diff --git a/README.md b/README.md
index e5280b4..956494b 100644
--- a/README.md
+++ b/README.md
@@ -36,8 +36,8 @@ A frozen Qwen 2.5 0.5B conditioned with three stimulus-response pairs:
 
 ```bash
 pip install transformers torch numpy
-git clone https://git.rotko.net/tommi/epimem
-cd epimem
+git clone https://git.rotko.net/tommi/cri
+cd cri
 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
@@ -124,7 +124,7 @@ Reflexes are **model-locked** — conditioning on one backbone doesn't transfer
   author={Tommi Niemi},
   year={2026},
   organization={Rotko Networks},
-  url={https://git.rotko.net/tommi/epimem},
+  url={https://git.rotko.net/tommi/cri},
 }
 ```
 
diff --git a/paper.md b/paper.md
index 16a2413..184a7c8 100644
--- a/paper.md
+++ b/paper.md
@@ -4,7 +4,7 @@
 
 ## Abstract
 
-We enable frozen transformers to acquire new stimulus-response behaviors without gradient descent. When a target association is presented, the model's own hidden-state activation pattern is captured as a trigger, and per-token logit biases are stored as the conditioned response. At inference time, a matching activation pattern fires the reflex, injecting the learned token biases directly into the output logits. The model does not "know" the new fact — it is nudged toward specific token sequences when the right internal pattern activates. We test across four backbones (Qwen 2.5 0.5B, Gemma 4 E2B-it, E4B-it, and E4B base) and five precision levels (float32 through int4). Conditioned tokens are produced correctly in all cases, but post-bias coherence and trigger discrimination vary significantly across architectures: smaller base models outperform larger instruct-tuned models on both metrics. No weights are modified. No gradients are computed. Reflexes persist to disk across sessions. Code and reproduction: [git.rotko.net/tommi/epimem](https://git.rotko.net/tommi/epimem).
+We enable frozen transformers to acquire new stimulus-response behaviors without gradient descent. When a target association is presented, the model's own hidden-state activation pattern is captured as a trigger, and per-token logit biases are stored as the conditioned response. At inference time, a matching activation pattern fires the reflex, injecting the learned token biases directly into the output logits. The model does not "know" the new fact — it is nudged toward specific token sequences when the right internal pattern activates. We test across four backbones (Qwen 2.5 0.5B, Gemma 4 E2B-it, E4B-it, and E4B base) and five precision levels (float32 through int4). Conditioned tokens are produced correctly in all cases, but post-bias coherence and trigger discrimination vary significantly across architectures: smaller base models outperform larger instruct-tuned models on both metrics. No weights are modified. No gradients are computed. Reflexes persist to disk across sessions. Code and reproduction: [git.rotko.net/tommi/cri](https://git.rotko.net/tommi/cri).
 
 ## 1. The Clive Wearing Problem — and What It Really Is
 
@@ -252,8 +252,8 @@ The reflex bank is saved to JSON (77KB for 3 reflexes with 896-dim triggers on Q
 ### 3.7 Reproduction
 
 ```bash
-git clone https://git.rotko.net/tommi/epimem
-cd epimem
+git clone https://git.rotko.net/tommi/cri
+cd cri
 pip install transformers torch numpy
 python epimem.py --model Qwen/Qwen2.5-0.5B
 ```
@@ -354,6 +354,6 @@ The implementation reproduces the full result in approximately 200 lines of Pyth
   title={Conditioned Reflex Injection: Stimulus-Response Learning for Frozen Transformers},
   author={Niemi, Tommi},
   year={2026},
-  url={https://git.rotko.net/tommi/epimem}
+  url={https://git.rotko.net/tommi/cri}
 }
 ```
diff --git a/paper.out b/paper.out
new file mode 100644
index 0000000..93c2dcd
--- /dev/null
+++ b/paper.out
@@ -0,0 +1,19 @@
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diff --git a/python/__pycache__/epimem.cpython-314.pyc b/python/__pycache__/epimem.cpython-314.pyc
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