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Vibe Coding2026-09-03 · source-backed
arXiv 2609.01985 fixes storage technologies, schema, entity-resolution algorithm and retrieval-filtering strategy in advance, then characterizes how a coding agent behaves on systems-level requirements: schema design, async orchestration, configuration correctness, retrieval-filtering tradeoffs. It's a defect taxonomy for the class of work agents are increasingly handed end to end, and the defects it finds are ones a passing test suite doesn't surface.
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RAGAS-style evaluation checks correctness against a frozen snapshot, which means routine document updates and corrections can silently break production without moving a dashboard. This ASE 2026 paper defines 11 mutation operators perturbing at both the pre-chunk index level an...
arXiv 2608.26197 stacked finite-state control, forced tool selection, output validation and bounded retries on two open-weight models, and got mixed results across all four model-task cells. Adding structured planning, where the plan is checked against a fixed schema before an...
RGA-Designer trains a reward model scoring both task correctness and structural compactness, then fine-tunes a graph generator against it to design communication topologies. arXiv For fan-out agent teams where inter-agent chatter dominates the bill, topology is a cost lever mo...
The failure they target is specific and under-discussed: a cached error page or a negative price returns in the *expected schema* and gets consumed as fact, unlike a timeout the agent can see. Outcome Monitors check results against contracts mined from task-disjoint traces or...
Nearly all cache-compaction research assumes a static context where future queries are known offline, which agents never have. Comparing token eviction against attention matching across proxy-query sources on BrowseComp-Plus and WideSearch, compacting a turn immediately often...
SWE-NFI builds 188 tasks from merged Python PRs and operationalizes non-functional improvement as 92 executable rules, cleanly separating "tests still pass" from "the code got better." Best agent: 70.0% functional correctness, 0.0-1.3 on structural improvement against a human...
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