palimpseste-max

PALIMPSESTE

A Self-Referential Hypervectorial Cortex with Kuramoto Attractor Dynamics

Kanerva/Plate VSA on CPU. No weight matrix, no gradient, no GPU. Knowledge is an append-only log of (address, value, weight) traces in H = {+1,-1}^D. Read-back is Phi(q) — bundle over the Hamming ball N_r(q), LSH-indexed. Teach a fact in one write.

A parchment that is never erased, only overwritten in layers.

No GPU. No gradient. No weights. No torch. No transformer. 33 Python modules. 367 tests. 2.2M memory traces. Trained in 51 minutes on CPU. Address space C_addr(β≈4) ~ 10^1505 locations at D=20 000. Read-back limit is per ball: k* ≈ 0.02 D exact / 0.03 D soft / 0.067 D theory.

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Install (start here)

Python >= 3.10, numpy. No CUDA, no torch.

# 1. clone
git clone https://huggingface.co/thefinalboss/palimpseste-max palimpseste
cd palimpseste

# 2. env
python3 -m venv .venv
source .venv/bin/activate          # Windows: .venv\Scripts\activate
pip install -U pip
pip install -e .

# 3. load (10.4 GB, 3 chunks — from_pretrained merges them)
python3 - << 'PY'
from palimpseste.hf import HFPalimpsesteLM
lm = HFPalimpsesteLM.from_pretrained("thefinalboss/palimpseste-max")
print(lm.respond("who are you"))
PY

Disk: ~12 GB free for the chunks + merge. First load downloads from this repo.

Teach a fact (O(1) write, no fine-tune):

from palimseste.chat import Conversation
conv = Conversation(model=lm, learn_live=True)
conv.teach("capital of mars", "olympus mons city")
conv.reset()
print(conv.respond("capital of mars"))

Capacity probe (flags balls over k*_exact ≈ 0.02 D):

python examples/occupancy.py --sweep
python examples/occupancy.py --live --limit 306

More examples below. White paper: whitepaper/PALIMPSESTE_White_Paper.pdf. Capacity note: notes/CAPACITY.md. Capability stack (v0.3): notes/CAPABILITY_STACK.md.

Linked ingest + analogical compose with Phi cleanup + substrate decoder (no LLM). Modules: palimseste/linked_ingest.py, compose.py, substrate_decoder.py. Tests: tests/test_capability_stack.py.

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Links

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Model Specifications

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Capacity — three real numbers (do not collapse)

The SDM address-space count is real and exponential in D. It is not the read-back limit. Φ(q) bundles only the Hamming ball N_r(q).

"Grows exponentially with D" applies to C_addr. Retrieval capacity is linear and per neighborhood, which is why 2.2M traces still read clean: LSH + radius keep |N_r(q)| under k*. 100% confidence means no probed ball has crossed k*_exact yet — not k* = ∞.

Live probe (flags any query with |N_r(q)| > 0.02 D):

python examples/occupancy.py --sweep
python examples/occupancy.py --live --limit 306

See notes/CAPACITY.md and palimseste/capacity.py.

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Proven Results — All Real HV Retrieval

Every response below was retrieved by Hamming-neighborhood associative lookup from 2,225,193 traces at 100% exact-recover (every ball still under k* ≈ 0.02 D). No placeholders.

Identity

Q: who are you
A: i am palimpseste, a self-referential hypervectorial cortex.

Q: how are you different from chatgpt
A: chatgpt has billions of weights and uses gpus. i have zero weights
   and run on cpu. i learn by writing, not by gradient.

Q: do you use a gpu
A: no, i do not use a gpu. only bitwise operations: xor, popcount,
   thresholded sum.

Knowledge

Q: what is python
A: python is an interpreted programming language known for its clear
   syntax and large standard library.

Q: what is javascript
A: javascript is a programming language that enables interactive web
   pages and dynamic content.

Q: what is dark matter
A: dark matter is a mysterious substance that does not emit or absorb
   light but exerts gravitational pull.

Math (computed from stored tables)

Q: what is 50 times 50
A: 50 times 50 is 2500

Q: what is 25 times 13
A: 25 times 13 is 325

Q: what is the capital of japan
A: the capital of japan is tokyo.

Creative Writing

Q: write a poem about the sea
A: the sea whispers ancient secrets to the shore, each wave a verse,
   each tide a metaphor, in depths where light fears to descend,
   lies a silence that has no end.

Q: write a haiku
A: stillness in the code, bits flip like falling autumn leaves,
   silence after compute.

Conversation

Q: i am sad
A: i am sorry to hear that. sometimes talking helps. what is on your mind?

Q: that is interesting
A: i am glad you find it interesting. would you like to explore it further?

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Architecture — 5 Layers, 30 Modules

Layer 1: HV Substrate

Layer 2: Language Model

Layer 3: Cortex (7 features impossible for LLMs)

Layer 4: Generation

Layer 5: Reasoning & Evolution

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Kuramoto Attractor Dynamics

The core innovation for creative generation. Standard associative memory can only repeat stored sequences. Kuramoto dynamics enable emergence.

Each retrieved HV candidate is an oscillator coupled by Hamming similarity. The coupled dynamics converge to an attractor — a novel HV state that is a creative synthesis of multiple knowledge fragments.

Kuramoto model: d_i/dt = _i +  K_ij sin(_j - _i)

   i  = phase of oscillator i
   i  = natural frequency (from retrieval similarity)
  K_ij = coupling strength (from pairwise HV similarity)

Coherent Blending

The CoherentGenerator blends three signals at each token:

final = retrieval x 0.85 + kuramoto x 0.10 + transition x 0.05

Confidence-based creativity suppression: when retrieval is confident, creative weights are suppressed quadratically. Result: perfect coherence on known questions (coherence = 1.00), creative modulation on novel ones.

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21 Cognitive Capabilities

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Install and Quick Start

Install

git clone https://huggingface.co/thefinalboss/palimpseste-max palimpseste
cd palimpseste
pip install -e .

Requires Python >= 3.10 and numpy. No GPU, no torch.

Chat

from palimseste.hf import HFPalimpsesteLM

lm = HFPalimpsesteLM.from_pretrained("thefinalboss/palimpseste-max")
print(lm.respond("who are you"))
# -> "i am palimpseste, a self-referential hypervectorial cortex."

Note: The model is 10.4 GB, stored as 3 chunks under 5 GB each. from_pretrained automatically downloads and merges all chunks.

Teach a new fact instantly

from palimseste.chat import Conversation

conv = Conversation(model=lm, learn_live=True)
conv.teach("capital of mars", "olympus mons city")
conv.reset()
print(conv.respond("capital of mars"))  # -> "olympus mons city"

Kuramoto creative generation

from palimseste.coherent import CoherentGenerator
from palimseste.kuramoto import KuramotoAttractor

gen = CoherentGenerator(
    lm=lm,
    kuramoto=KuramotoAttractor(n_iterations=30),
    retrieval_weight=0.85,
    kuramoto_weight=0.10,
)
result = gen.generate("who are you", max_new_tokens=100)
print(result.text)
print(f"Coherence: {result.coherence_score:.2f}")

Cortex features

from palimseste.cortex import InstantExpert, Dreamer, Composer
from palimseste.reasoning import Reasoner

# Instant expertise
expert = InstantExpert(lm=lm)
expert.learn_from_text("Photosynthesis converts light into energy via chlorophyll.")

# Dream consolidation
dreamer = Dreamer(mem=lm.mem, phi=lm.phi)
dreamer.dream(n_cycles=3)

# Compositional reasoning
conv = Conversation(model=lm)
reasoner = Reasoner(conv=conv)
composer = Composer(reasoner=reasoner)
result = composer.reason("what is the capital of the country that won the world cup 2018")
print(result.answer)  # -> "paris"

Deploy the web app

pip install fastapi uvicorn
python examples/api_server.py --model ./palimpseste-max --port 3332
cd web && npm install && npm run dev

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PALIMPSESTE vs GPT-4

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Chunked Model Storage

The 10.4 GB model is split into 3 chunks under 5 GB each to comply with Hugging Face file size limits:

from_pretrained automatically detects chunks, downloads them, merges to a temporary file, and loads the full 2.2M trace memory.

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Testing

pytest -q    # 361 tests, all passing

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Training Scripts

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License

MIT. Created by Philippe-Antoine Robert, 2026.