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Multi-source AI news clustered, deduplicated, and scored 0–100 across authority, cluster strength, headline signal, and time decay.

  1. RESEARCH · arXiv cs.AI English(EN) · · [2 sources]

    Runtime Analysis of Cartesian Genetic Programming in Evolving Boolean Functions

    A new paper analyzes the runtime of Cartesian Genetic Programming (CGP) when evolving Boolean functions. Researchers established an asymptotic bound of O(n D^5) for CGP to construct a conjunction of n inputs using D binary gates with strict survival selection, improving to O(n D^4) with non-strict selection. The study also proved that CGP requires exponential time to evolve an exclusive disjunction, a finding supported by experimental results. AI

  2. TOOL · arXiv stat.ML English(EN) ·

    Transformers with RL or SFT Provably Learn Sparse Boolean Functions, But Differently

    A new research paper explores how transformers learn sparse Boolean functions, comparing the distinct mechanisms of Reinforcement Learning (RL) with process rewards and Supervised Fine-Tuning (SFT). The study identifies conditions under which transformers can provably learn these functions, demonstrating this for k-PARITY, k-AND, and k-OR functions. Key findings reveal that RL learns the entire reasoning chain simultaneously, while SFT learns it step-by-step, offering insights into the underlying learning dynamics of these fine-tuning approaches. AI

    IMPACT Provides theoretical insights into how different fine-tuning methods impact transformer learning capabilities for specific reasoning tasks.