✨AgentScope 1.0: A Developer-Centric Framework for Building Agentic Applications

📝 Summary:
AgentScope enhances agentic applications by providing flexible tool-based interactions, unified interfaces, and advanced infrastructure based on the ReAct paradigm, supporting efficient and safe devel...

🔹 Publication Date: Published on Aug 22, 2025

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.16279
• PDF: https://arxiv.org/pdf/2508.16279
• Github: https://github.com/agentscope-ai/agentscope

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✨MediaPipe: A Framework for Building Perception Pipelines

📝 Summary:
MediaPipe is a framework for building perception applications. It helps developers combine components, prototype, and measure performance across platforms, addressing key development challenges. This allows focusing on algorithm improvement with reproducible results.

🔹 Publication Date: Published on Jun 14, 2019

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/1906.08172
• PDF: https://arxiv.org/pdf/1906.08172
• Github: https://github.com/google-ai-edge/mediapipe

✨ Spaces citing this paper:
• https://huggingface.co/spaces/Jha-Pranav/PixelCare

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✨Multi-Agent Software Development through Cross-Team Collaboration

📝 Summary:
Cross-Team Collaboration improves software quality by enabling multiple LLM agent teams to propose and communicate decisions. AI-generated summary The latest breakthroughs in Large Language Models ( L...

🔹 Publication Date: Published on Jun 13, 2024

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2406.08979
• PDF: https://arxiv.org/pdf/2406.08979
• Github: https://github.com/OpenBMB/ChatDev

✨ Spaces citing this paper:
• https://huggingface.co/spaces/shanghengdu/LLM-Agent-Optimization-PaperList

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✨Scaling Large-Language-Model-based Multi-Agent Collaboration

📝 Summary:
This study proposes MacNet, a multi-agent collaboration network organizing agents via directed acyclic graphs. It shows MacNet outperforms baselines, scales to over a thousand agents, and reveals a collaborative scaling law with abilities emerging earlier than neural scaling.

🔹 Publication Date: Published on Jun 11, 2024

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2406.07155
• PDF: https://arxiv.org/pdf/2406.07155
• Project Page: https://github.com/OpenBMB/ChatDev/tree/macnet
• Github: https://github.com/OpenBMB/ChatDev/tree/macnet

✨ Spaces citing this paper:
• https://huggingface.co/spaces/shanghengdu/LLM-Agent-Optimization-PaperList

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✨Single-stream Policy Optimization

📝 Summary:
Single-stream Policy Optimization SPO improves LLM policy-gradient training by eliminating group-based issues. SPO uses a persistent value tracker and global advantage normalization for a stable learning signal, achieving higher accuracy and significant gains on hard math benchmarks.

🔹 Publication Date: Published on Sep 16, 2025

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2509.13232
• PDF: https://arxiv.org/pdf/2509.13232
• Project Page: https://zhongwenxu.notion.site/Single-stream-Policy-Optimization-26a1c4e140e380d78d51fa4567727f50
• Github: https://github.com/volcengine/verl

🔹 Models citing this paper:
• https://huggingface.co/jingyaogong/MiniMind2-gguf

✨ Datasets citing this paper:
• https://huggingface.co/datasets/dingzihan737/SPO_Qwen3-8B_DAPO_16k_ReTool_Binary

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✨Chaining the Evidence: Robust Reinforcement Learning for Deep Search Agents with Citation-Aware Rubric Rewards

📝 Summary:
This paper proposes Citation-aware Rubric Rewards CaRR and C-GRPO to enhance deep search agents. CaRR provides fine-grained, citation-aware rewards that promote factual, comprehensive, evidence-grounded reasoning, reducing shortcuts. C-GRPO outperforms standard RL baselines, offering robust agent...

🔹 Publication Date: Published on Jan 9

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.06021
• PDF: https://arxiv.org/pdf/2601.06021
• Github: https://github.com/THUDM/CaRR

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✨VideoAR: Autoregressive Video Generation via Next-Frame & Scale Prediction

📝 Summary:
VideoAR presents a large-scale visual autoregressive framework for video generation that combines multi-scale next-frame prediction with autoregressive modeling, achieving state-of-the-art results wit...

🔹 Publication Date: Published on Jan 9

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.05966
• PDF: https://arxiv.org/pdf/2601.05966

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✨DR-LoRA: Dynamic Rank LoRA for Mixture-of-Experts Adaptation

📝 Summary:
DR-LoRA dynamically adjusts LoRA ranks for experts in Mixture-of-Experts models based on task-specific demands, improving parameter efficiency and performance. AI-generated summary Mixture-of-Experts ...

🔹 Publication Date: Published on Jan 8

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.04823
• PDF: https://arxiv.org/pdf/2601.04823

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✨IIB-LPO: Latent Policy Optimization via Iterative Information Bottleneck

📝 Summary:
Latent Policy Optimization via Iterative Information Bottleneck addresses exploration collapse in LLM reasoning by enabling topological branching of reasoning trajectories through information bottlene...

🔹 Publication Date: Published on Jan 9

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.05870
• PDF: https://arxiv.org/pdf/2601.05870

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✨Goal Force: Teaching Video Models To Accomplish Physics-Conditioned Goals

📝 Summary:
Video generation models trained on synthetic physics primitives demonstrate zero-shot generalization to complex real-world scenarios by modeling force propagation through time and space. AI-generated ...

🔹 Publication Date: Published on Jan 9

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.05848
• PDF: https://arxiv.org/pdf/2601.05848
• Project Page: https://goal-force.github.io/

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✨GenCtrl -- A Formal Controllability Toolkit for Generative Models

📝 Summary:
Generative models' controllability is theoretically analyzed through a framework that estimates controllable sets with distribution-free bounds, revealing that controllability is fragile and context-d...

🔹 Publication Date: Published on Jan 9

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.05637
• PDF: https://arxiv.org/pdf/2601.05637

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✨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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✨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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✨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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✨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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✨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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✨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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✨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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✨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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✨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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