Consider a scenario where a decision-maker faces a row of slot machines, each offering unknown and varying payout rates. The objective is to maximize cumulative rewards, yet each action simultaneously provides new information. This problem—balancing the exploration of new options against the exploitation of known rewarding ones—is termed the multi-armed bandit problem. It has evolved from a simple gambling question into a fundamental tool for modern computer systems. This paper looks at three main ways to solve this problem: Explore-Then-Commit, Upper Confidence Bound, and Thompson Sampling. Through careful testing, Thompson Sampling stands out as the best performer, cutting total regret down to 0.60 where UCB reaches 3.63. It also handles delays well—when feedback comes 1000 steps late, Thompson Sampling's lead over UCB grows to nearly 4 times. The paper shows where these methods are used in real life: online ads, where they improve click rates by about 11%; movie and music suggestions, where they help new users find content they like; and medical trials, where they can put over 80% of patients on better treatments. The paper ends with a look at new research areas, including methods that adapt to changing conditions and handle complex choices.
Ziyue Zhou· Applied and Computational En...· 0 citations
Unit testing plays a critical role in ensuring software quality and reliability in large-scale industrial environments. While Large Language Models (LLMs) offer promising automated test generation capabilities, their practical deployment faces significant challenges due to hallucination problems. In this paper, we analyze compilation failures from LLM-generated unit tests in Ant Group's production systems and identify two fundamental types of hallucinations: extrinsic hallucinations caused by insufficient contextual information and intrinsic hallucinations stemming from model limitations even with adequate context. To address these issues, we propose DEHALL, an automated end-to-end unit test generation tool that systematically mitigates both types of hallucinations through comprehensive context construction and targeted static analysis-based repair. Our approach builds a heterogeneous graph to capture relevant context and employs specialized repair mechanisms for import, field, and method issues. Evaluation on Ant Group's internal datasets reveals that DEHALL achieves 71.56% line coverage and 67.18% branch coverage, significantly outperforming vanilla LLM approaches. In the public benchmarks, it also shows better performance on coverage and better defect detection capability than previous state-of-the-art approaches. DEHALL has been successfully deployed across multiple business domains at Ant Group, achieving an 81% developer adoption rate with positive user feedback on productivity improvements.
Programmable logic controllers (PLCs) run industrial plants, and large language models can already generate independent program organization units (POUs) for them. Whether such logic integrates into an existing PLC project and then runs correctly has been checked only in limited tests. We present \textsc{SemaPLC}, a project-grounded and verification-gated agent harness assembled from conventional tools but governed by a strict completion rule. Rather than stopping when the model judges its own output adequate, \textsc{SemaPLC} declares a task complete only when logged external checks confirm it. Those checks cover the specification, the compilation, and the behavior on a live runtime. On 117 independent-POU tasks matching existing benchmarks, it attains the highest strict verified pass rate on all seven models (72.6\% mean). On a project-context track of 65 tasks whose generated logic must compile and run inside a real project, it attains the highest mean on integrated compilation, static behavior, and dynamic behavior. Of the three layers, dynamic behavior is the most revealing. We measure it by deploying the generated and the reference logic to a live PLC runtime and comparing their executed traces. All methods fall within 10 static points of one another, whereas dynamic scores separate them sharply, from 22.4 to 31.4 for the baselines against 52.2 for \textsc{SemaPLC}. Overall, our verification-gated harness raises the mean at every layer and most sharply at runtime. Execution, not static scoring, is the faithful test of whether generated control logic actually works. \textsc{SemaPLC} is open-sourced at https://github.com/midea-ai/SemaPLC.
Yanlun Tu, Huacan Wang, Ziyue Zhou et al.· 0 citations