling sustained
self-improvement.
Agent Data Protocol: Unifying Datasets for Diverse, Effective
  Fine-tuning of LLM Agents
Authors: name: Yueqi Song, name: Ketan Ramaneti, name: Zaid Sheikh, name: Ziru Chen, name: Boyu Gou, name: Tianbao Xie, name: Yiheng Xu, name: Danyang Zhang, name: Apurva Gandhi, name: Fan Yang, name: Joseph Liu, name: Tianyue Ou, name: Zhihao Yuan, name: Frank Xu, name: Shuyan Zhou, name: Xingyao Wang, name: Xiang Yue, name: Tao Yu, name: Huan Sun, name: Yu Su, name: Graham Neubig
Published: 2025-10-28 17:53:13

Public research results on large-scale supervised finetuning of AI agents
remain relatively rare, since the collection of agent training data presents
unique challenges. In this work, we argue that the bottleneck is not a lack of
underlying data sources, but that a large variety of data is fragmented across
heterogeneous formats, tools, and interfaces. To this end, we introduce the
agent data protocol (ADP), a light-weight representation language that serves
as an "interlingua" between agent datasets in diverse formats and unified agent
training pipelines downstream. The design of ADP is expressive enough to
capture a large variety of tasks, including API/tool use, browsing, coding,
software engineering, and general agentic workflows, while remaining simple to
parse and train on without engineering at a per-dataset level. In experiments,
we unified a broad collection of 13 existing agent training datasets into ADP
format, and converted the standardized ADP data into training-ready formats for
multiple agent frameworks. We performed SFT on these data, and demonstrated an
average performance gain of ~20% over corresponding base models, and delivers
state-of-the-art or near-SOTA performance on standard coding, browsing, tool
use, and research benchmarks, without domain-specific tuning. All code and data
are released publicly, in the hope that ADP could help lower the barrier to
standardized, scalable, and reproducible agent training.
Optimize Any Topology: A Foundation Model for Shape- and Resolution-Free
  Structural Topology Optimization
Authors: name: Amin Heyrani Nobari, name: Lyle Regenwetter, name: Cyril Picard, name: Ligong Han, name: Faez Ahmed
Published: 2025-10-26 15:11:54

Structural topology optimization (TO) is central to engineering design but
remains computationally intensive due to complex physics and hard constraints.
Existing deep-learning methods are limited to fixed square grids, a few
hand-coded boundary conditions, and post-hoc optimization, preventing general
deployment. We introduce Optimize Any Topology (OAT), a foundation-model
framework that directly predicts minimum-compliance layouts for arbitrary
aspect ratios, resolutions, volume fractions, loads, and fixtures. OAT combines
a resolution- and shape-agnostic autoencoder with an implicit neural-field
decoder and a conditional latent-diffusion model trained on OpenTO, a new
corpus of 2.2 million optimized structures covering 2 million unique
boundary-condition configurations. On four public benchmarks and two
challenging unseen tests, OAT lowers mean compliance up to 90% relative to the
best prior models and delivers sub-1 second inference on a single GPU across
resolutions from 64 x 64 to 256 x 256 and aspect ratios as high as 10:1. These
results establish OAT as a general, fast, and resolution-free framework for
physics-aware topology optimization and provide a large-scale dataset to spur
further research in generative modeling for inverse design. Code & data can be
found at https://github.com/ahnobari/OptimizeAnyTopology.
UltraHR-100K: Enhancing UHR Image Synthesis with A Large-Scale
  High-Quality Dataset
Authors: name: Chen Zhao, name: En Ci, name: Yunzhe Xu, name: Tiehan Fan, name: Shanyan Guan, name: Yanhao Ge, name: Jian Yang, name: Ying Tai
Published: 2025-10-23 15:34:53

Ultra-high-resolution (UHR) text-to-image (T2I) generation has seen notable
progress. However, two key challenges remain : 1) the absence of a large-scale
high-quality UHR T2I dataset, and (2) the neglect of tailored training
strategies for fine-grained detail synthesis in UHR scenarios. To tackle the
first challenge, we introduce UltraHR-100K, a high-quality dataset of
100K UHR images with rich captions, offering diverse content and strong visual
fidelity. Each image exceeds 3K resolution and is rigorously curated based on
detail richness, content complexity, and aesthetic quality. To tackle the
second challenge, we propose a frequency-aware post-training method that
enhances fine-detail generation in T2I diffusion models. Specifically, we
design (i) Detail-Oriented Timestep Sampling (DOTS) to focus learning
on detail-critical denoising steps, and (ii) Soft-Weighting Frequency
Regularization (SWFR), which leverages Discrete Fourier Transform (DFT) to
softly constrain frequency components, encouraging high-frequency detail
preservation. Extensive experiments on our proposed UltraHR-eval4K benchmarks
demonstrate that our approach significantly improves the fine-grained detail
quality and overall fidelity of UHR image generation. The code is available at
https://github.com/NJU-PCALab/UltraHR-100k{here}.
From Spatial to Actions: Grounding Vision-Language-Action Model in
  Spatial Foundation Priors
Authors: name: Zhengshen Zhang, name: Hao Li, name: Yalun Dai, name: Zhengbang Zhu, name: Lei Zhou, name: Chenchen Liu, name: Dong Wang, name: Francis E. H. Tay, name: Sijin Chen, name: Ziwei Liu, name: Yuxiao Liu, name: Xinghang Li, name: Pan Zhou
Published: 2025-10-20 11:26:45

Existing vision-language-action (VLA) models act in 3D real-world but are
typically built on 2D encoders, leaving a spatial reasoning gap that limits
generalization and adaptability. Recent 3D integration techniques for VLAs
either require specialized sensors and transfer poorly across modalities, or
inject weak cues that lack geometry and degrade vision-language alignment. In
this work, we introduce FALCON (From Spatial to Action), a novel paradigm that
injects rich 3D spatial tokens into the action head. FALCON leverages spatial
foundation models to deliver strong geometric priors from RGB alone, and
includes an Embodied Spatial Model that can optionally fuse depth, or pose for
higher fidelity when available, without retraining or architectural changes. To
preserve language reasoning, spatial tokens are consumed by a Spatial-Enhanced
Action Head rather than being concatenated into the vision-language backbone.
These designs enable FALCON to address limitations in spatial representation,
modality transferability, and alignment. In comprehensive evaluations across
three simulation benchmarks and eleven real-world tasks, our proposed FALCON
achieves state-of-the-art performance, consistently surpasses competitive
baselines, and remains robust under clutter, spatial-prompt conditioning, and
variations in object scale and height.
PatenTEB: A Comprehensive Benchmark and Model Family for Patent Text
  Embedding
Authors: name: Iliass Ayaou, name: Denis Cavallucci
Published: 2025-10-25 12:01:46

Patent text embeddings enable prior art search, technology landscaping, and
patent analysis, yet existing benchmarks inadequately capture patent-specific
challenges. We introduce PatenTEB, a comprehensive benchmark comprising 15
tasks across retrieval, classification, paraphrase, and clustering, with 2.06
million examples. PatenTEB employs domain-stratified splits, domain specific
hard negative mining, and systematic coverage of asymmetric
fragment-to-document matching scenarios absent from general embedding
benchmarks. We develop the patembed model family through multi-task training,
spanning 67M to 344M parameters with context lengths up to 4096 tokens.
External validation shows strong generalization: patembed-base achieves
state-of-the-art on MTEB BigPatentClustering.v2 (0.494 V-measure vs. 0.445
previous best), while patembed-large achieves 0.377 NDCG@100 on DAPFAM.
Systematic ablations reveal that multi-task training improves external
generalization despite minor benchmark costs, and that domain-pretrained
initialization provides consistent advantages across task families. All
resources will be made available at https://github.com/iliass-y/patenteb.
Keywords: patent retrieval, sentence embeddings, multi-task learning,
asymmetric retrieval, benchmark evaluation, contrastive learning.
VisJudge-Bench: Aesthetics and Quality Assessment of Visualizations
Authors: name: Yupeng Xie, name: Zhiyang Zhang, name: Yifan Wu, name: Sirong Lu, name: Jiayi Zhang, name: Zhaoyang Yu, name: Jinlin Wang, name: Sirui Hong, name: Bang Liu, name: Chenglin Wu, name: Yuyu Luo
Published: 2025-10-25 17:31:02

Visualization, a domain-specific yet widely used form of imagery, is an
effective way to turn complex datasets into intuitive insights, and its value
depends on whether data are faithfully represented, clearly communicated, and
aesthetically designed. However, evaluating visualization quality is
challenging: unlike natural images, it requires simultaneous judgment across
data encoding accuracy, information expressiveness, and visual aesthetics.
Although multimodal large language models (MLLMs) have shown promising
performance in aesthetic assessment of natural images, no systematic benchmark
exists for measuring their capabilities in evaluating visualizations. To
address this, we propose VisJudge-Bench, the first comprehensive benchmark for
evaluating MLLMs' performance in assessing visualization aesthetics and
quality. It contains 3,090 expert-annotated samples from real-world scenarios,
covering single visualizations, multiple visualizations, and dashboards across
32 chart types. Systematic testing on this benchmark reveals that even the most
advanced MLLMs (such as GPT-5) still exhibit significant gaps compared to human
experts in judgment, with a Mean Absolute Error (MAE) of 0.551 and a
correlation with human ratings of only 0.429. To address this issue, we propose
VisJudge, a model specifically designed for visualization aesthetics and
quality assessment. Experimental results demonstrate that VisJudge
significantly narrows the gap with human judgment, reducing the MAE to 0.442 (a
19.8% reduction) and increasing the consistency with human experts to 0.681 (a
58.7% improvement) compared to GPT-5. The benchmark is available at
https://github.com/HKUSTDial/VisJudgeBench.
PartNeXt: A Next-Generation Dataset for Fine-Grained and Hierarchical 3D
  Part Understanding
Authors: name: Penghao Wang, name: Yiyang He, name: Xin Lv, name: Yukai Zhou, name: Lan Xu, name: Jingyi Yu, name: Jiayuan Gu
Published: 2025-10-23 03:06:08

Understanding objects at the level of their constituent parts is fundamental
to advancing computer vision, graphics, and robotics. While datasets like
PartNet have driven progress in 3D part understanding, their reliance on
untextured geometries and expert-dependent annotation limits scalability and
usability. We introduce PartNeXt, a next-generation dataset addressing these
gaps with over 23,000 high-quality, textured 3D models annotated with
fine-grained, hierarchical part labels across 50 categories. We benchmark
PartNeXt on two tasks: (1) class-agnostic part segmentation, where
state-of-the-art methods (e.g., PartField, SAMPart3D) struggle with
fine-grained and leaf-level parts, and (2) 3D part-centric question answering,
a new benchmark for 3D-LLMs that reveals significant gaps in open-vocabulary
part grounding. Additionally, training Point-SAM on PartNeXt yields substantial
gains over PartNet, underscoring the dataset's superior quality and diversity.
By combining scalable annotation, texture-aware labels, and multi-task
evaluation, PartNeXt opens new avenues for research in structured 3D
understanding.
Uniform Discrete Diffusion with Metric Path for Video Generation
Authors: name: Haoge Deng, name: Ting Pan, name: Fan Zhang, name: Yang Liu, name: Zhuoyan Luo, name: Yufeng Cui, name: Wenxuan Wang, name: Chunhua Shen, name: Shiguang Shan, name: Zhaoxiang Zhang, name: Xinlong Wang
Published: 2025-10-28 17:59:57

Continuous-space video generation has advanced rapidly, while discrete
approaches lag behind due to error accumulation and long-context inconsistency.
In this work, we revisit discrete generative modeling and present Uniform
discRete diffuSion with metric pAth (URSA), a simple yet powerful framework
that bridges the gap with continuous approaches for the scalable video
generation. At its core, URSA formulates the video generation task as an
iterative global refinement of discrete spatiotemporal tokens. It integrates
two key designs: a Linearized Metric Path and a Resolution-dependent Timestep
Shifting mechanism. These designs enable URSA to scale efficiently to
high-resolution image synthesis and long-duration video generation, while
requiring significantly fewer inference steps. Additionally, we introduce an
asynchronous temporal fine-tuning strategy that unifies versatile tasks within
a single model, including interpolation and image-to-video generation.
Extensive experiments on challenging video and image generation benchmarks
demonstrate that URSA consistently outperforms existing discrete methods and
achieves performance comparable to state-of-the-art continuous diffusion
methods. Code and models are available at https://github.com/baaivision/URSA