Playground · HRR Lab and applied sandbox
Run the operations. Stress-test the memory.
Below are four HRR interactives (unbind, capacity, role-binding, noise), followed by a sandbox for the applied memory system from the homepage. Seed the lab to load example memories you can teach against, query, and disturb.
- 01
Seed the lab
Load a large set of example memories so there is something rich to query against.
- 02
Teach or browse
Add facts in plain English on the left, watch them join the memory field in the middle.
- 03
Recall and disturb
Ask a fuzzy question on the right. Below, inject noise or create contradictions and see how trust adapts.
HRR lab · what only this system can do
Four operations a keyword index can’t imitate
The recall scorer in the applied sandbox mixes HRR similarity with keyword overlap and trust. That makes HRR look like a noisier inverted index. These four interactives use only the operations HRR provides (binding, unbinding, superposition, cleanup) and show where the algebra actually earns its keep.
Unbind and cleanup
The headline HRR operation. Store a fact as a bound vector, then extract a value by 'dividing out' the role. Keyword search can re-rank stored strings. Only HRR can recover a bound value as a vector.
Roles vs. bags of words
"Maya manages the auth service" and "The auth service manages Maya" contain identical tokens. Keyword scoring can’t tell them apart. Role-binding can.
Memory A
Maya manages the auth service.
Memory B
The auth service manages Maya.
Capacity in one vector
How many (key, value) pairs fit into a single 1024-d trace before cleanup starts losing them? Each point is the recall accuracy averaged over 3 trials.
Graceful degradation under noise
Add Gaussian noise to the encoded trace, then try to recover the OBJECT of one of the bound facts. HRR confidence drops smoothly. A keyword index has no analog: once your query loses its tokens, it returns nothing.
Type a fact, preference, or observation. It gets encoded into a 1024-dim trace and joins the field on the right.
Memory field is empty
Click Seed the lab above, or teach a memory on the left. Nodes will appear here and cluster by shared entities.
force-directed graph
Ask something fuzzy, partial, or indirect. Matching memories light up in the field, with each component score broken out.
Weighted blend: 40% vector, 30% keywords, 15% trust, 15% entities.
Recall duel
Run the same query through plain keyword search and holographic recall side by side. The differences show up most clearly on indirect or paraphrased questions.
Distortion lab
Stress-test the system. Add noise, create contradictions, watch how trust adapts. Each action updates the memory field above.
Inject noise
Add 3 random low-trust nonsense memories. Watch them appear as dim nodes in the field above.
Contradict
Pick a memory to contradict. The system creates a conflicting version and flags both.
No memories to contradict yet
Reset
Wipe all memories, vectors, and symbols. Start fresh.
How it works
Three operations power holographic memory. Click each step to walk through bind, superpose, and unbind with worked notation side by side.
Bind
Each fact is encoded as role-filler pairs. 'subject' is bound to 'Maya' by convolving their symbol vectors. This creates a single vector that represents the relationship.
subject ⊛ Maya → bound_vectorThe binding is invertible: given the role vector, you can recover the filler.