🔥 Continuous-Time Distribution Matching for Few-Step Diffusion Distillation

💡 The paper introduces Continuous-Time Distribution Matching, a new method for accelerating diffusion models through distillation. The existing Distribution Matching Distillation method has limitations, as it relies on sparse supervision at discrete timesteps, leading to visual artifacts and over-smoothed outputs. To address this, the authors propose a continuous-time optimization approach that replaces the fixed discrete schedule with a dynamic continuous schedule, allowing distribution matching to be enforced at arbitrary points along sampling trajectories. Additionally, they introduce a continuous-time alignment objective that performs active off-trajectory matching on latents extrapolated via the student's velocity field, improving generalization and preserving fine visual details. The results show that this new method, called Continuous-Time Distribution Matching, provides highly competitive visual fidelity for few-step image generation without relying on complex auxiliary objectives, outperforming existing methods on various architectures. The code for the method is made available, demonstrating the effectiveness of this new approach for diffusion model distillation.

📅 Published on May 7

🔗 Links:
• arXiv: https://arxiv.org/abs/2605.06376
• PDF: https://arxiv.org/pdf/2605.06376
• Project Page: https://byliutao.github.io/cdm_page/
• GitHub: https://github.com/byliutao/cdm ⭐ 22

🤖 Models citing this paper:
• https://huggingface.co/byliutao/stable-diffusion-3-medium-turbo
• https://huggingface.co/byliutao/Longcat-Image-Turbo

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📢 By: https://xn--r1a.website/PaperNexus

#DiffusionModelDistillation #ContinuousTimeDistributionMatching #FewStepDiffusion #DistributionMatchingDistillation #DiffusionBasedImageSynthesis

🔥 MARBLE: Multi-Aspect Reward Balance for Diffusion RL

💡 The paper introduces MARBLE, a novel gradient-space optimization framework for multi-reward reinforcement learning fine-tuning of diffusion models. The problem addressed is that existing methods for handling multiple rewards either train separate models for each reward or use a weighted-sum reward aggregation, which can lead to poor performance due to sample-level mismatch. This mismatch occurs because most rollouts are highly informative for certain reward dimensions but irrelevant for others, causing the weighted summation to dilute their supervision.

To address this issue, MARBLE maintains independent advantage estimators for each reward and computes per-reward policy gradients. These gradients are then harmonized into a single update direction without manual reward weighting, by solving a quadratic programming problem. This approach allows for a unified model that can be jointly trained on all rewards, eliminating the need for heavy manual tuning and sequential training.

The authors also propose an amortized formulation that reduces the computational cost of MARBLE, making it more efficient. Additionally, they use exponential moving average smoothing on the balancing coefficients to stabilize updates against transient fluctuations.

The results show that MARBLE improves all five reward dimensions simultaneously on the SD3.5 Medium dataset, outperforming the baseline method. Specifically, MARBLE turns the worst-aligned reward's gradient cosine from negative to consistently positive, indicating better alignment with human preferences. Furthermore, MARBLE runs at nearly the same training speed as the baseline method, with only a 3% slowdown. Overall, MARBLE provides a more effective and efficient approach to multi-reward reinforcement learning fine-tuning of diffusion models.

📅 Published on May 7

🔗 Links:
• arXiv: https://arxiv.org/abs/2605.06507
• PDF: https://arxiv.org/pdf/2605.06507
• Project Page: https://aim-uofa.github.io/MARBLE/
• GitHub: https://github.com/aim-uofa/MARBLE ⭐ 24

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📢 By: https://xn--r1a.website/PaperNexus

#MultiRewardReinforcementLearning #DiffusionModels #GradientSpaceOptimization #MultiAspectRewardBalance #ReinforcementLearningFineTuning

🔥 SwiftI2V: Efficient High-Resolution Image-to-Video Generation via Conditional Segment-wise Generation

💡 The paper proposes SwiftI2V, an efficient framework for high resolution image to video generation. The problem addressed is that existing methods for generating high resolution videos from images are either computationally expensive or lack fidelity to the input image. High resolution image to video generation aims to synthesize realistic temporal dynamics while preserving fine grained appearance details of the input image, but at 2K resolution, this becomes extremely challenging. Existing solutions suffer from weaknesses such as high memory and latency costs, or hallucinating details and drifting from input specific local structures.

The proposed method, SwiftI2V, addresses these weaknesses by using a two stage design. First, it generates a low resolution motion reference to reduce token costs and ease the modeling burden. Then, it performs a strongly image conditioned 2K synthesis guided by the motion to recover input faithful details with controlled overhead. To make generation more scalable, SwiftI2V introduces Conditional Segment wise Generation, which synthesizes videos segment by segment with a bounded per step token budget. It also adopts bidirectional contextual interaction within each segment to improve cross segment coherence and input fidelity.

The results show that SwiftI2V achieves performance comparable to end to end baselines while reducing total GPU time by 202 times on the VBench I2V benchmark at 2K resolution. This enables practical 2K image to video generation on a single datacenter GPU or consumer GPU, making it a significant contribution to the field of image to video generation. Overall, SwiftI2V provides an efficient and scalable solution for high resolution image to video generation, addressing the efficiency fidelity dilemma and achieving state of the art results.

📅 Published on May 7

🔗 Links:
• arXiv: https://arxiv.org/abs/2605.06356
• PDF: https://arxiv.org/pdf/2605.06356
• Project Page: https://hkust-longgroup.github.io/SwiftI2V/
• GitHub: https://github.com/HKUST-LongGroup/SwiftI2V ⭐ 13

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📢 By: https://xn--r1a.website/PaperNexus

#ImageToVideoGeneration #HighResolutionVideoSynthesis #ConditionalSegmentWiseGeneration #EfficientVideoGeneration #ImageBasedVideoRendering

🔥 DeepCode: Open Agentic Coding

💡 The paper introduces DeepCode, a fully autonomous framework that addresses the challenge of converting documents into codebases, such as turning scientific papers into code. The existing methods for doing this have significant limitations due to the large amount of information in documents and the limited context that large language models can handle. DeepCode solves this problem by optimizing the flow of information through four key operations: source compression, structured indexing, knowledge injection, and error correction.

The source compression operation uses blueprint distillation to reduce the amount of information in the document. The structured indexing operation uses stateful code memory to organize the information in a way that makes it easier to access and use. The knowledge injection operation uses retrieval-augmented generation to add relevant knowledge to the code. The error correction operation uses closed-loop error correction to ensure that the code is accurate and reliable.

The paper evaluates DeepCode on a benchmark called PaperBench and finds that it achieves state-of-the-art performance, outperforming leading commercial agents and even surpassing PhD-level human experts. This means that DeepCode can take a scientific paper and turn it into code that is comparable in quality to code written by a human expert. The results of this paper have significant implications for the field of autonomous scientific reproduction, as they demonstrate the potential for AI systems to accelerate research evaluation and discovery by automating the process of converting scientific papers into code. Overall, the paper presents a major breakthrough in the field of document-to-codebase synthesis and has the potential to revolutionize the way that scientific research is conducted.

📅 Published on Dec 8, 2025

🔗 Links:
• arXiv: https://arxiv.org/abs/2512.07921
• PDF: https://arxiv.org/pdf/2512.07921
• GitHub: https://github.com/HKUDS/DeepCode ⭐ 15.4k
• Project Page: https://huggingface.co/papers/2511.03404

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📢 By: https://xn--r1a.website/PaperNexus

#AgenticCoding #AutonomousCodeGeneration #DocumentToCode #CodeMemoryArchitecture #LargeLanguageModelOptimization

🔥 Advances and Challenges in Foundation Agents: From Brain-Inspired Intelligence to Evolutionary, Collaborative, and Safe Systems

💡 This paper provides a comprehensive survey of the design, evaluation, and improvement of intelligent agents based on modular, brain-inspired architectures. The authors discuss the challenges and opportunities in developing advanced intelligent agents capable of sophisticated reasoning, robust perception, and versatile action across diverse domains. The survey is structured into four parts, starting with the modular foundation of intelligent agents, where the authors map cognitive, perceptual, and operational modules onto analogous human brain functionalities, including memory, world modeling, reward processing, and emotion-like systems.

The second part of the survey focuses on self-enhancement and adaptive evolution mechanisms, exploring how agents can autonomously refine their capabilities, adapt to dynamic environments, and achieve continual learning through automated optimization paradigms, including emerging AutoML and LLM-driven optimization strategies. The authors also examine collaborative and evolutionary multi-agent systems, investigating the collective intelligence emerging from agent interactions, cooperation, and societal structures, and highlighting parallels to human social dynamics.

Finally, the paper addresses the critical imperative of building safe, secure, and beneficial AI systems, emphasizing intrinsic and extrinsic security threats, ethical alignment, robustness, and practical mitigation strategies necessary for trustworthy real-world deployment. The authors provide a comprehensive overview of the current state of intelligent agents, framing their design, evaluation, and improvement within a modular, brain-inspired architecture that integrates principles from cognitive science, neuroscience, and computational research.

The paper's contributions include a systematic mapping of intelligent agent modules onto human brain functionalities, an exploration of self-enhancement and adaptive evolution mechanisms, an examination of collaborative and evolutionary multi-agent systems, and an emphasis on building safe, secure, and beneficial AI systems. The authors provide a comprehensive framework for understanding the design, evaluation, and improvement of intelligent agents, and highlight the importance of addressing the challenges and opportunities in developing advanced intelligent agents capable of sophisticated reasoning, robust perception, and versatile action across diverse domains.

📅 Published on Mar 31, 2025

🔗 Links:
• arXiv: https://arxiv.org/abs/2504.01990
• PDF: https://arxiv.org/pdf/2504.01990
• GitHub: https://github.com/FoundationAgents/awesome-foundation-agents ⭐ 2.1k

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📢 By: https://xn--r1a.website/PaperNexus

#BrainInspiredIntelligence #EvolutionaryRobotics #CollaborativeAI #SafeAutonomousSystems #ModularIntelligentAgents

🔥 Beyond Semantic Similarity: Rethinking Retrieval for Agentic Search via Direct Corpus Interaction

💡 The paper discusses the limitations of traditional retrieval methods in complex tasks, particularly in agentic search where agents need to query raw text directly. Conventional retrieval systems use a fixed similarity interface that compresses access into a single step, making it difficult to implement tasks that require multiple steps, such as discovering intermediate entities, combining weak clues, and revising plans. To address this limitation, the authors propose direct corpus interaction, where an agent searches the raw corpus directly using general-purpose tools without any embedding model or retrieval API. This approach requires no offline indexing and adapts naturally to evolving local corpora. The authors evaluate this approach on several benchmarks and tasks, including IR benchmarks and end-to-end agentic search tasks, and find that it substantially outperforms strong baselines, including sparse, dense, and reranking methods. The results indicate that direct corpus interaction is a simple yet effective approach that opens a broader interface-design space for agentic search, and that retrieval quality depends not only on reasoning ability but also on the resolution of the interface through which the model interacts with the corpus. Overall, the paper contributes to the development of more effective agentic search systems by introducing a new approach to retrieval that allows for more flexible and direct interaction with the corpus.

📅 Published on May 3

🔗 Links:
• arXiv: https://arxiv.org/abs/2605.05242
• PDF: https://arxiv.org/pdf/2605.05242
• GitHub: https://github.com/DCI-Agent/DCI-Agent-Lite ⭐ 20

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📢 By: https://xn--r1a.website/PaperNexus

#AgenticSearch #DirectCorpusInteraction #SemanticRetrieval #InformationRetrievalSystems #QueryingRawText

🔥 UI-TARS-2 Technical Report: Advancing GUI Agent with Multi-Turn Reinforcement Learning

💡 The paper presents UI-TARS-2, a native GUI-centered agent model that addresses challenges in data scalability, multi-turn reinforcement learning, and environment stability. The development of autonomous agents for graphical user interfaces is a major challenge in artificial intelligence, with open problems in data scalability, multi-turn reinforcement learning, and environment stability. To address these challenges, the authors propose a systematic training methodology that includes a data flywheel for scalable data generation, a stabilized multi-turn reinforcement learning framework, a hybrid GUI environment that integrates file systems and terminals, and a unified sandbox platform for large-scale rollouts.

The authors evaluate UI-TARS-2 on various benchmarks, including GUI benchmarks such as Online-Mind2Web, OSWorld, WindowsAgentArena, and AndroidWorld, as well as game environments and software engineering benchmarks. The results show that UI-TARS-2 achieves significant improvements over its predecessor UI-TARS-1.5 and strong baselines such as Claude and OpenAI agents. Specifically, UI-TARS-2 reaches high scores on GUI benchmarks, attains a mean normalized score of 59.8 across a 15-game suite, and remains competitive with frontier proprietary models on LMGame-Bench.

The model also generalizes to long-horizon information-seeking tasks and software engineering benchmarks, highlighting its robustness across diverse agent tasks. The authors provide detailed analyses of training dynamics, which provide insights into achieving stability and efficiency in large-scale agent reinforcement learning. Overall, the paper demonstrates UI-TARS-2's potential to advance the state of GUI agents and exhibit strong generalization to real-world interactive scenarios. The contributions of the paper include the development of a systematic training methodology, the evaluation of UI-TARS-2 on various benchmarks, and the analysis of training dynamics, which provide insights into achieving stability and efficiency in large-scale agent reinforcement learning.

📅 Published on Sep 2, 2025

🔗 Links:
• arXiv: https://arxiv.org/abs/2509.02544
• PDF: https://arxiv.org/pdf/2509.02544
• Project Page: https://seed-tars.com/showcase/ui-tars-2/
• GitHub: https://github.com/bytedance/ui-tars ⭐ 10.3k

🤖 Models citing this paper:
• https://huggingface.co/meituan/EvoCUA-32B-20260105
• https://huggingface.co/meituan/EvoCUA-8B-20260105

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📢 By: https://xn--r1a.website/PaperNexus

#GraphicalUserInterfaceLearning #MultiTurnReinforcementLearning #GUIAgentDevelopment #AutonomousAgentDesign #ReinforcementLearningForGUI

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🔥 QuantAgent: Price-Driven Multi-Agent LLMs for High-Frequency Trading

💡 The paper introduces QuantAgent, a multi-agent large language model framework designed specifically for high-frequency trading. High-frequency trading requires rapid and precise decisions based on short-term market signals, which is different from traditional financial applications that involve long-term semantic reasoning. Existing large language models are not well-suited for high-frequency trading due to their lack of structured reasoning capabilities and domain-specific tools.

To address this problem, the QuantAgent framework decomposes trading into four specialized agents: Indicator, Pattern, Trend, and Risk. Each agent is equipped with domain-specific tools and structured reasoning capabilities to capture distinct aspects of market dynamics over short temporal windows. The Indicator agent focuses on technical indicators, the Pattern agent focuses on chart patterns, the Trend agent focuses on trend-based features, and the Risk agent focuses on risk management.

The results show that QuantAgent outperforms strong neural and rule-based baselines in terms of predictive accuracy and cumulative return over 4-hour trading intervals. The evaluation was conducted across ten financial instruments, including Bitcoin and Nasdaq futures, using zero-shot evaluations. The findings suggest that combining structured financial priors with language-native reasoning can unlock new potential for real-time decision systems in high-frequency financial markets.

The main contribution of the paper is the introduction of a multi-agent large language model framework that is specifically designed for high-frequency trading. The framework's ability to decompose trading into specialized agents and leverage domain-specific tools and structured reasoning capabilities makes it well-suited for the high-speed and precision-critical demands of high-frequency trading. The results demonstrate the effectiveness of the QuantAgent framework and highlight its potential for use in real-world high-frequency trading applications.

📅 Published on Sep 12, 2025

🔗 Links:
• arXiv: https://arxiv.org/abs/2509.09995
• PDF: https://arxiv.org/pdf/2509.09995
• Project Page: https://Y-Research-SBU.github.io/QuantAgent/
• GitHub: https://github.com/Y-Research-SBU/QuantAgent ⭐ 2.5k

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📢 By: https://xn--r1a.website/PaperNexus

#HighFrequencyTrading #MultiAgentSystems #LargeLanguageModels #FinancialMachineLearning #AlgorithmicTrading

🔥 3D Gaussian Splatting for Real-Time Radiance Field Rendering

💡 This paper presents a method for real-time novel-view synthesis of scenes using 3D Gaussian splatting for radiance field rendering. The problem addressed is that current methods for novel-view synthesis require costly neural networks to achieve high visual quality, and faster methods trade off speed for quality, making it difficult to achieve real-time rendering at high resolutions.

The proposed method represents the scene using 3D Gaussians, which preserve the desirable properties of continuous volumetric radiance fields while avoiding unnecessary computation in empty space. The method consists of three key elements: first, it starts with sparse points produced during camera calibration and represents the scene with 3D Gaussians; second, it performs interleaved optimization and density control of the 3D Gaussians, optimizing anisotropic covariance to achieve an accurate representation of the scene; and third, it develops a fast visibility-aware rendering algorithm that supports anisotropic splatting and accelerates both training and real-time rendering.

The results demonstrate state-of-the-art visual quality and real-time rendering at 1080p resolution, achieving display rates of at least 30 frames per second. The method is evaluated on several established datasets, showing its effectiveness in achieving high-quality real-time novel-view synthesis. Overall, the paper contributes a novel approach to radiance field rendering, enabling high-quality and efficient rendering of scenes in real-time.

📅 Published on Aug 8, 2023

🔗 Links:
• arXiv: https://arxiv.org/abs/2308.04079
• PDF: https://arxiv.org/pdf/2308.04079
• GitHub: https://github.com/graphdeco-inria/gaussian-splatting ⭐ 21.8k

🤖 Models citing this paper:
• https://huggingface.co/stevee00/InteriorGen3D

📊 Datasets citing this paper:
• https://huggingface.co/datasets/Voxel51/gaussian_splatting
• https://huggingface.co/datasets/Arkanos25/gaussian_splatting

🚀 Spaces citing this paper:
• https://huggingface.co/spaces/Voxel51/2025-ai-timeline

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📢 By: https://xn--r1a.website/PaperNexus

#3DGaussianSplatting #RealTimeRadianceFieldRendering #NovelViewSynthesis #VolumetricRendering #RadianceFieldRepresentation

🔥 Fish Audio S2 Technical Report

💡 The paper introduces Fish Audio S2, an open source text to speech system that features multi speaker capabilities, multi turn generation, and instruction following control through natural language descriptions. The system utilizes a multi stage training approach, which includes a staged data pipeline covering video captioning, speech captioning, voice quality assessment, and reward modeling. This approach allows for scalable training and improves the overall performance of the system. The authors also release their model weights, fine tuning code, and an inference engine, making it production ready for streaming. The inference engine achieves a real time factor of 0.195 and a time to first audio of below 100 milliseconds, indicating its efficiency and speed. The code and weights are made available on GitHub and Hugging Face, and users are encouraged to try custom voices on the website. Overall, the paper contributes to the advancement of open source text to speech systems, providing a robust and efficient solution for generating high quality speech.

📅 Published on Mar 9

🔗 Links:
• arXiv: https://arxiv.org/abs/2603.08823
• PDF: https://arxiv.org/pdf/2603.08823
• Project Page: https://fish.audio/
• GitHub: https://github.com/fishaudio/fish-speech ⭐ 30.2k

🤖 Models citing this paper:
• https://huggingface.co/fishaudio/s2-pro
• https://huggingface.co/drbaph/s2-pro-fp8
• https://huggingface.co/mlx-community/fish-audio-s2-pro-bf16

📊 Datasets citing this paper:
• https://huggingface.co/datasets/Izzyzlin/CFSDD

🚀 Spaces citing this paper:
• https://huggingface.co/spaces/artificialguybr/fish-s2-pro-zero
• https://huggingface.co/spaces/fguilleme/fish-s2-pro-zero
• https://huggingface.co/spaces/MAYA-AI/fish-s2-pro-zero

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📢 By: https://xn--r1a.website/PaperNexus

#TextToSpeechSystems #MultispeakerSynthesis #NaturalLanguageProcessing #SpeechGenerationModels #RealTimeAudioProcessing