Orionnnn/hub-stats huggingface dataset:
ReviewerToo: Should AI Join The Program Committee? A Look At The Future
  of Peer Review
Authors: name: Gaurav Sahu, name: Hugo Larochelle, name: Laurent Charlin, name: Christopher Pal
Published: 2025-10-09 23:53:19

Peer review is the cornerstone of scientific publishing, yet it suffers from
inconsistencies, reviewer subjectivity, and scalability challenges. We
introduce ReviewerToo, a modular framework for studying and deploying
AI-assisted peer review to complement human judgment with systematic and
consistent assessments. ReviewerToo supports systematic experiments with
specialized reviewer personas and structured evaluation criteria, and can be
partially or fully integrated into real conference workflows. We validate
ReviewerToo on a carefully curated dataset of 1,963 paper submissions from ICLR
2025, where our experiments with the gpt-oss-120b model achieves 81.8% accuracy
for the task of categorizing a paper as accept/reject compared to 83.9% for the
average human reviewer. Additionally, ReviewerToo-generated reviews are rated
as higher quality than the human average by an LLM judge, though still trailing
the strongest expert contributions. Our analysis highlights domains where AI
reviewers excel (e.g., fact-checking, literature coverage) and where they
struggle (e.g., assessing methodological novelty and theoretical
contributions), underscoring the continued need for human expertise. Based on
these findings, we propose guidelines for integrating AI into peer-review
pipelines, showing how AI can enhance consistency, coverage, and fairness while
leaving complex evaluative judgments to domain experts. Our work provides a
foundation for systematic, hybrid peer-review systems that scale with the
growth of scientific publishing.
TAG:Tangential Amplifying Guidance for Hallucination-Resistant Diffusion
  Sampling
Authors: name: Hyunmin Cho, name: Donghoon Ahn, name: Susung Hong, name: Jee Eun Kim, name: Seungryong Kim, name: Kyong Hwan Jin
Published: 2025-10-06 06:53:29

Recent diffusion models achieve the state-of-the-art performance in image
generation, but often suffer from semantic inconsistencies or hallucinations.
While various inference-time guidance methods can enhance generation, they
often operate indirectly by relying on external signals or architectural
modifications, which introduces additional computational overhead. In this
paper, we propose Tangential Amplifying Guidance (TAG), a more efficient and
direct guidance method that operates solely on trajectory signals without
modifying the underlying diffusion model. TAG leverages an intermediate sample
as a projection basis and amplifies the tangential components of the estimated
scores with respect to this basis to correct the sampling trajectory. We
formalize this guidance process by leveraging a first-order Taylor expansion,
which demonstrates that amplifying the tangential component steers the state
toward higher-probability regions, thereby reducing inconsistencies and
enhancing sample quality. TAG is a plug-and-play, architecture-agnostic module
that improves diffusion sampling fidelity with minimal computational addition,
offering a new perspective on diffusion guidance.
Dyna-Mind: Learning to Simulate from Experience for Better AI Agents
Authors: name: Xiao Yu, name: Baolin Peng, name: Michel Galley, name: Hao Cheng, name: Qianhui Wu, name: Janardhan Kulkarni, name: Suman Nath, name: Zhou Yu, name: Jianfeng Gao
Published: 2025-10-10 17:30:18

Reasoning models have recently shown remarkable progress in domains such as
math and coding. However, their expert-level abilities in math and coding
contrast sharply with their performance in long-horizon, interactive tasks such
as web navigation and computer/phone-use. Inspired by literature on human
cognition, we argue that current AI agents need ''vicarious trial and error'' -
the capacity to mentally simulate alternative futures before acting - in order
to enhance their understanding and performance in complex interactive
environments. We introduce Dyna-Mind, a two-stage training framework that
explicitly teaches (V)LM agents to integrate such simulation into their
reasoning. In stage 1, we introduce Reasoning with Simulations (ReSim), which
trains the agent to generate structured reasoning traces from expanded search
trees built from real experience gathered through environment interactions.
ReSim thus grounds the agent's reasoning in faithful world dynamics and equips
it with the ability to anticipate future states in its reasoning. In stage 2,
we propose Dyna-GRPO, an online reinforcement learning method to further
strengthen the agent's simulation and decision-making ability by using both
outcome rewards and intermediate states as feedback from real rollouts.
Experiments on two synthetic benchmarks (Sokoban and ALFWorld) and one
realistic benchmark (AndroidWorld) demonstrate that (1) ReSim effectively
infuses simulation ability into AI agents, and (2) Dyna-GRPO leverages outcome
and interaction-level signals to learn better policies for long-horizon,
planning-intensive tasks. Together, these results highlight the central role of
simulation in enabling AI agents to reason, plan, and act more effectively in
the ever more challenging environments.
Don't Waste Mistakes: Leveraging Negative RL-Groups via Confidence
  Reweighting
Authors: name: Yunzhen Feng, name: Parag Jain, name: Anthony Hartshorn, name: Yaqi Duan, name: Julia Kempe
Published: 2025-10-09 18:01:44

Reinforcement learning with verifiable rewards (RLVR) has become a standard
recipe for improving large language models (LLMs) on reasoning tasks, with
Group Relative Policy Optimization (GRPO) widely used in practice. Yet GRPO
wastes substantial compute on negative groups: groups in which no sampled
response is correct yield zero advantage and thus no gradient. We ask whether
negative groups can be leveraged without extra supervision. Starting from a
maximum-likelihood (MLE) objective in reward modeling, we show that the MLE
gradient is equivalent to a policy gradient for a modified value function. This
value function adds a confidence-weighted penalty on incorrect responses,
imposing larger penalties on more confident mistakes. We refer to this as
Likelihood Estimation with Negative Samples
(LENS). LENS modifies GRPO to assign non-zero, confidence-dependent
rewards to incorrect generations, making negative groups informative and
converting previously wasted samples into useful gradient updates. On the MATH
benchmark with Llama-3.1-8B and Qwen-2.5-3B, the proposed variant consistently
outperforms GRPO baseline, with significant gains on harder items. These
results demonstrate a principled and practical way to "rescue" negative groups,
improving efficiency and performance in RLVR.
Pseudo2Real: Task Arithmetic for Pseudo-Label Correction in Automatic
  Speech Recognition
Authors: name: Yi-Cheng Lin, name: Yu-Hsuan Li Liang, name: Hsuan Su, name: Tzu-Quan Lin, name: Shang-Tse Chen, name: Yun-Nung Chen, name: Hung-yi Lee
Published: 2025-10-09 10:31:47

Robust ASR under domain shift is crucial because real-world systems encounter
unseen accents and domains with limited labeled data. Although pseudo-labeling
offers a practical workaround, it often introduces systematic, accent-specific
errors that filtering fails to fix. We ask: How can we correct these recurring
biases without target ground truth? We propose a simple parameter-space
correction: in a source domain containing both real and pseudo-labeled data,
two ASR models are fine-tuned from the same initialization, one on ground-truth
labels and the other on pseudo-labels, and their weight difference forms a
correction vector that captures pseudo-label biases. When applied to a
pseudo-labeled target model, this vector enhances recognition, achieving up to
a 35% relative Word Error Rate (WER) reduction on AfriSpeech-200 across ten
African accents with the Whisper tiny model.
OpenRubrics: Towards Scalable Synthetic Rubric Generation for Reward
  Modeling and LLM Alignment
Authors: name: Tianci Liu, name: Ran Xu, name: Tony Yu, name: Ilgee Hong, name: Carl Yang, name: Tuo Zhao, name: Haoyu Wang
Published: 2025-10-09 03:31:26

Reward modeling lies at the core of reinforcement learning from human
feedback (RLHF), yet most existing reward models rely on scalar or pairwise
judgments that fail to capture the multifaceted nature of human preferences.
Recent studies have explored rubrics-as-rewards (RaR) that uses structured
natural language criteria that capture multiple dimensions of response quality.
However, producing rubrics that are both reliable and scalable remains a key
challenge. In this work, we introduce OpenRubrics, a diverse, large-scale
collection of (prompt, rubric) pairs for training rubric-generation and
rubric-based reward models. To elicit discriminative and comprehensive
evaluation signals, we introduce Contrastive Rubric Generation (CRG), which
derives both hard rules (explicit constraints) and principles (implicit
qualities) by contrasting preferred and rejected responses. We further improve
reliability by enforcing preference-label consistency via rejection sampling to
remove noisy rubrics. Across multiple reward-modeling benchmarks, our
rubric-based reward model, Rubric-RM, surpasses strong size-matched baselines
by 6.8%. These gains transfer to policy models on instruction-following and
biomedical benchmarks. Our results show that rubrics provide scalable alignment
signals that narrow the gap between costly human evaluation and automated
reward modeling, enabling a new principle-driven paradigm for LLM alignment.
Use the Online Network If You Can: Towards Fast and Stable Reinforcement
  Learning
Authors: name: Ahmed Hendawy, name: Henrik Metternich, name: Théo Vincent, name: Mahdi Kallel, name: Jan Peters, name: Carlo D'Eramo
Published: 2025-10-02 21:48:01

The use of target networks is a popular approach for estimating value
functions in deep Reinforcement Learning (RL). While effective, the target
network remains a compromise solution that preserves stability at the cost of
slowly moving targets, thus delaying learning. Conversely, using the online
network as a bootstrapped target is intuitively appealing, albeit well-known to
lead to unstable learning. In this work, we aim to obtain the best out of both
worlds by introducing a novel update rule that computes the target using the
MINimum estimate between the Target and Online network, giving rise to our
method, MINTO. Through this simple, yet effective modification, we show that
MINTO enables faster and stable value function learning, by mitigating the
potential overestimation bias of using the online network for bootstrapping.
Notably, MINTO can be seamlessly integrated into a wide range of value-based
and actor-critic algorithms with a negligible cost. We evaluate MINTO
extensively across diverse benchmarks, spanning online and offline RL, as well
as discrete and continuous action spaces. Across all benchmarks, MINTO
consistently improves performance, demonstrating its broad applicability and
effectiveness.
DreamOmni2: Multimodal Instruction-based Editing and Generation
Authors: name: Bin Xia, name: Bohao Peng, name: Yuechen Zhang, name: Junjia Huang, name: Jiyang Liu, name: Jingyao Li, name: Haoru Tan, name: Sitong Wu, name: Chengyao Wang, name: Yitong Wang, name: Xinglong Wu, name: Bei Yu, name: Jiaya Jia
Published: 2025-10-08 06:07:14

Recent advancements in instruction-based image editing and subject-driven
generation have garnered significant attention, yet both tasks still face
limitations in meeting practical user needs. Instruction-based editing relies
solely on language instructions, which often fail to capture specific editing
details, making reference images necessary. Meanwhile, subject-driven
generation is limited to combining concrete objects or people, overlooking
broader, abstract concepts. To address these challenges, we propose two novel
tasks: multimodal instruction-based editing and generation. These tasks support
both text and image instructions and extend the scope to include both concrete
and abstract concepts, greatly enhancing their practical applications. We
introduce DreamOmni2, tackling two primary challenges: data creation and model
framework design. Our data synthesis pipeline consists of three steps: (1)
using a feature mixing method to create extraction data for both abstract and
concrete concepts, (2) generating multimodal instruction-based editing training
data using the editing and extraction models, and (3) further applying the
extraction model to create training data for multimodal instruction-based
editing. For the framework, to handle multi-image input, we propose an index
encoding and position encoding shift scheme, which helps the model distinguish
images and avoid pixel confusion. Additionally, we introduce joint training
with the VLM and our generation/editing model to better process complex
instructions. In addition, we have proposed comprehensive benchmarks for these
two new tasks to drive their development. Experiments show that DreamOmni2 has
achieved impressive results. Models and codes will be released.
OmniRetarget: Interaction-Preserving Data Generation for Humanoid
  Whole-Body Loco-Manipulation and Scene Interaction
Authors: name: Lujie Yang, name: Xiaoyu Huang, name: Zhen Wu, name: Angjoo Kanazawa, name: Pieter Abbeel, name: Carmelo Sferrazza, name: C. Karen Liu, name: Rocky Duan, name: Guanya Shi
Published: 2025-09-30 17:59:02

A dominant paradigm for teaching humanoid robots complex skills is to
retarget human motions as kinematic references to train reinforcement learning
(RL) policies. However, existing retargeting pipelines often struggle with the
significant embodiment gap between humans and robots, producing physically
implausible artifacts like foot-skating and penetration. More importantly,
common retargeting methods neglect the rich human-object and human-environment
interactions essential for expressive locomotion and loco-manipulation. To
address this, we introduce OmniRetarget, an interaction-preserving data
generation engine based on an interaction mesh that explicitly models and
preserves the crucial spatial and contact relationships between an agent, the
terrain, and manipulated objects. By minimizing the Laplacian deformation
between the human and robot meshes while enforcing kinematic constraints,
OmniRetarget generates kinematically feasible trajectories. Moreover,
preserving task-relevant interactions enables efficient data augmentation, from
a single demonstration to different robot embodiments, terrains, and object
configurations. We comprehensively evaluate OmniRetarget by retargeting motions
from OMOMO, LAFAN1, and our in-house MoCap datasets, generating over 8-hour
trajectories that achieve better kinematic constraint satisfaction and contact
preservation than widely used baselines. Such high-quality data enables
proprioceptive RL policies to successfully execute long-horizon (up to 30
seconds) parkour and loco-manipulation skills on a Unitree G1 humanoid, trained
with only 5 reward terms and simple domain randomization shared by all tasks,
without any learning curriculum.
PickStyle: Video-to-Video Style Transfer with Context-Style Adapters
Authors: name: Soroush Mehraban, name: Vida Adeli, name: Jacob Rommann, name: Babak Taati, name: Kyryl Truskovskyi
Published: 2025-10-08 21:02:55

We address the task of video style transfer with diffusion models, where the
goal is to preserve the context of an input video while rendering it in a
target style specified by a text prompt. A major challenge is the lack of
paired video data for supervision. We propose PickStyle, a video-to-video style
transfer framework that augments pretrained video diffusion backbones with
style adapters and benefits from paired still image data with source-style
correspondences for training. PickStyle inserts low-rank adapters into the
self-attention layers of conditioning modules, enabling efficient
specialization for motion-style transfer while maintaining strong alignment
between video content and style. To bridge the gap between static image
supervision and dynamic video, we construct synthetic training clips from
paired images by applying shared augmentations that simulate camera motion,
ensuring temporal priors are preserved. In addition, we introduce Context-Style
Classifier-Free Guidance (CS-CFG), a novel factorization of classifier-free
guidance into independent text (style) and video (context) directions. CS-CFG
ensures that context is preserved in generated video while the style is
effectively transferred. Experiments across benchmarks show that our approach
achieves temporally coherent, style-faithful, and content-preserving video
translations, outperforming existing baselines both qualitatively and
quantitatively.
Drive&Gen: Co-Evaluating End-to-End Driving and Video Generation Models
Authors: name: Jiahao Wang, name: Zhenpei Yang, name: Yijing Bai, name: Yingwei Li, name: Yuliang Zou, name: Bo Sun, name: Abhijit Kundu, name: Jose Lezama, name: Luna Yue Huang, name: Zehao Zhu, name: Jyh-Jing Hwang, name: Dragomir Anguelov, name: Mingxing Tan, name: Chiyu Max Jiang
Published: 2025-10-07 17:58:32

Recent advances in generative models have sparked exciting new possibilities
in the field of autonomous vehicles. Specifically, video generation models are
now being explored as controllable virtual testing environments.
Simultaneously, end-to-end (E2E) driving models have emerged as a streamlined
alternative to conventional modular autonomous driving systems, gaining
popularity for their simplicity and scalability. However, the application of
these techniques to simulation and planning raises important questions. First,
while video generation models can generate increasingly realistic videos, can
these videos faithfully adhere to the specified conditions and be realistic
enough for E2E autonomous planner evaluation? Second, given that data is
crucial for understanding and controlling E2E planners, how can we gain deeper
insights into their biases and improve their ability to generalize to
out-of-distribution scenarios? In this work, we bridge the gap between the
driving models and generative world models (Drive&Gen) to address these
questions. We propose novel statistical measures leveraging E2E drivers to
evaluate the realism of generated videos. By exploiting the controllability of
the video generation model, we conduct targeted experiments to investigate
distribution gaps affecting E2E planner performance. Finally, we show that
synthetic data produced by the video generation model offers a cost-effective
alternative to real-world data collection. This synthetic data effectively
improves E2E model generalization beyond existing Operational Design Domains,
facilitating the expansion of autonomous vehicle services into new operational
contexts.
GyroSwin: 5D Surrogates for Gyrokinetic Plasma Turbulence Simulations
Authors: name: Fabian Paischer, name: Gianluca Galletti, name: William Hornsby, name: Paul Setinek, name: Lorenzo Zanisi, name: Naomi Carey, name: Stanislas Pamela, name: Johannes Brandstetter
Published: 2025-10-08 17:59:10

Nuclear fusion plays a pivotal role in the quest for reliable and sustainable
energy production. A major roadblock to viable fusion power is understanding
plasma turbulence, which significantly impairs plasma confinement, and is vital
for next-generation reactor design. Plasma turbulence is governed by the
nonlinear gyrokinetic equation, which evolves a 5D distribution function over
time. Due to its high computational cost, reduced-order models are often
employed in practice to approximate turbulent transport of energy. However,
they omit nonlinear effects unique to the full 5D dynamics. To tackle this, we
introduce GyroSwin, the first scalable 5D neural surrogate that can model 5D
nonlinear gyrokinetic simulations, thereby capturing the physical phenomena
neglected by reduced models, while providing accurate estimates of turbulent
heat transport.GyroSwin (i) extends hierarchical Vision Transformers to 5D,
(ii) introduces cross-attention and integration modules for latent
3Dleftrightarrow5D interactions between electrostatic potential fields and
the distribution function, and (iii) performs channelwise mode separation
inspired by nonlinear physics. We demonstrate that GyroSwin outperforms widely
used reduced numerics on heat flux prediction, captures the turbulent energy
cascade, and reduces the cost of fully resolved nonlinear gyrokinetics by three
orders of magnitude while remaining physically verifiable. GyroSwin shows
promising scaling laws, tested up to one billion parameters, paving the way for
scalable neural surrogates for gyrokinetic simulations of plasma turbulence.
SViM3D: Stable Video Material Diffusion for Single Image 3D Generation
Authors: name: Andreas Engelhardt, name: Mark Boss, name: Vikram Voletti, name: Chun-Han Yao, name: Hendrik P. A. Lensch, name: Varun Jampani
Published: 2025-10-09 14:29:47

We present Stable Video Materials 3D (SViM3D), a framework to predict
multi-view consistent physically based rendering (PBR) materials, given a
single image. Recently, video diffusion models have been successfully used to
reconstruct 3D objects from a single image efficiently. However, reflectance is
still represented by simple material models or needs to be estimated in
additional steps to enable relighting and controlled appearance edits. We
extend a latent video diffusion model to output spatially varying PBR
parameters and surface normals jointly with each generated view based on
explicit camera control. This unique setup allows for relighting and generating
a 3D asset using our model as neural prior. We introduce various mechanisms to
this pipeline that improve quality in this ill-posed setting. We show
state-of-the-art relighting and novel view synthesis performance on multiple
object-centric datasets. Our method generalizes to diverse inputs, enabling the
generation of relightable 3D assets useful in AR/VR, movies, games and other
visual media.
GCPO: When Contrast Fails, Go Gold
Authors: name: Hao Wu, name: Wei Liu
Published: 2025-10-09 05:09:06

Reinforcement learning has been widely applied to enhance the reasoning
capabilities of large language models. Extending the inference limits of
smaller models has become a prominent research focus. However, algorithms such
as Group Relative Policy Optimization (GRPO) suffer from a clear drawback: the
upper bound of a model's rollout responses is entirely determined by the model
itself, preventing the acquisition of knowledge from samples that are either
all incorrect or all correct. In this paper, we introduce Group Contrastive
Policy Optimization (GCPO), a method that incorporates external standard
reference answers. When the model cannot solve a problem, the reference answer
supplies the correct response, steering the model toward an unequivocally
accurate update direction. This approach offers two main advantages: (1) it
improves training efficiency by fully utilizing every sample; (2) it enables
the model to emulate the problem solving strategy of the reference answer
during training, thereby enhancing generalization in reasoning. GCPO achieves
outstanding results across multiple benchmark datasets, yielding substantial
improvements over the baseline model. Our code is available at:
https://github.com/AchoWu/GCPO.