arXiv:2607. 28126v2 Announce Type: replace Abstract: Long-horizon steel-equipment inspection requires reasoning over heterogeneous records accumulated across repeated inspection cycles.
Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.
Long-horizon steel-equipment inspection requires reasoning over heterogeneous records accumulated across repeated inspection cycles. Existing retrieval-augmented generation systems treat historical logs as a static corpus and retain records without estimating their diagnostic value, failing to report early risk.
arXiv:2608. 07770v1 Announce Type: new Abstract: Automated anomaly detection methods often report strong performance on curated academic benchmarks, but their behavior under real-world industrial conditions is less clear.
arXiv:2609.22231v1 Announce Type: new Abstract: Long-horizon interactions with LLM-based assistants require memory systems that preserve and update user states, preferences, and interaction histories...
The paper introduces D$^2$ACCI, a dual-loop diagnostic protocol designed to improve evidence-preserving memory in large language model agents. It provides a structured framework that uses paired evidence, protected-slice monitoring, and trace-level localizability to decide whether to promote, flag, or reject memory interventions. The authors also present DCR, a metric for measuring failure localizability, and D$^2$ACCI‑Eval, a reusable artifact for gate replay, demonstrating significant performance gains on three public benchmarks and highlighting the importance of traceable, statistically grounded diagnostics.
arXiv:2604. 26633v2 Announce Type: replace-cross Abstract: Industrial surface defect inspection suffers from a fundamental data bottleneck: defects are rare, annotations require expert knowledge, and collecting balanced training sets is slow and costly.
The paper introduces D$^2$ACCI, a dual-loop diagnostic protocol designed to improve evidence-preserving memory in large language model agents. It provides stage-level diagnostic traces, protected-slice monitoring, and a graded observability metric (DCR) to localize failures within the memory pipeline. Applied to MemStack and evaluated on three benchmarks, the protocol achieves high accuracy and demonstrates statistically significant gains from specific memory interventions.