Orionnnn/hub-stats huggingface dataset:
A Survey of Vibe Coding with Large Language Models
Authors: name: Yuyao Ge, name: Lingrui Mei, name: Zenghao Duan, name: Tianhao Li, name: Yujia Zheng, name: Yiwei Wang, name: Lexin Wang, name: Jiayu Yao, name: Tianyu Liu, name: Yujun Cai, name: Baolong Bi, name: Fangda Guo, name: Jiafeng Guo, name: Shenghua Liu, name: Xueqi Cheng
Published: 2025-10-14 11:26:56

The advancement of large language models (LLMs) has catalyzed a paradigm
shift from code generation assistance to autonomous coding agents, enabling a
novel development methodology termed "Vibe Coding" where developers validate
AI-generated implementations through outcome observation rather than
line-by-line code comprehension. Despite its transformative potential, the
effectiveness of this emergent paradigm remains under-explored, with empirical
evidence revealing unexpected productivity losses and fundamental challenges in
human-AI collaboration. To address this gap, this survey provides the first
comprehensive and systematic review of Vibe Coding with large language models,
establishing both theoretical foundations and practical frameworks for this
transformative development approach. Drawing from systematic analysis of over
1000 research papers, we survey the entire vibe coding ecosystem, examining
critical infrastructure components including LLMs for coding, LLM-based coding
agent, development environment of coding agent, and feedback mechanisms. We
first introduce Vibe Coding as a formal discipline by formalizing it through a
Constrained Markov Decision Process that captures the dynamic triadic
relationship among human developers, software projects, and coding agents.
Building upon this theoretical foundation, we then synthesize existing
practices into five distinct development models: Unconstrained Automation,
Iterative Conversational Collaboration, Planning-Driven, Test-Driven, and
Context-Enhanced Models, thus providing the first comprehensive taxonomy in
this domain. Critically, our analysis reveals that successful Vibe Coding
depends not merely on agent capabilities but on systematic context engineering,
well-established development environments, and human-agent collaborative
development models.
SRUM: Fine-Grained Self-Rewarding for Unified Multimodal Models
Authors: name: Weiyang Jin, name: Yuwei Niu, name: Jiaqi Liao, name: Chengqi Duan, name: Aoxue Li, name: Shenghua Gao, name: Xihui Liu
Published: 2025-10-14 17:56:11

Recently, remarkable progress has been made in Unified Multimodal Models
(UMMs), which integrate vision-language generation and understanding
capabilities within a single framework. However, a significant gap exists where
a model's strong visual understanding often fails to transfer to its visual
generation. A model might correctly understand an image based on user
instructions, yet be unable to generate a faithful image from text prompts.
This phenomenon directly raises a compelling question: Can a model achieve
self-improvement by using its understanding module to reward its generation
module? To bridge this gap and achieve self-improvement, we introduce SRUM, a
self-rewarding post-training framework that can be directly applied to existing
UMMs of various designs. SRUM creates a feedback loop where the model's own
understanding module acts as an internal ``evaluator'', providing corrective
signals to improve its generation module, without requiring additional
human-labeled data. To ensure this feedback is comprehensive, we designed a
global-local dual reward system. To tackle the inherent structural complexity
of images, this system offers multi-scale guidance: a global reward
ensures the correctness of the overall visual semantics and layout, while a
local reward refines fine-grained, object-level fidelity. SRUM leads
to powerful capabilities and shows strong generalization, boosting performance
on T2I-CompBench from 82.18 to 88.37 and on T2I-ReasonBench from 43.82
to 46.75. Overall, our work establishes a powerful new paradigm for
enabling a UMMs' understanding module to guide and enhance its own generation
via self-rewarding.
SAIL-Embedding Technical Report: Omni-modal Embedding Foundation Model
Authors: name: Lin Lin, name: Jiefeng Long, name: Zhihe Wan, name: Yuchi Wang, name: Dingkang Yang, name: Shuang Yang, name: Yueyang Yao, name: Xu Chen, name: Zirui Guo, name: Shengqiang Li, name: Weiran Li, name: Hanyu Li, name: Yaling Mou, name: Yan Qiu, name: Haiyang Yu, name: Xiao Liang, name: Hongsheng Li, name: Chao Feng
Published: 2025-10-14 16:43:22

Multimodal embedding models aim to yield informative unified representations
that empower diverse cross-modal tasks. Despite promising developments in the
evolution from CLIP-based dual-tower architectures to large vision-language
models, prior works still face unavoidable challenges in real-world
applications and business scenarios, such as the limited modality support,
unstable training mechanisms, and industrial domain gaps. In this work, we
introduce SAIL-Embedding, an omni-modal embedding foundation model that
addresses these issues through tailored training strategies and architectural
design. In the optimization procedure, we propose a multi-stage training scheme
to boost the multifaceted effectiveness of representation learning.
Specifically, the content-aware progressive training aims to enhance the
model's adaptability to diverse downstream tasks and master enriched
cross-modal proficiency. The collaboration-aware recommendation enhancement
training further adapts multimodal representations for recommendation scenarios
by distilling knowledge from sequence-to-item and ID-to-item embeddings while
mining user historical interests. Concurrently, we develop the stochastic
specialization and dataset-driven pattern matching to strengthen model training
flexibility and generalizability. Experimental results show that SAIL-Embedding
achieves SOTA performance compared to other methods in different retrieval
tasks. In online experiments across various real-world scenarios integrated
with our model, we observe a significant increase in Lifetime (LT), which is a
crucial indicator for the recommendation experience. For instance, the model
delivers the 7-day LT gain of +0.158% and the 14-day LT gain of +0.144% in the
Douyin-Selected scenario. For the Douyin feed rank model, the match features
produced by SAIL-Embedding yield a +0.08% AUC gain.
FlashVSR: Towards Real-Time Diffusion-Based Streaming Video
  Super-Resolution
Authors: name: Junhao Zhuang, name: Shi Guo, name: Xin Cai, name: Xiaohui Li, name: Yihao Liu, name: Chun Yuan, name: Tianfan Xue
Published: 2025-10-14 17:25:54

Diffusion models have recently advanced video restoration, but applying them
to real-world video super-resolution (VSR) remains challenging due to high
latency, prohibitive computation, and poor generalization to ultra-high
resolutions. Our goal in this work is to make diffusion-based VSR practical by
achieving efficiency, scalability, and real-time performance. To this end, we
propose FlashVSR, the first diffusion-based one-step streaming framework
towards real-time VSR. FlashVSR runs at approximately 17 FPS for 768x1408
videos on a single A100 GPU by combining three complementary innovations: (i) a
train-friendly three-stage distillation pipeline that enables streaming
super-resolution, (ii) locality-constrained sparse attention that cuts
redundant computation while bridging the train-test resolution gap, and (iii) a
tiny conditional decoder that accelerates reconstruction without sacrificing
quality. To support large-scale training, we also construct VSR-120K, a new
dataset with 120k videos and 180k images. Extensive experiments show that
FlashVSR scales reliably to ultra-high resolutions and achieves
state-of-the-art performance with up to 12x speedup over prior one-step
diffusion VSR models. We will release the code, pretrained models, and dataset
to foster future research in efficient diffusion-based VSR.
LLM Reasoning for Machine Translation: Synthetic Data Generation over
  Thinking Tokens
Authors: name: Armel Zebaze, name: Rachel Bawden, name: Benoît Sagot
Published: 2025-10-13 20:41:01

Large reasoning models (LRMs) have led to new possibilities in terms of
problem-solving, through the devising of a natural language thought process
prior to answering a query. While their capabilities are well known across
mathematics and coding tasks, their impact on the task of machine translation
(MT) remains underexplored. In this work, we explore the benefits of the
generation of intermediate tokens when performing MT across multiple language
pairs of different levels of resourcedness and multiple setups. We find that
"thinking tokens" do not help LRMs better perform MT. This result generalizes
to models fine-tuned to reason before translating using distilled chain of
thought (CoT) inspired by human translators' practices. Specifically,
fine-tuning a model with synthetic CoT explanations detailing how to translate
step-by-step does not outperform standard input-output fine-tuning. However,
constructing the intermediate tokens by combining the outputs of modular
translation-specific prompting strategies results in improvements. Our findings
underscore that the contribution of intermediate tokens during fine-tuning
highly depends on the presence of translation attempts within them. More
broadly, our results suggest that using a teacher to refine target translations
or to expand parallel corpora is more impactful than distilling their CoT
explanations into "thinking" MT models.
Advancing End-to-End Pixel Space Generative Modeling via Self-supervised
  Pre-training
Authors: name: Jiachen Lei, name: Keli Liu, name: Julius Berner, name: Haiming Yu, name: Hongkai Zheng, name: Jiahong Wu, name: Xiangxiang Chu
Published: 2025-10-14 14:41:16

Pixel-space generative models are often more difficult to train and generally
underperform compared to their latent-space counterparts, leaving a persistent
performance and efficiency gap. In this paper, we introduce a novel two-stage
training framework that closes this gap for pixel-space diffusion and
consistency models. In the first stage, we pre-train encoders to capture
meaningful semantics from clean images while aligning them with points along
the same deterministic sampling trajectory, which evolves points from the prior
to the data distribution. In the second stage, we integrate the encoder with a
randomly initialized decoder and fine-tune the complete model end-to-end for
both diffusion and consistency models. Our training framework demonstrates
strong empirical performance on ImageNet dataset. Specifically, our diffusion
model reaches an FID of 2.04 on ImageNet-256 and 2.35 on ImageNet-512 with 75
number of function evaluations (NFE), surpassing prior pixel-space methods by a
large margin in both generation quality and efficiency while rivaling leading
VAE-based models at comparable training cost. Furthermore, on ImageNet-256, our
consistency model achieves an impressive FID of 8.82 in a single sampling step,
significantly surpassing its latent-space counterpart. To the best of our
knowledge, this marks the first successful training of a consistency model
directly on high-resolution images without relying on pre-trained VAEs or
diffusion models.
Memory as Action: Autonomous Context Curation for Long-Horizon Agentic
  Tasks
Authors: name: Yuxiang Zhang, name: Jiangming Shu, name: Ye Ma, name: Xueyuan Lin, name: Shangxi Wu, name: Jitao Sang
Published: 2025-10-14 15:29:57

Large Language Models face challenges in long-horizon agentic tasks as their
constrained memory is easily overwhelmed by distracting or irrelevant context.
Existing working memory methods typically rely on external, heuristic
mechanisms that are decoupled from the agent's core policy. In this work, we
reframe working memory management as a learnable, intrinsic capability. We
propose a novel framework, Memory-as-Action, where an agent actively manages
its working memory by executing explicit editing operations as part of a
unified policy. This formulation allows an agent, trained via reinforcement
learning, to balance memory curation against long-term task objectives under
given resource constraints. However, such memory editing actions break the
standard assumption of a continuously growing prefix in LLM interactions,
leading to what we call trajectory fractures. These non-prefix changes disrupt
the causal continuity required by standard policy gradient methods, making
those methods inapplicable. To address this, we propose a new algorithm,
Dynamic Context Policy Optimization, which enables stable end-to-end
reinforcement learning by segmenting trajectories at memory action points and
applying trajectory-level advantages to the resulting action segments. Our
results demonstrate that jointly optimizing for task reasoning and memory
management in an end-to-end fashion not only reduces overall computational
consumption but also improves task performance, driven by adaptive context
curation strategies tailored to the model's intrinsic capabilities.
Detect Anything via Next Point Prediction
Authors: name: Qing Jiang, name: Junan Huo, name: Xingyu Chen, name: Yuda Xiong, name: Zhaoyang Zeng, name: Yihao Chen, name: Tianhe Ren, name: Junzhi Yu, name: Lei Zhang
Published: 2025-10-14 17:59:54

Object detection has long been dominated by traditional coordinate
regression-based models, such as YOLO, DETR, and Grounding DINO. Although
recent efforts have attempted to leverage MLLMs to tackle this task, they face
challenges like low recall rate, duplicate predictions, coordinate
misalignment, etc. In this work, we bridge this gap and propose Rex-Omni, a
3B-scale MLLM that achieves state-of-the-art object perception performance. On
benchmarks like COCO and LVIS, Rex-Omni attains performance comparable to or
exceeding regression-based models (e.g., DINO, Grounding DINO) in a zero-shot
setting. This is enabled by three key designs: 1) Task Formulation: we use
special tokens to represent quantized coordinates from 0 to 999, reducing the
model's learning difficulty and improving token efficiency for coordinate
prediction; 2) Data Engines: we construct multiple data engines to generate
high-quality grounding, referring, and pointing data, providing semantically
rich supervision for training; \3) Training Pipelines: we employ a two-stage
training process, combining supervised fine-tuning on 22 million data with
GRPO-based reinforcement post-training. This RL post-training leverages
geometry-aware rewards to effectively bridge the discrete-to-continuous
coordinate prediction gap, improve box accuracy, and mitigate undesirable
behaviors like duplicate predictions that stem from the teacher-guided nature
of the initial SFT stage. Beyond conventional detection, Rex-Omni's inherent
language understanding enables versatile capabilities such as object referring,
pointing, visual prompting, GUI grounding, spatial referring, OCR and
key-pointing, all systematically evaluated on dedicated benchmarks. We believe
that Rex-Omni paves the way for more versatile and language-aware visual
perception systems.
UniFusion: Vision-Language Model as Unified Encoder in Image Generation
Authors: name: Kevin Li, name: Manuel Brack, name: Sudeep Katakol, name: Hareesh Ravi, name: Ajinkya Kale
Published: 2025-10-14 17:57:56

Although recent advances in visual generation have been remarkable, most
existing architectures still depend on distinct encoders for images and text.
This separation constrains diffusion models' ability to perform cross-modal
reasoning and knowledge transfer. Prior attempts to bridge this gap often use
the last layer information from VLM, employ multiple visual encoders, or train
large unified models jointly for text and image generation, which demands
substantial computational resources and large-scale data, limiting its
accessibility.We present UniFusion, a diffusion-based generative model
conditioned on a frozen large vision-language model (VLM) that serves as a
unified multimodal encoder. At the core of UniFusion is the Layerwise Attention
Pooling (LAP) mechanism that extracts both high level semantics and low level
details from text and visual tokens of a frozen VLM to condition a diffusion
generative model. We demonstrate that LAP outperforms other shallow fusion
architectures on text-image alignment for generation and faithful transfer of
visual information from VLM to the diffusion model which is key for editing. We
propose VLM-Enabled Rewriting Injection with Flexibile Inference (VERIFI),
which conditions a diffusion transformer (DiT) only on the text tokens
generated by the VLM during in-model prompt rewriting. VERIFI combines the
alignment of the conditioning distribution with the VLM's reasoning
capabilities for increased capabilities and flexibility at inference. In
addition, finetuning on editing task not only improves text-image alignment for
generation, indicative of cross-modality knowledge transfer, but also exhibits
tremendous generalization capabilities. Our model when trained on single image
editing, zero-shot generalizes to multiple image references further motivating
the unified encoder design of UniFusion.
DeepMMSearch-R1: Empowering Multimodal LLMs in Multimodal Web Search
Authors: name: Kartik Narayan, name: Yang Xu, name: Tian Cao, name: Kavya Nerella, name: Vishal M. Patel, name: Navid Shiee, name: Peter Grasch, name: Chao Jia, name: Yinfei Yang, name: Zhe Gan
Published: 2025-10-14 17:59:58

Multimodal Large Language Models (MLLMs) in real-world applications require
access to external knowledge sources and must remain responsive to the dynamic
and ever-changing real-world information in order to address
information-seeking and knowledge-intensive user queries. Existing approaches,
such as retrieval augmented generation (RAG) methods, search agents, and search
equipped MLLMs, often suffer from rigid pipelines, excessive search calls, and
poorly constructed search queries, which result in inefficiencies and
suboptimal outcomes. To address these limitations, we present DeepMMSearch-R1,
the first multimodal LLM capable of performing on-demand, multi-turn web
searches and dynamically crafting queries for both image and text search tools.
Specifically, DeepMMSearch-R1 can initiate web searches based on relevant crops
of the input image making the image search more effective, and can iteratively
adapt text search queries based on retrieved information, thereby enabling
self-reflection and self-correction. Our approach relies on a two-stage
training pipeline: a cold start supervised finetuning phase followed by an
online reinforcement learning optimization. For training, we introduce
DeepMMSearchVQA, a novel multimodal VQA dataset created through an automated
pipeline intermixed with real-world information from web search tools. This
dataset contains diverse, multi-hop queries that integrate textual and visual
information, teaching the model when to search, what to search for, which
search tool to use and how to reason over the retrieved information. We conduct
extensive experiments across a range of knowledge-intensive benchmarks to
demonstrate the superiority of our approach. Finally, we analyze the results
and provide insights that are valuable for advancing multimodal web-search.
GAR: Generative Adversarial Reinforcement Learning for Formal Theorem
  Proving
Authors: name: Ruida Wang, name: Jiarui Yao, name: Rui Pan, name: Shizhe Diao, name: Tong Zhang
Published: 2025-10-13 17:56:25

Solving math problems through verifiable languages such as Lean has
significantly impacted both the mathematics and computer science communities.
Current state-of-the-art models are often trained with expensive online
Reinforcement Learning (RL) or expert iteration. However, these approaches rely
on fixed problem sets, which causes inefficient training and limits the model
to tackle complex problems. To overcome these limitations, we propose GAR:
Generative Adversarial Reinforcement learning, a comprehensive RL training
framework that jointly trains the problem composer and solver in an adversarial
loop. GAR introduces an implicit curriculum learning mechanism, which aligns
task difficulty with the prover's evolving capability. It thereby improves the
training efficiency and enables stronger performance of proving advanced
theorems. Experiments show that with GAR training, Goedel-Prover-V2-8B and
DeepSeek-Prover-V2-7B achieve an average relative improvement in pass@32 of
4.20% on MiniF2F-Test benchmark, while DeepSeek-Prover-V2's pass@32 on
ProofNet-Test increases from 22.58% to 25.81%. Beyond formal proving, GAR
establishes a general RL paradigm for co-evolution of problem generation and
solving under verifiable environments.
VLM-Guided Adaptive Negative Prompting for Creative Generation
Authors: name: Shelly Golan, name: Yotam Nitzan, name: Zongze Wu, name: Or Patashnik
Published: 2025-10-12 17:34:59

Creative generation is the synthesis of new, surprising, and valuable samples
that reflect user intent yet cannot be envisioned in advance. This task aims to
extend human imagination, enabling the discovery of visual concepts that exist
in the unexplored spaces between familiar domains. While text-to-image
diffusion models excel at rendering photorealistic scenes that faithfully match
user prompts, they still struggle to generate genuinely novel content. Existing
approaches to enhance generative creativity either rely on interpolation of
image features, which restricts exploration to predefined categories, or
require time-intensive procedures such as embedding optimization or model
fine-tuning. We propose VLM-Guided Adaptive Negative-Prompting, a
training-free, inference-time method that promotes creative image generation
while preserving the validity of the generated object. Our approach utilizes a
vision-language model (VLM) that analyzes intermediate outputs of the
generation process and adaptively steers it away from conventional visual
concepts, encouraging the emergence of novel and surprising outputs. We
evaluate creativity through both novelty and validity, using statistical
metrics in the CLIP embedding space. Through extensive experiments, we show
consistent gains in creative novelty with negligible computational overhead.
Moreover, unlike existing methods that primarily generate single objects, our
approach extends to complex scenarios, such as generating coherent sets of
creative objects and preserving creativity within elaborate compositional
prompts. Our method integrates seamlessly into existing diffusion pipelines,
offering a practical route to producing creative outputs that venture beyond
the constraints of textual descriptions.