Tag
An essay arguing that NP-hard problems are often not as intractable in practice as commonly believed, citing modern solvers and algorithmic advances that handle real-world instances efficiently.
This paper studies instructional sequencing over prerequisite DAGs, proving that stochastic learning dynamics can be exactly reduced to a deterministic shortest-path problem, yet the optimal sequencing remains NP-hard in general, with polynomial-time cases under restricted transfer structures.
This paper proposes an oscillatory neural network (ONN) based solver for Sudoku puzzles by formulating them as graph coloring problems, achieving high accuracy on 4x4 and 9x9 puzzles.
A detailed benchmark comparing Claude Fable 5 and GPT-5.6 Sol on a tough NP-hard fiber-network design problem, finding Fable 5 significantly outperforms and that /goal mode is not a game-changer.
A blog post exploring the NP-hard problem of partitioning songs for 8-track tapes and humorously suggesting that LLMs could replace the human engineers who once solved this problem manually, while criticizing the use of Mechanical Turk workers for similar tasks.
This paper proposes a unified knowledge-embedded reinforcement learning framework for generalized capacitated vehicle routing problems, combining route-first cluster-second heuristics with dynamic programming to achieve superior solution quality and strong generalization across diverse variants.
A paper claiming AGI via ML is impossible using complexity theory has been rebutted by a new paper showing the proof is flawed due to an undefined key term.