activity
20242026
most citedSelf-Resource Allocation in Multi-Agent LLM Systems

3 citations · 3 across the 2 of their papers we have counts for

collaborators

5 papers

cs.SE2026

Planning to Explore: Curiosity-Driven Planning for LLM Test Generation

Alfonso Amayuelas, Firas Laakom, Piotr Piękos +5

The use of LLMs for code generation has naturally extended to code testing and evaluation. As codebases grow in size and complexity, so does the need for automated test generation.…

cs.AI2025

Agents of Change: Self-Evolving LLM Agents for Strategic Planning

Nikolas Belle, Dakota Barnes, Alfonso Amayuelas +3

We address the long-horizon gap in large language model (LLM) agents by enabling them to sustain coherent strategies in adversarial, stochastic environments. Settlers of Catan prov…

cs.MA20253 cited

Self-Resource Allocation in Multi-Agent LLM Systems

Alfonso Amayuelas, Jingbo Yang, Saaket Agashe +4

With the development of LLMs as agents, there is a growing interest in connecting multiple agents into multi-agent systems to solve tasks concurrently, focusing on their role in ta…

cs.CL2025

SOPBench: Evaluating Language Agents at Following Standard Operating Procedures and Constraints

Zekun Li, Shinda Huang, Jiangtian Wang +8

As language agents increasingly automate critical tasks, their ability to follow domain-specific standard operating procedures (SOPs), policies, and constraints when taking actions…

cs.AI2024

SWE-Search: Enhancing Software Agents with Monte Carlo Tree Search and Iterative Refinement

Antonis Antoniades, Albert Örwall, Kexun Zhang +3

Software engineers operating in complex and dynamic environments must continuously adapt to evolving requirements, learn iteratively from experience, and reconsider their approache…