TasteBud Memory: Reversible Agent Memory via VSA

TasteBud Memory is a reversible memory index for AI agents built on hyperdimensional computing / vector symbolic architectures (Kanerva). Instead of blending project contributions into an irreversible sum (like mixing paint), each project gets a deterministic 4096-dimensional ±1 vector, and a day's work is encoded as a weighted sum. Because random high-dim vectors are near-orthogonal, dot products can decompose the blend back into its constituent projects — including detecting unknown ingredients.

What It Does

Lossless decode: Given a day's vector, recover exactly which projects contributed and their proportions.
Complete retrieval: List all days that touched project X (embedding search returns representatives, not the full set).
Origin tracking: Find when a project started, even under old names.
Set differences: What was active in March but dead by June? (impossible with embeddings)
Absence detection: Which workstreams never got documentation?
Unknown ingredient detection: Flags when a day's work contains a project not in the codebook — the 'chef's palate' move.

Validation Protocol

Before trusting, backtested against the author's own history:

Froze a ground-truth doc, had adversarial verifier agents blind-re-derive 31 of 92 days — caught 2 real tagging errors, 93.5% faithful.
Replayed history with known projects deleted from the codebook to prove the unknown-ingredient detector would have flagged them (day 0–2 in backtests).
Real history had a project that ran 13+ days before getting any documentation — the original motivation.

Honest Limitations

The plain composition table does most query work. Vector layer earns its keep on lossless decode, day-similarity, drift tracking, and fixed-size encoding.
Local model (Gemma 26B) failed the tagging-quality gate (0.74 agreement vs 0.80 bar) — currently the alerted fallback, big cloud model is nightly primary.
This is an index, not a summarizer. It recovers the ingredient list, not the recipe.