✨Over-Searching in Search-Augmented Large Language Models
📝 Summary:
Search-augmented large language models suffer from over-searching behavior that wastes computational resources and introduces hallucinations, with findings showing varied impacts across model types an...
🔹 Publication Date: Published on Jan 9
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.05503
• PDF: https://arxiv.org/pdf/2601.05503
==================================
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📝 Summary:
Search-augmented large language models suffer from over-searching behavior that wastes computational resources and introduces hallucinations, with findings showing varied impacts across model types an...
🔹 Publication Date: Published on Jan 9
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.05503
• PDF: https://arxiv.org/pdf/2601.05503
==================================
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✨NeoVerse: Enhancing 4D World Model with in-the-wild Monocular Videos
📝 Summary:
NeoVerse is a scalable 4D world model that enables pose-free reconstruction and novel-trajectory video generation from monocular videos with state-of-the-art performance. AI-generated summary In this ...
🔹 Publication Date: Published on Jan 1
🔹 Paper Links:
• arXiv Page: https://arxivexplained.com/papers/neoverse-enhancing-4d-world-model-with-in-the-wild-monocular-videos
• PDF: https://arxiv.org/pdf/2601.00393
• Project Page: https://neoverse-4d.github.io/
• Github: https://neoverse-4d.github.io
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📝 Summary:
NeoVerse is a scalable 4D world model that enables pose-free reconstruction and novel-trajectory video generation from monocular videos with state-of-the-art performance. AI-generated summary In this ...
🔹 Publication Date: Published on Jan 1
🔹 Paper Links:
• arXiv Page: https://arxivexplained.com/papers/neoverse-enhancing-4d-world-model-with-in-the-wild-monocular-videos
• PDF: https://arxiv.org/pdf/2601.00393
• Project Page: https://neoverse-4d.github.io/
• Github: https://neoverse-4d.github.io
==================================
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✨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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