arXiv AI

Measuring What a Specification Determines: A Formal Semantic-Block Model and an Execution-Judged Benchmark

arXiv:2608. 19475v1 Announce Type: cross Abstract: This work introduces a formal semantic-block model for specifications and an execution-judged benchmark for evaluating specification quality independently of model capability.

Hugging Face Trending Papers
Aug 20

Specification-delta-driven data governance: an empirical study of the «spec-delta» as the unit of change in lakehouse data platforms

Spec Driven Development SDD has consolidated the idea that the specification rather than the code should be the primary artefact governing AI assisted work. Tools such as GitHub Spec Kit, and proposals such as Constitutional SDD, have formalised this principle in the software domain, while the executable data-contracts literature has extended it to schema and quality enforcement at run time.

arXiv AI
4d ago

ESQ-Bench: A Multi-Tier Enterprise Oracle Benchmark for Evaluating NL2SQL Dialect Generalization and Silent Semantic Divergence

ESQ‑Bench is an Oracle‑first NL2SQL benchmark that introduces systematic complexity tiers and silent‑divergence evaluation across three enterprise schema levels. It provides six populated schemas (465 tables, 164,682 rows) on Oracle, PostgreSQL, MySQL, and SQL Server, along with 550 gold‑validated question‑query pairs and a four‑metric evaluation harness. The benchmark reveals that state‑of‑the‑art models such as GPT‑4o and Claude Sonnet 4.6 experience significant drops in execution accuracy and high silent‑divergence rates as schema complexity increases, highlighting a gap between closed‑API models and open‑weight baselines on enterprise Oracle schemas.

By Sanjay Mishra, Divya Chukkapalli, Ganesh R. Naik
arXiv AI
2d ago

DRL: A Deterministic Relational Middleware Layer for Transaction-Safe Enterprise NL2SQL Under Schema-Graph Scaling

The paper introduces DRL, a deterministic relational middleware layer designed to enable transaction-safe natural‑language to SQL (NL2SQL) interfaces over large enterprise OLTP catalogs. DRL interposes between front‑ends and SQL back‑ends, employing dynamic context pruning, relational AST typing, and transactional safeguards (EXPLAIN gating and NULL guards) to keep context within LLM attention limits and detect silent divergence. Experiments on PostgreSQL and MySQL show significant context reductions (up to 92%) and high execution match rates (≈53%) for GPT‑4o, Claude Sonnet 4.5, and Gemini 2.5 Flash, while also revealing that evaluation code quality can materially affect reported performance gaps.

By Sanjay Mishra, Divya Chukkapalli, Ganesh R. Naik
Hugging Face Trending Papers
Aug 12

SemPlan: Benchmarking Structured Semantic Planning for LLM-Based Queries over Enterprise Data

Natural-language interfaces to enterprise data must translate underspecified requests into governed, executable behavior while controlling invalid queries, policy failures, cost, and nondeterminism. SemPlan Benchmark evaluates this architectural design space with a deterministic synthetic bilingual benchmark containing 1,800 cases in English and Brazilian Portuguese; 1,200 cases form the frozen scientific evaluation subset.

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
arXiv Machine Learning
Aug 4

Who Belongs in the Eval Set? A Capability-Taxonomy-Driven Pipeline for Curating Regression Eval Sets in Agent-Extensibility Platforms

arXiv:2608. 01004v1 Announce Type: new Abstract: Platform teams hosting agent-extensibility surfaces face a regression-economics paradox: every onboarding customer ships an evaluation set tuned to their domain, but the platform's regression set must live under a hard query-count ceiling bounded by release cadence.

By Tezan Sahu, Aritra Das, Pankaj Mittal, Sudipta Das
Hugging Face Trending Papers
Jun 24

Constraint Tax in Open-Weight LLMs: An Empirical Study of Tool Calling Suppression Under Structured Output Constraints

Tool Calling and Structured Output are two core capabilities of modern Agent systems, yet their interaction under joint deployment conditions remains insufficiently understood. This paper reports a reproducible phenomenon observed in a production Agent system: when Tool Calling and JSON Schema constraints are simultaneously enabled, multiple open-weight models cease invoking tools despite maintaining high schema compliance.