✨Reverse Personalization
📝 Summary:
A reverse personalization framework using conditional diffusion inversion enables attribute-controllable face anonymization, balancing identity removal and image quality. AI-generated summary Recent t...
🔹 Publication Date: Published on Dec 28
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.22984
• PDF: https://arxiv.org/pdf/2512.22984
• Github: https://github.com/hanweikung/reverse-personalization
==================================
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📝 Summary:
A reverse personalization framework using conditional diffusion inversion enables attribute-controllable face anonymization, balancing identity removal and image quality. AI-generated summary Recent t...
🔹 Publication Date: Published on Dec 28
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.22984
• PDF: https://arxiv.org/pdf/2512.22984
• Github: https://github.com/hanweikung/reverse-personalization
==================================
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❤1
✨Introducing TrGLUE and SentiTurca: A Comprehensive Benchmark for Turkish General Language Understanding and Sentiment Analysis
📝 Summary:
Evaluating the performance of various model architectures, such as transformers, large language models (LLMs), and other NLP systems, requires comprehensive benchmarks that measure performance across ...
🔹 Publication Date: Published on Dec 26
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.22100
• PDF: https://arxiv.org/pdf/2512.22100
✨ Datasets citing this paper:
• https://huggingface.co/datasets/turkish-nlp-suite/TrGLUE
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📝 Summary:
Evaluating the performance of various model architectures, such as transformers, large language models (LLMs), and other NLP systems, requires comprehensive benchmarks that measure performance across ...
🔹 Publication Date: Published on Dec 26
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.22100
• PDF: https://arxiv.org/pdf/2512.22100
✨ Datasets citing this paper:
• https://huggingface.co/datasets/turkish-nlp-suite/TrGLUE
==================================
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❤1
✨Robo-Dopamine: General Process Reward Modeling for High-Precision Robotic Manipulation
📝 Summary:
The primary obstacle for applying reinforcement learning (RL) to real-world robotics is the design of effective reward functions. While recently learning-based Process Reward Models (PRMs) are a promi...
🔹 Publication Date: Published on Dec 29
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.23703
• PDF: https://arxiv.org/pdf/2512.23703
• Project Page: https://robo-dopamine.github.io/
• Github: https://github.com/FlagOpen/Robo-Dopamine
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📝 Summary:
The primary obstacle for applying reinforcement learning (RL) to real-world robotics is the design of effective reward functions. While recently learning-based Process Reward Models (PRMs) are a promi...
🔹 Publication Date: Published on Dec 29
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.23703
• PDF: https://arxiv.org/pdf/2512.23703
• Project Page: https://robo-dopamine.github.io/
• Github: https://github.com/FlagOpen/Robo-Dopamine
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❤1
✨KernelEvolve: Scaling Agentic Kernel Coding for Heterogeneous AI Accelerators at Meta
📝 Summary:
KernelEvolve is an agentic kernel coding framework for deep learning recommendation models. It automates kernel generation and optimization for diverse AI accelerators, significantly improving performance and reducing development time on heterogeneous hardware at scale.
🔹 Publication Date: Published on Dec 29
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.23236
• PDF: https://arxiv.org/pdf/2512.23236
==================================
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📝 Summary:
KernelEvolve is an agentic kernel coding framework for deep learning recommendation models. It automates kernel generation and optimization for diverse AI accelerators, significantly improving performance and reducing development time on heterogeneous hardware at scale.
🔹 Publication Date: Published on Dec 29
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.23236
• PDF: https://arxiv.org/pdf/2512.23236
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✨Shape of Thought: When Distribution Matters More than Correctness in Reasoning Tasks
📝 Summary:
We present the surprising finding that a language model's reasoning capabilities can be improved by training on synthetic datasets of chain-of-thought (CoT) traces from more capable models, even when ...
🔹 Publication Date: Published on Dec 24
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.22255
• PDF: https://arxiv.org/pdf/2512.22255
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📝 Summary:
We present the surprising finding that a language model's reasoning capabilities can be improved by training on synthetic datasets of chain-of-thought (CoT) traces from more capable models, even when ...
🔹 Publication Date: Published on Dec 24
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.22255
• PDF: https://arxiv.org/pdf/2512.22255
==================================
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✨Self-Evaluation Unlocks Any-Step Text-to-Image Generation
📝 Summary:
Self-E is a novel self-evaluating text-to-image model trained from scratch that supports any-step generation and combines local learning with self-driven global matching to achieve high quality even a...
🔹 Publication Date: Published on Dec 26
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.22374
• PDF: https://arxiv.org/pdf/2512.22374
• Project Page: https://xinyu-andy.github.io/SelfE-project/
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📝 Summary:
Self-E is a novel self-evaluating text-to-image model trained from scratch that supports any-step generation and combines local learning with self-driven global matching to achieve high quality even a...
🔹 Publication Date: Published on Dec 26
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.22374
• PDF: https://arxiv.org/pdf/2512.22374
• Project Page: https://xinyu-andy.github.io/SelfE-project/
==================================
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✨LeVo: High-Quality Song Generation with Multi-Preference Alignment
📝 Summary:
LeVo enhances lyrics-to-song generation. It uses an LM to parallelly model mixed and dual-track audio tokens for vocal-instrument harmony and sound quality. Direct Preference Optimization improves musicality and instruction following, outperforming existing methods.
🔹 Publication Date: Published on Jun 9
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2506.07520
• PDF: https://arxiv.org/pdf/2506.07520
• Project Page: https://levo-demo.github.io/
• Github: https://github.com/tencent-ailab/songgeneration
🔹 Models citing this paper:
• https://huggingface.co/tencent/SongGeneration
• https://huggingface.co/waytan22/SongGeneration-v1.5-beta
• https://huggingface.co/chaitnya26/SongGeneration-fork
✨ Spaces citing this paper:
• https://huggingface.co/spaces/tencent/SongGeneration
• https://huggingface.co/spaces/NeoPy/SongGeneration
• https://huggingface.co/spaces/Open-Hat-Lab/Song-Generator
==================================
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📝 Summary:
LeVo enhances lyrics-to-song generation. It uses an LM to parallelly model mixed and dual-track audio tokens for vocal-instrument harmony and sound quality. Direct Preference Optimization improves musicality and instruction following, outperforming existing methods.
🔹 Publication Date: Published on Jun 9
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2506.07520
• PDF: https://arxiv.org/pdf/2506.07520
• Project Page: https://levo-demo.github.io/
• Github: https://github.com/tencent-ailab/songgeneration
🔹 Models citing this paper:
• https://huggingface.co/tencent/SongGeneration
• https://huggingface.co/waytan22/SongGeneration-v1.5-beta
• https://huggingface.co/chaitnya26/SongGeneration-fork
✨ Spaces citing this paper:
• https://huggingface.co/spaces/tencent/SongGeneration
• https://huggingface.co/spaces/NeoPy/SongGeneration
• https://huggingface.co/spaces/Open-Hat-Lab/Song-Generator
==================================
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arXiv.org
LeVo: High-Quality Song Generation with Multi-Preference Alignment
Recent advances in large language models (LLMs) and audio language models have significantly improved music generation, particularly in lyrics-to-song generation. However, existing approaches...
✨DreamOmni3: Scribble-based Editing and Generation
📝 Summary:
DreamOmni3 introduces scribble-based editing and generation for more flexible image creation beyond text prompts. It proposes new tasks, data synthesis, and a joint input scheme using colored scribbles on source images for precise localization and complex edits.
🔹 Publication Date: Published on Dec 27
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.22525
• PDF: https://arxiv.org/pdf/2512.22525
• Github: https://github.com/dvlab-research/DreamOmni3
==================================
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📝 Summary:
DreamOmni3 introduces scribble-based editing and generation for more flexible image creation beyond text prompts. It proposes new tasks, data synthesis, and a joint input scheme using colored scribbles on source images for precise localization and complex edits.
🔹 Publication Date: Published on Dec 27
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.22525
• PDF: https://arxiv.org/pdf/2512.22525
• Github: https://github.com/dvlab-research/DreamOmni3
==================================
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✨End-to-End Test-Time Training for Long Context
📝 Summary:
This paper introduces End-to-End Test-Time Training TTT-E2E for long-context language models. It uses a standard Transformer that continually learns from context at test time, compressing information into its weights. TTT-E2E scales well with context length and offers constant inference latency, ...
🔹 Publication Date: Published on Dec 29
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.23675
• PDF: https://arxiv.org/pdf/2512.23675
• Github: https://github.com/test-time-training/e2e
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📝 Summary:
This paper introduces End-to-End Test-Time Training TTT-E2E for long-context language models. It uses a standard Transformer that continually learns from context at test time, compressing information into its weights. TTT-E2E scales well with context length and offers constant inference latency, ...
🔹 Publication Date: Published on Dec 29
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.23675
• PDF: https://arxiv.org/pdf/2512.23675
• Github: https://github.com/test-time-training/e2e
==================================
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✨GraphLocator: Graph-guided Causal Reasoning for Issue Localization
📝 Summary:
The issue localization task aims to identify the locations in a software repository that requires modification given a natural language issue description. This task is fundamental yet challenging in a...
🔹 Publication Date: Published on Dec 27
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.22469
• PDF: https://arxiv.org/pdf/2512.22469
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📝 Summary:
The issue localization task aims to identify the locations in a software repository that requires modification given a natural language issue description. This task is fundamental yet challenging in a...
🔹 Publication Date: Published on Dec 27
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.22469
• PDF: https://arxiv.org/pdf/2512.22469
==================================
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✨PhyGDPO: Physics-Aware Groupwise Direct Preference Optimization for Physically Consistent Text-to-Video Generation
📝 Summary:
Recent advances in text-to-video (T2V) generation have achieved good visual quality, yet synthesizing videos that faithfully follow physical laws remains an open challenge. Existing methods mainly bas...
🔹 Publication Date: Published on Dec 31, 2025
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.24551
• PDF: https://arxiv.org/pdf/2512.24551
• Project Page: https://caiyuanhao1998.github.io/project/PhyGDPO/
• Github: https://github.com/caiyuanhao1998/Open-PhyGDPO
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📝 Summary:
Recent advances in text-to-video (T2V) generation have achieved good visual quality, yet synthesizing videos that faithfully follow physical laws remains an open challenge. Existing methods mainly bas...
🔹 Publication Date: Published on Dec 31, 2025
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.24551
• PDF: https://arxiv.org/pdf/2512.24551
• Project Page: https://caiyuanhao1998.github.io/project/PhyGDPO/
• Github: https://github.com/caiyuanhao1998/Open-PhyGDPO
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✨Figure It Out: Improving the Frontier of Reasoning with Active Visual Thinking
📝 Summary:
Complex reasoning problems often involve implicit spatial, geometric, and structural relationships that are not explicitly encoded in text. While recent reasoning models have achieved strong performan...
🔹 Publication Date: Published on Dec 30, 2025
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.24297
• PDF: https://arxiv.org/pdf/2512.24297
• Github: https://github.com/chenmeiqii/FIGR
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📝 Summary:
Complex reasoning problems often involve implicit spatial, geometric, and structural relationships that are not explicitly encoded in text. While recent reasoning models have achieved strong performan...
🔹 Publication Date: Published on Dec 30, 2025
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.24297
• PDF: https://arxiv.org/pdf/2512.24297
• Github: https://github.com/chenmeiqii/FIGR
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✨Pretraining Frame Preservation in Autoregressive Video Memory Compression
📝 Summary:
We present PFP, a neural network structure to compress long videos into short contexts, with an explicit pretraining objective to preserve the high-frequency details of single frames at arbitrary temp...
🔹 Publication Date: Published on Dec 29, 2025
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.23851
• PDF: https://arxiv.org/pdf/2512.23851
• Github: https://github.com/lllyasviel/PFP
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📝 Summary:
We present PFP, a neural network structure to compress long videos into short contexts, with an explicit pretraining objective to preserve the high-frequency details of single frames at arbitrary temp...
🔹 Publication Date: Published on Dec 29, 2025
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.23851
• PDF: https://arxiv.org/pdf/2512.23851
• Github: https://github.com/lllyasviel/PFP
==================================
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✨Factorized Learning for Temporally Grounded Video-Language Models
📝 Summary:
Video-language models struggle with temporal grounding from coupled tasks. Our D^2VLM framework decouples grounding and textual response using evidence tokens. Factorized preference optimization explicitly optimizes temporal grounding for both tasks.
🔹 Publication Date: Published on Dec 30, 2025
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.24097
• PDF: https://arxiv.org/pdf/2512.24097
• Project Page: https://github.com/nusnlp/d2vlm
• Github: https://github.com/nusnlp/d2vlm
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📝 Summary:
Video-language models struggle with temporal grounding from coupled tasks. Our D^2VLM framework decouples grounding and textual response using evidence tokens. Factorized preference optimization explicitly optimizes temporal grounding for both tasks.
🔹 Publication Date: Published on Dec 30, 2025
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.24097
• PDF: https://arxiv.org/pdf/2512.24097
• Project Page: https://github.com/nusnlp/d2vlm
• Github: https://github.com/nusnlp/d2vlm
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✨JavisGPT: A Unified Multi-modal LLM for Sounding-Video Comprehension and Generation
📝 Summary:
This paper presents JavisGPT, the first unified multimodal large language model (MLLM) for Joint Audio-Video (JAV) comprehension and generation. JavisGPT adopts a concise encoder-LLM-decoder architect...
🔹 Publication Date: Published on Dec 28, 2025
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2503.23377
• PDF: https://arxiv.org/pdf/2512.22905
• Project Page: https://javisverse.github.io/JavisGPT-page/
• Github: https://github.com/JavisVerse/JavisGPT
🔹 Models citing this paper:
• https://huggingface.co/JavisVerse/JavisGPT-v0.1-7B-Instruct
✨ Datasets citing this paper:
• https://huggingface.co/datasets/JavisVerse/MM-PreTrain
• https://huggingface.co/datasets/JavisVerse/JavisUnd-Eval
• https://huggingface.co/datasets/JavisVerse/AV-FineTune
==================================
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📝 Summary:
This paper presents JavisGPT, the first unified multimodal large language model (MLLM) for Joint Audio-Video (JAV) comprehension and generation. JavisGPT adopts a concise encoder-LLM-decoder architect...
🔹 Publication Date: Published on Dec 28, 2025
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2503.23377
• PDF: https://arxiv.org/pdf/2512.22905
• Project Page: https://javisverse.github.io/JavisGPT-page/
• Github: https://github.com/JavisVerse/JavisGPT
🔹 Models citing this paper:
• https://huggingface.co/JavisVerse/JavisGPT-v0.1-7B-Instruct
✨ Datasets citing this paper:
• https://huggingface.co/datasets/JavisVerse/MM-PreTrain
• https://huggingface.co/datasets/JavisVerse/JavisUnd-Eval
• https://huggingface.co/datasets/JavisVerse/AV-FineTune
==================================
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arXiv.org
JavisDiT: Joint Audio-Video Diffusion Transformer with...
This paper introduces JavisDiT, a novel Joint Audio-Video Diffusion Transformer designed for synchronized audio-video generation (JAVG). Built upon the powerful Diffusion Transformer (DiT)...
✨Forging Spatial Intelligence: A Roadmap of Multi-Modal Data Pre-Training for Autonomous Systems
📝 Summary:
The rapid advancement of autonomous systems, including self-driving vehicles and drones, has intensified the need to forge true Spatial Intelligence from multi-modal onboard sensor data. While foundat...
🔹 Publication Date: Published on Dec 30, 2025
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.24385
• PDF: https://arxiv.org/pdf/2512.24385
• Github: https://github.com/worldbench/awesome-spatial-intelligence
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📝 Summary:
The rapid advancement of autonomous systems, including self-driving vehicles and drones, has intensified the need to forge true Spatial Intelligence from multi-modal onboard sensor data. While foundat...
🔹 Publication Date: Published on Dec 30, 2025
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.24385
• PDF: https://arxiv.org/pdf/2512.24385
• Github: https://github.com/worldbench/awesome-spatial-intelligence
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✨Valori: A Deterministic Memory Substrate for AI Systems
📝 Summary:
Valori introduces a deterministic AI memory substrate using fixed-point arithmetic, ensuring bit-identical results across platforms. This eliminates non-determinism from floating-point operations in vector embeddings and search, making AI systems trustworthy and verifiable.
🔹 Publication Date: Published on Dec 25, 2025
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.22280
• PDF: https://arxiv.org/pdf/2512.22280
• Project Page: https://valori.systems/
• Github: https://github.com/varshith-Git/Valori-Kernel
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📝 Summary:
Valori introduces a deterministic AI memory substrate using fixed-point arithmetic, ensuring bit-identical results across platforms. This eliminates non-determinism from floating-point operations in vector embeddings and search, making AI systems trustworthy and verifiable.
🔹 Publication Date: Published on Dec 25, 2025
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.22280
• PDF: https://arxiv.org/pdf/2512.22280
• Project Page: https://valori.systems/
• Github: https://github.com/varshith-Git/Valori-Kernel
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✨BEDA: Belief Estimation as Probabilistic Constraints for Performing Strategic Dialogue Acts
📝 Summary:
A framework called BEDA uses probabilistic constraints on belief estimation to improve strategic dialogue through formalized adversarial and alignment acts, outperforming baselines across multiple tas...
🔹 Publication Date: Published on Dec 31, 2025
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.24885
• PDF: https://arxiv.org/pdf/2512.24885
==================================
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📝 Summary:
A framework called BEDA uses probabilistic constraints on belief estimation to improve strategic dialogue through formalized adversarial and alignment acts, outperforming baselines across multiple tas...
🔹 Publication Date: Published on Dec 31, 2025
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.24885
• PDF: https://arxiv.org/pdf/2512.24885
==================================
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✨On the Role of Discreteness in Diffusion LLMs
📝 Summary:
This paper examines diffusion language models, highlighting five properties separating diffusion mechanics from language requirements. Existing approaches face structural trade-offs. Key issues identified are uniform corruption and token-wise marginal training, urging development of diffusion pro...
🔹 Publication Date: Published on Dec 27, 2025
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.22630
• PDF: https://arxiv.org/pdf/2512.22630
==================================
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📝 Summary:
This paper examines diffusion language models, highlighting five properties separating diffusion mechanics from language requirements. Existing approaches face structural trade-offs. Key issues identified are uniform corruption and token-wise marginal training, urging development of diffusion pro...
🔹 Publication Date: Published on Dec 27, 2025
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.22630
• PDF: https://arxiv.org/pdf/2512.22630
==================================
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✨DiffThinker: Towards Generative Multimodal Reasoning with Diffusion Models
📝 Summary:
DiffThinker introduces a generative multimodal reasoning framework using diffusion models. It reframes vision-centric tasks as image-to-image generation for superior logical consistency and spatial precision. DiffThinker significantly outperforms existing MLLMs across various domains, showcasing ...
🔹 Publication Date: Published on Dec 30, 2025
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.24165
• PDF: https://arxiv.org/pdf/2512.24165
• Project Page: https://diffthinker-project.github.io/
• Github: https://github.com/lcqysl/DiffThinker
==================================
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📝 Summary:
DiffThinker introduces a generative multimodal reasoning framework using diffusion models. It reframes vision-centric tasks as image-to-image generation for superior logical consistency and spatial precision. DiffThinker significantly outperforms existing MLLMs across various domains, showcasing ...
🔹 Publication Date: Published on Dec 30, 2025
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.24165
• PDF: https://arxiv.org/pdf/2512.24165
• Project Page: https://diffthinker-project.github.io/
• Github: https://github.com/lcqysl/DiffThinker
==================================
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❤1