Orionnnn/hub-stats huggingface dataset:
Unlocking the Potential of MLLMs in Referring Expression Segmentation
  via a Light-weight Mask Decode
Authors: name: Jingchao Wang, name: Zhijian Wu, name: Dingjiang Huang, name: Yefeng Zheng, name: Hong Wang
Published: 2025-08-06 06:06:52

Reference Expression Segmentation (RES) aims to segment image regions
specified by referring expressions and has become popular with the rise of
multimodal large models (MLLMs). While MLLMs excel in semantic understanding,
their token-generation paradigm struggles with pixel-level dense prediction.
Existing RES methods either couple MLLMs with the parameter-heavy Segment
Anything Model (SAM) with 632M network parameters or adopt SAM-free lightweight
pipelines that sacrifice accuracy. To address the trade-off between performance
and cost, we specifically propose MLLMSeg, a novel framework that fully
exploits the inherent visual detail features encoded in the MLLM vision encoder
without introducing an extra visual encoder. Besides, we propose a
detail-enhanced and semantic-consistent feature fusion module (DSFF) that fully
integrates the detail-related visual feature with the semantic-related feature
output by the large language model (LLM) of MLLM. Finally, we establish a
light-weight mask decoder with only 34M network parameters that optimally
leverages detailed spatial features from the visual encoder and semantic
features from the LLM to achieve precise mask prediction. Extensive experiments
demonstrate that our method generally surpasses both SAM-based and SAM-free
competitors, striking a better balance between performance and cost. Code is
available at https://github.com/jcwang0602/MLLMSeg.
Attention Basin: Why Contextual Position Matters in Large Language
  Models
Authors: name: Zihao Yi, name: Delong Zeng, name: Zhenqing Ling, name: Haohao Luo, name: Zhe Xu, name: Wei Liu, name: Jian Luan, name: Wanxia Cao, name: Ying Shen
Published: 2025-08-07 08:08:08

The performance of Large Language Models (LLMs) is significantly sensitive to
the contextual position of information in the input. To investigate the
mechanism behind this positional bias, our extensive experiments reveal a
consistent phenomenon we term the attention basin: when presented with a
sequence of structured items (e.g., retrieved documents or few-shot examples),
models systematically assign higher attention to the items at the beginning and
end of the sequence, while neglecting those in the middle. Crucially, our
analysis further reveals that allocating higher attention to critical
information is key to enhancing model performance. Based on these insights, we
introduce Attention-Driven Reranking (AttnRank), a two-stage framework that (i)
estimates a model's intrinsic positional attention preferences using a small
calibration set, and (ii) reorders retrieved documents or few-shot examples to
align the most salient content with these high-attention positions. AttnRank is
a model-agnostic, training-free, and plug-and-play method with minimal
computational overhead. Experiments on multi-hop QA and few-shot in-context
learning tasks demonstrate that AttnRank achieves substantial improvements
across 10 large language models of varying architectures and scales, without
modifying model parameters or training procedures.
Visual Document Understanding and Question Answering: A Multi-Agent
  Collaboration Framework with Test-Time Scaling
Authors: name: Xinlei Yu, name: Zhangquan Chen, name: Yudong Zhang, name: Shilin Lu, name: Ruolin Shen, name: Jiangning Zhang, name: Xiaobin Hu, name: Yanwei Fu, name: Shuicheng Yan
Published: 2025-08-05 12:52:09

Existing vision-language models (VLMs), whether generalists or specialists,
remain constrained by their parameter scale, lack robust self-correction
capabilities, and underperform in tasks involving long visual contexts and
complex reasoning, resulting in suboptimal performance on document-based tasks.
To address this, we propose MACT, a Multi-Agent Collaboration framework with
Test-Time scaling, tailored for visual document understanding and visual
question answering (VQA). It comprises four distinct small-scale agents, i.e.,
planning, execution, judgment, and answer agents, with clearly defined roles
and effective collaboration. Notably, the judgment agent exclusively verifies
correctness and redirects to prior agents for revisions, outperforming
conventional correction strategies. To further expand the capability boundaries
of the framework, we propose mixed reward modeling that balances agent-specific
abilities and global collaboration, as well as agent-wise hybrid test-time
scaling, which customizes different scaling strategies for each agent based on
their functions. Evaluated on benchmarks spanning both document-based and
non-document-based settings, our MACT shows superior performance with a smaller
parameter scale without sacrificing the ability of general and mathematical
tasks. Especially, it stands out in benchmarks involving long visual contexts
and complicated reasoning. The three variants of MACT consistently hold the top
three positions in average scores, leading in 13 of the 15 benchmarks. Code
will be available at: https://github.com/YU-deep/MACT.git.
Steering One-Step Diffusion Model with Fidelity-Rich Decoder for Fast
  Image Compression
Authors: name: Zheng Chen, name: Mingde Zhou, name: Jinpei Guo, name: Jiale Yuan, name: Yifei Ji, name: Yulun Zhang
Published: 2025-08-07 02:24:03

Diffusion-based image compression has demonstrated impressive perceptual
performance. However, it suffers from two critical drawbacks: (1) excessive
decoding latency due to multi-step sampling, and (2) poor fidelity resulting
from over-reliance on generative priors. To address these issues, we propose
SODEC, a novel single-step diffusion image compression model. We argue that in
image compression, a sufficiently informative latent renders multi-step
refinement unnecessary. Based on this insight, we leverage a pre-trained
VAE-based model to produce latents with rich information, and replace the
iterative denoising process with a single-step decoding. Meanwhile, to improve
fidelity, we introduce the fidelity guidance module, encouraging output that is
faithful to the original image. Furthermore, we design the rate annealing
training strategy to enable effective training under extremely low bitrates.
Extensive experiments show that SODEC significantly outperforms existing
methods, achieving superior rate-distortion-perception performance. Moreover,
compared to previous diffusion-based compression models, SODEC improves
decoding speed by more than 20times. Code is released at:
https://github.com/zhengchen1999/SODEC.
I2CR: Intra- and Inter-modal Collaborative Reflections for Multimodal
  Entity Linking
Authors: name: Ziyan Liu, name: Junwen Li, name: Kaiwen Li, name: Tong Ruan, name: Chao Wang, name: Xinyan He, name: Zongyu Wang, name: Xuezhi Cao, name: Jingping Liu
Published: 2025-08-04 09:43:54

Multimodal entity linking plays a crucial role in a wide range of
applications. Recent advances in large language model-based methods have become
the dominant paradigm for this task, effectively leveraging both textual and
visual modalities to enhance performance. Despite their success, these methods
still face two challenges, including unnecessary incorporation of image data in
certain scenarios and the reliance only on a one-time extraction of visual
features, which can undermine their effectiveness and accuracy. To address
these challenges, we propose a novel LLM-based framework for the multimodal
entity linking task, called Intra- and Inter-modal Collaborative Reflections.
This framework prioritizes leveraging text information to address the task.
When text alone is insufficient to link the correct entity through intra- and
inter-modality evaluations, it employs a multi-round iterative strategy that
integrates key visual clues from various aspects of the image to support
reasoning and enhance matching accuracy. Extensive experiments on three widely
used public datasets demonstrate that our framework consistently outperforms
current state-of-the-art methods in the task, achieving improvements of 3.2%,
5.1%, and 1.6%, respectively. Our code is available at
https://github.com/ziyan-xiaoyu/I2CR/.
RPCANet++: Deep Interpretable Robust PCA for Sparse Object Segmentation
Authors: name: Fengyi Wu, name: Yimian Dai, name: Tianfang Zhang, name: Yixuan Ding, name: Jian Yang, name: Ming-Ming Cheng, name: Zhenming Peng
Published: 2025-08-06 08:19:37

Robust principal component analysis (RPCA) decomposes an observation matrix
into low-rank background and sparse object components. This capability has
enabled its application in tasks ranging from image restoration to
segmentation. However, traditional RPCA models suffer from computational
burdens caused by matrix operations, reliance on finely tuned hyperparameters,
and rigid priors that limit adaptability in dynamic scenarios. To solve these
limitations, we propose RPCANet++, a sparse object segmentation framework that
fuses the interpretability of RPCA with efficient deep architectures. Our
approach unfolds a relaxed RPCA model into a structured network comprising a
Background Approximation Module (BAM), an Object Extraction Module (OEM), and
an Image Restoration Module (IRM). To mitigate inter-stage transmission loss in
the BAM, we introduce a Memory-Augmented Module (MAM) to enhance background
feature preservation, while a Deep Contrast Prior Module (DCPM) leverages
saliency cues to expedite object extraction. Extensive experiments on diverse
datasets demonstrate that RPCANet++ achieves state-of-the-art performance under
various imaging scenarios. We further improve interpretability via visual and
numerical low-rankness and sparsity measurements. By combining the theoretical
strengths of RPCA with the efficiency of deep networks, our approach sets a new
baseline for reliable and interpretable sparse object segmentation. Codes are
available at our Project Webpage https://fengyiwu98.github.io/rpcanetx.
Marco-Voice Technical Report
Authors: name: Fengping Tian, name: Chenyang Lyu, name: Xuanfan Ni, name: Haoqin Sun, name: Qingjuan Li, name: Zhiqiang Qian, name: Haijun Li, name: Longyue Wang, name: Zhao Xu, name: Weihua Luo, name: Kaifu Zhang
Published: 2025-08-04 04:08:22

This paper presents a multifunctional speech synthesis system that integrates
voice cloning and emotion control speech synthesis within a unified framework.
The goal of this work is to address longstanding challenges in achieving highly
expressive, controllable, and natural speech generation that faithfully
preserves speaker identity across diverse linguistic and emotional contexts.
Our approach introduces an effective speaker-emotion disentanglement mechanism
with in-batch contrastive learning, enabling independent manipulation of
speaker identity and eemotional style, as well as rotational emotional
embedding integration method for smooth emotion control. To support
comprehensive training and evaluation, we construct CSEMOTIONS, a high-quality
emotional speech dataset containing 10 hours of Mandarin speech from six
professional speakers across seven emotional categories. Extensive experiments
demonstrate that our system, Marco-Voice, achieves substantial improvements in
both objective and subjective metrics. Comprehensive evaluations and analysis
were conducted, results show that MarcoVoice delivers competitive performance
in terms of speech clarity and emotional richness, representing a substantial
advance in the field of expressive neural speech synthesis.
CoAct-1: Computer-using Agents with Coding as Actions
Authors: name: Linxin Song, name: Yutong Dai, name: Viraj Prabhu, name: Jieyu Zhang, name: Taiwei Shi, name: Li Li, name: Junnan Li, name: Silvio Savarese, name: Zeyuan Chen, name: Jieyu Zhao, name: Ran Xu, name: Caiming Xiong
Published: 2025-08-05 21:33:36

Autonomous agents that operate computers via Graphical User Interfaces (GUIs)
often struggle with efficiency and reliability on complex, long-horizon tasks.
While augmenting these agents with planners can improve task decomposition,
they remain constrained by the inherent limitations of performing all actions
through GUI manipulation, leading to brittleness and inefficiency. In this
work, we introduce a more robust and flexible paradigm: enabling agents to use
coding as a enhanced action. We present CoAct-1, a novel multi-agent system
that synergistically combines GUI-based control with direct programmatic
execution. CoAct-1 features an Orchestrator that dynamically delegates subtasks
to either a conventional GUI Operator or a specialized Programmer agent, which
can write and execute Python or Bash scripts. This hybrid approach allows the
agent to bypass inefficient GUI action sequences for tasks like file management
and data processing, while still leveraging visual interaction when necessary.
We evaluate our system on the challenging OSWorld benchmark, where CoAct-1
achieves a new state-of-the-art success rate of 60.76%, significantly
outperforming prior methods. Furthermore, our approach dramatically improves
efficiency, reducing the average number of steps required to complete a task to
just 10.15, compared to 15 for leading GUI agents. Our results demonstrate that
integrating coding as a core action provides a more powerful, efficient, and
scalable path toward generalized computer automation.
InfiAlign: A Scalable and Sample-Efficient Framework for Aligning LLMs
  to Enhance Reasoning Capabilities
Authors: name: Shuo Cai, name: Su Lu, name: Qi Zhou, name: Kejing Yang, name: Zhijie Sang, name: Congkai Xie, name: Hongxia Yang
Published: 2025-08-07 15:34:06

Large language models (LLMs) have exhibited impressive reasoning abilities on
a wide range of complex tasks. However, enhancing these capabilities through
post-training remains resource intensive, particularly in terms of data and
computational cost. Although recent efforts have sought to improve sample
efficiency through selective data curation, existing methods often rely on
heuristic or task-specific strategies that hinder scalability. In this work, we
introduce InfiAlign, a scalable and sample-efficient post-training framework
that integrates supervised fine-tuning (SFT) with Direct Preference
Optimization (DPO) to align LLMs for enhanced reasoning. At the core of
InfiAlign is a robust data selection pipeline that automatically curates
high-quality alignment data from open-source reasoning datasets using
multidimensional quality metrics. This pipeline enables significant performance
gains while drastically reducing data requirements and remains extensible to
new data sources. When applied to the Qwen2.5-Math-7B-Base model, our SFT model
achieves performance on par with DeepSeek-R1-Distill-Qwen-7B, while using only
approximately 12% of the training data, and demonstrates strong generalization
across diverse reasoning tasks. Additional improvements are obtained through
the application of DPO, with particularly notable gains in mathematical
reasoning tasks. The model achieves an average improvement of 3.89% on AIME
24/25 benchmarks. Our results highlight the effectiveness of combining
principled data selection with full-stage post-training, offering a practical
solution for aligning large reasoning models in a scalable and data-efficient
manner. The model checkpoints are available at
https://huggingface.co/InfiX-ai/InfiAlign-Qwen-7B-SFT.
REINA: Regularized Entropy Information-Based Loss for Efficient
  Simultaneous Speech Translation
Authors: name: Nameer Hirschkind, name: Joseph Liu, name: Mahesh Kumar Nandwana, name: Xiao Yu
Published: 2025-08-07 00:25:58

Simultaneous Speech Translation (SimulST) systems stream in audio while
simultaneously emitting translated text or speech. Such systems face the
significant challenge of balancing translation quality and latency. We
introduce a strategy to optimize this tradeoff: wait for more input only if you
gain information by doing so. Based on this strategy, we present Regularized
Entropy INformation Adaptation (REINA), a novel loss to train an adaptive
policy using an existing non-streaming translation model. We derive REINA from
information theory principles and show that REINA helps push the reported
Pareto frontier of the latency/quality tradeoff over prior works. Utilizing
REINA, we train a SimulST model on French, Spanish and German, both from and
into English. Training on only open source or synthetically generated data, we
achieve state-of-the-art (SOTA) streaming results for models of comparable
size. We also introduce a metric for streaming efficiency, quantitatively
showing REINA improves the latency/quality trade-off by as much as 21% compared
to prior approaches, normalized against non-streaming baseline BLEU scores.
PRvL: Quantifying the Capabilities and Risks of Large Language Models
  for PII Redaction
Authors: name: Leon Garza, name: Anantaa Kotal, name: Aritran Piplai, name: Lavanya Elluri, name: Prajit Das, name: Aman Chadha
Published: 2025-08-07 16:22:49

Redacting Personally Identifiable Information (PII) from unstructured text is
critical for ensuring data privacy in regulated domains. While earlier
approaches have relied on rule-based systems and domain-specific Named Entity
Recognition (NER) models, these methods fail to generalize across formats and
contexts. Recent advances in Large Language Models (LLMs) offer a promising
alternative, yet the effect of architectural and training choices on redaction
performance remains underexplored. LLMs have demonstrated strong performance in
tasks that require contextual language understanding, including the redaction
of PII in free-form text. Prior work suggests that with appropriate adaptation,
LLMs can become effective contextual privacy learners. However, the
consequences of architectural and training choices for PII Redaction remain
underexplored. In this work, we present a comprehensive analysis of LLMs as
privacy-preserving PII Redaction systems. We evaluate a range of LLM
architectures and training strategies for their effectiveness in PII Redaction.
Our analysis measures redaction performance, semantic preservation, and PII
leakage, and compares these outcomes against latency and computational cost.
The results provide practical guidance for configuring LLM-based redactors that
are accurate, efficient, and privacy-aware. To support reproducibility and
real-world deployment, we release PRvL, an open-source suite of fine-tuned
models, and evaluation tools for general-purpose PII Redaction. PRvL is built
entirely on open-source LLMs and supports multiple inference settings for
flexibility and compliance. It is designed to be easily customized for
different domains and fully operable within secure, self-managed environments.
This enables data owners to perform redactions without relying on third-party
services or exposing sensitive content beyond their own infrastructure.
I Think, Therefore I Am Under-Qualified? A Benchmark for Evaluating
  Linguistic Shibboleth Detection in LLM Hiring Evaluations
Authors: name: Julia Kharchenko, name: Tanya Roosta, name: Aman Chadha, name: Chirag Shah
Published: 2025-08-06 23:51:03

This paper introduces a comprehensive benchmark for evaluating how Large
Language Models (LLMs) respond to linguistic shibboleths: subtle linguistic
markers that can inadvertently reveal demographic attributes such as gender,
social class, or regional background. Through carefully constructed interview
simulations using 100 validated question-response pairs, we demonstrate how
LLMs systematically penalize certain linguistic patterns, particularly hedging
language, despite equivalent content quality. Our benchmark generates
controlled linguistic variations that isolate specific phenomena while
maintaining semantic equivalence, which enables the precise measurement of
demographic bias in automated evaluation systems. We validate our approach
along multiple linguistic dimensions, showing that hedged responses receive
25.6% lower ratings on average, and demonstrate the benchmark's effectiveness
in identifying model-specific biases. This work establishes a foundational
framework for detecting and measuring linguistic discrimination in AI systems,
with broad applications to fairness in automated decision-making contexts.
Can Large Multimodal Models Actively Recognize Faulty Inputs? A
  Systematic Evaluation Framework of Their Input Scrutiny Ability
Authors: name: Haiqi Yang, name: Jinzhe Li, name: Gengxu Li, name: Yi Chang, name: Yuan Wu
Published: 2025-08-06 02:13:46

Large Multimodal Models (LMMs) have witnessed remarkable growth, showcasing
formidable capabilities in handling intricate multimodal tasks with exceptional
performance. Recent research has underscored the inclination of large language
models to passively accept defective inputs, often resulting in futile
reasoning on invalid prompts. However, the same critical question of whether
LMMs can actively detect and scrutinize erroneous inputs still remains
unexplored. To address this gap, we introduce the Input Scrutiny Ability
Evaluation Framework (ISEval), which encompasses seven categories of flawed
premises and three evaluation metrics. Our extensive evaluation of ten advanced
LMMs has identified key findings. Most models struggle to actively detect
flawed textual premises without guidance, which reflects a strong reliance on
explicit prompts for premise error identification. Error type affects
performance: models excel at identifying logical fallacies but struggle with
surface-level linguistic errors and certain conditional flaws. Modality trust
varies-Gemini 2.5 pro and Claude Sonnet 4 balance visual and textual info,
while aya-vision-8b over-rely on text in conflicts. These insights underscore
the urgent need to enhance LMMs' proactive verification of input validity and
shed novel insights into mitigating the problem. The code is available at
https://github.com/MLGroupJLU/LMM_ISEval.
Are We on the Right Way for Assessing Document Retrieval-Augmented
  Generation?
Authors: name: Wenxuan Shen, name: Mingjia Wang, name: Yaochen Wang, name: Dongping Chen, name: Junjie Yang, name: Yao Wan, name: Weiwei Lin
Published: 2025-08-05 16:55:02

Retrieval-Augmented Generation (RAG) systems using Multimodal Large Language
Models (MLLMs) show great promise for complex document understanding, yet their
development is critically hampered by inadequate evaluation. Current benchmarks
often focus on specific part of document RAG system and use synthetic data with
incomplete ground truth and evidence labels, therefore failing to reflect
real-world bottlenecks and challenges. To overcome these limitations, we
introduce Double-Bench: a new large-scale, multilingual, and multimodal
evaluation system that is able to produce fine-grained assessment to each
component within document RAG systems. It comprises 3,276 documents (72,880
pages) and 5,168 single- and multi-hop queries across 6 languages and 4
document types with streamlined dynamic update support for potential data
contamination issues. Queries are grounded in exhaustively scanned evidence
pages and verified by human experts to ensure maximum quality and completeness.
Our comprehensive experiments across 9 state-of-the-art embedding models, 4
MLLMs and 4 end-to-end document RAG frameworks demonstrate the gap between text
and visual embedding models is narrowing, highlighting the need in building
stronger document retrieval models. Our findings also reveal the
over-confidence dilemma within current document RAG frameworks that tend to
provide answer even without evidence support. We hope our fully open-source
Double-Bench provide a rigorous foundation for future research in advanced
document RAG systems. We plan to retrieve timely corpus and release new
benchmarks on an annual basis.