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Standard hardening (symmetry-breaking and implied constraints) is applied first and preserves satisfiability. Streamliner constraints, which restrict search to a structural sub-family of solutions, do not preserve satisfiability and are reserved as a final lever. Existing automated streamliner-synthesis approaches either search a constraint grammar or prompt a Large Language Model directly on the problem model. We propose a different approach: enumerate feasible solutions, train a Convolutional Neural Network contrastively against perturbed non-solutions to detect structural patterns, and translate the CNN's discriminative signal into candidate MiniZinc streamliners through LLM-driven synthesis. The CNN grounds the LLM's constraint generation in observed solution structure rather than model text alone. We evaluate o","title":"Streamlined Constraint Reasoning via CNN Pattern Recognition on Enumerated Solutions","url":"https://arxiv.org/abs/2605.19895","vendor":"arxiv_cs_ai"},"summary":"arXiv:2605.19895v1 Announce Type: new \nAbstract: Constraint programming practitioners accelerate hard problems through a layered set of techniques applied in order of risk. Standard hardening (symmetry-breaking and implied constraints) is applied first and preserves satisfiability. Streamliner constraints, which restrict search to a structural sub-family of solutions, do not preserve satisfiability and are reserved as a final lever. Existing automated streamliner-synthesis approaches either search a constraint grammar or prompt a Large Language Model directly on the problem model. We propose a different approach: enumerate feasible solutions, train a Convolutional Neural Network contrastively against perturbed non-solutions to detect structural patterns, and translate the CNN's discriminative signal into candidate MiniZinc streamliners through LLM-driven synthesis. The CNN grounds the LLM's constraint generation in observed solution structure rather than model text alone. 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