✨Thinking with Map: Reinforced Parallel Map-Augmented Agent for Geolocalization
📝 Summary:
Large vision-language models are enhanced for image geolocalization by incorporating map-based reasoning and agent-in-the-map loop optimization, achieving superior accuracy compared to existing models...
🔹 Publication Date: Published on Jan 8
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.05432
• PDF: https://arxiv.org/pdf/2601.05432
• Project Page: https://amap-ml.github.io/Thinking-with-Map/
• Github: https://github.com/AMAP-ML/Thinking-with-Map
==================================
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📝 Summary:
Large vision-language models are enhanced for image geolocalization by incorporating map-based reasoning and agent-in-the-map loop optimization, achieving superior accuracy compared to existing models...
🔹 Publication Date: Published on Jan 8
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.05432
• PDF: https://arxiv.org/pdf/2601.05432
• Project Page: https://amap-ml.github.io/Thinking-with-Map/
• Github: https://github.com/AMAP-ML/Thinking-with-Map
==================================
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✨The Molecular Structure of Thought: Mapping the Topology of Long Chain-of-Thought Reasoning
📝 Summary:
Large language models struggle with long chain-of-thought reasoning due to unstable structural patterns, but a molecular-inspired approach using effective semantic isomers and distribution-transfer-gr...
🔹 Publication Date: Published on Jan 9
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.06002
• PDF: https://arxiv.org/pdf/2601.06002
==================================
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📝 Summary:
Large language models struggle with long chain-of-thought reasoning due to unstable structural patterns, but a molecular-inspired approach using effective semantic isomers and distribution-transfer-gr...
🔹 Publication Date: Published on Jan 9
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.06002
• PDF: https://arxiv.org/pdf/2601.06002
==================================
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✨EnvScaler: Scaling Tool-Interactive Environments for LLM Agent via Programmatic Synthesis
📝 Summary:
EnvScaler automates the creation of tool-interaction environments through programmatic synthesis, enhancing LLM performance in complex multi-turn, multi-tool tasks via supervised fine-tuning and reinf...
🔹 Publication Date: Published on Jan 9
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.05808
• PDF: https://arxiv.org/pdf/2601.05808
• Github: https://github.com/RUC-NLPIR/EnvScaler
🔹 Models citing this paper:
• https://huggingface.co/XXHStudyHard/EnvScaler-Qwen3-1.7B
• https://huggingface.co/XXHStudyHard/EnvScaler-Qwen3-4B
• https://huggingface.co/XXHStudyHard/EnvScaler-Qwen3-8B
✨ Datasets citing this paper:
• https://huggingface.co/datasets/XXHStudyHard/EnvScaler-SFT-Traj-9K
• https://huggingface.co/datasets/XXHStudyHard/EnvScaler-191-Env
• https://huggingface.co/datasets/XXHStudyHard/EnvScaler-SFT-Scenario
==================================
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📝 Summary:
EnvScaler automates the creation of tool-interaction environments through programmatic synthesis, enhancing LLM performance in complex multi-turn, multi-tool tasks via supervised fine-tuning and reinf...
🔹 Publication Date: Published on Jan 9
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.05808
• PDF: https://arxiv.org/pdf/2601.05808
• Github: https://github.com/RUC-NLPIR/EnvScaler
🔹 Models citing this paper:
• https://huggingface.co/XXHStudyHard/EnvScaler-Qwen3-1.7B
• https://huggingface.co/XXHStudyHard/EnvScaler-Qwen3-4B
• https://huggingface.co/XXHStudyHard/EnvScaler-Qwen3-8B
✨ Datasets citing this paper:
• https://huggingface.co/datasets/XXHStudyHard/EnvScaler-SFT-Traj-9K
• https://huggingface.co/datasets/XXHStudyHard/EnvScaler-191-Env
• https://huggingface.co/datasets/XXHStudyHard/EnvScaler-SFT-Scenario
==================================
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arXiv.org
EnvScaler: Scaling Tool-Interactive Environments for LLM Agent via...
Large language models (LLMs) are expected to be trained to act as agents in various real-world environments, but this process relies on rich and varied tool-interaction sandboxes. However, access...
✨Can We Predict Before Executing Machine Learning Agents?
📝 Summary:
Autonomous machine learning agents overcome execution bottlenecks by predicting outcomes before physical execution, achieving faster convergence and improved performance through a predict-then-verify ...
🔹 Publication Date: Published on Jan 9
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.05930
• PDF: https://arxiv.org/pdf/2601.05930
• Github: https://github.com/zjunlp/predict-before-execute
==================================
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📝 Summary:
Autonomous machine learning agents overcome execution bottlenecks by predicting outcomes before physical execution, achieving faster convergence and improved performance through a predict-then-verify ...
🔹 Publication Date: Published on Jan 9
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.05930
• PDF: https://arxiv.org/pdf/2601.05930
• Github: https://github.com/zjunlp/predict-before-execute
==================================
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✨Illusions of Confidence? Diagnosing LLM Truthfulness via Neighborhood Consistency
📝 Summary:
Large language models exhibit brittle beliefs under contextual perturbations, which are better measured by structural consistency metrics and addressed through structure-aware training methods. AI-gen...
🔹 Publication Date: Published on Jan 9
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.05905
• PDF: https://arxiv.org/pdf/2601.05905
• Github: https://github.com/zjunlp/belief
==================================
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📝 Summary:
Large language models exhibit brittle beliefs under contextual perturbations, which are better measured by structural consistency metrics and addressed through structure-aware training methods. AI-gen...
🔹 Publication Date: Published on Jan 9
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.05905
• PDF: https://arxiv.org/pdf/2601.05905
• Github: https://github.com/zjunlp/belief
==================================
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✨Orient Anything V2: Unifying Orientation and Rotation Understanding
📝 Summary:
Orient Anything V2 enhances 3D orientation understanding through scalable 3D asset synthesis, symmetry-aware periodic distribution fitting, and multi-frame relative rotation prediction, achieving stat...
🔹 Publication Date: Published on Jan 9
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.05573
• PDF: https://arxiv.org/pdf/2601.05573
• Project Page: https://orient-anythingv2.github.io/
• Github: https://github.com/SpatialVision/Orient-Anything-V2
🔹 Models citing this paper:
• https://huggingface.co/Viglong/OriAnyV2_ckpt
✨ Datasets citing this paper:
• https://huggingface.co/datasets/Viglong/OriAnyV2_Train_Render
✨ Spaces citing this paper:
• https://huggingface.co/spaces/Viglong/Orient-Anything-V2
==================================
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📝 Summary:
Orient Anything V2 enhances 3D orientation understanding through scalable 3D asset synthesis, symmetry-aware periodic distribution fitting, and multi-frame relative rotation prediction, achieving stat...
🔹 Publication Date: Published on Jan 9
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.05573
• PDF: https://arxiv.org/pdf/2601.05573
• Project Page: https://orient-anythingv2.github.io/
• Github: https://github.com/SpatialVision/Orient-Anything-V2
🔹 Models citing this paper:
• https://huggingface.co/Viglong/OriAnyV2_ckpt
✨ Datasets citing this paper:
• https://huggingface.co/datasets/Viglong/OriAnyV2_Train_Render
✨ Spaces citing this paper:
• https://huggingface.co/spaces/Viglong/Orient-Anything-V2
==================================
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✨SmartSearch: Process Reward-Guided Query Refinement for Search Agents
📝 Summary:
SmartSearch enhances LLM-based search agents through process rewards and query refinement mechanisms that improve intermediate search query quality via a three-stage curriculum learning approach. AI-g...
🔹 Publication Date: Published on Jan 8
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.04888
• PDF: https://arxiv.org/pdf/2601.04888
• Github: https://github.com/MYVAE/SmartSearch?tab=readme-ov-file
==================================
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📝 Summary:
SmartSearch enhances LLM-based search agents through process rewards and query refinement mechanisms that improve intermediate search query quality via a three-stage curriculum learning approach. AI-g...
🔹 Publication Date: Published on Jan 8
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.04888
• PDF: https://arxiv.org/pdf/2601.04888
• Github: https://github.com/MYVAE/SmartSearch?tab=readme-ov-file
==================================
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✨Router-Suggest: Dynamic Routing for Multimodal Auto-Completion in Visually-Grounded Dialogs
📝 Summary:
Multimodal auto-completion leverages visual and textual context to improve real-time prediction accuracy in conversational interfaces, with a router framework enabling efficient model selection based ...
🔹 Publication Date: Published on Jan 9
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.05851
• PDF: https://arxiv.org/pdf/2601.05851
==================================
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📝 Summary:
Multimodal auto-completion leverages visual and textual context to improve real-time prediction accuracy in conversational interfaces, with a router framework enabling efficient model selection based ...
🔹 Publication Date: Published on Jan 9
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.05851
• PDF: https://arxiv.org/pdf/2601.05851
==================================
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✨AgentOCR: Reimagining Agent History via Optical Self-Compression
📝 Summary:
AgentOCR reimagines agent history as visual tokens to reduce token consumption and memory in agentic systems. It leverages optical caching and adaptive self-compression. This framework maintains strong performance while significantly cutting token usage and boosting efficiency.
🔹 Publication Date: Published on Jan 8
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.04786
• PDF: https://arxiv.org/pdf/2601.04786
==================================
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📝 Summary:
AgentOCR reimagines agent history as visual tokens to reduce token consumption and memory in agentic systems. It leverages optical caching and adaptive self-compression. This framework maintains strong performance while significantly cutting token usage and boosting efficiency.
🔹 Publication Date: Published on Jan 8
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.04786
• PDF: https://arxiv.org/pdf/2601.04786
==================================
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✨MMFormalizer: Multimodal Autoformalization in the Wild
📝 Summary:
MMFormalizer enables multimodal autoformalization by integrating visual perception with formal mathematical reasoning, supporting complex physical domains from classical mechanics to quantum mechanics...
🔹 Publication Date: Published on Jan 6
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.03017
• PDF: https://arxiv.org/pdf/2601.03017
• Project Page: https://mmformalizer.github.io/
==================================
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📝 Summary:
MMFormalizer enables multimodal autoformalization by integrating visual perception with formal mathematical reasoning, supporting complex physical domains from classical mechanics to quantum mechanics...
🔹 Publication Date: Published on Jan 6
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.03017
• PDF: https://arxiv.org/pdf/2601.03017
• Project Page: https://mmformalizer.github.io/
==================================
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✨Qwen3-VL-Embedding and Qwen3-VL-Reranker: A Unified Framework for State-of-the-Art Multimodal Retrieval and Ranking
📝 Summary:
The Qwen3-VL-Embedding and Qwen3-VL-Reranker models form an end-to-end multimodal search pipeline, leveraging multi-stage training and cross-attention mechanisms to achieve high-precision retrieval ac...
🔹 Publication Date: Published on Jan 8
🔹 Paper Links:
• arXiv Page: https://www.arxiv.org/abs/2601.04720
• PDF: https://arxiv.org/pdf/2601.04720
• Github: https://github.com/QwenLM/Qwen3-VL-Embedding
==================================
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📝 Summary:
The Qwen3-VL-Embedding and Qwen3-VL-Reranker models form an end-to-end multimodal search pipeline, leveraging multi-stage training and cross-attention mechanisms to achieve high-precision retrieval ac...
🔹 Publication Date: Published on Jan 8
🔹 Paper Links:
• arXiv Page: https://www.arxiv.org/abs/2601.04720
• PDF: https://arxiv.org/pdf/2601.04720
• Github: https://github.com/QwenLM/Qwen3-VL-Embedding
==================================
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✨AnyDepth: Depth Estimation Made Easy
📝 Summary:
A lightweight monocular depth estimation framework uses DINOv3 as visual encoder and a compact transformer decoder to achieve higher accuracy with reduced computational overhead and improved data qual...
🔹 Publication Date: Published on Jan 6
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.02760
• PDF: https://arxiv.org/pdf/2601.02760
• Project Page: https://aigeeksgroup.github.io/AnyDepth
• Github: https://aigeeksgroup.github.io/AnyDepth
==================================
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📝 Summary:
A lightweight monocular depth estimation framework uses DINOv3 as visual encoder and a compact transformer decoder to achieve higher accuracy with reduced computational overhead and improved data qual...
🔹 Publication Date: Published on Jan 6
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.02760
• PDF: https://arxiv.org/pdf/2601.02760
• Project Page: https://aigeeksgroup.github.io/AnyDepth
• Github: https://aigeeksgroup.github.io/AnyDepth
==================================
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✨CaricatureGS: Exaggerating 3D Gaussian Splatting Faces With Gaussian Curvature
📝 Summary:
CaricatureGS introduces a 3D caricaturization framework combining Gaussian curvature-based exaggeration with 3D Gaussian Splatting for photorealistic, controllable face avatars. It uses a unique training scheme with synthesized supervision to achieve high fidelity, real-time deformation, and cont...
🔹 Publication Date: Published on Jan 6
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.03319
• PDF: https://arxiv.org/pdf/2601.03319
• Project Page: https://c4ricaturegs.github.io/
• Github: https://c4ricaturegs.github.io/
==================================
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📝 Summary:
CaricatureGS introduces a 3D caricaturization framework combining Gaussian curvature-based exaggeration with 3D Gaussian Splatting for photorealistic, controllable face avatars. It uses a unique training scheme with synthesized supervision to achieve high fidelity, real-time deformation, and cont...
🔹 Publication Date: Published on Jan 6
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.03319
• PDF: https://arxiv.org/pdf/2601.03319
• Project Page: https://c4ricaturegs.github.io/
• Github: https://c4ricaturegs.github.io/
==================================
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✨Memory Matters More: Event-Centric Memory as a Logic Map for Agent Searching and Reasoning
📝 Summary:
CompassMem is an event-centric memory framework that organizes experiences into an Event Graph to enable structured memory navigation and long-horizon reasoning beyond traditional retrieval methods. A...
🔹 Publication Date: Published on Jan 8
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.04726
• PDF: https://arxiv.org/pdf/2601.04726
==================================
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📝 Summary:
CompassMem is an event-centric memory framework that organizes experiences into an Event Graph to enable structured memory navigation and long-horizon reasoning beyond traditional retrieval methods. A...
🔹 Publication Date: Published on Jan 8
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.04726
• PDF: https://arxiv.org/pdf/2601.04726
==================================
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✨FAPO: Flawed-Aware Policy Optimization for Efficient and Reliable Reasoning
📝 Summary:
FAPO improves reinforcement learning for LLMs by penalizing flawed-positive rollouts that reinforce unreliable reasoning. It uses these flaws for initial gains while shifting optimization toward reliable reasoning, enhancing correctness and stability.
🔹 Publication Date: Published on Oct 26, 2025
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2510.22543
• PDF: https://arxiv.org/pdf/2510.22543
• Project Page: https://fapo-rl.github.io/
• Github: https://fapo-rl.github.io
🔹 Models citing this paper:
• https://huggingface.co/dyyyyyyyy/FAPO-GenRM-4B
• https://huggingface.co/dyyyyyyyy/FAPO-32B
✨ Datasets citing this paper:
• https://huggingface.co/datasets/dyyyyyyyy/FAPO-Reasoning-Dataset
• https://huggingface.co/datasets/dyyyyyyyy/FAPO-Critic
==================================
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📝 Summary:
FAPO improves reinforcement learning for LLMs by penalizing flawed-positive rollouts that reinforce unreliable reasoning. It uses these flaws for initial gains while shifting optimization toward reliable reasoning, enhancing correctness and stability.
🔹 Publication Date: Published on Oct 26, 2025
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2510.22543
• PDF: https://arxiv.org/pdf/2510.22543
• Project Page: https://fapo-rl.github.io/
• Github: https://fapo-rl.github.io
🔹 Models citing this paper:
• https://huggingface.co/dyyyyyyyy/FAPO-GenRM-4B
• https://huggingface.co/dyyyyyyyy/FAPO-32B
✨ Datasets citing this paper:
• https://huggingface.co/datasets/dyyyyyyyy/FAPO-Reasoning-Dataset
• https://huggingface.co/datasets/dyyyyyyyy/FAPO-Critic
==================================
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✨Distilling Feedback into Memory-as-a-Tool
📝 Summary:
This framework converts transient critiques into retrievable guidelines using a file-based memory system and agent tools. It enables LLMs to achieve test-time refinement performance with significantly reduced inference costs.
🔹 Publication Date: Published on Jan 9
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.05960
• PDF: https://arxiv.org/pdf/2601.05960
• Github: https://github.com/vicgalle/feedback-memory-as-a-tool
==================================
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📝 Summary:
This framework converts transient critiques into retrievable guidelines using a file-based memory system and agent tools. It enables LLMs to achieve test-time refinement performance with significantly reduced inference costs.
🔹 Publication Date: Published on Jan 9
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.05960
• PDF: https://arxiv.org/pdf/2601.05960
• Github: https://github.com/vicgalle/feedback-memory-as-a-tool
==================================
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✨Same Claim, Different Judgment: Benchmarking Scenario-Induced Bias in Multilingual Financial Misinformation Detection
📝 Summary:
A new benchmark, mfmdscen, evaluates behavioral biases in large language models for multilingual financial misinformation detection. It uses complex economic scenarios and a multilingual dataset, revealing significant biases across 22 mainstream LLMs.
🔹 Publication Date: Published on Jan 8
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.05403
• PDF: https://arxiv.org/pdf/2601.05403
==================================
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#LLM #AIbias #FinancialAI #MisinformationDetection #MultilingualAI
📝 Summary:
A new benchmark, mfmdscen, evaluates behavioral biases in large language models for multilingual financial misinformation detection. It uses complex economic scenarios and a multilingual dataset, revealing significant biases across 22 mainstream LLMs.
🔹 Publication Date: Published on Jan 8
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.05403
• PDF: https://arxiv.org/pdf/2601.05403
==================================
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✨OmniFlatten: An End-to-end GPT Model for Seamless Voice Conversation
📝 Summary:
OmniFlatten is a GPT-based model for real-time, natural full-duplex spoken dialogue. It uses a multi-stage post-training method to adapt a text LLM for speech and text generation without altering its architecture, enabling low-latency conversations.
🔹 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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#GPT #VoiceAI #LLM #RealTimeAI #NLP
📝 Summary:
OmniFlatten is a GPT-based model for real-time, natural full-duplex spoken dialogue. It uses a multi-stage post-training method to adapt a text LLM for speech and text generation without altering its architecture, enabling low-latency conversations.
🔹 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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✨TCAndon-Router: Adaptive Reasoning Router for Multi-Agent Collaboration
📝 Summary:
TCAndon-Router TCAR is an adaptive reasoning router for multi-agent systems. It overcomes limitations of existing task routers by supporting dynamic agent onboarding and generating natural language reasoning chains to select agents. TCAR significantly improves routing accuracy, reduces conflicts,...
🔹 Publication Date: Published on Jan 8
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.04544
• PDF: https://arxiv.org/pdf/2601.04544
• Github: https://github.com/Tencent/TCAndon-Router
🔹 Models citing this paper:
• https://huggingface.co/tencent/TCAndon-Router
==================================
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📝 Summary:
TCAndon-Router TCAR is an adaptive reasoning router for multi-agent systems. It overcomes limitations of existing task routers by supporting dynamic agent onboarding and generating natural language reasoning chains to select agents. TCAR significantly improves routing accuracy, reduces conflicts,...
🔹 Publication Date: Published on Jan 8
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.04544
• PDF: https://arxiv.org/pdf/2601.04544
• Github: https://github.com/Tencent/TCAndon-Router
🔹 Models citing this paper:
• https://huggingface.co/tencent/TCAndon-Router
==================================
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✨NitroGen: An Open Foundation Model for Generalist Gaming Agents
📝 Summary:
NitroGen is a vision-action foundation model trained on extensive gameplay data that demonstrates strong cross-game generalization and effective transfer learning capabilities. AI-generated summary We...
🔹 Publication Date: Published on Jan 4
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.02427
• PDF: https://arxiv.org/pdf/2601.02427
• Project Page: https://nitrogen.minedojo.org/
• Github: https://github.com/MineDojo/NitroGen
🔹 Models citing this paper:
• https://huggingface.co/nvidia/NitroGen
✨ Datasets citing this paper:
• https://huggingface.co/datasets/nvidia/NitroGen
✨ Spaces citing this paper:
• https://huggingface.co/spaces/dennny123/NitroGen-SuperstarSaga
• https://huggingface.co/spaces/blanchon/NitroGen-Pokemon
==================================
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📝 Summary:
NitroGen is a vision-action foundation model trained on extensive gameplay data that demonstrates strong cross-game generalization and effective transfer learning capabilities. AI-generated summary We...
🔹 Publication Date: Published on Jan 4
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.02427
• PDF: https://arxiv.org/pdf/2601.02427
• Project Page: https://nitrogen.minedojo.org/
• Github: https://github.com/MineDojo/NitroGen
🔹 Models citing this paper:
• https://huggingface.co/nvidia/NitroGen
✨ Datasets citing this paper:
• https://huggingface.co/datasets/nvidia/NitroGen
✨ Spaces citing this paper:
• https://huggingface.co/spaces/dennny123/NitroGen-SuperstarSaga
• https://huggingface.co/spaces/blanchon/NitroGen-Pokemon
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#AI #DataScience #MachineLearning #HuggingFace #Research