arXiv Machine Learning
Aug 11

Opportunity Is Not Realizability: Selection-Valid Diagnostics for Multi-LLM Routing

arXiv:2608. 08265v1 Announce Type: new Abstract: Oracle routing measures how much a pool of language models could gain from per-query selection, but the diagnostic has two flaws: testing against a best fixed model selected on the same examples invalidates paired inference, and a full-information oracle sees outcomes no deployable router observes.

By Ibne Farabi Shihab, Abu Sa-Adat Mohamed Moon-Im Al Ahsan, Md Najmus Swaqeeb
arXiv AI
Aug 11

Business Truth, not SQL Accuracy: A Rule-Gated 7B Analytics Agent Outperforms a Direct-Prompted 32B Baseline

arXiv:2608. 09254v1 Announce Type: new Abstract: LLM analytics agents are evaluated on SQL syntax accuracy, but production failures look different: questions with two valid business definitions, questions the warehouse cannot answer, deprecated columns after a schema change, and queries that execute successfully while returning the wrong business number.

By Morris Lee
arXiv AI
Jul 28

TLA$^{+}$-Bench: An Execution-Grounded Benchmark and Dataset for Natural-Language to TLA+ Specification Generation

arXiv:2607. 23425v1 Announce Type: cross Abstract: Large language models increasingly write TLA$^{+}$ formal specifications from natural-language descriptions, but progress is hard to measure: existing resources grade by resemblance to a reference or by whether the output parses, neither of which shows correctness.

By Arslan Bisharat, Eric Spencer, Brian Ortiz, Khushboo Bhadauria, Mujtaba Nazari, Beatriz Santos, Anisa Ramos, TaiNing Wang, George K. Thiruvathukal, Konstantin L\"aufer, Mohammed Abuhamad