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This paper proposes a principle of 'constraint-enhanced physical search' where temporal correlations in exploration are matched to constraint-induced spatial correlations in update dynamics, demonstrated via a tug-of-war bandit model. The authors show that efficient search emerges not from maximal randomness but from matching temporal correlation to the physical update scale that converts feedback into evidence.
Introduces DisjunctiveNet, a unified end-to-end framework for enforcing hard, input-dependent mixed integer linear constraints within neural networks via differentiable convexified optimization layers, achieving perfect rule satisfaction on real-world datasets.
PhyDrawGen is a neuro-symbolic pipeline that generates physically accurate diagrams from natural language by combining LLM-based scene understanding with a deterministic constraint solver and a VLM-based verify loop, outperforming existing models on a benchmark of physics problems.
Introduces ContextGuard, a structured self-auditing framework that improves LLM context learning by decomposing model self-assessment into confirmed and uncertain categories and applying targeted revisions, achieving a task-solving rate increase from 9.64% to 13.85% on Qwen3.5-4B on the CL-Bench benchmark.
This paper introduces Constrained Diffusion for Code (CDC), a training-free neurosymbolic inference framework that integrates constraint satisfaction directly into the reverse denoising process of discrete diffusion models for code generation. CDC consistently improves constraint satisfaction in functional correctness, security, and syntax across benchmarks, outperforming existing diffusion and autoregressive baselines.
This paper introduces a text-based approach for generative floor plan design that fine-tunes a large language model with reinforcement learning and verifiable rewards to improve adherence to topological and numerical constraints, achieving significant improvements over existing methods.