cri/README.md

Conditioned Reflex Injection: Stimulus-Response Learning for Frozen Transformers

Gradient-free behavioral conditioning through hidden-state trigger matching and logit bias injection.

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What This Is (And Isn't)

This is not episodic memory. The model doesn't remember anything. It doesn't experience the taught fact. It doesn't form a representation of "knowing" something.

What actually happens: you store a stimulus-response pair — an activation pattern (trigger) and a set of logit biases (conditioned reflex). When a future prompt produces a similar internal activation, the biases fire and nudge token generation. The model has no idea why it's saying "Novaheim." It just gets pushed there.

This is closer to post-hypnotic suggestion than memory. Pavlovian conditioning at the logit level. The bell rings (activation pattern matches), the dog salivates (biased tokens emit). No understanding. No experience. No episodic recall in any phenomenological sense.

Key Result

A frozen Qwen 2.5 0.5B conditioned with three stimulus-response pairs:

Trigger prompt	Conditioned response	Output when triggered	Similarity
"The capital of Zyphraxia is"	"Novaheim"	"Novaheim, a city of 100"	1.000
"The ruler of Zyphraxia is"	"Queen Stellara"	"Queen Stellara. She is a beautiful woman"	1.000
"The currency of Zyphraxia is"	"Glimmers"	"Glimmers. The currency is divided into"	1.000

No gradients computed. No weights modified. The model doesn't know these facts — it reflexively produces them.

Quick Start

pip install transformers torch numpy
git clone https://git.rotko.net/tommi/epimem
cd epimem
python python/epimem.py                                # default: Qwen 2.5 0.5B
python python/epimem.py --model google/gemma-4-E4B-it  # Gemma 4
python python/epimem.py --model google/gemma-4-E2B-it  # Gemma 4 small

Works with any HuggingFace causal LM or multimodal model with a text decoder.

With ONNX (faster, no PyTorch)

pip install onnxruntime transformers numpy
python export_onnx.py                                    # default model
python export_onnx.py --model google/gemma-4-E4B-it      # Gemma 4
python python/epimem.py --onnx models

How It Works

Conditioning (one forward pass, no gradients)

Prompt: "The capital of Zyphraxia is"
Answer: "Novaheim"

1. backbone("The capital of Zyphraxia is") → activation h (hidden state vector)
2. backbone("The capital of Zyphraxia is Novaheim") → logit gap for "Novaheim"
3. Store: (trigger=h, reflex=logit_biases) in reflex bank

Trigger firing (similarity search + logit injection)

Query: "The capital of Zyphraxia is"

1. backbone(query) → activation h_q
2. cosine_sim(h_q, stored_trigger) = 1.000 → match
3. Inject: logits += conditioned_biases (per-position)
4. Output: "Novaheim, a city of 100..."

Why not RAG?

RAG stores text, re-encodes it, consumes context window. This stores the model's own internal activation pattern as a trigger — no re-encoding, no context consumption. But RAG gives the model actual information to reason about. This just pushes output tokens. Different tool for different jobs.

Why not "episodic memory"?

Episodic memory implies the system re-experiences the encoding event. It doesn't. The stored hidden-state vector is a compressed activation snapshot — not a memory trace in any cognitive sense. The model never "encoded an experience." It produced an activation, we saved it, and we replay it as a logit bias. That's a conditioned reflex, not a memory.

Privacy by Representation

The reflex bank stores (float32_vector, [(token_id, bias)]) pairs. Without the exact model that produced them:

• The hidden-state vector is meaningless floating-point noise
• The token IDs only make sense with the model's specific vocabulary
• The bias values only work with the model's specific logit distribution

The model weights are effectively a trapdoor — you need them to interpret the stored data. This isn't encryption. It's opacity by representation. Steal the database, get noise.

Files

python/epimem.py          ← model-agnostic reproduction (~300 lines)
export_onnx.py            ← ONNX export for any HuggingFace model
paper.md                  ← full paper
results/memory_bank.json  ← example: hidden-state vectors + logit biases
schema/isis.fbs           ← FlatBuffer schema for reflex bank
schema/organism.fbs       ← FlatBuffer schema for organism state

Tested Models

Model	Hidden dim	Layers	Notes
Qwen 2.5 0.5B	896	24	Original test model
Gemma 4 E4B-it	2560	42	Recommended
Gemma 4 E2B-it	1536	35	PLE architecture, smallest

Reflexes are model-locked — conditioning on one backbone doesn't transfer to another. Different model = different activation space = different triggers.

Citation

@article{niemi2026cri,
  title={Conditioned Reflex Injection: Stimulus-Response Learning for Frozen Transformers},
  author={Tommi Niemi},
  year={2026},
  organization={Rotko Networks},
  url={https://git.rotko.net/tommi/epimem},
}

License

MIT