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Preprint Aug 2026

Architecture as Capability Equalizer for Coding Agents

A controlled experiment comparing five informationally equivalent specification formats across six models from three vendor families finds that structured architecture specifications serve as a capability equalizer, with value inversely proportional to model strength and the largest returns for cost-optimized deploymen...

A. Canedo · 1 citation
#natural language process... Preprint Sep 2026

The Answer Path and the Grounding Instruction in LLM Question Answering over Knowledge Graphs

A graph retrieval-augmented generation pipeline chooses which triples to put in the prompt, a syntax to write them in, an order to write them in, and a sentence telling the model what to do with them. We vary all four over six large language models and two knowledge-graph question answering benchmarks. Two of the four...

A. Canedo · 0 citations
#artificial intelligence Preprint Aug 2026

Invalidation Contracts for Cross-Episode Agent Memory

LLM agents that cache recovery suggestions from API errors can skip re-derivation in later episodes, spending fewer tokens and fewer model calls on constraints they have already learned. Server-side data drift turns those cached fixes into silent failures, and the usual remedy, re-deriving on every episode, gives the s...

Michael Wu, A. Canedo · 1 citation

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