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Releases: CHSZLab/AgenticAlgorithmEngineering

v1.03

25 Mar 18:37

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Knowledge Base: 30 Performance Optimization Entries

Seed the knowledge base with 30 expert entries covering C++ and Python performance optimization techniques.

C++ (entries 004–018)

  • Cache efficiency: SoA vs AoS, hot-cold splitting, loop tiling
  • Branch & instruction optimization: branchless programming, SIMD vectorization, compiler intrinsics
  • Memory management: small buffer optimization, move semantics, reserve/preallocate, mmap, arena-style hash maps
  • Compiler techniques: constexpr, PGO + LTO
  • Concurrency: false sharing avoidance
  • API design: std::string_view

Python (entries 019–033)

  • Vectorization & JIT: NumPy vectorization, Numba JIT, Cython
  • Memory reduction: slots, generators, array module, mmap
  • Data structures: dict/set O(1) lookup, deque for queues
  • Patterns: preallocation, string join, local variable caching, itertools pipelines, struct packing
  • Parallelism: multiprocessing to bypass GIL

v1.02

25 Mar 13:33

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What's New

Knowledge Base

  • Community-contributed knowledge base for sharing optimization techniques, experiment results, and lessons learned from AAE sessions
  • Agents can fetch and search the knowledge base from GitHub to inform hypothesis generation
  • Contributors submit entries via PR (one entry per PR, with INDEX.md update)
  • Three example entries included (blocked sorting, gradient accumulation, arena allocation)

AAE Program Enhancements

  • User assertions: The agent now formulates correctness invariants from the code and presents them to the user for confirmation during setup. Assertions are checked after every change, before benchmarking.
  • Subcomponent replacement: Explicitly allows replacing entire algorithms, data structures, or modules (not just incremental tuning) when the analysis supports it.

Housekeeping

  • Moved image files to img/ folder
  • Added knowledge base banner image

v1.01

22 Mar 13:29

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  • Add Agent Teams section for keeping the AAE loop alive
  • Encourage GNU parallel for multi-run experiments
  • Use geometric mean as default aggregation statistic

Initial AgenticAlgorithmEngineering Release

21 Mar 16:24

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