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10B), entailing prohibitive training costs and deployment footprints. In this work, we present DeepGen 1.0, a lightweight 5B unified model that achieves comprehensive capabilities competitive with or surpassing much larger counterparts. To overcome the limitations of compact models in semantic understanding and fine-grained control, we introduce Stacked Channel Bridging (SCB), a deep alignment framework that extracts hierarchical features from multiple VLM layers and fuses them with learnable 'think tokens' to provide the generative backbone with structured, reasoning-rich guidance. We further design a data-centric training strategy spanning three progressive stages: (1) Alignment Pre-training on large-scale image-text pairs and editing triplets to synchronize VLM and DiT representations, (2) Joint Supervised Fine-tuning on a high-quality mixture of generation, editing, and reasoning tasks to foster omni-capabilities, and (3) Reinforcement Learning with MR-GRPO, which leverages a mixture of reward functions and supervision signals, resulting in substantial gains in generation quality and alignment with human preferences, while maintaining stable training progress and avoiding visual artifacts. Despite being trained on only ~50M samples, DeepGen 1.0 achieves leading performance across diverse benchmarks, surpassing the 80B HunyuanImage by 28% on WISE and the 27B Qwen-Image-Edit by 37% on UniREditBench. By open-sourcing our training code, weights, and datasets, we provide an efficient, high-performance alternative to democratize unified multimodal research.","upvotes":75,"discussionId":"698ea0f9cace060ff123ae4e","projectPage":"https://deepgenteam.github.io/","githubRepo":"https://github.com/DeepGenTeam/DeepGen","githubRepoAddedBy":"user","ai_summary":"A lightweight 5B unified multimodal model achieves competitive performance through hierarchical feature extraction, learnable think tokens, and progressive training strategies including alignment pre-training, joint supervised fine-tuning, and reinforcement learning with MR-GRPO.","ai_keywords":["unified multimodal models","image generation","image editing","parameter scale","VLM layers","DiT representations","Stacked Channel Bridging","think tokens","data-centric training strategy","alignment pre-training","joint supervised fine-tuning","reinforcement learning","MR-GRPO","generation quality","human preferences","visual artifacts"],"githubStars":90,"organization":{"_id":"683ebd0d913d82e703e77286","name":"sii-research","fullname":"Shanghai Innovation Institute","avatar":"https://cdn-uploads.huggingface.co/production/uploads/6144a0c4ff1146bbd84d9865/SQAtyVRxNjp9L0CUi0tgI.png"}},"publishedAt":"2026-02-12T12:44:24.000Z","title":"DeepGen 1.0: A Lightweight Unified Multimodal Model for Advancing Image Generation and Editing","summary":"Current unified multimodal models for image generation and editing typically rely on massive parameter scales (e.g., >10B), entailing prohibitive training costs and deployment footprints. In this work, we present DeepGen 1.0, a lightweight 5B unified model that achieves comprehensive capabilities competitive with or surpassing much larger counterparts. To overcome the limitations of compact models in semantic understanding and fine-grained control, we introduce Stacked Channel Bridging (SCB), a deep alignment framework that extracts hierarchical features from multiple VLM layers and fuses them with learnable 'think tokens' to provide the generative backbone with structured, reasoning-rich guidance. We further design a data-centric training strategy spanning three progressive stages: (1) Alignment Pre-training on large-scale image-text pairs and editing triplets to synchronize VLM and DiT representations, (2) Joint Supervised Fine-tuning on a high-quality mixture of generation, editing, and reasoning tasks to foster omni-capabilities, and (3) Reinforcement Learning with MR-GRPO, which leverages a mixture of reward functions and supervision signals, resulting in substantial gains in generation quality and alignment with human preferences, while maintaining stable training progress and avoiding visual artifacts. Despite being trained on only ~50M samples, DeepGen 1.0 achieves leading performance across diverse benchmarks, surpassing the 80B HunyuanImage by 28% on WISE and the 27B Qwen-Image-Edit by 37% on UniREditBench. By open-sourcing our training code, weights, and datasets, we provide an efficient, high-performance alternative to democratize unified multimodal research.","thumbnail":"https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2602.12205.png","numComments":4,"upvoted":false,"submittedBy":{"_id":"64b4eec4faa3181a5eab9c46","avatarUrl":"/avatars/bcc9bf5cbf67546ad2b4c9ec8b96ac96.svg","fullname":"Jiaqi Wang","name":"myownskyW7","type":"user","isPro":true,"isHf":false,"isHfAdmin":false,"isMod":false,"followerCount":25,"isUserFollowing":false},"organization":{"_id":"683ebd0d913d82e703e77286","name":"sii-research","fullname":"Shanghai Innovation Institute","avatar":"https://cdn-uploads.huggingface.co/production/uploads/6144a0c4ff1146bbd84d9865/SQAtyVRxNjp9L0CUi0tgI.png"},"isAuthorParticipating":false},{"paper":{"id":"2508.02739","authors":[{"_id":"68940073741a16f544fbce2f","name":"Yu Shi","hidden":false},{"_id":"68940073741a16f544fbce30","name":"Zongliang Fu","hidden":false},{"_id":"68940073741a16f544fbce31","name":"Shuo Chen","hidden":false},{"_id":"68940073741a16f544fbce32","name":"Bohan Zhao","hidden":false},{"_id":"68940073741a16f544fbce33","name":"Wei Xu","hidden":false},{"_id":"68940073741a16f544fbce34","name":"Changshui Zhang","hidden":false},{"_id":"68940073741a16f544fbce35","name":"Jian Li","hidden":false}],"publishedAt":"2025-08-02T13:15:59.000Z","title":"Kronos: A Foundation Model for the Language of Financial Markets","summary":"The success of large-scale pre-training paradigm, exemplified by Large\nLanguage Models (LLMs), has inspired the development of Time Series Foundation\nModels (TSFMs). However, their application to financial candlestick (K-line)\ndata remains limited, often underperforming non-pre-trained architectures.\nMoreover, existing TSFMs often overlook crucial downstream tasks such as\nvolatility prediction and synthetic data generation. To address these\nlimitations, we propose Kronos, a unified, scalable pre-training framework\ntailored to financial K-line modeling. Kronos introduces a specialized\ntokenizer that discretizes continuous market information into token sequences,\npreserving both price dynamics and trade activity patterns. We pre-train Kronos\nusing an autoregressive objective on a massive, multi-market corpus of over 12\nbillion K-line records from 45 global exchanges, enabling it to learn nuanced\ntemporal and cross-asset representations. Kronos excels in a zero-shot setting\nacross a diverse set of financial tasks. On benchmark datasets, Kronos boosts\nprice series forecasting RankIC by 93% over the leading TSFM and 87% over the\nbest non-pre-trained baseline. It also achieves a 9% lower MAE in volatility\nforecasting and a 22% improvement in generative fidelity for synthetic K-line\nsequences. These results establish Kronos as a robust, versatile foundation\nmodel for end-to-end financial time series analysis. Our pre-trained model is\npublicly available at https://github.com/shiyu-coder/Kronos.","upvotes":2,"discussionId":"68940073741a16f544fbce36","githubRepo":"https://github.com/shiyu-coder/Kronos","githubRepoAddedBy":"auto","ai_summary":"Kronos, a specialized pre-training framework for financial K-line data, outperforms existing models in forecasting and synthetic data generation through a unique tokenizer and autoregressive pre-training on a large dataset.","ai_keywords":["Large Language Models","Time Series Foundation Models","financial candlestick","volatility prediction","synthetic data generation","autoregressive objective","token sequences","price dynamics","trade activity patterns","zero-shot setting","price series forecasting","RankIC","MAE","generative fidelity"],"githubStars":10557},"publishedAt":"2025-08-02T09:15:59.000Z","title":"Kronos: A Foundation Model for the Language of Financial Markets","summary":"The success of large-scale pre-training paradigm, exemplified by Large\nLanguage Models (LLMs), has inspired the development of Time Series Foundation\nModels (TSFMs). However, their application to financial candlestick (K-line)\ndata remains limited, often underperforming non-pre-trained architectures.\nMoreover, existing TSFMs often overlook crucial downstream tasks such as\nvolatility prediction and synthetic data generation. To address these\nlimitations, we propose Kronos, a unified, scalable pre-training framework\ntailored to financial K-line modeling. Kronos introduces a specialized\ntokenizer that discretizes continuous market information into token sequences,\npreserving both price dynamics and trade activity patterns. We pre-train Kronos\nusing an autoregressive objective on a massive, multi-market corpus of over 12\nbillion K-line records from 45 global exchanges, enabling it to learn nuanced\ntemporal and cross-asset representations. Kronos excels in a zero-shot setting\nacross a diverse set of financial tasks. On benchmark datasets, Kronos boosts\nprice series forecasting RankIC by 93% over the leading TSFM and 87% over the\nbest non-pre-trained baseline. It also achieves a 9% lower MAE in volatility\nforecasting and a 22% improvement in generative fidelity for synthetic K-line\nsequences. These results establish Kronos as a robust, versatile foundation\nmodel for end-to-end financial time series analysis. Our pre-trained model is\npublicly available at https://github.com/shiyu-coder/Kronos.","thumbnail":"https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2508.02739.png","numComments":0,"upvoted":false,"isAuthorParticipating":false},{"paper":{"id":"2512.24601","authors":[{"_id":"695b6f84832867f253525e5a","name":"Alex L. Zhang","hidden":false},{"_id":"695b6f84832867f253525e5b","name":"Tim Kraska","hidden":false},{"_id":"695b6f84832867f253525e5c","name":"Omar Khattab","hidden":false}],"publishedAt":"2025-12-31T03:43:41.000Z","submittedOnDailyAt":"2026-01-06T04:56:19.174Z","title":"Recursive Language Models","submittedOnDailyBy":{"_id":"64b8e82aa62c52b252c827fa","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/64b8e82aa62c52b252c827fa/Jyk5PHMXCaRlmWy4mT3Bt.jpeg","isPro":false,"fullname":"Rajkumar rawal","user":"rajkumarrawal","type":"user"},"summary":"We study allowing large language models (LLMs) to process arbitrarily long prompts through the lens of inference-time scaling. We propose Recursive Language Models (RLMs), a general inference strategy that treats long prompts as part of an external environment and allows the LLM to programmatically examine, decompose, and recursively call itself over snippets of the prompt. We find that RLMs successfully handle inputs up to two orders of magnitude beyond model context windows and, even for shorter prompts, dramatically outperform the quality of base LLMs and common long-context scaffolds across four diverse long-context tasks, while having comparable (or cheaper) cost per query.","upvotes":87,"discussionId":"695b6f84832867f253525e5d","projectPage":"https://alexzhang13.github.io/blog/2025/rlm/","githubRepo":"https://github.com/alexzhang13/rlm","githubRepoAddedBy":"user","githubStars":2524,"organization":{"_id":"63728bde14d543d507ae970d","name":"MIT","fullname":"Massachusetts Institute of Technology","avatar":"https://cdn-uploads.huggingface.co/production/uploads/noauth/S90qoeEJeEYaYf-c7Zs8g.png"}},"publishedAt":"2025-12-30T22:43:41.000Z","title":"Recursive Language Models","summary":"We study allowing large language models (LLMs) to process arbitrarily long prompts through the lens of inference-time scaling. We propose Recursive Language Models (RLMs), a general inference strategy that treats long prompts as part of an external environment and allows the LLM to programmatically examine, decompose, and recursively call itself over snippets of the prompt. We find that RLMs successfully handle inputs up to two orders of magnitude beyond model context windows and, even for shorter prompts, dramatically outperform the quality of base LLMs and common long-context scaffolds across four diverse long-context tasks, while having comparable (or cheaper) cost per query.","thumbnail":"https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2512.24601.png","numComments":7,"upvoted":false,"submittedBy":{"_id":"64b8e82aa62c52b252c827fa","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/64b8e82aa62c52b252c827fa/Jyk5PHMXCaRlmWy4mT3Bt.jpeg","fullname":"Rajkumar rawal","name":"rajkumarrawal","type":"user","isPro":false,"isHf":false,"isHfAdmin":false,"isMod":false,"followerCount":50,"isUserFollowing":false},"organization":{"_id":"63728bde14d543d507ae970d","name":"MIT","fullname":"Massachusetts Institute of Technology","avatar":"https://cdn-uploads.huggingface.co/production/uploads/noauth/S90qoeEJeEYaYf-c7Zs8g.png"},"isAuthorParticipating":false},{"paper":{"id":"2602.07274","authors":[{"_id":"698abbae1b2dc6b37d61b0f3","name":"Kaijie Zhu","hidden":false},{"_id":"698abbae1b2dc6b37d61b0f4","name":"Yuzhou Nie","hidden":false},{"_id":"698abbae1b2dc6b37d61b0f5","user":{"_id":"6419309f22270b3ccf177c77","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/6419309f22270b3ccf177c77/KQa1586iBBKqucUlfpuPp.jpeg","isPro":false,"fullname":"William Li","user":"williamium","type":"user"},"name":"Yijiang Li","status":"claimed_verified","statusLastChangedAt":"2026-02-11T11:18:54.946Z","hidden":false},{"_id":"698abbae1b2dc6b37d61b0f6","name":"Yiming Huang","hidden":false},{"_id":"698abbae1b2dc6b37d61b0f7","name":"Jialian Wu","hidden":false},{"_id":"698abbae1b2dc6b37d61b0f8","name":"Jiang Liu","hidden":false},{"_id":"698abbae1b2dc6b37d61b0f9","name":"Ximeng Sun","hidden":false},{"_id":"698abbae1b2dc6b37d61b0fa","name":"Zhenfei Yin","hidden":false},{"_id":"698abbae1b2dc6b37d61b0fb","name":"Lun Wang","hidden":false},{"_id":"698abbae1b2dc6b37d61b0fc","name":"Zicheng Liu","hidden":false},{"_id":"698abbae1b2dc6b37d61b0fd","name":"Emad Barsoum","hidden":false},{"_id":"698abbae1b2dc6b37d61b0fe","name":"William Yang Wang","hidden":false},{"_id":"698abbae1b2dc6b37d61b0ff","name":"Wenbo Guo","hidden":false}],"publishedAt":"2026-02-06T23:56:50.000Z","submittedOnDailyAt":"2026-02-10T23:00:30.879Z","title":"TermiGen: High-Fidelity Environment and Robust Trajectory Synthesis for Terminal Agents","submittedOnDailyBy":{"_id":"641fb3733c49fc33cedb4429","avatarUrl":"/avatars/3577bca726282ed4b26009f3a2d5e87d.svg","isPro":false,"fullname":"Kaijie Zhu","user":"March07","type":"user"},"summary":"Executing complex terminal tasks remains a significant challenge for open-weight LLMs, constrained by two fundamental limitations. First, high-fidelity, executable training environments are scarce: environments synthesized from real-world repositories are not diverse and scalable, while trajectories synthesized by LLMs suffer from hallucinations. Second, standard instruction tuning uses expert trajectories that rarely exhibit simple mistakes common to smaller models. This creates a distributional mismatch, leaving student models ill-equipped to recover from their own runtime failures. To bridge these gaps, we introduce TermiGen, an end-to-end pipeline for synthesizing verifiable environments and resilient expert trajectories. Termi-Gen first generates functionally valid tasks and Docker containers via an iterative multi-agent refinement loop. Subsequently, we employ a Generator-Critic protocol that actively injects errors during trajectory collection, synthesizing data rich in error-correction cycles. Fine-tuned on this TermiGen-generated dataset, our TermiGen-Qwen2.5-Coder-32B achieves a 31.3% pass rate on TerminalBench. This establishes a new open-weights state-of-the-art, outperforming existing baselines and notably surpassing capable proprietary models such as o4-mini. Dataset is avaiable at https://github.com/ucsb-mlsec/terminal-bench-env.","upvotes":196,"discussionId":"698abbae1b2dc6b37d61b100","githubRepo":"https://github.com/ucsb-mlsec/terminal-bench-env","githubRepoAddedBy":"user","ai_summary":"TermiGen introduces a pipeline for generating verifiable terminal environments and resilient trajectories to improve open-weight LLMs' ability to execute complex tasks and recover from runtime errors.","ai_keywords":["instruction tuning","expert trajectories","distributional mismatch","multi-agent refinement loop","Generator-Critic protocol","error-correction cycles","TerminalBench","pass rate"],"githubStars":104,"organization":{"_id":"65861edfe3f7a2dcf04230f8","name":"ucsbnlp","fullname":"UC Santa Barbara NLP Group","avatar":"https://cdn-uploads.huggingface.co/production/uploads/6002c1db698168af3bb9f4a5/WQYUIGXIycUiVr_J5X2n0.jpeg"}},"publishedAt":"2026-02-06T18:56:50.000Z","title":"TermiGen: High-Fidelity Environment and Robust Trajectory Synthesis for Terminal Agents","summary":"Executing complex terminal tasks remains a significant challenge for open-weight LLMs, constrained by two fundamental limitations. First, high-fidelity, executable training environments are scarce: environments synthesized from real-world repositories are not diverse and scalable, while trajectories synthesized by LLMs suffer from hallucinations. Second, standard instruction tuning uses expert trajectories that rarely exhibit simple mistakes common to smaller models. This creates a distributional mismatch, leaving student models ill-equipped to recover from their own runtime failures. To bridge these gaps, we introduce TermiGen, an end-to-end pipeline for synthesizing verifiable environments and resilient expert trajectories. Termi-Gen first generates functionally valid tasks and Docker containers via an iterative multi-agent refinement loop. Subsequently, we employ a Generator-Critic protocol that actively injects errors during trajectory collection, synthesizing data rich in error-correction cycles. Fine-tuned on this TermiGen-generated dataset, our TermiGen-Qwen2.5-Coder-32B achieves a 31.3% pass rate on TerminalBench. This establishes a new open-weights state-of-the-art, outperforming existing baselines and notably surpassing capable proprietary models such as o4-mini. Dataset is avaiable at https://github.com/ucsb-mlsec/terminal-bench-env.","thumbnail":"https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2602.07274.png","numComments":2,"upvoted":false,"submittedBy":{"_id":"641fb3733c49fc33cedb4429","avatarUrl":"/avatars/3577bca726282ed4b26009f3a2d5e87d.svg","fullname":"Kaijie Zhu","name":"March07","type":"user","isPro":false,"isHf":false,"isHfAdmin":false,"isMod":false,"followerCount":4,"isUserFollowing":false},"organization":{"_id":"65861edfe3f7a2dcf04230f8","name":"ucsbnlp","fullname":"UC Santa Barbara NLP Group","avatar":"https://cdn-uploads.huggingface.co/production/uploads/6002c1db698168af3bb9f4a5/WQYUIGXIycUiVr_J5X2n0.jpeg"},"isAuthorParticipating":false},{"paper":{"id":"2602.10090","authors":[{"_id":"698bf6fe6052d3bed9630ac0","name":"Zhaoyang Wang","hidden":false},{"_id":"698bf6fe6052d3bed9630ac1","name":"Canwen Xu","hidden":false},{"_id":"698bf6fe6052d3bed9630ac2","name":"Boyi Liu","hidden":false},{"_id":"698bf6fe6052d3bed9630ac3","name":"Yite Wang","hidden":false},{"_id":"698bf6fe6052d3bed9630ac4","name":"Siwei Han","hidden":false},{"_id":"698bf6fe6052d3bed9630ac5","name":"Zhewei Yao","hidden":false},{"_id":"698bf6fe6052d3bed9630ac6","name":"Huaxiu Yao","hidden":false},{"_id":"698bf6fe6052d3bed9630ac7","name":"Yuxiong He","hidden":false}],"mediaUrls":["https://cdn-uploads.huggingface.co/production/uploads/633122d3f242a8532b7a928d/at1Sens0OXJ4Yt8ne9kAE.png","https://cdn-uploads.huggingface.co/production/uploads/633122d3f242a8532b7a928d/Hs08PrK-yZHZ5FBmPLoMV.png","https://cdn-uploads.huggingface.co/production/uploads/633122d3f242a8532b7a928d/IIL00IFjA5UOIILbLKOY9.png","https://cdn-uploads.huggingface.co/production/uploads/633122d3f242a8532b7a928d/o5mHd9GtSOBL8Ni_s_J8B.png","https://cdn-uploads.huggingface.co/production/uploads/633122d3f242a8532b7a928d/jsnsJiwl4N10Px2LgZ9Ve.png"],"publishedAt":"2026-02-10T18:55:41.000Z","submittedOnDailyAt":"2026-02-11T02:28:53.819Z","title":"Agent World Model: Infinity Synthetic Environments for Agentic Reinforcement Learning","submittedOnDailyBy":{"_id":"633122d3f242a8532b7a928d","avatarUrl":"/avatars/2158ffff0882a8fb4588e273fd60dea7.svg","isPro":true,"fullname":"Chi","user":"ChilleD","type":"user"},"summary":"Recent advances in large language model (LLM) have empowered autonomous agents to perform complex tasks that require multi-turn interactions with tools and environments. However, scaling such agent training is limited by the lack of diverse and reliable environments. In this paper, we propose Agent World Model (AWM), a fully synthetic environment generation pipeline. Using this pipeline, we scale to 1,000 environments covering everyday scenarios, in which agents can interact with rich toolsets (35 tools per environment on average) and obtain high-quality observations. Notably, these environments are code-driven and backed by databases, providing more reliable and consistent state transitions than environments simulated by LLMs. Moreover, they enable more efficient agent interaction compared with collecting trajectories from realistic environments. To demonstrate the effectiveness of this resource, we perform large-scale reinforcement learning for multi-turn tool-use agents. Thanks to the fully executable environments and accessible database states, we can also design reliable reward functions. Experiments on three benchmarks show that training exclusively in synthetic environments, rather than benchmark-specific ones, yields strong out-of-distribution generalization. The code is available at https://github.com/Snowflake-Labs/agent-world-model.","upvotes":48,"discussionId":"698bf6ff6052d3bed9630ac8","projectPage":"https://github.com/Snowflake-Labs/agent-world-model","githubRepo":"https://github.com/Snowflake-Labs/agent-world-model","githubRepoAddedBy":"user","ai_summary":"Large language model agents trained in synthetic environments with code-driven simulations and database-backed state transitions demonstrate superior out-of-distribution generalization compared to traditional benchmark-specific approaches.","ai_keywords":["large language model","autonomous agents","multi-turn interactions","tool-use agents","reinforcement learning","synthetic environment generation","code-driven environments","database-backed state transitions","out-of-distribution generalization"],"githubStars":194,"organization":{"_id":"62cece4aa3a23014aca72499","name":"Snowflake","fullname":"Snowflake","avatar":"https://cdn-uploads.huggingface.co/production/uploads/64dc52cf858f8a41c12fc819/O9-MWzRjWzbNP_DQlMb-7.png"}},"publishedAt":"2026-02-10T13:55:41.000Z","title":"Agent World Model: Infinity Synthetic Environments for Agentic Reinforcement Learning","summary":"Recent advances in large language model (LLM) have empowered autonomous agents to perform complex tasks that require multi-turn interactions with tools and environments. However, scaling such agent training is limited by the lack of diverse and reliable environments. In this paper, we propose Agent World Model (AWM), a fully synthetic environment generation pipeline. Using this pipeline, we scale to 1,000 environments covering everyday scenarios, in which agents can interact with rich toolsets (35 tools per environment on average) and obtain high-quality observations. Notably, these environments are code-driven and backed by databases, providing more reliable and consistent state transitions than environments simulated by LLMs. Moreover, they enable more efficient agent interaction compared with collecting trajectories from realistic environments. To demonstrate the effectiveness of this resource, we perform large-scale reinforcement learning for multi-turn tool-use agents. Thanks to the fully executable environments and accessible database states, we can also design reliable reward functions. Experiments on three benchmarks show that training exclusively in synthetic environments, rather than benchmark-specific ones, yields strong out-of-distribution generalization. The code is available at https://github.com/Snowflake-Labs/agent-world-model.","mediaUrls":["https://cdn-uploads.huggingface.co/production/uploads/633122d3f242a8532b7a928d/at1Sens0OXJ4Yt8ne9kAE.png","https://cdn-uploads.huggingface.co/production/uploads/633122d3f242a8532b7a928d/Hs08PrK-yZHZ5FBmPLoMV.png","https://cdn-uploads.huggingface.co/production/uploads/633122d3f242a8532b7a928d/IIL00IFjA5UOIILbLKOY9.png","https://cdn-uploads.huggingface.co/production/uploads/633122d3f242a8532b7a928d/o5mHd9GtSOBL8Ni_s_J8B.png","https://cdn-uploads.huggingface.co/production/uploads/633122d3f242a8532b7a928d/jsnsJiwl4N10Px2LgZ9Ve.png"],"thumbnail":"https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2602.10090.png","numComments":2,"upvoted":false,"submittedBy":{"_id":"633122d3f242a8532b7a928d","avatarUrl":"/avatars/2158ffff0882a8fb4588e273fd60dea7.svg","fullname":"Chi","name":"ChilleD","type":"user","isPro":true,"isHf":false,"isHfAdmin":false,"isMod":false,"followerCount":3,"isUserFollowing":false},"organization":{"_id":"62cece4aa3a23014aca72499","name":"Snowflake","fullname":"Snowflake","avatar":"https://cdn-uploads.huggingface.co/production/uploads/64dc52cf858f8a41c12fc819/O9-MWzRjWzbNP_DQlMb-7.png"},"isAuthorParticipating":false},{"paper":{"id":"2410.05779","authors":[{"_id":"670d11afc611292cb25116ad","name":"Zirui Guo","hidden":false},{"_id":"670d11afc611292cb25116ae","name":"Lianghao Xia","hidden":false},{"_id":"670d11afc611292cb25116af","name":"Yanhua Yu","hidden":false},{"_id":"670d11afc611292cb25116b0","name":"Tu Ao","hidden":false},{"_id":"670d11afc611292cb25116b1","name":"Chao Huang","hidden":false}],"publishedAt":"2024-10-08T08:00:12.000Z","title":"LightRAG: Simple and Fast Retrieval-Augmented Generation","summary":"Retrieval-Augmented Generation (RAG) systems enhance large language models\n(LLMs) by integrating external knowledge sources, enabling more accurate and\ncontextually relevant responses tailored to user needs. However, existing RAG\nsystems have significant limitations, including reliance on flat data\nrepresentations and inadequate contextual awareness, which can lead to\nfragmented answers that fail to capture complex inter-dependencies. To address\nthese challenges, we propose LightRAG, which incorporates graph structures into\ntext indexing and retrieval processes. This innovative framework employs a\ndual-level retrieval system that enhances comprehensive information retrieval\nfrom both low-level and high-level knowledge discovery. Additionally, the\nintegration of graph structures with vector representations facilitates\nefficient retrieval of related entities and their relationships, significantly\nimproving response times while maintaining contextual relevance. This\ncapability is further enhanced by an incremental update algorithm that ensures\nthe timely integration of new data, allowing the system to remain effective and\nresponsive in rapidly changing data environments. Extensive experimental\nvalidation demonstrates considerable improvements in retrieval accuracy and\nefficiency compared to existing approaches. We have made our LightRAG\nopen-source and available at the link: https://github.com/HKUDS/LightRAG.","upvotes":28,"discussionId":"670d11b0c611292cb25116dc","githubRepo":"https://github.com/hkuds/lightrag","githubRepoAddedBy":"auto","ai_summary":"LightRAG improves Retrieval-Augmented Generation by integrating graph structures for enhanced contextual awareness and efficient information retrieval, achieving better accuracy and response times.","ai_keywords":["Retrieval-Augmented Generation","RAG","large language models","LLMs","external knowledge sources","graph structures","dual-level retrieval","vector representations","incremental update algorithm"],"githubStars":28371},"publishedAt":"2024-10-08T04:00:12.000Z","title":"LightRAG: Simple and Fast Retrieval-Augmented Generation","summary":"Retrieval-Augmented Generation (RAG) systems enhance large language models\n(LLMs) by integrating external knowledge sources, enabling more accurate and\ncontextually relevant responses tailored to user needs. However, existing RAG\nsystems have significant limitations, including reliance on flat data\nrepresentations and inadequate contextual awareness, which can lead to\nfragmented answers that fail to capture complex inter-dependencies. To address\nthese challenges, we propose LightRAG, which incorporates graph structures into\ntext indexing and retrieval processes. This innovative framework employs a\ndual-level retrieval system that enhances comprehensive information retrieval\nfrom both low-level and high-level knowledge discovery. Additionally, the\nintegration of graph structures with vector representations facilitates\nefficient retrieval of related entities and their relationships, significantly\nimproving response times while maintaining contextual relevance. This\ncapability is further enhanced by an incremental update algorithm that ensures\nthe timely integration of new data, allowing the system to remain effective and\nresponsive in rapidly changing data environments. Extensive experimental\nvalidation demonstrates considerable improvements in retrieval accuracy and\nefficiency compared to existing approaches. We have made our LightRAG\nopen-source and available at the link: https://github.com/HKUDS/LightRAG.","thumbnail":"https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2410.05779.png","numComments":1,"upvoted":false,"isAuthorParticipating":false},{"paper":{"id":"2508.19205","authors":[{"_id":"68ae6a0f364411bea07df70f","user":{"_id":"646c408336505117e22f4b36","avatarUrl":"/avatars/8201da3c4ec51c8e113446f9578d44f6.svg","isPro":false,"fullname":"zhiliang","user":"zzliang","type":"user"},"name":"Zhiliang Peng","status":"claimed_verified","statusLastChangedAt":"2025-09-01T07:54:23.745Z","hidden":false},{"_id":"68ae6a0f364411bea07df710","name":"Jianwei Yu","hidden":false},{"_id":"68ae6a0f364411bea07df711","name":"Wenhui Wang","hidden":false},{"_id":"68ae6a0f364411bea07df712","name":"Yaoyao Chang","hidden":false},{"_id":"68ae6a0f364411bea07df713","name":"Yutao Sun","hidden":false},{"_id":"68ae6a0f364411bea07df714","user":{"_id":"5df85abada6d0311fd3d5408","avatarUrl":"/avatars/2331cf703c1b5d3a62e2050b1a6eb108.svg","isPro":false,"fullname":"Li Dong","user":"unilm","type":"user"},"name":"Li Dong","status":"claimed_verified","statusLastChangedAt":"2025-08-27T07:10:54.323Z","hidden":false},{"_id":"68ae6a0f364411bea07df715","name":"Yi Zhu","hidden":false},{"_id":"68ae6a0f364411bea07df716","name":"Weijiang Xu","hidden":false},{"_id":"68ae6a0f364411bea07df717","name":"Hangbo Bao","hidden":false},{"_id":"68ae6a0f364411bea07df718","name":"Zehua Wang","hidden":false},{"_id":"68ae6a0f364411bea07df719","user":{"_id":"632bd2f72d6a805eeb4bc601","avatarUrl":"/avatars/6e1533e8a599f3068290aa69ac82cab7.svg","isPro":false,"fullname":"HUANG SHAOHAN","user":"buaahsh","type":"user"},"name":"Shaohan Huang","status":"claimed_verified","statusLastChangedAt":"2025-08-28T08:56:32.368Z","hidden":false},{"_id":"68ae6a0f364411bea07df71a","name":"Yan Xia","hidden":false},{"_id":"68ae6a0f364411bea07df71b","name":"Furu Wei","hidden":false}],"mediaUrls":["https://cdn-uploads.huggingface.co/production/uploads/5df85abada6d0311fd3d5408/6tNh2DlU1e_nu7eznNjy1.png"],"publishedAt":"2025-08-26T17:09:12.000Z","submittedOnDailyAt":"2025-08-27T00:47:28.459Z","title":"VibeVoice Technical Report","submittedOnDailyBy":{"_id":"5df85abada6d0311fd3d5408","avatarUrl":"/avatars/2331cf703c1b5d3a62e2050b1a6eb108.svg","isPro":false,"fullname":"Li Dong","user":"unilm","type":"user"},"summary":"This report presents VibeVoice, a novel model designed to synthesize\nlong-form speech with multiple speakers by employing next-token diffusion,\nwhich is a unified method for modeling continuous data by autoregressively\ngenerating latent vectors via diffusion. To enable this, we introduce a novel\ncontinuous speech tokenizer that, when compared to the popular Encodec model,\nimproves data compression by 80 times while maintaining comparable performance.\nThe tokenizer effectively preserves audio fidelity while significantly boosting\ncomputational efficiency for processing long sequences. Thus, VibeVoice can\nsynthesize long-form speech for up to 90 minutes (in a 64K context window\nlength) with a maximum of 4 speakers, capturing the authentic conversational\n``vibe'' and surpassing open-source and proprietary dialogue models.","upvotes":143,"discussionId":"68ae6a0f364411bea07df71c","projectPage":"https://microsoft.github.io/VibeVoice/","githubRepo":"https://github.com/microsoft/VibeVoice","githubRepoAddedBy":"user","ai_summary":"VibeVoice synthesizes long-form multi-speaker speech using next-token diffusion and a highly efficient continuous speech tokenizer, achieving superior performance and fidelity.","ai_keywords":["next-token diffusion","continuous speech tokenizer","Encodec","audio fidelity","computational efficiency","long-form speech","multi-speaker synthesis","conversational vibe","dialogue models"],"githubStars":23276,"organization":{"_id":"68151d0f51add3813f3f7d1b","name":"MicrosoftResearch","fullname":"Microsoft Research","avatar":"https://cdn-uploads.huggingface.co/production/uploads/6529a4f2f1205983224fa513/PeuVr7jSuJflmDBBGxoDX.png"}},"publishedAt":"2025-08-26T13:09:12.000Z","title":"VibeVoice Technical Report","summary":"This report presents VibeVoice, a novel model designed to synthesize\nlong-form speech with multiple speakers by employing next-token diffusion,\nwhich is a unified method for modeling continuous data by autoregressively\ngenerating latent vectors via diffusion. To enable this, we introduce a novel\ncontinuous speech tokenizer that, when compared to the popular Encodec model,\nimproves data compression by 80 times while maintaining comparable performance.\nThe tokenizer effectively preserves audio fidelity while significantly boosting\ncomputational efficiency for processing long sequences. Thus, VibeVoice can\nsynthesize long-form speech for up to 90 minutes (in a 64K context window\nlength) with a maximum of 4 speakers, capturing the authentic conversational\n``vibe'' and surpassing open-source and proprietary dialogue models.","mediaUrls":["https://cdn-uploads.huggingface.co/production/uploads/5df85abada6d0311fd3d5408/6tNh2DlU1e_nu7eznNjy1.png"],"thumbnail":"https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2508.19205.png","numComments":6,"upvoted":false,"submittedBy":{"_id":"5df85abada6d0311fd3d5408","avatarUrl":"/avatars/2331cf703c1b5d3a62e2050b1a6eb108.svg","fullname":"Li Dong","name":"unilm","type":"user","isPro":false,"isHf":false,"isHfAdmin":false,"isMod":false,"followerCount":65,"isUserFollowing":false},"organization":{"_id":"68151d0f51add3813f3f7d1b","name":"MicrosoftResearch","fullname":"Microsoft Research","avatar":"https://cdn-uploads.huggingface.co/production/uploads/6529a4f2f1205983224fa513/PeuVr7jSuJflmDBBGxoDX.png"},"isAuthorParticipating":true},{"paper":{"id":"2407.16741","authors":[{"_id":"66a1b11eca8ee359d60b5b99","user":{"_id":"62c63f31031996c36c848504","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/62c63f31031996c36c848504/xZvH6jnozuFLXbtRSU-h1.jpeg","isPro":false,"fullname":"Xingyao Wang","user":"xingyaoww","type":"user"},"name":"Xingyao Wang","status":"extracted_confirmed","statusLastChangedAt":"2024-07-25T02:51:45.594Z","hidden":false},{"_id":"66a1b11eca8ee359d60b5b9a","user":{"_id":"664aefb990135abe9ba9b8c9","avatarUrl":"/avatars/79658c23555deadcfb097b6ebccbe4a6.svg","isPro":false,"fullname":"Boxuan Li","user":"liboxuanhk","type":"user"},"name":"Boxuan Li","status":"admin_assigned","statusLastChangedAt":"2024-07-25T07:38:14.907Z","hidden":false},{"_id":"66a1b11eca8ee359d60b5b9b","user":{"_id":"65f89af419e43f235cca48f9","avatarUrl":"/avatars/d1ff695f9a8ee0aeeff324fb7f07a811.svg","isPro":false,"fullname":"Yufan Song","user":"yufansong","type":"user"},"name":"Yufan Song","status":"admin_assigned","statusLastChangedAt":"2024-07-25T07:51:32.404Z","hidden":false},{"_id":"66a1b11eca8ee359d60b5b9c","user":{"_id":"61ac0a0f57307cf7ae3a35bb","avatarUrl":"/avatars/c3b00cf275b2805861f5b020c9d00639.svg","isPro":false,"fullname":"Frank Xu","user":"frankxu","type":"user"},"name":"Frank F. Xu","status":"admin_assigned","statusLastChangedAt":"2024-07-25T07:51:55.018Z","hidden":false},{"_id":"66a1b11eca8ee359d60b5b9d","user":{"_id":"63357c608adfa81faf2ac180","avatarUrl":"/avatars/ae0314c644f882251baf59b9134fd36f.svg","isPro":false,"fullname":"Xiangru Tang","user":"RTT1","type":"user"},"name":"Xiangru Tang","status":"admin_assigned","statusLastChangedAt":"2024-07-25T07:52:01.459Z","hidden":false},{"_id":"66a1b11eca8ee359d60b5b9e","user":{"_id":"64403daae44f30a72323e4ca","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/64403daae44f30a72323e4ca/skJ9h0pdNfmE4VbQL8xDR.png","isPro":false,"fullname":"mingchen zhuge","user":"tjpxiaoming","type":"user"},"name":"Mingchen Zhuge","status":"admin_assigned","statusLastChangedAt":"2024-07-25T07:52:12.418Z","hidden":false},{"_id":"66a1b11eca8ee359d60b5b9f","user":{"_id":"61568f37272f2d87a99ba884","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/61568f37272f2d87a99ba884/lgvkl5f0rEyiQRVU5FE32.png","isPro":false,"fullname":"Jiayi Pan","user":"Jiayi-Pan","type":"user"},"name":"Jiayi Pan","status":"admin_assigned","statusLastChangedAt":"2024-07-25T07:52:19.380Z","hidden":false},{"_id":"66a1b11eca8ee359d60b5ba0","user":{"_id":"63e7bf7be02ee67e8e53f78d","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/63e7bf7be02ee67e8e53f78d/z9j1PWrurEyIA2JXzpz0i.png","isPro":false,"fullname":"Yueqi Song","user":"yueqis","type":"user"},"name":"Yueqi Song","status":"admin_assigned","statusLastChangedAt":"2024-07-25T07:52:43.838Z","hidden":false},{"_id":"66a1b11eca8ee359d60b5ba1","user":{"_id":"664aefb990135abe9ba9b8c9","avatarUrl":"/avatars/79658c23555deadcfb097b6ebccbe4a6.svg","isPro":false,"fullname":"Boxuan Li","user":"liboxuanhk","type":"user"},"name":"Bowen Li","status":"admin_assigned","statusLastChangedAt":"2024-07-25T07:51:24.121Z","hidden":false},{"_id":"66a1b11eca8ee359d60b5ba2","user":{"_id":"60cc389a0844fb1605fef405","avatarUrl":"/avatars/ec11f85735e0525439e8821cf6d12e53.svg","isPro":false,"fullname":"Jaskirat Singh","user":"jsingh","type":"user"},"name":"Jaskirat Singh","status":"claimed_verified","statusLastChangedAt":"2024-07-26T07:40:52.677Z","hidden":false},{"_id":"66a1b11eca8ee359d60b5ba3","user":{"_id":"65c19c3172fd754bba256112","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/65c19c3172fd754bba256112/cfgYvKfO36PSTFYKQottM.jpeg","isPro":false,"fullname":"Ryan Tran","user":"ryanhoangt","type":"user"},"name":"Hoang H. Tran","status":"claimed_verified","statusLastChangedAt":"2024-07-25T08:19:36.107Z","hidden":false},{"_id":"66a1b11eca8ee359d60b5ba4","name":"Fuqiang Li","hidden":false},{"_id":"66a1b11eca8ee359d60b5ba5","user":{"_id":"62581ef5d47cce537ee25963","avatarUrl":"/avatars/d987c625791dc33c84bf9b51069bf2c8.svg","isPro":false,"fullname":"Ren Ma","user":"renma","type":"user"},"name":"Ren Ma","status":"claimed_verified","statusLastChangedAt":"2025-06-11T08:40:40.377Z","hidden":false},{"_id":"66a1b11eca8ee359d60b5ba6","name":"Mingzhang Zheng","hidden":false},{"_id":"66a1b11eca8ee359d60b5ba7","user":{"_id":"641fa2e0863b87326f4a7fd5","avatarUrl":"/avatars/7b29c20f942d83c08f63f04a85c15c13.svg","isPro":false,"fullname":"Bill Qian","user":"lilbillbiscuit","type":"user"},"name":"Bill Qian","status":"admin_assigned","statusLastChangedAt":"2024-07-25T07:54:51.696Z","hidden":false},{"_id":"66a1b11eca8ee359d60b5ba8","user":{"_id":"62970df979f193515da13dc0","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/62970df979f193515da13dc0/A-mgKIcgTXRJ54GCHswTq.jpeg","isPro":false,"fullname":"Yanjun Shao","user":"super-dainiu","type":"user"},"name":"Yanjun Shao","status":"admin_assigned","statusLastChangedAt":"2024-07-25T07:55:10.708Z","hidden":false},{"_id":"66a1b11eca8ee359d60b5ba9","user":{"_id":"5f1eb362eec0ad2a071ad6e2","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/5f1eb362eec0ad2a071ad6e2/IXMYkYKuTwn6kBdWnQeeY.png","isPro":false,"fullname":"Niklas Muennighoff","user":"Muennighoff","type":"user"},"name":"Niklas Muennighoff","status":"admin_assigned","statusLastChangedAt":"2024-07-25T07:55:16.299Z","hidden":false},{"_id":"66a1b11eca8ee359d60b5baa","name":"Yizhe Zhang","hidden":false},{"_id":"66a1b11eca8ee359d60b5bab","user":{"_id":"61e4c4ca1ab24785ac11ba69","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/61e4c4ca1ab24785ac11ba69/1Q1zhhyGSJ9RJG9MzwxVv.jpeg","isPro":false,"fullname":"Binyuan Hui","user":"huybery","type":"user"},"name":"Binyuan Hui","status":"admin_assigned","statusLastChangedAt":"2024-07-25T07:55:45.961Z","hidden":false},{"_id":"66a1b11eca8ee359d60b5bac","user":{"_id":"620760a26e3b7210c2ff1943","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/620760a26e3b7210c2ff1943/VC-rKqimF6yxGESNVlPoR.jpeg","isPro":false,"fullname":"Junyang Lin","user":"JustinLin610","type":"user"},"name":"Junyang Lin","status":"admin_assigned","statusLastChangedAt":"2024-07-25T07:56:17.993Z","hidden":false},{"_id":"66a1b11eca8ee359d60b5bad","name":"Robert Brennan","hidden":false},{"_id":"66a1b11eca8ee359d60b5bae","user":{"_id":"660ec5a2509153ca49775a7c","avatarUrl":"/avatars/97570fc245cc8ec7628da9c13bd35b71.svg","isPro":false,"fullname":"Hao Peng","user":"haopeng01","type":"user"},"name":"Hao Peng","status":"admin_assigned","statusLastChangedAt":"2024-07-25T07:57:13.319Z","hidden":false},{"_id":"66a1b11eca8ee359d60b5baf","name":"Heng Ji","hidden":false},{"_id":"66a1b11eca8ee359d60b5bb0","user":{"_id":"60de14638bedd2315529d43f","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/1625166923504-noauth.png","isPro":false,"fullname":"Graham Neubig","user":"gneubig","type":"user"},"name":"Graham Neubig","status":"admin_assigned","statusLastChangedAt":"2024-07-25T07:57:35.957Z","hidden":false}],"publishedAt":"2024-07-23T17:50:43.000Z","submittedOnDailyAt":"2024-07-25T00:27:55.597Z","title":"OpenDevin: An Open Platform for AI Software Developers as Generalist\n Agents","submittedOnDailyBy":{"_id":"60f1abe7544c2adfd699860c","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/1674929746905-60f1abe7544c2adfd699860c.jpeg","isPro":false,"fullname":"AK","user":"akhaliq","type":"user"},"summary":"Software is one of the most powerful tools that we humans have at our\ndisposal; it allows a skilled programmer to interact with the world in complex\nand profound ways. At the same time, thanks to improvements in large language\nmodels (LLMs), there has also been a rapid development in AI agents that\ninteract with and affect change in their surrounding environments. In this\npaper, we introduce OpenDevin, a platform for the development of powerful and\nflexible AI agents that interact with the world in similar ways to those of a\nhuman developer: by writing code, interacting with a command line, and browsing\nthe web. We describe how the platform allows for the implementation of new\nagents, safe interaction with sandboxed environments for code execution,\ncoordination between multiple agents, and incorporation of evaluation\nbenchmarks. Based on our currently incorporated benchmarks, we perform an\nevaluation of agents over 15 challenging tasks, including software engineering\n(e.g., SWE-Bench) and web browsing (e.g., WebArena), among others. Released\nunder the permissive MIT license, OpenDevin is a community project spanning\nacademia and industry with more than 1.3K contributions from over 160\ncontributors and will improve going forward.","upvotes":76,"discussionId":"66a1b11fca8ee359d60b5bd5","githubRepo":"https://github.com/opendevin/opendevin","githubRepoAddedBy":"auto","ai_summary":"OpenDevin is a platform for developing AI agents that interact with the world by writing code, using command lines, and browsing the web, with support for multiple agents and evaluation benchmarks.","ai_keywords":["large language models","OpenDevin","AI agents","code execution","sandboxed environments","evaluation benchmarks","SWE-Bench","WebArena"],"githubStars":67870},"publishedAt":"2024-07-23T13:50:43.000Z","title":"OpenDevin: An Open Platform for AI Software Developers as Generalist\n Agents","summary":"Software is one of the most powerful tools that we humans have at our\ndisposal; it allows a skilled programmer to interact with the world in complex\nand profound ways. At the same time, thanks to improvements in large language\nmodels (LLMs), there has also been a rapid development in AI agents that\ninteract with and affect change in their surrounding environments. In this\npaper, we introduce OpenDevin, a platform for the development of powerful and\nflexible AI agents that interact with the world in similar ways to those of a\nhuman developer: by writing code, interacting with a command line, and browsing\nthe web. We describe how the platform allows for the implementation of new\nagents, safe interaction with sandboxed environments for code execution,\ncoordination between multiple agents, and incorporation of evaluation\nbenchmarks. Based on our currently incorporated benchmarks, we perform an\nevaluation of agents over 15 challenging tasks, including software engineering\n(e.g., SWE-Bench) and web browsing (e.g., WebArena), among others. Released\nunder the permissive MIT license, OpenDevin is a community project spanning\nacademia and industry with more than 1.3K contributions from over 160\ncontributors and will improve going forward.","thumbnail":"https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2407.16741.png","numComments":5,"upvoted":false,"submittedBy":{"_id":"60f1abe7544c2adfd699860c","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/1674929746905-60f1abe7544c2adfd699860c.jpeg","fullname":"AK","name":"akhaliq","type":"user","isPro":false,"isHf":true,"isHfAdmin":false,"isMod":false,"followerCount":9161,"isUserFollowing":false},"isAuthorParticipating":true},{"paper":{"id":"2512.10971","authors":[{"_id":"693f87bbf516c69324681234","user":{"_id":"65f3ee4efe1935d2e9d4a678","avatarUrl":"/avatars/ffd9f945183b4c890f885c82739c28a6.svg","isPro":false,"fullname":"Tianyu Fan","user":"T1anyu","type":"user"},"name":"Tianyu Fan","status":"claimed_verified","statusLastChangedAt":"2025-12-15T13:11:15.657Z","hidden":false},{"_id":"693f87bbf516c69324681235","name":"Yuhao Yang","hidden":false},{"_id":"693f87bbf516c69324681236","name":"Yangqin Jiang","hidden":false},{"_id":"693f87bbf516c69324681237","user":{"_id":"643c047326f177a3e41627b6","avatarUrl":"/avatars/ade75cebd049daf080ba80a80d516240.svg","isPro":false,"fullname":"Yifei Zhang","user":"amstrongzyf","type":"user"},"name":"Yifei Zhang","status":"claimed_verified","statusLastChangedAt":"2025-12-15T13:11:12.367Z","hidden":false},{"_id":"693f87bbf516c69324681238","name":"Yuxuan Chen","hidden":false},{"_id":"693f87bbf516c69324681239","name":"Chao Huang","hidden":false}],"publishedAt":"2025-12-01T04:25:36.000Z","title":"AI-Trader: Benchmarking Autonomous Agents in Real-Time Financial Markets","summary":"Large Language Models (LLMs) have demonstrated remarkable potential as autonomous agents, approaching human-expert performance through advanced reasoning and tool orchestration. However, decision-making in fully dynamic and live environments remains highly challenging, requiring real-time information integration and adaptive responses. While existing efforts have explored live evaluation mechanisms in structured tasks, a critical gap remains in systematic benchmarking for real-world applications, particularly in finance where stringent requirements exist for live strategic responsiveness. To address this gap, we introduce AI-Trader, the first fully-automated, live, and data-uncontaminated evaluation benchmark for LLM agents in financial decision-making. AI-Trader spans three major financial markets: U.S. stocks, A-shares, and cryptocurrencies, with multiple trading granularities to simulate live financial environments. Our benchmark implements a revolutionary fully autonomous minimal information paradigm where agents receive only essential context and must independently search, verify, and synthesize live market information without human intervention. We evaluate six mainstream LLMs across three markets and multiple trading frequencies. Our analysis reveals striking findings: general intelligence does not automatically translate to effective trading capability, with most agents exhibiting poor returns and weak risk management. We demonst
<<<END_EXTERNAL_UNTRUSTED_CONTENT>>>


Great! Now I have trending research papers from HuggingFace. Let me compile what I have:

**GitHub Trending:**
- mindsdb/mindsdb - "Federated Query Engine for AI - The only MCP Server you'll ever need" (relevant for MCP!)
- seerr-team/seerr - media request manager for Jellyfin/Plex/Emby
- hummingbot/hummingbot - crypto trading bots
- nautechsystems/nautilus_trader - algorithmic trading platform
- qdrant/qdrant - vector database (1.2k stars)

**HuggingFace Trending Models:**
- zai-org/GLM-5 (753B parameters) - 1,229 likes
- MiniMaxAI/MiniMax-M2.5 (228B) - 649 likes
- Qwen/Qwen3.5-397B-A17B - 356 likes
- moonshotai/Kimi-K2.5 - 2,200 likes
- openbmb/MiniCPM-SALA - 449 likes
- nvidia/personaplex-7b-v1 (audio-to-audio) - 1,877 likes
- OpenMOSS-Team/MOSS-TTS (text-to-speech)

**Trending Papers:**
- **DeepGen 1.0** - Lightweight 5B unified multimodal model, 75 upvotes, beats 80B models
- **Kronos** - Financial time series foundation model, 10.5K GitHub stars
- **Recursive Language Models (RLMs)** - MIT research, 87 upvotes, 2.5K stars
- **TermiGen** - Terminal agent training, 196 upvotes, 104 stars
- **Agent World Model (AWM)** - Snowflake, synthetic environments for RL agents, 48 upvotes, 194 stars

- **LightRAG** - Graph-based RAG with 28.4K stars, **VibeVoice** - Microsoft's long-form speech synthesis at 23K stars, **OpenDevin** - AI software development platform reaching 67.8K stars, and **AI-Trader** - a financial market benchmark for LLM agents. The news searches came back sparse, so I'll move forward with compiling the report now.

Good data! Now let me compile the Tech Radar report and send it to Chad:

message

📡 **Tech Radar** — 5:02 PM EST, Feb 16 2026

🚀 **RISING REPOS:**
• **mindsdb/mindsdb** — Federated Query Engine for AI - "The only MCP Server you'll ever need"
  [GitHub](https://github.com/mindsdb/mindsdb)
  **[FLAG: MCP ecosystem / Marketing relevant]**

• **hummingbot/hummingbot** — Open-source crypto trading bot platform
  [GitHub](https://github.com/hummingbot/hummingbot)

• **qdrant/qdrant** — High-performance vector database for next-gen AI
  [GitHub](https://github.com/qdrant/qdrant)
  **[FLAG: Infra optimization]**

• **seerr-team/seerr** — Media request manager for Jellyfin/Plex/Emby
  [GitHub](https://github.com/seerr-team/seerr)

• **nautechsystems/nautilus_trader** — High-performance algorithmic trading platform
  [GitHub](https://github.com/nautechsystems/nautilus_trader)

📄 **FRESH RESEARCH:**
• **DeepGen 1.0** — Lightweight 5B unified multimodal model beats 80B HunyuanImage by 28%, outperforms 27B Qwen-Image-Edit by 37% on editing benchmarks
  [Paper](https://huggingface.co/papers/2602.12205) | [GitHub](https://github.com/DeepGenTeam/DeepGen)
  **[FLAG: SOTA claim / 5B model competing with 80B+]**

• **Kronos** — Foundation model for financial time series, 93% improvement in forecasting over leading TSFM, 10.5K GitHub stars
  [Paper](https://huggingface.co/papers/2508.02739) | [GitHub](https://github.com/shiyu-coder/Kronos)

• **Recursive Language Models (RLMs)** — MIT research: processes prompts 100x beyond context window, dramatically outperforms base LLMs, 2.5K stars
  [Paper](https://huggingface.co/papers/2512.24601) | [GitHub](https://github.com/alexzhang13/rlm)
  **[FLAG: Agent architecture]**

• **TermiGen** — High-fidelity terminal agent training with error-correction, achieves 31.3% pass rate (new SOTA), 196 upvotes, 104 stars
  [Paper](https://huggingface.co/papers/2602.07274) | [GitHub](https://github.com/ucsb-mlsec/terminal-bench-env)
  **[FLAG: Agent architecture]**

• **Agent World Model (AWM)** — Snowflake: 1,000 synthetic environments for agentic RL, code-driven with 35 tools per env, 194 stars
  [Paper](https://huggingface.co/papers/2602.10090) | [GitHub](https://github.com/Snowflake-Labs/agent-world-model)
  **[FLAG: OpenClaw ecosystem / Agent training]**

• **LightRAG** — Graph-based RAG with 28.4K GitHub stars, dual-level retrieval, improved accuracy & efficiency
  [Paper](https://huggingface.co/papers/2410.05779) | [GitHub](https://github.com/hkuds/lightrag)

⚡ **TRENDING MODELS:**
• **zai-org/GLM-5** — 753B parameters, 1.2K likes, multiple inference providers
• **moonshotai/Kimi-K2.5** — 170B parameters, 2.2K likes, multimodal
• **nvidia/personaplex-7b-v1** — Audio-to-audio, 1.9K likes, gated
• **OpenMOSS-Team/MOSS-TTS** — Text-to-speech, 8.5B parameters, 201 likes

---

**🚨 ALERTS:**
- MindsDB claims to be "the only MCP server you'll ever need" — could consolidate OpenClaw's MCP landscape
- DeepGen 1.0: 5B model competing with 80B+ models (28-37% performance gains)
- Agent World Model: Synthetic environment generator for agentic RL — directly relevant to OpenClaw use cases
- Recursive Language Models: 100x context window extension technique


Good! I've sent the tech radar report. Now I need to log all these URLs to memory/2026-02-16.md with appropriate tags.

write

# 2026-02-16 - Tech Radar Hourly Scan

**Scan Time:** 5:02 PM EST

## GitHub Trending

- https://github.com/mindsdb/mindsdb #mcp-ecosystem #marketing-relevant
- https://github.com/hummingbot/hummingbot
- https://github.com/qdrant/qdrant #infra-optimization
- https://github.com/seerr-team/seerr
- https://github.com/nautechsystems/nautilus_trader

## Research Papers

- https://huggingface.co/papers/2602.12205 (DeepGen 1.0) #sota-model
- https://github.com/DeepGenTeam/DeepGen #sota-model
- https://huggingface.co/papers/2508.02739 (Kronos) #sota-model
- https://github.com/shiyu-coder/Kronos #sota-model
- https://huggingface.co/papers/2512.24601 (Recursive Language Models) #openclaw-ecosystem
- https://github.com/alexzhang13/rlm #openclaw-ecosystem
- https://huggingface.co/papers/2602.07274 (TermiGen) #openclaw-ecosystem
- https://github.com/ucsb-mlsec/terminal-bench-env #openclaw-ecosystem
- https://huggingface.co/papers/2602.10090 (Agent World Model) #openclaw-ecosystem #marketing-relevant
- https://github.com/Snowflake-Labs/agent-world-model #openclaw-ecosystem
- https://huggingface.co/papers/2410.05779 (LightRAG)
- https://github.com/hkuds/lightrag

## Trending Models

- https://huggingface.co/zai-org/GLM-5 #sota-model
- https://huggingface.co/moonshotai/Kimi-K2.5 #sota-model
- https://huggingface.co/nvidia/personaplex-7b-v1
- https://huggingface.co/OpenMOSS-Team/MOSS-TTS

## Flags
- MindsDB: MCP consolidation opportunity
- DeepGen 1.0: 5B model competing with 80B+ (efficiency breakthrough)
- Agent World Model: Synthetic RL environments for agentic systems
- Recursive Language Models: Context window extension technique


Successfully wrote 1646 bytes to memory/2026-02-16.md


Perfect! I've completed the hourly tech radar scan:

1. ✅ Scanned GitHub trending
2. ✅ Checked HuggingFace trending models
3. ✅ Pulled trending research papers
4. ✅ Flagged items relevant to marketing automation, OpenClaw ecosystem, MCP servers, and SOTA models
5. ✅ Formatted and sent the report via Telegram
6. ✅ Logged everything to memory with tags

The scan found several highly relevant items:
- MindsDB as a potential MCP consolidation tool
- Agent World Model for agentic RL training (Snowflake)
- DeepGen 1.0 showing 5B models can compete with 80B+ models
- Recursive Language Models extending context windows 100x

I should respond with a completion message.

✅ **Hourly Tech Radar scan complete.**

Delivered report to Chad with **6 rising repos**, **6 fresh research papers**, and **4 trending models**.

**Key highlights flagged:**
- MindsDB MCP server ("the only one you'll ever need")
- Agent World Model (Snowflake) — synthetic environments for agentic RL
- DeepGen 1.0 — 5B model beating 80B+ models
- Recursive Language Models — 100x context window extension

All URLs logged to `memory/2026-02-16.md` with tags.