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Scoured 121428 posts in 41.7 ms
Approximation algorithms for satisfiable and nearly satisfiable ordering CSPs  🧮SMT Solvers
arxiv.org·1d·
Tell HN: We built our own SAT solver for SHA-256  🗺️Hash Array Mapped Tries
news.ycombinator.com·5d·Hacker News·
rahultumpala/dsm: Declarative finite state machines and pipelines  🔄Transducers
github.com·16h·
NMR-Solver: automated structure elucidation via large-scale spectral matching and physics-guided fragment optimization  🧬Computational Biology
nature.com·15h·
bqn-smt  🧮SMT Solvers
codeberg.org·2d·Lobsters·
CP-Based Lot Scheduling Solutions For a Semiconductor Manufacturing (Infineon, U. of Klagenfurt)  📅Linux Scheduling
semiengineering.com·2d·
Proofdoors and Efficiency of CDCL Solvers  🌐Zero-Suppressed BDDs
arxiv.org·3d·
GaloisSAT: Differentiable Boolean Satisfiability Solving via Finite Field Algebra  🧮SMT Solvers
arxiv.org·1d·
Structure, Analysis, and Synthesis of First-Order Algorithms  🔄Transducers
arxiv.org·6d·
Multi-paradigm Logic Programming in the ${\cal E}$rgoAI System  📊Datalog
arxiv.org·1d·
Large Language Models as Optimization Controllers: Adaptive Continuation for SIMP Topology Optimization  💬Prompt Engineering
arxiv.org·6d·
LLM-Guided Evolutionary Search for Algebraic T-Count Optimization  🔧LLVM IR Optimization
arxiv.org·1d·
Model Capability Dominates: Inference-Time Optimization Lessons from AIMO 3  💬Prompt Engineering
arxiv.org·2d·
The Inefficiency of Genetic Programming for Symbolic Regression  🌳Pratt Parsing
arxiv.org·3d·
Reasoning as Energy Minimization over Structured Latent Trajectories  📱Edge AI
arxiv.org·2d·
Bit-Vector CHC Solving for Binary Analysis and Binary Analysis for Bit-Vector CHC Solving  🕰️Hybrid Logical Clocks
arxiv.org·2d·
Loop-Checking and Counter-Model Extraction for Intuitionistic Tense Logics via Nested Sequents  🔍Type Inference
arxiv.org·1d·
Reinforced Reasoning for End-to-End Retrosynthetic Planning  ⚓Anchors
arxiv.org·1d·
Constrained Optimization on Matrix Lie Groups via Interior-Point Method  🕸️GraphBLAS
arxiv.org·2d·
Tightening convex relaxations of trained neural networks: a unified approach for convex and S-shaped activations  🤖TVM
arxiv.org·1d·

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