arXiv AI
Detecting Inconsistencies in Model Specifications with LLM-as-Verifier Reasoning
Read the original on arXiv AI →The Flow has not summarised this story yet — read it at arXiv AI.
Related stories
arXiv Machine Learning
Jun 5
Can LLMs Write Correct TLA+ Specifications? Evaluating Natural-Language-to-TLA+ Generation
arXiv:2606. 05792v1 Announce Type: cross Abstract: TLA+ has supported industrial verification at companies such as Amazon and Microsoft, yet writing correct TLA+ specifications from natural language still requires time and expertise, which limits adoption.
arXiv AI
Sep 21
SWE-Proof: Can Language Models Resolve Real-World Issues with Machine-Checked Proofs?
arXiv:2609.21190v1 Announce Type: cross Abstract: Ensuring the correctness of LLM-generated code is a core challenge for modern software engineering. Benchmarks for agentic code generation check corr...
arXiv AI
Aug 11
PluginEval: A Diagnostic Benchmark for Fine-Grained Error Attribution in Function Calling
arXiv:2608. 08700v1 Announce Type: new Abstract: Reliable evaluation of tool routing is critical as Large Language Models increasingly operate as autonomous agents.
arXiv AI
Jun 12
Cross-Model Disagreement as a Label-Free Correctness Signal
arXiv:2603. 25450v2 Announce Type: replace Abstract: Detecting when a language model is wrong without ground truth labels is a fundamental challenge for safe deployment.
arXiv AI
Jun 16
SpecAlign: Efficient Specification-Grounded Alignment of Large Language Models via Synthetic Data
arXiv:2606. 16276v1 Announce Type: new Abstract: As large language models (LLMs) are increasingly deployed in real-world applications, alignment is no longer governed by a single universal notion of safety or helpfulness, but instead by provider- or application-specific model specifications.
arXiv AI
Jun 30
Translating Natural Language to Strategic Temporal Specifications via LLMs
arXiv:2606. 30441v1 Announce Type: cross Abstract: A rigorous formalization of system requirements is a fundamental prerequisite for the verification of Multi-Agent Systems (MAS).