arXiv subjects

Yang Wang

Publications and source records attributed to Yang Wang.

2 recordsLinked to original sources

arXiv2026

Augur: Predicting View Serializability Violations in Relational Data Store Applications

Data stores are widely used because they provide persistence, scalability, and fault tolerance with a simple interface. However, most data store applications configure the data store to use weak isolation to achieve scalable performance, resulting in sporadic unserializable executions that are incorrect or fail. Prior work uses dynamic predictive analysis to infer violations from execution traces, but it cannot handle relational (i.e., SQL) queries with complex predicates, and it predicts executions that do not violate View Serializability. This paper introduces Augur, the first dynamic predictive program analysis that (1) supports data store applications with complex relational queries and (2) reports only executions that violate View Serializability. The evaluation demonstrates that Augur finds feasible, unserializable executions in the widely used OLTP-Bench programs and in the widely used e-commerce application Spree.

cs.PL↗

arXiv2026

Stop Before You Fail: Operational Capability Boundaries for Mitigating Unproductive Reasoning in Large Reasoning Models

Current answering paradigms for Large Reasoning Models (LRMs) often fail to account for the fact that some questions may lie beyond the model's operational capability boundary, leading to long but unproductive reasoning. In this paper, we study whether LRMs expose early signals predictive of such cases, and whether these signals can be used to mitigate unproductive reasoning. In black-box settings, we find that reasoning expressions contain failure-predictive signals. In white-box settings, we show that the hidden states of the last input token contain information that is predictive of whether a question will not be solved correctly under our evaluation setup. Building on these observations, we propose two test-time monitoring strategies: reasoning expression monitoring and hidden states monitoring, that reduce token usage by 62.7-93.6%, substantially improving efficiency and reliability while largely preserving accuracy.

cs.AI↗