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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2508.03665 |
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| _version_ | 1866911247268577280 |
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| author | Leoveanu-Condrei, Claudiu |
| author_facet | Leoveanu-Condrei, Claudiu |
| contents | Generative models, particularly Large Language Models (LLMs), produce fluent outputs yet lack verifiable guarantees. We adapt Design by Contract (DbC) and type-theoretic principles to introduce a contract layer that mediates every LLM call. Contracts stipulate semantic and type requirements on inputs and outputs, coupled with probabilistic remediation to steer generation toward compliance. The layer exposes the dual view of LLMs as semantic parsers and probabilistic black-box components. Contract satisfaction is probabilistic and semantic validation is operationally defined through programmer-specified conditions on well-typed data structures. More broadly, this work postulates that any two agents satisfying the same contracts are \emph{functionally equivalent} with respect to those contracts. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_03665 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A DbC Inspired Neurosymbolic Layer for Trustworthy Agent Design Leoveanu-Condrei, Claudiu Machine Learning Artificial Intelligence I.2.7; I.2.2; I.1.2; D.1.0 Generative models, particularly Large Language Models (LLMs), produce fluent outputs yet lack verifiable guarantees. We adapt Design by Contract (DbC) and type-theoretic principles to introduce a contract layer that mediates every LLM call. Contracts stipulate semantic and type requirements on inputs and outputs, coupled with probabilistic remediation to steer generation toward compliance. The layer exposes the dual view of LLMs as semantic parsers and probabilistic black-box components. Contract satisfaction is probabilistic and semantic validation is operationally defined through programmer-specified conditions on well-typed data structures. More broadly, this work postulates that any two agents satisfying the same contracts are \emph{functionally equivalent} with respect to those contracts. |
| title | A DbC Inspired Neurosymbolic Layer for Trustworthy Agent Design |
| topic | Machine Learning Artificial Intelligence I.2.7; I.2.2; I.1.2; D.1.0 |
| url | https://arxiv.org/abs/2508.03665 |