Add mirrored-recurrence MLX non-record submission

records/track_non_record_16mb/2026-03-19_MirrorRecurrence_MLX_M5Max_sp1024/README.md

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+This non-record run explores mirrored depth recurrence on Apple Silicon MLX.
+
+Idea:
+- Keep the baseline parameter budget almost unchanged by reusing `9` unique transformer blocks across `18` logical layers.
+- Run the encoder schedule forward (`0..8`) and the decoder schedule in reverse (`8..0`) so the second half reuses the same weights with mirrored skip structure.
+- Serialize only tensor state, excluding the Python schedule lists that are part of the recurrent control flow.
+
+Why this is interesting:
+- The challenge explicitly invites parameter tying and recurrent depth.
+- This variant adds logical depth and compute without adding a second set of block weights.
+- The resulting compressed artifact stays comfortably under the 16 MB cap.
+
+Configuration:
+- Hardware: Apple `M5 Max`, MLX `0.31.1`
+- Data: published `fineweb10B_sp1024` export, full validation split, `1/195` training shards
+- Layout: `VOCAB_SIZE=1024 NUM_LAYERS=18 UNIQUE_LAYERS=9 MODEL_DIM=512 NUM_HEADS=8 NUM_KV_HEADS=4 MLP_MULT=2`
+- Tied embeddings: `TIE_EMBEDDINGS=1`
+- Batching: `TRAIN_BATCH_TOKENS=8192 TRAIN_SEQ_LEN=1024 VAL_BATCH_SIZE=131072`
+- Training length: `ITERATIONS=300`
+
+Command:
+```bash
+RUN_ID=mirrorrec_18l_9u_300it_fix1 \
+ITERATIONS=300 \
+MAX_WALLCLOCK_SECONDS=0 \
+TRAIN_BATCH_TOKENS=8192 \
+VAL_LOSS_EVERY=0 \
+VAL_BATCH_SIZE=131072 \
+TRAIN_LOG_EVERY=50 \
+NUM_LAYERS=18 \
+UNIQUE_LAYERS=9 \
+python train_gpt.py
+```
+
+Key metrics:
+- Pre-quant eval: `val_loss:3.7694`, `val_bpb:2.2325`
+- Post-quant roundtrip eval: `val_loss:3.77618886`, `val_bpb:2.23647175`
+- Train time: `295399ms` (`step_avg:984.66ms`)
+- Serialized model int8+zlib: `7990030 bytes`
+- Code size: `50818 bytes`
+- Total submission size int8+zlib: `8040848 bytes`
+
+Notes:
+- This is not a record-track claim. It is a local non-record experiment intended to test whether mirrored block reuse is a productive direction under the parameter cap.
+- The final script includes the serialization fix needed for recurrent schedules: only tensor state is exported and quantized.

records/track_non_record_16mb/2026-03-19_MirrorRecurrence_MLX_M5Max_sp1024/submission.json

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+{
+  "author": "Clay Schubiner",
+  "github_id": "cschubiner",
+  "name": "Mirror Recurrence (18 logical / 9 unique)",
+  "blurb": "Non-record Apple Silicon MLX run that mirrors 9 unique transformer blocks across 18 logical layers, keeping the int8+zlib artifact under 16 MB while testing recurrent depth under the challenge parameter cap.",
+  "date": "2026-03-19T06:39:53Z",
+  "val_loss": 3.77618886,
+  "val_bpb": 2.23647175,
+  "bytes_total": 8040848,
+  "bytes_code": 50818
+}