arXiv AI
MoFlow: Multi-Objective Agentic Workflow Generation
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arXiv AI
Sep 17
Designing Agentic AI Workflow Portfolios under Imperfect Selection and Compute Cost
The paper investigates how to design portfolios of agentic AI workflows that vary in reasoning strategy, verification structure, and compute cost. It proposes a portfolio-and-selector framework where multiple workflow executions are run and the best output is chosen, balancing additional compute with potential gains in accuracy. The authors develop exact and approximate optimization methods, evaluate them on three datasets, and show modest improvements over the best single workflow.
arXiv AI
Jun 11
FlowBank: Query-Adaptive Agentic Workflows Optimization through Precompute-and-Reuse
arXiv:2606. 11290v1 Announce Type: cross Abstract: Large Language Model (LLM)-based multi-agent systems are increasingly powerful, but current agentic workflow optimization paradigms make an unsatisfying trade-off.
Hugging Face Trending Papers
Sep 16
Designing Agentic AI Workflow Portfolios under Imperfect Selection and Compute Cost
Agentic AI systems often approach the same task through multiple workflows that differ in reasoning strategy, verification structure, and compute cost. A natural deployment policy is to use the workfl...
arXiv AI
Aug 13
Backtrader-Bench: Benchmarking LLM Agents on Algorithmic Trading with Self-Generated MCQs
arXiv:2608. 11232v1 Announce Type: cross Abstract: Evaluating LLM coding agents in algorithmic trading is difficult because static benchmarks risk data contamination and numerical backtest outputs require ground truth from actual code execution.
arXiv AI
Sep 2
AgentFactory: Towards Automated Agentic System Design and Optimization
arXiv:2609.01045v1 Announce Type: new Abstract: Large Language Models (LLMs) have demonstrated remarkable capabilities as powerful components in agentic systems, enabling sophisticated reasoning and...
arXiv AI
Jul 21
FormulaCode: Evaluating Agentic Optimization on Large Codebases
arXiv:2603. 16011v3 Announce Type: replace-cross Abstract: Large language model (LLM) coding agents increasingly operate at the repository level, motivating benchmarks that evaluate their ability to optimize entire codebases under realistic constraints.