2026· International Conference on Conceptual Structures· pp. 473-487· 0 citations· 22 references
Computer Science
TL;DR
This study presents an exploration of an alternative approach in which LLMs serve as external assistants rather than as agents within simulations, called XABM (eX-plainable Generative ABM), which aims to make computational modeling more interpretable and reproducible.
VISA is presented, a structured, symbol-based description protocol that specifies a model in eight interconnected tables---four at the agent level (Agent, Variable, Sensing, Internal Function) and four at the model level (Associated Data, Input/Output, Schedule, Validation)---under the principle of minimality with comp...
Simulations based on large language models (LLMs) have proven to be powerful for understanding human behavior, making them valuable additions to the social scientific toolkit. However, LLMs are ultimately black boxes based on deep neural networks which limits their value for social science. This is because of a lack of...
Jia-Run Fan, Arul Murugan, Shreyas R. Krishnan et al.· 0 citations
This article introduces a framework for designing and running simulated experiments with LLM‐powered agents and applies the framework to the exploration–exploitation dilemma and shows that LLM‐based experiments reproduce patterns observed among human participants.
Experiments reveal that models with similar end-to-end accuracy can exhibit markedly different agentic capability profiles, demonstrating that process-level evaluation is crucial for interpreting the true potential of LLMs and guiding the development of next-generation mathematical agents.
Jiayi Kuang, Ying-Hui Li, Yun-Ze Song et al.· 0 citations
Analytical performance models --- derivations of throughput or speedup from hardware parameters --- make claims independently verifiable and expose binding constraints, yet rarely accompany architecture papers because building one by hand takes weeks of expert effort. We present Rosetta, a multi-agent LLM pipeline that...
An environment-grounded audit is introduced in which every intermediate proposal receives an exact outcome in an evolutionary Contexto search whose feedback function assigns every valid guess an exact rank without human annotation.
En-Rong Pan, Ryan Zhou, Ting Hu· Inquiry@Queen's Undergraduat...· 0 citations
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