GraphFlow uses a unified wGraph to dynamically instantiate workflows and manage KV caches for LLM agents, reporting 4.95 pp average gains and 4x memory reduction on five benchmarks.
Autoflow: Automated workflow generation for large language model agents.CoRR, abs/2407.12821
9 Pith papers cite this work. Polarity classification is still indexing.
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A VOI-based controller for dual inference budgets improves multi-hop QA performance by prioritizing search actions and selectively finalizing answers.
A policy-agnostic metric and controllable 2D grid environments with task DAGs enable measurement of exploration and exploitation errors in language model agents from observed actions.
Meta Agent Search uses a meta-agent to iteratively program novel agentic systems in code, producing agents that outperform state-of-the-art hand-designed ones across coding, science, and math while transferring across domains and models.
AgentCo-op retrieves and assembles existing agents and tools into interoperable workflows for open-world scientific tasks, showing effectiveness in genomics case studies and competitive benchmark results with lower costs.
ParaManager is a lightweight orchestrator that learns unified parallel agent-tool orchestration through a two-stage SFT and RL pipeline, achieving strong benchmark performance and generalization.
BLAST combines LLM agents with blockchain for decentralized spectrum trading, where Vickrey auctions achieve up to 71% of theoretical social welfare surplus and outperform non-LLM baselines in competition and efficiency.
The paper delivers the first systematic review of self-evolving agents, structured around what components evolve, when adaptation occurs, and how it is implemented.
Position paper calls for designing robotic AI to detect and recover from its own errors in continuous interactions, using nuclear glovebox operations as an illustrative case.
citing papers explorer
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GraphFlow: A Graph-Based Workflow Management for Efficient LLM-Agent Serving
GraphFlow uses a unified wGraph to dynamically instantiate workflows and manage KV caches for LLM agents, reporting 4.95 pp average gains and 4x memory reduction on five benchmarks.
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Inference-Time Budget Control for LLM Search Agents
A VOI-based controller for dual inference budgets improves multi-hop QA performance by prioritizing search actions and selectively finalizing answers.
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Exploration and Exploitation Errors Are Measurable for Language Model Agents
A policy-agnostic metric and controllable 2D grid environments with task DAGs enable measurement of exploration and exploitation errors in language model agents from observed actions.
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Automated Design of Agentic Systems
Meta Agent Search uses a meta-agent to iteratively program novel agentic systems in code, producing agents that outperform state-of-the-art hand-designed ones across coding, science, and math while transferring across domains and models.
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AgentCo-op: Retrieval-Based Synthesis of Interoperable Multi-Agent Workflows
AgentCo-op retrieves and assembles existing agents and tools into interoperable workflows for open-world scientific tasks, showing effectiveness in genomics case studies and competitive benchmark results with lower costs.
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Small Model as Master Orchestrator: Learning Unified Agent-Tool Orchestration with Parallel Subtask Decomposition
ParaManager is a lightweight orchestrator that learns unified parallel agent-tool orchestration through a two-stage SFT and RL pipeline, achieving strong benchmark performance and generalization.
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BLAST: Blockchain-based LLM-powered Agentic Spectrum Trading
BLAST combines LLM agents with blockchain for decentralized spectrum trading, where Vickrey auctions achieve up to 71% of theoretical social welfare surplus and outperform non-LLM baselines in competition and efficiency.
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A Survey of Self-Evolving Agents: What, When, How, and Where to Evolve on the Path to Artificial Super Intelligence
The paper delivers the first systematic review of self-evolving agents, structured around what components evolve, when adaptation occurs, and how it is implemented.
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Designing for Error Recovery in Human-Robot Interaction
Position paper calls for designing robotic AI to detect and recover from its own errors in continuous interactions, using nuclear glovebox operations as an illustrative case.