arXiv AI
Jun 2
MOSAIC: Modular Orchestration for Structured Agentic Intelligence and Composition
arXiv:2606. 00708v1 Announce Type: new Abstract: Automated data science is a structured model-selection problem.
Declarative Skills for AI Agents in Knowledge-Grounded Tool-Use Workflows
arXiv:2606. 06923v1 Announce Type: new Abstract: We study orchestration mechanisms for tool-using AI agents in realistic customer-service workflows over an unstructured knowledge base.
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arXiv AI
Jul 21
Agentic ERP: Multi-Agent Large Language Model Architecture for Autonomous Enterprise Resource Planning
arXiv:2607. 17331v1 Announce Type: new Abstract: Enterprise Resource Planning (ERP) systems record transactions reliably but still delegate almost all operational decision-making to human specialists, because classical rule-based automation cannot reason about exceptions and monolithic AI assistants degrade when asked to coordinate across functional boundaries.
Hugging Face Trending Papers
Jun 19
Training the Orchestrator: A Supervised Approach to End-to-End PDDL Planning with LLM Agents
Translating natural-language planning intent into verified plans is a longstanding challenge: people communicate goals in language, while classical planners require formal PDDL specifications. Recent agentic frameworks bridge this gap by orchestrating a pool of specialized repair agents inside a verifier-checked refinement loop, but the orchestrator at the centre is itself a prompted frontier LLM, paying a frontier-LLM API call at every refinement step.
arXiv AI
Sep 10
Inference-Time Graph Engineering for Multi-Agent LLM Workflows
arXiv:2609.05774v1 Announce Type: new Abstract: Recent multi-agent LLM systems increasingly rely on graph-structured communication to coordinate specialized agents. We revisit multi-agent orchestrati...
arXiv AI
4d ago
COLLATOR: Compositional Multi-Agent Orchestration with Counterfactual Reinforcement Learning
arXiv:2605.14483v2 Announce Type: replace Abstract: Large language models (LLMs) provide a flexible foundation for multi-agent systems, but their effectiveness and computational cost depend criticall...
arXiv AI
Aug 18
Semantic Uncertainty-Guided Orchestration in Hierarchical Multi-Agent Systems
arXiv:2608. 14707v1 Announce Type: new Abstract: As large language model (LLM)-based multi-agent systems become increasingly capable, coordinating agents under uncertainty becomes a fundamental challenge.
arXiv AI
Jun 18
Skill-MAS: Evolving Meta-Skill for Automatic Multi-Agent Systems
arXiv:2606. 18837v1 Announce Type: cross Abstract: Large Language Model (LLM)-based automatic Multi-Agent Systems (MAS) generation has become a crucial frontier for tackling complex tasks.
arXiv AI
Jul 7
The Remarkable Effectiveness of Providing AI Agents with Natural Language Tools: A Replication Study Validating NLT Performance Across 14 Models
arXiv:2607. 03953v1 Announce Type: cross Abstract: This study independently replicates and extends the Natural Language Tools (NLT) framework of Johnson et al.
arXiv Computation and Language
Sep 10
Subagents vs Agent Skills: Executing Reusable Knowledge for Long-Horizon Agentic Tasks
arXiv:2609.09233v1 Announce Type: cross Abstract: How can language model agents effectively leverage libraries of reusable knowledge to solve long-horizon tasks? Recent work has increasingly focused...
arXiv AI
Jun 19
Navigating Unreliable Parametric and Contextual Knowledge: Explicit Knowledge Conflict Resolution for LLM Inference
arXiv:2606. 20245v1 Announce Type: new Abstract: Large language models (LLMs) have achieved strong performance across a wide range of language-based tasks by leveraging both extensive parametric knowledge and in-context learning ability, enabling them to incorporate external information provided in the input prompt.
arXiv AI
Aug 14
Learning Latency-Aware Orchestration for Multi-Agent Systems
arXiv:2601. 10560v2 Announce Type: replace-cross Abstract: Multi-agent systems (MAS) coordinate multiple LLM-powered agents through structured workflows, gaining reasoning power but incurring high inference latency from multi-step execution and repeated model invocations.
arXiv AI
Jul 28
Focus Is All You Need: Adaptive Goal-aware Attention Orchestration for Multi-Agent Graph Systems
arXiv:2607. 23678v1 Announce Type: new Abstract: Large language models (LLMs) enable autonomous agents for reasoning, planning, and tool use.