✨Solving a Million-Step LLM Task with Zero Errors
📝 Summary:
MAKER solves million-step LLM tasks with zero errors. It uses extreme task decomposition for microagents and applies error correction at each step with multi-agent voting. This offers a new scalable approach for complex LLM processes.
🔹 Publication Date: Published on Nov 12
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.09030
• PDF: https://arxiv.org/pdf/2511.09030
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For more data science resources:
✓ https://xn--r1a.website/DataScienceT
#LLM #AI #ErrorCorrection #MultiAgent #TaskDecomposition
📝 Summary:
MAKER solves million-step LLM tasks with zero errors. It uses extreme task decomposition for microagents and applies error correction at each step with multi-agent voting. This offers a new scalable approach for complex LLM processes.
🔹 Publication Date: Published on Nov 12
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.09030
• PDF: https://arxiv.org/pdf/2511.09030
==================================
For more data science resources:
✓ https://xn--r1a.website/DataScienceT
#LLM #AI #ErrorCorrection #MultiAgent #TaskDecomposition
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✨D-CORE: Incentivizing Task Decomposition in Large Reasoning Models for Complex Tool Use
📝 Summary:
D-CORE is a two-stage training framework improving large reasoning models' task decomposition and reasoning. It overcomes Lazy Reasoning using self-distillation and diversity-aware reinforcement learning. D-CORE achieves superior tool-use performance, setting new state-of-the-art results even wit...
🔹 Publication Date: Published on Feb 2
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.02160
• PDF: https://arxiv.org/pdf/2602.02160
• Github: https://github.com/alibaba/EfficientAI
🔹 Models citing this paper:
• https://huggingface.co/bowiehsu/D-CORE-8B
==================================
For more data science resources:
✓ https://xn--r1a.website/DataScienceT
#LLM #TaskDecomposition #ToolUse #ReinforcementLearning #AIResearch
📝 Summary:
D-CORE is a two-stage training framework improving large reasoning models' task decomposition and reasoning. It overcomes Lazy Reasoning using self-distillation and diversity-aware reinforcement learning. D-CORE achieves superior tool-use performance, setting new state-of-the-art results even wit...
🔹 Publication Date: Published on Feb 2
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.02160
• PDF: https://arxiv.org/pdf/2602.02160
• Github: https://github.com/alibaba/EfficientAI
🔹 Models citing this paper:
• https://huggingface.co/bowiehsu/D-CORE-8B
==================================
For more data science resources:
✓ https://xn--r1a.website/DataScienceT
#LLM #TaskDecomposition #ToolUse #ReinforcementLearning #AIResearch
✨Intelligent AI Delegation
📝 Summary:
AI agents require better task decomposition and robust delegation. This paper proposes an adaptive framework for intelligent AI delegation, incorporating authority transfer, responsibility, and trust to handle dynamic environments and failures in complex AI and human networks.
🔹 Publication Date: Published on Feb 12
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.11865
• PDF: https://arxiv.org/pdf/2602.11865
==================================
For more data science resources:
✓ https://xn--r1a.website/DataScienceT
#AIDelegation #AIagents #TaskDecomposition #HumanAICollaboration #MultiAgentSystems
📝 Summary:
AI agents require better task decomposition and robust delegation. This paper proposes an adaptive framework for intelligent AI delegation, incorporating authority transfer, responsibility, and trust to handle dynamic environments and failures in complex AI and human networks.
🔹 Publication Date: Published on Feb 12
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.11865
• PDF: https://arxiv.org/pdf/2602.11865
==================================
For more data science resources:
✓ https://xn--r1a.website/DataScienceT
#AIDelegation #AIagents #TaskDecomposition #HumanAICollaboration #MultiAgentSystems