🔹 Title: Kimi Linear: An Expressive, Efficient Attention Architecture
📝 Summary:
Kimi Linear is a new hybrid linear attention architecture that, for the first time, outperforms full attention across various contexts. It achieves superior performance and efficiency, reducing KV cache and increasing decoding throughput, making it a powerful drop-in replacement.
🔹 Publication Date: Published on Oct 30
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2510.26692
• PDF: https://arxiv.org/pdf/2510.26692
• Github: https://github.com/MoonshotAI/Kimi-Linear
🔹 Models citing this paper:
• https://huggingface.co/moonshotai/Kimi-Linear-48B-A3B-Instruct
• https://huggingface.co/moonshotai/Kimi-Linear-48B-A3B-Base
• https://huggingface.co/aiqtech/Kimi-Linear-48B-A3B-Instruct
==================================
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📝 Summary:
Kimi Linear is a new hybrid linear attention architecture that, for the first time, outperforms full attention across various contexts. It achieves superior performance and efficiency, reducing KV cache and increasing decoding throughput, making it a powerful drop-in replacement.
🔹 Publication Date: Published on Oct 30
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2510.26692
• PDF: https://arxiv.org/pdf/2510.26692
• Github: https://github.com/MoonshotAI/Kimi-Linear
🔹 Models citing this paper:
• https://huggingface.co/moonshotai/Kimi-Linear-48B-A3B-Instruct
• https://huggingface.co/moonshotai/Kimi-Linear-48B-A3B-Base
• https://huggingface.co/aiqtech/Kimi-Linear-48B-A3B-Instruct
==================================
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🔹 Title: Emu3.5: Native Multimodal Models are World Learners
📝 Summary:
Emu3.5 is a multimodal world model natively predicting vision and language states. Trained on vast video data, it uses Discrete Diffusion Adaptation for 20x faster image inference. It excels at multimodal generation, world modeling, and performs competitively.
🔹 Publication Date: Published on Oct 30
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2510.26583
• PDF: https://arxiv.org/pdf/2510.26583
• Project Page: https://emu.world/
• Github: https://github.com/baaivision/Emu3.5
🔹 Models citing this paper:
• https://huggingface.co/BAAI/Emu3.5
• https://huggingface.co/BAAI/Emu3.5-Image
• https://huggingface.co/BAAI/Emu3.5-VisionTokenizer
==================================
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📝 Summary:
Emu3.5 is a multimodal world model natively predicting vision and language states. Trained on vast video data, it uses Discrete Diffusion Adaptation for 20x faster image inference. It excels at multimodal generation, world modeling, and performs competitively.
🔹 Publication Date: Published on Oct 30
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2510.26583
• PDF: https://arxiv.org/pdf/2510.26583
• Project Page: https://emu.world/
• Github: https://github.com/baaivision/Emu3.5
🔹 Models citing this paper:
• https://huggingface.co/BAAI/Emu3.5
• https://huggingface.co/BAAI/Emu3.5-Image
• https://huggingface.co/BAAI/Emu3.5-VisionTokenizer
==================================
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🔹 Title: olmOCR: Unlocking Trillions of Tokens in PDFs with Vision Language Models
📝 Summary:
olmOCR is an open-source toolkit using a fine-tuned vision language model to convert diverse PDFs into clean, structured plain text. It preserves formatting like tables and equations, and is optimized for cost-effective large-scale batch processing, unlocking tokens for language model training.
🔹 Publication Date: Published on Feb 25
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2502.18443
• PDF: https://arxiv.org/pdf/2502.18443
• Github: https://github.com/allenai/olmocr
🔹 Datasets citing this paper:
• https://huggingface.co/datasets/davanstrien/test-olmocr2
• https://huggingface.co/datasets/davanstrien/newspapers-olmocr2
• https://huggingface.co/datasets/stckmn/ocr-output-Directive017-1761355297
==================================
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✓ https://news.1rj.ru/str/DataScienceT
📝 Summary:
olmOCR is an open-source toolkit using a fine-tuned vision language model to convert diverse PDFs into clean, structured plain text. It preserves formatting like tables and equations, and is optimized for cost-effective large-scale batch processing, unlocking tokens for language model training.
🔹 Publication Date: Published on Feb 25
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2502.18443
• PDF: https://arxiv.org/pdf/2502.18443
• Github: https://github.com/allenai/olmocr
🔹 Datasets citing this paper:
• https://huggingface.co/datasets/davanstrien/test-olmocr2
• https://huggingface.co/datasets/davanstrien/newspapers-olmocr2
• https://huggingface.co/datasets/stckmn/ocr-output-Directive017-1761355297
==================================
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🔹 Title: PaddleOCR-VL: Boosting Multilingual Document Parsing via a 0.9B Ultra-Compact Vision-Language Model
📝 Summary:
PaddleOCR-VL is a compact 0.9B vision-language model for multilingual document parsing. It achieves state-of-the-art performance on 109 languages with minimal resources and fast inference. It efficiently recognizes complex elements like text and tables.
🔹 Publication Date: Published on Oct 16
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2510.14528
• PDF: https://arxiv.org/pdf/2510.14528
• Github: https://github.com/PaddlePaddle/PaddleOCR
🔹 Models citing this paper:
• https://huggingface.co/PaddlePaddle/PaddleOCR-VL
• https://huggingface.co/PaddlePaddle/PP-DocLayoutV2
• https://huggingface.co/lvyufeng/PaddleOCR-VL-0.9B
🔹 Spaces citing this paper:
• https://huggingface.co/spaces/PaddlePaddle/PaddleOCR-VL_Online_Demo
• https://huggingface.co/spaces/markobinario/PaddleOCR-VL_Online_Demo
• https://huggingface.co/spaces/waytoAGI/PaddleOCR-VL_Online_Demo
==================================
For more data science resources:
✓ https://news.1rj.ru/str/DataScienceT
📝 Summary:
PaddleOCR-VL is a compact 0.9B vision-language model for multilingual document parsing. It achieves state-of-the-art performance on 109 languages with minimal resources and fast inference. It efficiently recognizes complex elements like text and tables.
🔹 Publication Date: Published on Oct 16
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2510.14528
• PDF: https://arxiv.org/pdf/2510.14528
• Github: https://github.com/PaddlePaddle/PaddleOCR
🔹 Models citing this paper:
• https://huggingface.co/PaddlePaddle/PaddleOCR-VL
• https://huggingface.co/PaddlePaddle/PP-DocLayoutV2
• https://huggingface.co/lvyufeng/PaddleOCR-VL-0.9B
🔹 Spaces citing this paper:
• https://huggingface.co/spaces/PaddlePaddle/PaddleOCR-VL_Online_Demo
• https://huggingface.co/spaces/markobinario/PaddleOCR-VL_Online_Demo
• https://huggingface.co/spaces/waytoAGI/PaddleOCR-VL_Online_Demo
==================================
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arXiv.org
PaddleOCR-VL: Boosting Multilingual Document Parsing via a 0.9B...
In this report, we propose PaddleOCR-VL, a SOTA and resource-efficient model tailored for document parsing. Its core component is PaddleOCR-VL-0.9B, a compact yet powerful vision-language model...
🔹 Title: Concerto: Joint 2D-3D Self-Supervised Learning Emerges Spatial Representations
📝 Summary:
Concerto combines 3D self-distillation and 2D-3D joint embedding to learn superior spatial features. It significantly outperforms existing self-supervised models and achieves new state-of-the-art results in scene understanding and open-world perception.
🔹 Publication Date: Published on Oct 27
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2510.23607
• PDF: https://arxiv.org/pdf/2510.23607
• Project Page: https://pointcept.github.io/Concerto/
• Github: https://github.com/Pointcept/Pointcept
🔹 Models citing this paper:
• https://huggingface.co/Pointcept/Concerto
🔹 Spaces citing this paper:
• https://huggingface.co/spaces/Pointcept/Concerto
==================================
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📝 Summary:
Concerto combines 3D self-distillation and 2D-3D joint embedding to learn superior spatial features. It significantly outperforms existing self-supervised models and achieves new state-of-the-art results in scene understanding and open-world perception.
🔹 Publication Date: Published on Oct 27
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2510.23607
• PDF: https://arxiv.org/pdf/2510.23607
• Project Page: https://pointcept.github.io/Concerto/
• Github: https://github.com/Pointcept/Pointcept
🔹 Models citing this paper:
• https://huggingface.co/Pointcept/Concerto
🔹 Spaces citing this paper:
• https://huggingface.co/spaces/Pointcept/Concerto
==================================
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🔹 Title: LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Models
📝 Summary:
LlamaFactory is a unified framework that enables efficient, no-code fine-tuning of over 100 large language models. It simplifies adapting LLMs to various tasks using a web-based user interface.
🔹 Publication Date: Published on Mar 20, 2024
🔹 Paper Links:
• arXiv Page: https://arxivexplained.com/papers/llamafactory-unified-efficient-fine-tuning-of-100-language-models
• PDF: https://arxiv.org/pdf/2403.13372
• Project Page: https://huggingface.co/spaces/hiyouga/LLaMA-Board
• Github: https://github.com/hiyouga/LLaMA-Factory
🔹 Models citing this paper:
• https://huggingface.co/AELLM/Llama-3.2-Chibi-3B
• https://huggingface.co/GXMZU/Qwen3-14B-ai-expert-250925
• https://huggingface.co/XavierSpycy/Meta-Llama-3-8B-Instruct-zh-10k
🔹 Spaces citing this paper:
• https://huggingface.co/spaces/Justinrune/LLaMA-Factory
• https://huggingface.co/spaces/featherless-ai/try-this-model
• https://huggingface.co/spaces/Darok/Featherless-Feud
==================================
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📝 Summary:
LlamaFactory is a unified framework that enables efficient, no-code fine-tuning of over 100 large language models. It simplifies adapting LLMs to various tasks using a web-based user interface.
🔹 Publication Date: Published on Mar 20, 2024
🔹 Paper Links:
• arXiv Page: https://arxivexplained.com/papers/llamafactory-unified-efficient-fine-tuning-of-100-language-models
• PDF: https://arxiv.org/pdf/2403.13372
• Project Page: https://huggingface.co/spaces/hiyouga/LLaMA-Board
• Github: https://github.com/hiyouga/LLaMA-Factory
🔹 Models citing this paper:
• https://huggingface.co/AELLM/Llama-3.2-Chibi-3B
• https://huggingface.co/GXMZU/Qwen3-14B-ai-expert-250925
• https://huggingface.co/XavierSpycy/Meta-Llama-3-8B-Instruct-zh-10k
🔹 Spaces citing this paper:
• https://huggingface.co/spaces/Justinrune/LLaMA-Factory
• https://huggingface.co/spaces/featherless-ai/try-this-model
• https://huggingface.co/spaces/Darok/Featherless-Feud
==================================
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Arxivexplained
LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Models - Explained Simply
By Yaowei Zheng, Richong Zhang, Junhao Zhang et al.. # LlamaFactory: The Game-Changer That Makes AI Customization Accessible to Everyone
**The Problem:*...
**The Problem:*...
🔹 Title: Mem0: Building Production-Ready AI Agents with Scalable Long-Term Memory
📝 Summary:
Mem0 is a memory-centric architecture, with an enhanced graph-based version, that improves LLMs long-term conversational coherence. It surpasses other memory systems in accuracy and drastically cuts computational costs, enabling more reliable AI agents.
🔹 Publication Date: Published on Apr 28
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2504.19413
• PDF: https://arxiv.org/pdf/2504.19413
• Github: https://github.com/mem0ai/mem0
==================================
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📝 Summary:
Mem0 is a memory-centric architecture, with an enhanced graph-based version, that improves LLMs long-term conversational coherence. It surpasses other memory systems in accuracy and drastically cuts computational costs, enabling more reliable AI agents.
🔹 Publication Date: Published on Apr 28
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2504.19413
• PDF: https://arxiv.org/pdf/2504.19413
• Github: https://github.com/mem0ai/mem0
==================================
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🔹 Title: PokeeResearch: Effective Deep Research via Reinforcement Learning from AI Feedback and Robust Reasoning Scaffold
📝 Summary:
PokeeResearch-7B is a 7B-parameter deep research agent. It uses Reinforcement Learning from AI Feedback and chain-of-thought reasoning to enhance robustness. This achieves state-of-the-art performance on deep research benchmarks.
🔹 Publication Date: Published on Oct 17
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2510.15862
• PDF: https://arxiv.org/pdf/2510.15862
• Github: https://github.com/Pokee-AI/PokeeResearchOSS
🔹 Models citing this paper:
• https://huggingface.co/PokeeAI/pokee_research_7b
• https://huggingface.co/Mungert/pokee_research_7b-GGUF
==================================
For more data science resources:
✓ https://news.1rj.ru/str/DataScienceT
📝 Summary:
PokeeResearch-7B is a 7B-parameter deep research agent. It uses Reinforcement Learning from AI Feedback and chain-of-thought reasoning to enhance robustness. This achieves state-of-the-art performance on deep research benchmarks.
🔹 Publication Date: Published on Oct 17
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2510.15862
• PDF: https://arxiv.org/pdf/2510.15862
• Github: https://github.com/Pokee-AI/PokeeResearchOSS
🔹 Models citing this paper:
• https://huggingface.co/PokeeAI/pokee_research_7b
• https://huggingface.co/Mungert/pokee_research_7b-GGUF
==================================
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🔹 Title: TradingAgents: Multi-Agents LLM Financial Trading Framework
📝 Summary:
TradingAgents introduces a multi-agent LLM framework for stock trading, simulating real-world firms with specialized agent roles. This collaborative system, featuring analysts and traders, significantly improves trading performance metrics. It outperforms baseline models in cumulative returns, Sh...
🔹 Publication Date: Published on Dec 28, 2024
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2412.20138
• PDF: https://arxiv.org/pdf/2412.20138
• Github: https://github.com/tauricresearch/tradingagents
🔹 Spaces citing this paper:
• https://huggingface.co/spaces/shanghengdu/LLM-Agent-Optimization-PaperList
• https://huggingface.co/spaces/Ervin2077/qiu
==================================
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📝 Summary:
TradingAgents introduces a multi-agent LLM framework for stock trading, simulating real-world firms with specialized agent roles. This collaborative system, featuring analysts and traders, significantly improves trading performance metrics. It outperforms baseline models in cumulative returns, Sh...
🔹 Publication Date: Published on Dec 28, 2024
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2412.20138
• PDF: https://arxiv.org/pdf/2412.20138
• Github: https://github.com/tauricresearch/tradingagents
🔹 Spaces citing this paper:
• https://huggingface.co/spaces/shanghengdu/LLM-Agent-Optimization-PaperList
• https://huggingface.co/spaces/Ervin2077/qiu
==================================
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🔹 Title: OmniFlatten: An End-to-end GPT Model for Seamless Voice Conversation
📝 Summary:
OmniFlatten is an end-to-end GPT model enabling real-time, natural full-duplex spoken dialogue. It uses a multi-stage post-training scheme to adapt a text-based LLM for speech and text generation without altering its original architecture, handling complex conversation dynamics with low latency.
🔹 Publication Date: Published on Oct 23, 2024
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2410.17799
• PDF: https://arxiv.org/pdf/2410.17799
• Github: https://github.com/karpathy/nanogpt
==================================
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📝 Summary:
OmniFlatten is an end-to-end GPT model enabling real-time, natural full-duplex spoken dialogue. It uses a multi-stage post-training scheme to adapt a text-based LLM for speech and text generation without altering its original architecture, handling complex conversation dynamics with low latency.
🔹 Publication Date: Published on Oct 23, 2024
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2410.17799
• PDF: https://arxiv.org/pdf/2410.17799
• Github: https://github.com/karpathy/nanogpt
==================================
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🔹 Title: MinerU2.5: A Decoupled Vision-Language Model for Efficient High-Resolution Document Parsing
📝 Summary:
MinerU2.5 is a document parsing model using a two-stage coarse-to-fine strategy. It first analyzes layout on downsampled images, then recognizes content on native-resolution crops. This achieves state-of-the-art accuracy with high efficiency.
🔹 Publication Date: Published on Sep 26
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2509.22186
• PDF: https://arxiv.org/pdf/2509.22186
• Project Page: https://opendatalab.github.io/MinerU/
• Github: https://github.com/opendatalab/MinerU
🔹 Models citing this paper:
• https://huggingface.co/opendatalab/MinerU2.5-2509-1.2B
• https://huggingface.co/freakynit/MinerU2.5-2509-1.2B
• https://huggingface.co/Mungert/MinerU2.5-2509-1.2B-GGUF
🔹 Spaces citing this paper:
• https://huggingface.co/spaces/opendatalab/MinerU
• https://huggingface.co/spaces/xiaoye-winters/MinerU-API
• https://huggingface.co/spaces/ApeAITW/MinerU_2.5_Test
==================================
For more data science resources:
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📝 Summary:
MinerU2.5 is a document parsing model using a two-stage coarse-to-fine strategy. It first analyzes layout on downsampled images, then recognizes content on native-resolution crops. This achieves state-of-the-art accuracy with high efficiency.
🔹 Publication Date: Published on Sep 26
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2509.22186
• PDF: https://arxiv.org/pdf/2509.22186
• Project Page: https://opendatalab.github.io/MinerU/
• Github: https://github.com/opendatalab/MinerU
🔹 Models citing this paper:
• https://huggingface.co/opendatalab/MinerU2.5-2509-1.2B
• https://huggingface.co/freakynit/MinerU2.5-2509-1.2B
• https://huggingface.co/Mungert/MinerU2.5-2509-1.2B-GGUF
🔹 Spaces citing this paper:
• https://huggingface.co/spaces/opendatalab/MinerU
• https://huggingface.co/spaces/xiaoye-winters/MinerU-API
• https://huggingface.co/spaces/ApeAITW/MinerU_2.5_Test
==================================
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arXiv.org
MinerU2.5: A Decoupled Vision-Language Model for Efficient...
We introduce MinerU2.5, a 1.2B-parameter document parsing vision-language model that achieves state-of-the-art recognition accuracy while maintaining exceptional computational efficiency. Our...
❤1
🔹 Title: MinerU: An Open-Source Solution for Precise Document Content Extraction
📝 Summary:
MinerU is an open-source solution for high-precision document content extraction. It leverages fine-tuned models and pre/postprocessing rules to achieve consistent accuracy across diverse document types, addressing challenges in existing tools.
🔹 Publication Date: Published on Sep 27, 2024
🔹 Paper Links:
• arXiv Page: https://arxiv.org/pdf/2409.18839
• PDF: https://huggingface.co/spaces/Echo9k/PDF_reader
• Github: https://github.com/opendatalab/MinerU
🔹 Spaces citing this paper:
• https://huggingface.co/spaces/opendatalab/MinerU
• https://huggingface.co/spaces/xiaoye-winters/MinerU-API
• https://huggingface.co/spaces/ApeAITW/MinerU_2.5_Test
==================================
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📝 Summary:
MinerU is an open-source solution for high-precision document content extraction. It leverages fine-tuned models and pre/postprocessing rules to achieve consistent accuracy across diverse document types, addressing challenges in existing tools.
🔹 Publication Date: Published on Sep 27, 2024
🔹 Paper Links:
• arXiv Page: https://arxiv.org/pdf/2409.18839
• PDF: https://huggingface.co/spaces/Echo9k/PDF_reader
• Github: https://github.com/opendatalab/MinerU
🔹 Spaces citing this paper:
• https://huggingface.co/spaces/opendatalab/MinerU
• https://huggingface.co/spaces/xiaoye-winters/MinerU-API
• https://huggingface.co/spaces/ApeAITW/MinerU_2.5_Test
==================================
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🔹 Title: IndexTTS: An Industrial-Level Controllable and Efficient Zero-Shot Text-To-Speech System
📝 Summary:
IndexTTS enhances XTTS and Tortoise for superior naturalness and zero-shot voice cloning. It uses hybrid character-pinyin modeling and optimized VQ, offering controllable, efficient TTS with better performance.
🔹 Publication Date: Published on Feb 8
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2502.05512
• PDF: https://arxiv.org/pdf/2502.05512
• Github: https://github.com/index-tts/index-tts
🔹 Models citing this paper:
• https://huggingface.co/IndexTeam/IndexTTS-2
• https://huggingface.co/IndexTeam/Index-TTS
• https://huggingface.co/Toxzic/indextts-colab
🔹 Spaces citing this paper:
• https://huggingface.co/spaces/IndexTeam/IndexTTS
• https://huggingface.co/spaces/Pendrokar/TTS-Spaces-Arena
• https://huggingface.co/spaces/jairwaal/image
==================================
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📝 Summary:
IndexTTS enhances XTTS and Tortoise for superior naturalness and zero-shot voice cloning. It uses hybrid character-pinyin modeling and optimized VQ, offering controllable, efficient TTS with better performance.
🔹 Publication Date: Published on Feb 8
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2502.05512
• PDF: https://arxiv.org/pdf/2502.05512
• Github: https://github.com/index-tts/index-tts
🔹 Models citing this paper:
• https://huggingface.co/IndexTeam/IndexTTS-2
• https://huggingface.co/IndexTeam/Index-TTS
• https://huggingface.co/Toxzic/indextts-colab
🔹 Spaces citing this paper:
• https://huggingface.co/spaces/IndexTeam/IndexTTS
• https://huggingface.co/spaces/Pendrokar/TTS-Spaces-Arena
• https://huggingface.co/spaces/jairwaal/image
==================================
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🔹 Title: DeepAnalyze: Agentic Large Language Models for Autonomous Data Science
📝 Summary:
DeepAnalyze-8B is an agentic LLM that autonomously completes the entire data science pipeline. It uses curriculum-based training and data-grounded trajectory synthesis. DeepAnalyze-8B outperforms prior workflow-based agents on various data tasks.
🔹 Publication Date: Published on Oct 19
🔹 Paper Links:
• arXiv Page: https://arxivexplained.com/papers/deepanalyze-agentic-large-language-models-for-autonomous-data-science
• PDF: https://arxiv.org/pdf/2510.16872
• Project Page: https://ruc-deepanalyze.github.io/
• Github: https://github.com/ruc-datalab/DeepAnalyze
🔹 Models citing this paper:
• https://huggingface.co/RUC-DataLab/DeepAnalyze-8B
🔹 Datasets citing this paper:
• https://huggingface.co/datasets/RUC-DataLab/DataScience-Instruct-500K
• https://huggingface.co/datasets/fantos/DataScience-Instruct-500K
==================================
For more data science resources:
✓ https://news.1rj.ru/str/DataScienceT
📝 Summary:
DeepAnalyze-8B is an agentic LLM that autonomously completes the entire data science pipeline. It uses curriculum-based training and data-grounded trajectory synthesis. DeepAnalyze-8B outperforms prior workflow-based agents on various data tasks.
🔹 Publication Date: Published on Oct 19
🔹 Paper Links:
• arXiv Page: https://arxivexplained.com/papers/deepanalyze-agentic-large-language-models-for-autonomous-data-science
• PDF: https://arxiv.org/pdf/2510.16872
• Project Page: https://ruc-deepanalyze.github.io/
• Github: https://github.com/ruc-datalab/DeepAnalyze
🔹 Models citing this paper:
• https://huggingface.co/RUC-DataLab/DeepAnalyze-8B
🔹 Datasets citing this paper:
• https://huggingface.co/datasets/RUC-DataLab/DataScience-Instruct-500K
• https://huggingface.co/datasets/fantos/DataScience-Instruct-500K
==================================
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🔹 Title: DeepAgent: A General Reasoning Agent with Scalable Toolsets
📝 Summary:
DeepAgent is an end-to-end deep reasoning agent that autonomously performs thinking, tool discovery, and action execution. It uses memory folding and an RL strategy ToolPO to learn tool use and manage interactions. DeepAgent significantly outperforms baselines on diverse tool-use and application ...
🔹 Publication Date: Published on Oct 24
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2510.21618
• PDF: https://arxiv.org/pdf/2510.21618
• Github: https://github.com/RUC-NLPIR/DeepAgent
==================================
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✓ https://news.1rj.ru/str/DataScienceT
📝 Summary:
DeepAgent is an end-to-end deep reasoning agent that autonomously performs thinking, tool discovery, and action execution. It uses memory folding and an RL strategy ToolPO to learn tool use and manage interactions. DeepAgent significantly outperforms baselines on diverse tool-use and application ...
🔹 Publication Date: Published on Oct 24
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2510.21618
• PDF: https://arxiv.org/pdf/2510.21618
• Github: https://github.com/RUC-NLPIR/DeepAgent
==================================
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🔹 Title: ReCode: Unify Plan and Action for Universal Granularity Control
📝 Summary:
ReCode unifies LLM agent planning and action through recursive code generation. It treats plans as functions decomposed into primitive actions, enabling dynamic granularity control. This boosts performance and data efficiency.
🔹 Publication Date: Published on Oct 27
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2510.23564
• PDF: https://arxiv.org/pdf/2510.23564
• Github: https://github.com/FoundationAgents/ReCode
==================================
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📝 Summary:
ReCode unifies LLM agent planning and action through recursive code generation. It treats plans as functions decomposed into primitive actions, enabling dynamic granularity control. This boosts performance and data efficiency.
🔹 Publication Date: Published on Oct 27
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2510.23564
• PDF: https://arxiv.org/pdf/2510.23564
• Github: https://github.com/FoundationAgents/ReCode
==================================
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🔹 Title: WebDancer: Towards Autonomous Information Seeking Agency
📝 Summary:
This paper presents WebDancer, a four-stage training paradigm for autonomous information seeking agents. It combines data construction, supervised fine-tuning, and reinforcement learning to achieve strong performance on challenging benchmarks.
🔹 Publication Date: Published on May 28
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2505.22648
• PDF: https://arxiv.org/pdf/2505.22648
• Github: https://github.com/Alibaba-NLP/WebAgent
🔹 Models citing this paper:
• https://huggingface.co/Alibaba-NLP/WebDancer-32B
🔹 Spaces citing this paper:
• https://huggingface.co/spaces/frucht/Alibaba-NLP-WebDancer-32B
==================================
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✓ https://news.1rj.ru/str/DataScienceT
📝 Summary:
This paper presents WebDancer, a four-stage training paradigm for autonomous information seeking agents. It combines data construction, supervised fine-tuning, and reinforcement learning to achieve strong performance on challenging benchmarks.
🔹 Publication Date: Published on May 28
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2505.22648
• PDF: https://arxiv.org/pdf/2505.22648
• Github: https://github.com/Alibaba-NLP/WebAgent
🔹 Models citing this paper:
• https://huggingface.co/Alibaba-NLP/WebDancer-32B
🔹 Spaces citing this paper:
• https://huggingface.co/spaces/frucht/Alibaba-NLP-WebDancer-32B
==================================
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🔹 Title: Scaling Agents via Continual Pre-training
📝 Summary:
AgentFounder proposes Agentic Continual Pre-training to build powerful agentic foundation models. This resolves post-training optimization issues, achieving state-of-the-art agentic performance with strong tool-use.
🔹 Publication Date: Published on Sep 16
🔹 Paper Links:
• arXiv Page: https://arxiv.org/pdf/2502.06589
• PDF: https://arxiv.org/pdf/2509.13310
• Project Page: https://tongyi-agent.github.io/blog/
• Github: https://tongyi-agent.github.io/blog/
==================================
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📝 Summary:
AgentFounder proposes Agentic Continual Pre-training to build powerful agentic foundation models. This resolves post-training optimization issues, achieving state-of-the-art agentic performance with strong tool-use.
🔹 Publication Date: Published on Sep 16
🔹 Paper Links:
• arXiv Page: https://arxiv.org/pdf/2502.06589
• PDF: https://arxiv.org/pdf/2509.13310
• Project Page: https://tongyi-agent.github.io/blog/
• Github: https://tongyi-agent.github.io/blog/
==================================
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✓ https://news.1rj.ru/str/DataScienceT