🔥 IndexTTS: An Industrial-Level Controllable and Efficient Zero-Shot Text-To-Speech System

💡 The paper introduces IndexTTS, an enhanced text-to-speech system that combines the XTTS and Tortoise models to achieve improved naturalness, voice cloning, and controllable usage. The system addresses the limitations of existing text-to-speech systems, particularly in Chinese scenarios where polyphonic characters and long-tail characters can be challenging to pronounce. To overcome this, the authors propose a hybrid character-pinyin modeling approach that allows for more controllable pronunciations.

The authors also compare Vector Quantization with Finite-Scalar Quantization for codebook utilization of acoustic speech tokens, and introduce a conformer-based speech conditional encoder and BigVGAN2 to enhance voice cloning. The results show that IndexTTS achieves significant improvements in naturalness, content consistency, and zero-shot voice cloning compared to the XTTS model.

In comparison to other popular open-source text-to-speech systems, IndexTTS has a relatively simple training process, more controllable usage, and faster inference speed, while also surpassing their performance. The system is designed to be efficient and controllable, making it suitable for industrial-level applications. The authors provide demos of the system, which are available for evaluation. Overall, the paper presents a novel approach to text-to-speech synthesis that achieves state-of-the-art results and has the potential for practical applications.

📅 Published on Feb 8, 2025

🔗 Links:
• GitHub: https://github.com/huggingface
• arXiv: https://arxiv.org/abs/2502.05512
• PDF: https://arxiv.org/pdf/2502.05512
• Project Page: https://index-tts.github.io

🤖 Models citing this paper:
• https://huggingface.co/IndexTeam/IndexTTS-2
• https://huggingface.co/IndexTeam/Index-TTS
• https://huggingface.co/taraskurtizan/IndexTTS-2

📊 Datasets citing this paper:
• https://huggingface.co/datasets/echodict/index-tts

🚀 Spaces citing this paper:
• https://huggingface.co/spaces/IndexTeam/IndexTTS
• https://huggingface.co/spaces/Pendrokar/TTS-Spaces-Arena
• https://huggingface.co/spaces/alexnasa/OutofLipSync

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

#TextToSpeechSystems #ZeroShotLearning #VoiceCloningTechnology #ControllableSpeechSynthesis #SpeechRecognitionModels

🔥 QUEST: Training Frontier Deep Research Agents with Fully Synthetic Tasks

💡 The paper introduces QUEST, a family of open deep research agents that can perform well across diverse long horizon search tasks. The problem addressed is that existing open agents often generalize poorly across different task types, while frontier systems remain proprietary. To solve this, the authors propose a training recipe that combines mid training, supervised fine tuning, and reinforcement learning. A key component of this recipe is a curated data synthesis pipeline that applies to different task types and enables synthesizing training data with verifiable rewards without human annotation. The pipeline uses unified rubric trees to generate tasks. The authors also incorporate a built in context management mechanism that enables effective long horizon reasoning and knowledge synthesis. The results show that QUEST approaches or surpasses frontier closed source agents across eight deep research benchmarks using only 8K synthesized tasks. The models, data, and training scripts are released, making it possible for others to use and build upon the work. The contributions of the paper are the proposed training recipe, the data synthesis pipeline, and the release of the QUEST models, which provide a general purpose deep research agent that can handle a wide range of tasks, including fact seeking, citation grounding, and report synthesis.

📅 Published on May 22

🔗 Links:
• GitHub: https://github.com/huggingface
• arXiv: https://arxiv.org/abs/2605.24218
• PDF: https://arxiv.org/pdf/2605.24218
• Project Page: https://osu-nlp-group.github.io/QUEST/

🤖 Models citing this paper:
• https://huggingface.co/osunlp/QUEST-35B-RL
• https://huggingface.co/osunlp/QUEST-35B-MT-Plus-SFT
• https://huggingface.co/osunlp/QUEST-9B

📊 Datasets citing this paper:
• https://huggingface.co/datasets/osunlp/QUEST-RL-Data
• https://huggingface.co/datasets/osunlp/QUEST-SFT-Data-Objective
• https://huggingface.co/datasets/osunlp/QUEST-SFT-Data-Open-ended

🚀 Spaces citing this paper:
• https://huggingface.co/spaces/osunlp/QUEST

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

#DeepResearchAgents #LongHorizonSearchTasks #SyntheticTaskGeneration #ReinforcementLearningMethods #OpenAgentArchitectures

🔥 PaperFlow: Profiling, Recommending, and Adapting Across Daily Paper Streams

💡 The paper introduces PaperFlow, a framework for scientific paper recommendation that addresses the dynamic nature of scientific reading, where user interests shift and feedback accumulates over time. The traditional approach to scientific paper recommendation evaluates static ranking over a fixed candidate set, which does not reflect real-world reading behavior. PaperFlow organizes the recommendation process into three stages: Profiling, Recommending, and Adapting.

In the Profiling stage, a structured and inspectable scholarly profile is constructed and maintained from heterogeneous evidence, even in cases of cold start where limited information is available. The Recommending stage ranks each daily paper stream through multi-signal aggregation, considering a fixed display budget. The Adapting stage updates the user state from semantically distinct feedback signals and models interest drift across days.

To evaluate PaperFlow, the authors define a longitudinal user-day benchmark that consists of 24 simulated research users, 50 daily paper streams, and 1200 user-day episodes. This benchmark also includes 20727 unique papers and 497448 episode-paper records. The authors also specify a blind human-evaluation protocol to validate the alignment between automatic metrics and expert judgments.

The results show that PaperFlow outperforms five scientific recommendation baselines in terms of oracle-based ranking, behavioral alignment with simulated reading selections, and blind human-evaluation score. This indicates that PaperFlow is effective in capturing user interests and adapting to changes in their reading behavior over time. Overall, the paper contributes to the development of a more dynamic and personalized scientific paper recommendation system that reflects real-world reading behavior.

📅 Published on Jun 5

🔗 Links:
• GitHub: https://github.com/huggingface
• arXiv: https://arxiv.org/abs/2606.07454
• PDF: https://arxiv.org/pdf/2606.07454
• Project Page: https://openraiser.github.io/PaperFlow

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

#ScientificPaperRecommendation #DynamicUserModeling #PersonalizedRecommendationSystems #ScholarlyProfileConstruction #AdaptiveInformationRetrieval

🔥 Enhancing Financial Sentiment Analysis via Retrieval Augmented Large Language Models

💡 The paper addresses the challenge of financial sentiment analysis, which is crucial for investment decision-making. Traditional natural language processing models are limited by their size and training data, resulting in poor generalization and effectiveness. Large Language Models, despite their superior performance in various NLP tasks, also face challenges in financial sentiment analysis due to the discrepancy between their pre-training objective and the task of predicting sentiment labels. Additionally, the concise nature of financial news often lacks sufficient context, which can compromise the reliability of Large Language Models' sentiment analysis.

To overcome these challenges, the authors propose a retrieval-augmented Large Language Model framework. This framework consists of two modules: an instruction-tuned Large Language Model module that ensures the model behaves as a predictor of sentiment labels, and a retrieval-augmentation module that retrieves additional context from reliable external sources. This approach enables the model to leverage external context to improve its sentiment analysis capabilities.

The authors evaluate their framework against traditional models and other Large Language Models, such as ChatGPT and LLaMA. The results show that their approach achieves a significant performance gain, with improvements in accuracy and F1 score ranging from 15% to 48%. This demonstrates the effectiveness of the proposed retrieval-augmented Large Language Model framework in enhancing financial sentiment analysis. Overall, the paper contributes to the development of more accurate and reliable financial sentiment analysis models, which can inform better investment decisions.

📅 Published on Oct 6, 2023

🔗 Links:
• GitHub: https://github.com/huggingface
• arXiv: https://arxiv.org/abs/2310.04027
• PDF: https://arxiv.org/pdf/2310.04027

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

#FinancialSentimentAnalysis #RetrievalAugmentedModels #LargeLanguageModels #NaturalLanguageProcessing #FinancialTextAnalysis

🔥 Moebius: 0.2B Lightweight Image Inpainting Framework with 10B-Level Performance

💡 The paper presents Moebius, a lightweight image inpainting framework that achieves high fidelity results with significantly reduced parameters and inference time. The problem addressed is that current industrial foundation models for image inpainting have high computational costs, making them impractical for deployment. To solve this, the authors propose a novel approach that reconstructs the diffusion backbone using local-global interaction blocks and adaptive distillation strategies.

The method involves introducing the Local-λ Mix Interaction block, which consists of Local-λ and Interactive-λ modules. This block summarizes spatial contexts and global semantic priors into fixed-size linear matrices, preserving complex latent interactions while reducing parameters. The authors also propose an adaptive multi-granularity distillation strategy that operates within the latent space to avoid expensive pixel-space decoding. This strategy dynamically balances multiple gradient-based losses to achieve high-fidelity alignment.

The results show that Moebius achieves high-fidelity image inpainting results that rival or surpass those of the 10B-level industrial generalist FLUX.1-Fill-Dev, while using less than 2% of the parameters and delivering a more than 15 times acceleration in total inference time. The Moebius framework has 0.22 billion parameters, compared to 11.9 billion parameters in the industrial generalist model, and achieves a new efficiency standard for high-fidelity inpainting. Overall, the paper presents a highly efficient and optimized image inpainting framework that can be practically deployed.

📅 Published on Jun 17

🔗 Links:
• GitHub: https://github.com/huggingface
• arXiv: https://arxiv.org/abs/2606.19195
• PDF: https://arxiv.org/pdf/2606.19195
• Project Page: https://hustvl.github.io/Moebius

🤖 Models citing this paper:
• https://huggingface.co/hustvl/Moebius
• https://huggingface.co/simonw/Moebius-ONNX

🚀 Spaces citing this paper:
• https://huggingface.co/spaces/multimodalart/Moebius
• https://huggingface.co/spaces/Mike0021/moebius
• https://huggingface.co/spaces/jonatei/MoebiusDemo

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

#ImageInpainting #DiffusionBackbone #LightweightDeepLearning #LocalGlobalInteraction #EfficientComputerVision

🔥 GateMem: Benchmarking Memory Governance in Multi-Principal Shared-Memory Agents

💡 The paper introduces GateMem, a benchmark for evaluating the performance of multi-principal shared-memory agents. The problem addressed is that current memory agents are not reliable in shared institutional settings, such as hospitals, workplaces, and households, where multiple users with different roles and authorization contexts access and update a common memory pool. The challenge is to balance utility, access control, and forgetting in these settings.

The GateMem benchmark is designed to evaluate memory agents in terms of their ability to provide utility for legitimate requests, control access to sensitive information, and forget information that is no longer needed or has been explicitly deleted. The benchmark consists of a set of tasks and evaluation metrics that span multiple domains, including medical, office, education, and household settings.

The results of the paper show that current memory agents are not able to achieve strong performance on all three aspects of utility, access control, and forgetting. The authors evaluated several baseline models and backbone architectures, and found that no method is able to simultaneously achieve high utility, robust access control, and reliable forgetting. The results also show that long-context prompting can achieve good governance scores, but at a high computational cost, while retrieval-based and external-memory methods can reduce the cost but may leak unauthorized or deleted information.

Overall, the paper highlights the need for more research on developing reliable multi-principal shared-memory agents that can balance utility, access control, and forgetting in shared institutional settings. The GateMem benchmark provides a useful tool for evaluating the performance of these agents and identifying areas for improvement.

📅 Published on Jun 17

🔗 Links:
• GitHub: https://github.com/huggingface
• arXiv: https://arxiv.org/abs/2606.18829
• PDF: https://arxiv.org/pdf/2606.18829
• Project Page: https://rzhub.github.io/GateMem/project.html

📊 Datasets citing this paper:
• https://huggingface.co/datasets/Ray368/GateMem

🚀 Spaces citing this paper:
• https://huggingface.co/spaces/Ray368/GateMem-Submit

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

#MultiPrincipalSystems #SharedMemoryAgents #MemoryGovernance #AccessControlMechanisms #BenchmarkingArtificialIntelligence

🔥 Unlimited OCR Works

💡 The paper introduces Unlimited OCR, a model designed to efficiently transcribe long documents by addressing the growing memory consumption issue in existing end-to-end OCR models. The problem with current models is that as the output sequence length increases, memory consumption also increases due to the accumulated KV cache, leading to slower generation. To solve this, the authors propose Reference Sliding Window Attention, a new attention mechanism that reduces computation costs while maintaining a constant KV cache throughout the decoding process. This mechanism is applied to the decoder of the DeepSeek OCR model, replacing all attention layers. The resulting Unlimited OCR model can transcribe dozens of pages of documents in a single forward pass, making it more efficient. The proposed attention mechanism is also general-purpose and can be applied to other tasks such as speech recognition and translation. The authors make their code and model weights publicly available, allowing others to build upon their work. Overall, the paper contributes to the development of more efficient OCR models that can handle long documents without a significant decrease in performance.

📅 Published on Jun 22

🔗 Links:
• GitHub: https://github.com/huggingface
• arXiv: https://arxiv.org/abs/2606.23050
• PDF: https://arxiv.org/pdf/2606.23050

🤖 Models citing this paper:
• https://huggingface.co/baidu/Unlimited-OCR
• https://huggingface.co/Yehor/Unlimited-OCR

🚀 Spaces citing this paper:
• https://huggingface.co/spaces/akhaliq/Unlimited-OCR
• https://huggingface.co/spaces/Dlcastro/composa-unlimited-ocr-bench

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

#OpticalCharacterRecognition #EndToEndOCR #DeepLearningForOCR #ReferenceSlidingWindowAttention #EfficientDocumentTranscription

🔥 PlanBench-XL: Evaluating Long-Horizon Planning of LLM Tool-Use Agents in Large-Scale Tool Ecosystems

💡 The paper introduces PlanBench-XL, a benchmark for evaluating the ability of large language model agents to plan and adapt in complex environments with limited visibility and dynamic disruptions. The problem addressed is that existing benchmarks rarely evaluate planning under retrieval-limited tool visibility, where agents must discover relevant tools and adapt to dynamic environments over long horizons. To address this gap, PlanBench-XL is an interactive benchmark consisting of 327 retail tasks and 1665 tools, testing whether agents can iteratively retrieve usable tools and invoke them to achieve a final goal. The benchmark also features a blocking mechanism that simulates real-world unpredictability by introducing missing, failing, or distracting tool functions, forcing agents to detect disrupted paths and adapt at runtime. The method used is to evaluate ten leading large language models on PlanBench-XL, with and without the blocking mechanism. The results show that massive-tool planning remains challenging, with the best performing model achieving 51.90% accuracy in block-free settings but collapsing to 11.36% under the most severe blocking condition. Further analysis reveals that agents are vulnerable when failures lack explicit error signals or when recovery requires longer alternative tool-use paths. The paper's contributions establish PlanBench-XL as a testbed for diagnosing agentic planning failures and highlight the need for robust adaptive planning in long-horizon tasks with large, imperfect tool environments.

📅 Published on Jun 21

🔗 Links:
• GitHub: https://github.com/huggingface
• arXiv: https://arxiv.org/abs/2606.22388
• PDF: https://arxiv.org/pdf/2606.22388
• Project Page: https://planbench-xl.github.io/

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

#LongHorizonPlanning #LLMToolUse #LargeScaleToolEcosystems #PlanningUnderUncertainty #ToolVisibilityEvaluation

🔥 MeshFlow: Mesh Generation with Equivariant Flow Matching

💡 MeshFlow is a method for generating triangle meshes directly using equivariant optimal-transport flow matching models. The problem of generating meshes is challenging due to the symmetries present in the representation, including permutation invariance of faces and vertices. Traditional autoregressive methods serialize meshes into long sequences, which can be slow and inefficient.

MeshFlow addresses this problem by learning to generate triangle meshes as triangle soups, which are unordered collections of triangles. The method uses equivariant optimal-transport flow matching models that respect the symmetries of triangle soups, including arbitrary permutations of faces and permutations of vertices within each face.

To achieve this, the authors propose a modification to the Diffusion Transformer architecture, resulting in a scalable network that can model a velocity field while maintaining the desired equivariance. The authors also introduce an optimal-transport-based training objective that improves convergence by eliminating supervision signals that violate these symmetries.

The results show that MeshFlow achieves mesh quality comparable to state-of-the-art autoregressive mesh generators, but provides a significant speedup of about 18 times during inference. This makes MeshFlow a more efficient and effective method for generating high-quality triangle meshes. Overall, the contributions of MeshFlow include a novel method for generating triangle meshes, a scalable and equivariant network architecture, and an optimal-transport-based training objective that improves convergence and mesh quality.

📅 Published on Jun 22

🔗 Links:
• GitHub: https://github.com/huggingface
• arXiv: https://arxiv.org/abs/2606.23489
• PDF: https://arxiv.org/pdf/2606.23489
• Project Page: https://qiisun.github.io/MeshFlow/

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

#EquivariantFlowMatching #MeshGeneration #OptimalTransportModels #TriangleMeshes #GeometricDeepLearning

🔥 DataClaw0: Agentic Tailoring Multimodal Data from Raw Streams

💡 The paper introduces a new paradigm called Agentic Data Tailoring which aims to structure high entropy multimodal data streams using learnable data processing. The problem addressed is that existing methods for processing unstructured multimodal data are costly and inefficient, failing to unlock the deep procedural logic embedded in the data. The proposed method uses a two stage pipeline, first generating a large scale dataset using generative semantic synthesis and deterministic factual anchors, and then training a model called DataClaw0-9B using supervised fine tuning and group relative policy optimization. The DataClaw0-9B model is able to achieve robust alignment with complex refinement and tailoring intents. The paper also introduces a new benchmark called DataClaw0-val for evaluating data refinement capabilities. The results show that the DataClaw0 model is able to deliver high information density tailored data, facilitating efficient model adaptation to new tasks with limited training data. The evaluations are done on tasks such as video generation, real world visual question answering, and GUI navigation, and the results confirm the effectiveness of the proposed method. Overall, the paper proposes a new paradigm for data processing and provides a method and benchmark for evaluating data refinement capabilities, with results showing the potential of the proposed method for efficient model adaptation and high quality data processing.

📅 Published on Jun 19

🔗 Links:
• GitHub: https://github.com/huggingface
• arXiv: https://arxiv.org/abs/2606.21337
• PDF: https://arxiv.org/pdf/2606.21337
• Project Page: https://czjdsg.github.io/MakeAnyData/#cases

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

#MultimodalDataProcessing #AgenticDataTailoring #LearnableDataProcessing #MultimodalStreamAnalysis #DeepProceduralLogicExtraction