Aman Hassan | Searcharxiv

arXiv2026

Generalizing and accelerating consistency checking for non-transactional distributed storage systems

Linearizability checkers check if an operation history, observed by concurrent clients, is linearizable. They are used in testing distributed storage systems, and use the classic Wing-Gong (WG) linearizability checking algorithm. In this paper, we generalize the WG algorithm to make linearizability checkers more versatile: we can check other non-transactional consistency guarantees, like ordered sequential consistency provided by Zookeeper. Equipped with this generalization, we can also check for system-specific consistency guarantees that introduce additional ordering constraints over operations in a history, as per the system's specification. Our experiments with 8 distributed storage systems show that checking for system-specific consistency guarantees is easy to realize, reduces false negatives in testing, helps debug consistency violations, can be up to 370x faster, and can scale to more concurrent clients within the same checking time budget. We report 6 new consistency violation bugs, out of which 5 could not be found with existing consistency checkers.

cs.DC↗

arXiv2025

MIR: Methodology Inspiration Retrieval for Scientific Research Problems

There has been a surge of interest in harnessing the reasoning capabilities of Large Language Models (LLMs) to accelerate scientific discovery. While existing approaches rely on grounding the discovery process within the relevant literature, effectiveness varies significantly with the quality and nature of the retrieved literature. We address the challenge of retrieving prior work whose concepts can inspire solutions for a given research problem, a task we define as Methodology Inspiration Retrieval (MIR). We construct a novel dataset tailored for training and evaluating retrievers on MIR, and establish baselines. To address MIR, we build the Methodology Adjacency Graph (MAG); capturing methodological lineage through citation relationships. We leverage MAG to embed an "intuitive prior" into dense retrievers for identifying patterns of methodological inspiration beyond superficial semantic similarity. This achieves significant gains of +5.4 in Recall@3 and +7.8 in Mean Average Precision (mAP) over strong baselines. Further, we adapt LLM-based re-ranking strategies to MIR, yielding additional improvements of +4.5 in Recall@3 and +4.8 in mAP. Through extensive ablation studies and qualitative analyses, we exhibit the promise of MIR in enhancing automated scientific discovery and outline avenues for advancing inspiration-driven retrieval.

cs.AI↗