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A Two-Dimensional Framework for AI Agent Design Patterns: Cognitive Function and Execution Topology

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arXiv:2605. 13850v1 Announce Type: new Abstract: Existing frameworks for LLM-based agent architectures describe systems from a single perspective: industry guides (Anthropic, Google, LangChain) focus on execution topology -- how data flows -- while cognitive science surveys focus on cognitive function -- what the agent does.

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Authors: Jia Huang, Joey Tianyi Zhou
Released: 2026-05-16 (2mo ago)
Topic: Related to current project
Relation to my work: Read next because A Two-Dimensional Framework for AI Agent Design Patterns: Cognitive Function and Execution Topology overlaps with clean result "LoRA persona trained on <A> alone emits <B> at 23.5% when a co-trained partner learns <A>...<B>, vs 0% control on Qwen2.5-7B-Instruct (MODERATE confidence)", clean result "Leakage rate is a usable signal for recovering trigger-shaped phrases on Gaperon-1125-1B without knowing the hidden trigger itself (MODERATE confidence)", clean result "Language-mismatch LoRA SFT on Qwen2.5-7B leaks the trained completion language into bystander directives the model was never trained on, absent under same-language SFT (LOW confidence)". Matching terms: text, class, anth, rate, implement, disambiguate, alone, does. Source: arxiv cs.AI (Artificial Intelligence).
arXiv: 2605.13850

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