Orionnnn/hub-stats huggingface dataset:
Adaptive Classifier-Free Guidance via Dynamic Low-Confidence Masking
Authors: name: Pengxiang Li, name: Shilin Yan, name: Joey Tsai, name: Renrui Zhang, name: Ruichuan An, name: Ziyu Guo, name: Xiaowei Gao
Published: 2025-05-26 16:40:22

Classifier-Free Guidance (CFG) significantly enhances controllability in
generative models by interpolating conditional and unconditional predictions.
However, standard CFG often employs a static unconditional input, which can be
suboptimal for iterative generation processes where model uncertainty varies
dynamically. We introduce Adaptive Classifier-Free Guidance (A-CFG), a novel
method that tailors the unconditional input by leveraging the model's
instantaneous predictive confidence. At each step of an iterative (masked)
diffusion language model, A-CFG identifies tokens in the currently generated
sequence for which the model exhibits low confidence. These tokens are
temporarily re-masked to create a dynamic, localized unconditional input. This
focuses CFG's corrective influence precisely on areas of ambiguity, leading to
more effective guidance. We integrate A-CFG into a state-of-the-art masked
diffusion language model and demonstrate its efficacy. Experiments on diverse
language generation benchmarks show that A-CFG yields substantial improvements
over standard CFG, achieving, for instance, a 3.9 point gain on GPQA. Our work
highlights the benefit of dynamically adapting guidance mechanisms to model
uncertainty in iterative generation.
Table-R1: Inference-Time Scaling for Table Reasoning
Authors: name: Zheyuan Yang, name: Lyuhao Chen, name: Arman Cohan, name: Yilun Zhao
Published: 2025-05-29 16:28:50

In this work, we present the first study to explore inference-time scaling on
table reasoning tasks. We develop and evaluate two post-training strategies to
enable inference-time scaling: distillation from frontier model reasoning
traces and reinforcement learning with verifiable rewards (RLVR). For
distillation, we introduce a large-scale dataset of reasoning traces generated
by DeepSeek-R1, which we use to fine-tune LLMs into the Table-R1-SFT model. For
RLVR, we propose task-specific verifiable reward functions and apply the GRPO
algorithm to obtain the Table-R1-Zero model. We evaluate our Table-R1-series
models across diverse table reasoning tasks, including short-form QA, fact
verification, and free-form QA. Notably, the Table-R1-Zero model matches or
exceeds the performance of GPT-4.1 and DeepSeek-R1, while using only a
7B-parameter LLM. It also demonstrates strong generalization to out-of-domain
datasets. Extensive ablation and qualitative analyses reveal the benefits of
instruction tuning, model architecture choices, and cross-task generalization,
as well as emergence of essential table reasoning skills during RL training.
VideoReasonBench: Can MLLMs Perform Vision-Centric Complex Video
  Reasoning?
Authors: name: Yuanxin Liu, name: Kun Ouyang, name: Haoning Wu, name: Yi Liu, name: Lin Sui, name: Xinhao Li, name: Yan Zhong, name: Y. Charles, name: Xinyu Zhou, name: Xu Sun
Published: 2025-05-29 11:33:43

Recent studies have shown that long chain-of-thought (CoT) reasoning can
significantly enhance the performance of large language models (LLMs) on
complex tasks. However, this benefit is yet to be demonstrated in the domain of
video understanding, since most existing benchmarks lack the reasoning depth
required to demonstrate the advantages of extended CoT chains. While recent
efforts have proposed benchmarks aimed at video reasoning, the tasks are often
knowledge-driven and do not rely heavily on visual content. To bridge this gap,
we introduce VideoReasonBench, a benchmark designed to evaluate vision-centric,
complex video reasoning. To ensure visual richness and high reasoning
complexity, each video in VideoReasonBench depicts a sequence of fine-grained
operations on a latent state that is only visible in part of the video. The
questions evaluate three escalating levels of video reasoning skills: recalling
observed visual information, inferring the content of latent states, and
predicting information beyond the video. Under such task setting, models have
to precisely recall multiple operations in the video, and perform step-by-step
reasoning to get correct final answers for these questions. Using
VideoReasonBench, we comprehensively evaluate 18 state-of-the-art multimodal
LLMs (MLLMs), finding that most perform poorly on complex video reasoning,
e.g., GPT-4o achieves only 6.9% accuracy, while the thinking-enhanced
Gemini-2.5-Pro significantly outperforms others with 56.0% accuracy. Our
investigations on "test-time scaling" further reveal that extended thinking
budget, while offering none or minimal benefits on existing video benchmarks,
is essential for improving the performance on VideoReasonBench.
MAGREF: Masked Guidance for Any-Reference Video Generation
Authors: name: Yufan Deng, name: Xun Guo, name: Yuanyang Yin, name: Jacob Zhiyuan Fang, name: Yiding Yang, name: Yizhi Wang, name: Shenghai Yuan, name: Angtian Wang, name: Bo Liu, name: Haibin Huang, name: Chongyang Ma
Published: 2025-05-29 17:58:15

Video generation has made substantial strides with the emergence of deep
generative models, especially diffusion-based approaches. However, video
generation based on multiple reference subjects still faces significant
challenges in maintaining multi-subject consistency and ensuring high
generation quality. In this paper, we propose MAGREF, a unified framework for
any-reference video generation that introduces masked guidance to enable
coherent multi-subject video synthesis conditioned on diverse reference images
and a textual prompt. Specifically, we propose (1) a region-aware dynamic
masking mechanism that enables a single model to flexibly handle various
subject inference, including humans, objects, and backgrounds, without
architectural changes, and (2) a pixel-wise channel concatenation mechanism
that operates on the channel dimension to better preserve appearance features.
Our model delivers state-of-the-art video generation quality, generalizing from
single-subject training to complex multi-subject scenarios with coherent
synthesis and precise control over individual subjects, outperforming existing
open-source and commercial baselines. To facilitate evaluation, we also
introduce a comprehensive multi-subject video benchmark. Extensive experiments
demonstrate the effectiveness of our approach, paving the way for scalable,
controllable, and high-fidelity multi-subject video synthesis. Code and model
can be found at: https://github.com/MAGREF-Video/MAGREF
Spatial-MLLM: Boosting MLLM Capabilities in Visual-based Spatial
  Intelligence
Authors: name: Diankun Wu, name: Fangfu Liu, name: Yi-Hsin Hung, name: Yueqi Duan
Published: 2025-05-29 17:59:04

Recent advancements in Multimodal Large Language Models (MLLMs) have
significantly enhanced performance on 2D visual tasks. However, improving their
spatial intelligence remains a challenge. Existing 3D MLLMs always rely on
additional 3D or 2.5D data to incorporate spatial awareness, restricting their
utility in scenarios with only 2D inputs, such as images or videos. In this
paper, we present Spatial-MLLM, a novel framework for visual-based spatial
reasoning from purely 2D observations. Unlike conventional video MLLMs which
rely on CLIP-based visual encoders optimized for semantic understanding, our
key insight is to unleash the strong structure prior from the feed-forward
visual geometry foundation model. Specifically, we propose a dual-encoder
architecture: a pretrained 2D visual encoder to extract semantic features, and
a spatial encoder-initialized from the backbone of the visual geometry model-to
extract 3D structure features. A connector then integrates both features into
unified visual tokens for enhanced spatial understanding. Furthermore, we
propose a space-aware frame sampling strategy at inference time, which selects
the spatially informative frames of a video sequence, ensuring that even under
limited token length, the model focuses on frames critical for spatial
reasoning. Beyond architecture improvements, we construct the Spatial-MLLM-120k
dataset and train the model on it using supervised fine-tuning and GRPO.
Extensive experiments on various real-world datasets demonstrate that our
spatial-MLLM achieves state-of-the-art performance in a wide range of
visual-based spatial understanding and reasoning tasks. Project page:
https://diankun-wu.github.io/Spatial-MLLM/.
On-Policy RL with Optimal Reward Baseline
Authors: name: Yaru Hao, name: Li Dong, name: Xun Wu, name: Shaohan Huang, name: Zewen Chi, name: Furu Wei
Published: 2025-05-29 15:58:04

Reinforcement learning algorithms are fundamental to align large language
models with human preferences and to enhance their reasoning capabilities.
However, current reinforcement learning algorithms often suffer from training
instability due to loose on-policy constraints and computational inefficiency
due to auxiliary models. In this work, we propose On-Policy RL with Optimal
reward baseline (OPO), a novel and simplified reinforcement learning algorithm
designed to address these challenges. OPO emphasizes the importance of exact
on-policy training, which empirically stabilizes the training process and
enhances exploration. Moreover, OPO introduces the optimal reward baseline that
theoretically minimizes gradient variance. We evaluate OPO on mathematical
reasoning benchmarks. The results demonstrate its superior performance and
training stability without additional models or regularization terms.
Furthermore, OPO achieves lower policy shifts and higher output entropy,
encouraging more diverse and less repetitive responses. These results highlight
OPO as a promising direction for stable and effective reinforcement learning in
large language model alignment and reasoning tasks. The implementation is
provided at https://github.com/microsoft/LMOps/tree/main/opo.
ATLAS: Learning to Optimally Memorize the Context at Test Time
Authors: name: Ali Behrouz, name: Zeman Li, name: Praneeth Kacham, name: Majid Daliri, name: Yuan Deng, name: Peilin Zhong, name: Meisam Razaviyayn, name: Vahab Mirrokni
Published: 2025-05-29 17:57:16

Transformers have been established as the most popular backbones in sequence
modeling, mainly due to their effectiveness in in-context retrieval tasks and
the ability to learn at scale. Their quadratic memory and time complexity,
however, bound their applicability in longer sequences and so has motivated
researchers to explore effective alternative architectures such as modern
recurrent neural networks (a.k.a long-term recurrent memory module). Despite
their recent success in diverse downstream tasks, they struggle in tasks that
requires long context understanding and extrapolation to longer sequences. We
observe that these shortcomings come from three disjoint aspects in their
design: (1) limited memory capacity that is bounded by the architecture of
memory and feature mapping of the input; (2) online nature of update, i.e.,
optimizing the memory only with respect to the last input; and (3) less
expressive management of their fixed-size memory. To enhance all these three
aspects, we present ATLAS, a long-term memory module with high capacity that
learns to memorize the context by optimizing the memory based on the current
and past tokens, overcoming the online nature of long-term memory models.
Building on this insight, we present a new family of Transformer-like
architectures, called DeepTransformers, that are strict generalizations of the
original Transformer architecture. Our experimental results on language
modeling, common-sense reasoning, recall-intensive, and long-context
understanding tasks show that ATLAS surpasses the performance of Transformers
and recent linear recurrent models. ATLAS further improves the long context
performance of Titans, achieving +80\% accuracy in 10M context length of
BABILong benchmark.
SWE-bench Goes Live!
Authors: name: Linghao Zhang, name: Shilin He, name: Chaoyun Zhang, name: Yu Kang, name: Bowen Li, name: Chengxing Xie, name: Junhao Wang, name: Maoquan Wang, name: Yufan Huang, name: Shengyu Fu, name: Elsie Nallipogu, name: Qingwei Lin, name: Yingnong Dang, name: Saravan Rajmohan, name: Dongmei Zhang
Published: 2025-05-29 13:09:44

The issue-resolving task, where a model generates patches to fix real-world
bugs, has emerged as a critical benchmark for evaluating the capabilities of
large language models (LLMs). While SWE-bench and its variants have become
standard in this domain, they suffer from key limitations: they have not been
updated since their initial releases, cover a narrow set of repositories, and
depend heavily on manual effort for instance construction and environment
setup. These factors hinder scalability and introduce risks of overfitting and
data contamination. In this work, we present SWE-bench-Live, a
live-updatable benchmark designed to overcome these challenges. Our
initial release consists of 1,319 tasks derived from real GitHub issues created
since 2024, spanning 93 repositories. Each task is accompanied by a dedicated
Docker image to ensure reproducible execution. Central to our benchmark is
\method, an automated curation pipeline that streamlines the entire process
from instance creation to environment setup, removing manual bottlenecks and
enabling scalability and continuous updates. We evaluate a range of
state-of-the-art agent frameworks and LLMs on SWE-bench-Live, revealing a
substantial performance gap compared to static benchmarks like SWE-bench, even
under controlled evaluation conditions. To better understand this discrepancy,
we perform detailed analyses across repository origin, issue recency, and task
difficulty. By providing a fresh, diverse, and executable benchmark grounded in
live repository activity, SWE-bench-Live facilitates rigorous,
contamination-resistant evaluation of LLMs and agents in dynamic, real-world
software development settings.
The Climb Carves Wisdom Deeper Than the Summit: On the Noisy Rewards in
  Learning to Reason
Authors: name: Ang Lv, name: Ruobing Xie, name: Xingwu Sun, name: Zhanhui Kang, name: Rui Yan
Published: 2025-05-28 17:59:03

Recent studies on post-training large language models (LLMs) for reasoning
through reinforcement learning (RL) typically focus on tasks that can be
accurately verified and rewarded, such as solving math problems. In contrast,
our research investigates the impact of reward noise, a more practical
consideration for real-world scenarios involving the post-training of LLMs
using reward models. We found that LLMs demonstrate strong robustness to
substantial reward noise. For example, manually flipping 40% of the reward
function's outputs in math tasks still allows a Qwen-2.5-7B model to achieve
rapid convergence, improving its performance on math tasks from 5% to 72%,
compared to the 75% accuracy achieved by a model trained with noiseless
rewards. Surprisingly, by only rewarding the appearance of key reasoning
phrases (namely reasoning pattern reward, RPR), such as ``first, I need
to''-without verifying the correctness of answers, the model achieved peak
downstream performance (over 70% accuracy for Qwen-2.5-7B) comparable to models
trained with strict correctness verification and accurate rewards. Recognizing
the importance of the reasoning process over the final results, we combined RPR
with noisy reward models. RPR helped calibrate the noisy reward models,
mitigating potential false negatives and enhancing the LLM's performance on
open-ended tasks. These findings suggest the importance of improving models'
foundational abilities during the pre-training phase while providing insights
for advancing post-training techniques. Our code and scripts are available at
https://github.com/trestad/Noisy-Rewards-in-Learning-to-Reason.
Satori-SWE: Evolutionary Test-Time Scaling for Sample-Efficient Software
  Engineering
Authors: name: Guangtao Zeng, name: Maohao Shen, name: Delin Chen, name: Zhenting Qi, name: Subhro Das, name: Dan Gutfreund, name: David Cox, name: Gregory Wornell, name: Wei Lu, name: Zhang-Wei Hong, name: Chuang Gan
Published: 2025-05-29 16:15:36

Language models (LMs) perform well on standardized coding benchmarks but
struggle with real-world software engineering tasks such as resolving GitHub
issues in SWE-Bench, especially when model parameters are less than 100B. While
smaller models are preferable in practice due to their lower computational
cost, improving their performance remains challenging. Existing approaches
primarily rely on supervised fine-tuning (SFT) with high-quality data, which is
expensive to curate at scale. An alternative is test-time scaling: generating
multiple outputs, scoring them using a verifier, and selecting the best one.
Although effective, this strategy often requires excessive sampling and costly
scoring, limiting its practical application. We propose Evolutionary Test-Time
Scaling (EvoScale), a sample-efficient method that treats generation as an
evolutionary process. By iteratively refining outputs via selection and
mutation, EvoScale shifts the output distribution toward higher-scoring
regions, reducing the number of samples needed to find correct solutions. To
reduce the overhead from repeatedly sampling and selection, we train the model
to self-evolve using reinforcement learning (RL). Rather than relying on
external verifiers at inference time, the model learns to self-improve the
scores of its own generations across iterations. Evaluated on
SWE-Bench-Verified, EvoScale enables our 32B model, Satori-SWE-32B, to match or
exceed the performance of models with over 100B parameters while using a few
samples. Code, data, and models will be fully open-sourced.
ZeroGUI: Automating Online GUI Learning at Zero Human Cost
Authors: name: Chenyu Yang, name: Shiqian Su, name: Shi Liu, name: Xuan Dong, name: Yue Yu, name: Weijie Su, name: Xuehui Wang, name: Zhaoyang Liu, name: Jinguo Zhu, name: Hao Li, name: Wenhai Wang, name: Yu Qiao, name: Xizhou Zhu, name: Jifeng Dai
Published: 2025-05-29 17:59:51

The rapid advancement of large Vision-Language Models (VLMs) has propelled
the development of pure-vision-based GUI Agents, capable of perceiving and
operating Graphical User Interfaces (GUI) to autonomously fulfill user
instructions. However, existing approaches usually adopt an offline learning
framework, which faces two core limitations: (1) heavy reliance on high-quality
manual annotations for element grounding and action supervision, and (2)
limited adaptability to dynamic and interactive environments. To address these
limitations, we propose ZeroGUI, a scalable, online learning framework for
automating GUI Agent training at Zero human cost. Specifically, ZeroGUI
integrates (i) VLM-based automatic task generation to produce diverse training
goals from the current environment state, (ii) VLM-based automatic reward
estimation to assess task success without hand-crafted evaluation functions,
and (iii) two-stage online reinforcement learning to continuously interact with
and learn from GUI environments. Experiments on two advanced GUI Agents
(UI-TARS and Aguvis) demonstrate that ZeroGUI significantly boosts performance
across OSWorld and AndroidLab environments. The code is available at
https://github.com/OpenGVLab/ZeroGUI.
Can LLMs Deceive CLIP? Benchmarking Adversarial Compositionality of
  Pre-trained Multimodal Representation via Text Updates
Authors: name: Jaewoo Ahn, name: Heeseung Yun, name: Dayoon Ko, name: Gunhee Kim
Published: 2025-05-28 23:45:55

While pre-trained multimodal representations (e.g., CLIP) have shown
impressive capabilities, they exhibit significant compositional vulnerabilities
leading to counterintuitive judgments. We introduce Multimodal Adversarial
Compositionality (MAC), a benchmark that leverages large language models (LLMs)
to generate deceptive text samples to exploit these vulnerabilities across
different modalities and evaluates them through both sample-wise attack success
rate and group-wise entropy-based diversity. To improve zero-shot methods, we
propose a self-training approach that leverages rejection-sampling fine-tuning
with diversity-promoting filtering, which enhances both attack success rate and
sample diversity. Using smaller language models like Llama-3.1-8B, our approach
demonstrates superior performance in revealing compositional vulnerabilities
across various multimodal representations, including images, videos, and
audios.
ToMAP: Training Opponent-Aware LLM Persuaders with Theory of Mind
Authors: name: Peixuan Han, name: Zijia Liu, name: Jiaxuan You
Published: 2025-05-29 01:03:41

Large language models (LLMs) have shown promising potential in persuasion,
but existing works on training LLM persuaders are still preliminary. Notably,
while humans are skilled in modeling their opponent's thoughts and opinions
proactively and dynamically, current LLMs struggle with such Theory of Mind
(ToM) reasoning, resulting in limited diversity and opponent awareness. To
address this limitation, we introduce Theory of Mind Augmented Persuader
(ToMAP), a novel approach for building more flexible persuader agents by
incorporating two theory of mind modules that enhance the persuader's awareness
and analysis of the opponent's mental state. Specifically, we begin by
prompting the persuader to consider possible objections to the target central
claim, and then use a text encoder paired with a trained MLP classifier to
predict the opponent's current stance on these counterclaims. Our carefully
designed reinforcement learning schema enables the persuader learns how to
analyze opponent-related information and utilize it to generate more effective
arguments. Experiments show that the ToMAP persuader, while containing only 3B
parameters, outperforms much larger baselines, like GPT-4o, with a relative
gain of 39.4% across multiple persuadee models and diverse corpora. Notably,
ToMAP exhibits complex reasoning chains and reduced repetition during training,
which leads to more diverse and effective arguments. The opponent-aware feature
of ToMAP also makes it suitable for long conversations and enables it to employ
more logical and opponent-aware strategies. These results underscore our
method's effectiveness and highlight its potential for developing more
persuasive language agents. Code is available at:
https://github.com/ulab-uiuc/ToMAP.
To Trust Or Not To Trust Your Vision-Language Model's Prediction
Authors: name: Hao Dong, name: Moru Liu, name: Jian Liang, name: Eleni Chatzi, name: Olga Fink
Published: 2025-05-29 17:59:01

Vision-Language Models (VLMs) have demonstrated strong capabilities in
aligning visual and textual modalities, enabling a wide range of applications
in multimodal understanding and generation. While they excel in zero-shot and
transfer learning scenarios, VLMs remain susceptible to misclassification,
often yielding confident yet incorrect predictions. This limitation poses a
significant risk in safety-critical domains, where erroneous predictions can
lead to severe consequences. In this work, we introduce TrustVLM, a
training-free framework designed to address the critical challenge of
estimating when VLM's predictions can be trusted. Motivated by the observed
modality gap in VLMs and the insight that certain concepts are more distinctly
represented in the image embedding space, we propose a novel confidence-scoring
function that leverages this space to improve misclassification detection. We
rigorously evaluate our approach across 17 diverse datasets, employing 4
architectures and 2 VLMs, and demonstrate state-of-the-art performance, with
improvements of up to 51.87% in AURC, 9.14% in AUROC, and 32.42% in FPR95
compared to existing baselines. By improving the reliability of the model
without requiring retraining, TrustVLM paves the way for safer deployment of
VLMs in real-world applications. The code will be available at
https://github.com/EPFL-IMOS/TrustVLM.