Orionnnn/hub-stats huggingface dataset:
Rex-Thinker: Grounded Object Referring via Chain-of-Thought Reasoning
Authors: name: Qing Jiang, name: Xingyu Chen, name: Zhaoyang Zeng, name: Junzhi Yu, name: Lei Zhang
Published: 2025-06-04 14:56:57

Object referring aims to detect all objects in an image that match a given
natural language description. We argue that a robust object referring model
should be grounded, meaning its predictions should be both explainable and
faithful to the visual content. Specifically, it should satisfy two key
properties: 1) Verifiable, by producing interpretable reasoning that justifies
its predictions and clearly links them to visual evidence; and 2) Trustworthy,
by learning to abstain when no object in the image satisfies the given
expression. However, most methods treat referring as a direct bounding box
prediction task, offering limited interpretability and struggling to reject
expressions with no matching object. In this work, we propose Rex-Thinker, a
model that formulates object referring as an explicit CoT reasoning task. Given
a referring expression, we first identify all candidate object instances
corresponding to the referred object category. Rex-Thinker then performs
step-by-step reasoning over each candidate to assess whether it matches the
given expression, before making a final prediction. To support this paradigm,
we construct a large-scale CoT-style referring dataset named HumanRef-CoT by
prompting GPT-4o on the HumanRef dataset. Each reasoning trace follows a
structured planning, action, and summarization format, enabling the model to
learn decomposed, interpretable reasoning over object candidates. We then train
Rex-Thinker in two stages: a cold-start supervised fine-tuning phase to teach
the model how to perform structured reasoning, followed by GRPO-based RL
learning to improve accuracy and generalization. Experiments show that our
approach outperforms standard baselines in both precision and interpretability
on in-domain evaluation, while also demonstrating improved ability to reject
hallucinated outputs and strong generalization in out-of-domain settings.
TimeHC-RL: Temporal-aware Hierarchical Cognitive Reinforcement Learning
  for Enhancing LLMs' Social Intelligence
Authors: name: Guiyang Hou, name: Xing Gao, name: Yuchuan Wu, name: Xiang Huang, name: Wenqi Zhang, name: Zhe Zheng, name: Yongliang Shen, name: Jialu Du, name: Fei Huang, name: Yongbin Li, name: Weiming Lu
Published: 2025-05-30 12:01:06

Recently, Large Language Models (LLMs) have made significant progress in
IQ-related domains that require careful thinking, such as mathematics and
coding. However, enhancing LLMs' cognitive development in social domains,
particularly from a post-training perspective, remains underexplored.
Recognizing that the social world follows a distinct timeline and requires a
richer blend of cognitive modes (from intuitive reactions (System 1) and
surface-level thinking to deliberate thinking (System 2)) than mathematics,
which primarily relies on System 2 cognition (careful, step-by-step reasoning),
we introduce Temporal-aware Hierarchical Cognitive Reinforcement Learning
(TimeHC-RL) for enhancing LLMs' social intelligence. In our experiments, we
systematically explore improving LLMs' social intelligence and validate the
effectiveness of the TimeHC-RL method, through five other post-training
paradigms and two test-time intervention paradigms on eight datasets with
diverse data patterns. Experimental results reveal the superiority of our
proposed TimeHC-RL method compared to the widely adopted System 2 RL method. It
gives the 7B backbone model wings, enabling it to rival the performance of
advanced models like DeepSeek-R1 and OpenAI-O3. Additionally, the systematic
exploration from post-training and test-time interventions perspectives to
improve LLMs' social intelligence has uncovered several valuable insights.
DLP: Dynamic Layerwise Pruning in Large Language Models
Authors: name: Yuli Chen, name: Bo Cheng, name: Jiale Han, name: Yingying Zhang, name: Yingting Li, name: Shuhao Zhang
Published: 2025-05-27 07:35:00

Pruning has recently been widely adopted to reduce the parameter scale and
improve the inference efficiency of Large Language Models (LLMs). Mainstream
pruning techniques often rely on uniform layerwise pruning strategies, which
can lead to severe performance degradation at high sparsity levels. Recognizing
the varying contributions of different layers in LLMs, recent studies have
shifted their focus toward non-uniform layerwise pruning. However, these
approaches often rely on pre-defined values, which can result in suboptimal
performance. To overcome these limitations, we propose a novel method called
Dynamic Layerwise Pruning (DLP). This approach adaptively determines the
relative importance of each layer by integrating model weights with input
activation information, assigning pruning rates accordingly. Experimental
results show that DLP effectively preserves model performance at high sparsity
levels across multiple LLMs. Specifically, at 70% sparsity, DLP reduces the
perplexity of LLaMA2-7B by 7.79 and improves the average accuracy by 2.7%
compared to state-of-the-art methods. Moreover, DLP is compatible with various
existing LLM compression techniques and can be seamlessly integrated into
Parameter-Efficient Fine-Tuning (PEFT). We release the code at
https://github.com/ironartisan/DLP to facilitate future research.
Image Editing As Programs with Diffusion Models
Authors: name: Yujia Hu, name: Songhua Liu, name: Zhenxiong Tan, name: Xingyi Yang, name: Xinchao Wang
Published: 2025-06-04 16:57:24

While diffusion models have achieved remarkable success in text-to-image
generation, they encounter significant challenges with instruction-driven image
editing. Our research highlights a key challenge: these models particularly
struggle with structurally inconsistent edits that involve substantial layout
changes. To mitigate this gap, we introduce Image Editing As Programs (IEAP), a
unified image editing framework built upon the Diffusion Transformer (DiT)
architecture. At its core, IEAP approaches instructional editing through a
reductionist lens, decomposing complex editing instructions into sequences of
atomic operations. Each operation is implemented via a lightweight adapter
sharing the same DiT backbone and is specialized for a specific type of edit.
Programmed by a vision-language model (VLM)-based agent, these operations
collaboratively support arbitrary and structurally inconsistent
transformations. By modularizing and sequencing edits in this way, IEAP
generalizes robustly across a wide range of editing tasks, from simple
adjustments to substantial structural changes. Extensive experiments
demonstrate that IEAP significantly outperforms state-of-the-art methods on
standard benchmarks across various editing scenarios. In these evaluations, our
framework delivers superior accuracy and semantic fidelity, particularly for
complex, multi-step instructions. Codes are available at
https://github.com/YujiaHu1109/IEAP.
DiffDecompose: Layer-Wise Decomposition of Alpha-Composited Images via
  Diffusion Transformers
Authors: name: Zitong Wang, name: Hang Zhao, name: Qianyu Zhou, name: Xuequan Lu, name: Xiangtai Li, name: Yiren Song
Published: 2025-05-24 16:08:04

Diffusion models have recently motivated great success in many generation
tasks like object removal. Nevertheless, existing image decomposition methods
struggle to disentangle semi-transparent or transparent layer occlusions due to
mask prior dependencies, static object assumptions, and the lack of datasets.
In this paper, we delve into a novel task: Layer-Wise Decomposition of
Alpha-Composited Images, aiming to recover constituent layers from single
overlapped images under the condition of semi-transparent/transparent alpha
layer non-linear occlusion. To address challenges in layer ambiguity,
generalization, and data scarcity, we first introduce AlphaBlend, the first
large-scale and high-quality dataset for transparent and semi-transparent layer
decomposition, supporting six real-world subtasks (e.g., translucent flare
removal, semi-transparent cell decomposition, glassware decomposition).
Building on this dataset, we present DiffDecompose, a diffusion
Transformer-based framework that learns the posterior over possible layer
decompositions conditioned on the input image, semantic prompts, and blending
type. Rather than regressing alpha mattes directly, DiffDecompose performs
In-Context Decomposition, enabling the model to predict one or multiple layers
without per-layer supervision, and introduces Layer Position Encoding Cloning
to maintain pixel-level correspondence across layers. Extensive experiments on
the proposed AlphaBlend dataset and public LOGO dataset verify the
effectiveness of DiffDecompose. The code and dataset will be available upon
paper acceptance. Our code will be available at:
https://github.com/Wangzt1121/DiffDecompose.
Improving Knowledge Distillation Under Unknown Covariate Shift Through
  Confidence-Guided Data Augmentation
Authors: name: Niclas Popp, name: Kevin Alexander Laube, name: Matthias Hein, name: Lukas Schott
Published: 2025-06-02 22:15:59

Large foundation models trained on extensive datasets demonstrate strong
zero-shot capabilities in various domains. To replicate their success when data
and model size are constrained, knowledge distillation has become an
established tool for transferring knowledge from foundation models to small
student networks. However, the effectiveness of distillation is critically
limited by the available training data. This work addresses the common
practical issue of covariate shift in knowledge distillation, where spurious
features appear during training but not at test time. We ask the question: when
these spurious features are unknown, yet a robust teacher is available, is it
possible for a student to also become robust to them? We address this problem
by introducing a novel diffusion-based data augmentation strategy that
generates images by maximizing the disagreement between the teacher and the
student, effectively creating challenging samples that the student struggles
with. Experiments demonstrate that our approach significantly improves worst
group and mean group accuracy on CelebA and SpuCo Birds as well as the spurious
mAUC on spurious ImageNet under covariate shift, outperforming state-of-the-art
diffusion-based data augmentation baselines
TRiSM for Agentic AI: A Review of Trust, Risk, and Security Management
  in LLM-based Agentic Multi-Agent Systems
Authors: name: Shaina Raza, name: Ranjan Sapkota, name: Manoj Karkee, name: Christos Emmanouilidis
Published: 2025-06-04 16:26:11

Agentic AI systems, built on large language models (LLMs) and deployed in
multi-agent configurations, are redefining intelligent autonomy, collaboration
and decision-making across enterprise and societal domains. This review
presents a structured analysis of Trust, Risk, and Security Management (TRiSM)
in the context of LLM-based agentic multi-agent systems (AMAS). We begin by
examining the conceptual foundations of agentic AI, its architectural
differences from traditional AI agents, and the emerging system designs that
enable scalable, tool-using autonomy. The TRiSM in the agentic AI framework is
then detailed through four pillars governance, explainability, ModelOps, and
privacy/security each contextualized for agentic LLMs. We identify unique
threat vectors and introduce a comprehensive risk taxonomy for the agentic AI
applications, supported by case studies illustrating real-world
vulnerabilities. Furthermore, the paper also surveys trust-building mechanisms,
transparency and oversight techniques, and state-of-the-art explainability
strategies in distributed LLM agent systems. Additionally, metrics for
evaluating trust, interpretability, and human-centered performance are reviewed
alongside open benchmarking challenges. Security and privacy are addressed
through encryption, adversarial defense, and compliance with evolving AI
regulations. The paper concludes with a roadmap for responsible agentic AI,
proposing research directions to align emerging multi-agent systems with robust
TRiSM principles for safe, accountable, and transparent deployment.
ByteMorph: Benchmarking Instruction-Guided Image Editing with Non-Rigid
  Motions
Authors: name: Di Chang, name: Mingdeng Cao, name: Yichun Shi, name: Bo Liu, name: Shengqu Cai, name: Shijie Zhou, name: Weilin Huang, name: Gordon Wetzstein, name: Mohammad Soleymani, name: Peng Wang
Published: 2025-06-03 17:39:47

Editing images with instructions to reflect non-rigid motions, camera
viewpoint shifts, object deformations, human articulations, and complex
interactions, poses a challenging yet underexplored problem in computer vision.
Existing approaches and datasets predominantly focus on static scenes or rigid
transformations, limiting their capacity to handle expressive edits involving
dynamic motion. To address this gap, we introduce ByteMorph, a comprehensive
framework for instruction-based image editing with an emphasis on non-rigid
motions. ByteMorph comprises a large-scale dataset, ByteMorph-6M, and a strong
baseline model built upon the Diffusion Transformer (DiT), named ByteMorpher.
ByteMorph-6M includes over 6 million high-resolution image editing pairs for
training, along with a carefully curated evaluation benchmark ByteMorph-Bench.
Both capture a wide variety of non-rigid motion types across diverse
environments, human figures, and object categories. The dataset is constructed
using motion-guided data generation, layered compositing techniques, and
automated captioning to ensure diversity, realism, and semantic coherence. We
further conduct a comprehensive evaluation of recent instruction-based image
editing methods from both academic and commercial domains.
Negative-Guided Subject Fidelity Optimization for Zero-Shot
  Subject-Driven Generation
Authors: name: Chaehun Shin, name: Jooyoung Choi, name: Johan Barthelemy, name: Jungbeom Lee, name: Sungroh Yoon
Published: 2025-06-04 06:59:25

We present Subject Fidelity Optimization (SFO), a novel comparative learning
framework for zero-shot subject-driven generation that enhances subject
fidelity. Beyond supervised fine-tuning methods that rely only on positive
targets and use the diffusion loss as in the pre-training stage, SFO introduces
synthetic negative targets and explicitly guides the model to favor positives
over negatives through pairwise comparison. For negative targets, we propose
Condition-Degradation Negative Sampling (CDNS), which automatically generates
distinctive and informative negatives by intentionally degrading visual and
textual cues without expensive human annotations. Moreover, we reweight the
diffusion timesteps to focus finetuning on intermediate steps where subject
details emerge. Extensive experiments demonstrate that SFO with CDNS
significantly outperforms baselines in terms of both subject fidelity and text
alignment on a subject-driven generation benchmark. Project page:
https://subjectfidelityoptimization.github.io/
Control-R: Towards controllable test-time scaling
Authors: name: Di Zhang, name: Weida Wang, name: Junxian Li, name: Xunzhi Wang, name: Jiatong Li, name: Jianbo Wu, name: Jingdi Lei, name: Haonan He, name: Peng Ye, name: Shufei Zhang, name: Wanli Ouyang, name: Yuqiang Li, name: Dongzhan Zhou
Published: 2025-05-30 19:59:44

This paper target in addressing the challenges of underthinking and
overthinking in long chain-of-thought (CoT) reasoning for Large Reasoning
Models (LRMs) by introducing Reasoning Control Fields (RCF)--a novel test-time
approach that injects structured control signals to guide reasoning from a tree
search perspective. RCF enables models to adjust reasoning effort according to
given control conditions when solving complex tasks. Additionally, we present
the Control-R-4K dataset, which consists of challenging problems annotated with
detailed reasoning processes and corresponding control fields. To further
enhance reasoning control, we propose a Conditional Distillation Finetuning
(CDF) method, which trains model--particularly Control-R-32B--to effectively
adjust reasoning effort during test time. Experimental results on benchmarks
such as AIME2024 and MATH500 demonstrate that our approach achieves
state-of-the-art performance at the 32B scale while enabling a controllable
Long CoT reasoning process (L-CoT). Overall, this work introduces an effective
paradigm for controllable test-time scaling reasoning.
Angles Don't Lie: Unlocking Training-Efficient RL Through the Model's
  Own Signals
Authors: name: Qinsi Wang, name: Jinghan Ke, name: Hancheng Ye, name: Yueqian Lin, name: Yuzhe Fu, name: Jianyi Zhang, name: Kurt Keutzer, name: Chenfeng Xu, name: Yiran Chen
Published: 2025-06-02 21:40:38

Current Reinforcement Fine-tuning (RFT) paradigms for Large Language Models
(LLMs) suffer from sample inefficiency due to the redundant exposure of
identical queries under uniform data sampling. While previous work has explored
curriculum learning via heuristic difficulty metrics, these strategies exhibit
limitations by neglecting the intrinsic learning signals generated by the model
itself, thus leading to suboptimal training regimes. In this paper, we identify
a model-inherent signal termed angle concentration that effectively reflects an
LLM's capacity to learn from specific data. We theoretically and empirically
demonstrate a correlation between the angular distribution of token hidden
state vectors and the resulting gradient, revealing a learning preference for
data exhibiting higher angle concentration. Inspired by this finding, we
propose GAIN-RL, a Gradient-driven Angle-Informed Navigated RL framework. By
leveraging the model's intrinsic angle concentration signal, GAIN-RL
dynamically selects training data in each epoch, ensuring consistently
impactful gradient updates and thus significantly enhancing overall training
efficiency. Empirical evaluations show that GAIN-RL (GRPO) achieves over a 2.5x
acceleration in training efficiency across diverse mathematical and coding
tasks and varying model scales. Furthermore, GAIN-RL (GRPO)'s efficient
sampling yields data-efficient training, achieving better performance with half
the original data compared to vanilla GRPO with full training data. Code is
realsed at https://github.com/wangqinsi1/GAINRL/tree/main.
Training Language Models to Generate Quality Code with Program Analysis
  Feedback
Authors: name: Feng Yao, name: Zilong Wang, name: Liyuan Liu, name: Junxia Cui, name: Li Zhong, name: Xiaohan Fu, name: Haohui Mai, name: Vish Krishnan, name: Jianfeng Gao, name: Jingbo Shang
Published: 2025-05-28 17:57:47

Code generation with large language models (LLMs), often termed vibe coding,
is increasingly adopted in production but fails to ensure code quality,
particularly in security (e.g., SQL injection vulnerabilities) and
maintainability (e.g., missing type annotations). Existing methods, such as
supervised fine-tuning and rule-based post-processing, rely on labor-intensive
annotations or brittle heuristics, limiting their scalability and
effectiveness. We propose REAL, a reinforcement learning framework that
incentivizes LLMs to generate production-quality code using program
analysis-guided feedback. Specifically, REAL integrates two automated signals:
(1) program analysis detecting security or maintainability defects and (2) unit
tests ensuring functional correctness. Unlike prior work, our framework is
prompt-agnostic and reference-free, enabling scalable supervision without
manual intervention. Experiments across multiple datasets and model scales
demonstrate that REAL outperforms state-of-the-art methods in simultaneous
assessments of functionality and code quality. Our work bridges the gap between
rapid prototyping and production-ready code, enabling LLMs to deliver both
speed and quality.
Motion-Aware Concept Alignment for Consistent Video Editing
Authors: name: Tong Zhang, name: Juan C Leon Alcazar, name: Bernard Ghanem
Published: 2025-06-01 13:28:04

We introduce MoCA-Video (Motion-Aware Concept Alignment in Video), a
training-free framework bridging the gap between image-domain semantic mixing
and video. Given a generated video and a user-provided reference image,
MoCA-Video injects the semantic features of the reference image into a specific
object within the video, while preserving the original motion and visual
context. Our approach leverages a diagonal denoising schedule and
class-agnostic segmentation to detect and track objects in the latent space and
precisely control the spatial location of the blended objects. To ensure
temporal coherence, we incorporate momentum-based semantic corrections and
gamma residual noise stabilization for smooth frame transitions. We evaluate
MoCA's performance using the standard SSIM, image-level LPIPS, temporal LPIPS,
and introduce a novel metric CASS (Conceptual Alignment Shift Score) to
evaluate the consistency and effectiveness of the visual shifts between the
source prompt and the modified video frames. Using self-constructed dataset,
MoCA-Video outperforms current baselines, achieving superior spatial
consistency, coherent motion, and a significantly higher CASS score, despite
having no training or fine-tuning. MoCA-Video demonstrates that structured
manipulation in the diffusion noise trajectory allows for controllable,
high-quality video synthesis.
Ctrl-Crash: Controllable Diffusion for Realistic Car Crashes
Authors: name: Anthony Gosselin, name: Ge Ya Luo, name: Luis Lara, name: Florian Golemo, name: Derek Nowrouzezahrai, name: Liam Paull, name: Alexia Jolicoeur-Martineau, name: Christopher Pal
Published: 2025-05-30 21:04:38

Video diffusion techniques have advanced significantly in recent years;
however, they struggle to generate realistic imagery of car crashes due to the
scarcity of accident events in most driving datasets. Improving traffic safety
requires realistic and controllable accident simulations. To tackle the
problem, we propose Ctrl-Crash, a controllable car crash video generation model
that conditions on signals such as bounding boxes, crash types, and an initial
image frame. Our approach enables counterfactual scenario generation where
minor variations in input can lead to dramatically different crash outcomes. To
support fine-grained control at inference time, we leverage classifier-free
guidance with independently tunable scales for each conditioning signal.
Ctrl-Crash achieves state-of-the-art performance across quantitative video
quality metrics (e.g., FVD and JEDi) and qualitative measurements based on a
human-evaluation of physical realism and video quality compared to prior
diffusion-based methods.
Controllable Human-centric Keyframe Interpolation with Generative Prior
Authors: name: Zujin Guo, name: Size Wu, name: Zhongang Cai, name: Wei Li, name: Chen Change Loy
Published: 2025-06-03 17:50:05

Existing interpolation methods use pre-trained video diffusion priors to
generate intermediate frames between sparsely sampled keyframes. In the absence
of 3D geometric guidance, these methods struggle to produce plausible results
for complex, articulated human motions and offer limited control over the
synthesized dynamics. In this paper, we introduce PoseFuse3D Keyframe
Interpolator (PoseFuse3D-KI), a novel framework that integrates 3D human
guidance signals into the diffusion process for Controllable Human-centric
Keyframe Interpolation (CHKI). To provide rich spatial and structural cues for
interpolation, our PoseFuse3D, a 3D-informed control model, features a novel
SMPL-X encoder that transforms 3D geometry and shape into the 2D latent
conditioning space, alongside a fusion network that integrates these 3D cues
with 2D pose embeddings. For evaluation, we build CHKI-Video, a new dataset
annotated with both 2D poses and 3D SMPL-X parameters. We show that
PoseFuse3D-KI consistently outperforms state-of-the-art baselines on
CHKI-Video, achieving a 9% improvement in PSNR and a 38% reduction in LPIPS.
Comprehensive ablations demonstrate that our PoseFuse3D model improves
interpolation fidelity.