We audit 2,318 LLM-generated patches drawn from public agent benchmarks and find that 28.6% fail to reproduce when re-run on a fresh container, even when the originating evaluation reported success.
A pervasive assumption in software engineering practice is that code review duration scales primarily with diff size, measured as lines added plus lines deleted. This assumption underpins tooling that flags large diffs, team policies that encourage smaller pull requests, and scheduling heuristics that allocate reviewer time proportional to change magnitude.
Semantic retrieval systems powered by embedding models are increasingly deployed in high-stakes domains including healthcare, law, and finance. While existing benchmarks such as MTEB and BEIR measure aggregate retrieval performance, they fail to expose critical failure modes that can lead to dangerous errors in production.
Coding agents are increasingly judged by whether they can finish tasks. In practice, teams also need help with a different question: once an agent proposes code, what should happen next?
Coding agents are increasingly judged by whether they can finish tasks. In practice, teams also need help with a different question: once an agent proposes code, what should happen next?
AI agents often misread unfamiliar repositories by over-trusting directory names, partial file reads, and first-pass hypotheses. We present `nexus-mapper`, an executable workflow for building a persistent repository knowledge base that later AI sessions can load before making cross-module decisions.