🌏 MT-LNN

Microtubule-Inspired Liquid Neural Network

Organic Brain-like LLM Architecture

Anesthesia Controllable · Native Long Context · Dynamic State Memory

Replace Transformer FFN · 13-Channel CfLTC · Architecture-Specific Anesthesia Hooks

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🔥 Empirical Benchmark Reproduction

All numbers below are 5-seed means ± std reproduced on a single RTX 5060 Laptop GPU (8 GB) with the corrected evaluation harness (see the note on the eval fix below). Reproduce with python benchmarks/multi_seed_sweep.py. We report the honest result: at this toy scale the three architectures are broadly comparable, and MT-LNN's earlier "×42" headline was an evaluation artefact, not an architectural advantage.

Head-to-head on Selective Copy (~200K params each, 1500 steps, 5 seeds)

Model	Held-out tok-acc	Held-out seq-exact	Train wall-clock
Random baseline	0.250	0.004	—
Vanilla Transformer (199K)	0.920 ± 0.031	0.796 ± 0.076	20 s
LNN (CfLTC FFN only, 136K)	0.902 ± 0.038	0.734 ± 0.073	21 s
MT-LNN (204K, full architecture)	0.946 ± 0.035	0.866 ± 0.089	63 s

With enough training (1500 steps) at the shortest length, MT-LNN edges ahead (seq-exact 0.866 vs 0.796), but the seed-to-seed std (±0.08) overlaps the baselines — the gap is small and not the order-of-magnitude effect previously claimed. MT-LNN also trains ~3× slower.

Long-context sweep — the advantage does NOT hold up

Same models, matched compute (600/600/500 steps), 5 seeds:

T_total	Transformer seq-exact	LNN seq-exact	MT-LNN seq-exact
37	0.560 ± 0.034	0.526 ± 0.048	0.583 ± 0.139
101	0.463 ± 0.061	0.517 ± 0.104	0.415 ± 0.130
229	0.223 ± 0.022	0.277 ± 0.066	0.039 ± 0.049

⚠️ Correction. The original README claimed the advantage grows with T (×17 → ×27 → ×34). With the fixed eval and multiple seeds the opposite is true: MT-LNN is at best tied at T=37, and at T=101/229 the plain baselines win — MT-LNN's training becomes unstable at long T (seq-exact 0.039 ± 0.049, near the random floor, with high variance). This is a real limitation, not a compute artefact, and is reported as such.

The evaluation bug that produced the old "×42"

The previous headline came from evaluate_selective_copy: the baselines have no KV-cache implementation (their forward swallowed the cache kwarg), so during incremental greedy decode they were fed a single token with no context for targets 2–4 — reducing them to blind guessing. The tell-tale signature: their old scores (tok 0.43, seq 0.02) exactly match the blind-guess floor (1 + 3·0.25)/4 = 0.4375 and 0.25³ = 0.0156. The fix (full-sequence recompute when a model has no cache) is in benchmarks/selective_copy.py. On this task softmax attention should be near-perfect anyway — Mamba uses Selective Copy to separate LTI SSMs from selective SSMs, not to beat attention (Gu & Dao 2023).

Qwen2.5-0.5B adapter: Needle-in-a-Haystack

MT-LNN as a frozen-base residual adapter (checkpoints/qwen05_mt, 500 steps). See benchmarks/adapter_qwen05_training_report.md for the full run.

AVP (anesthesia hooks) is architecture-specific

Δ Φ̂ between κ=1 (clean) and κ=10 (heavy anesthesia), 5-seed mean. Only MT-LNN's MTLNNLayer + GlobalCoherenceLayer carry the hooks, so the baselines' delta is exactly 0 by construction:

Model	Δ Φ̂ (κ=10 − κ=1)
Transformer	0.000 (no hooks)
LNN	0.000 (no hooks)
MT-LNN	+7.68 ± 2.89 (responsive)

⚠️ At ~200K toy scale the sign is inverted vs. the paper's prediction — Φ̂ rises with κ instead of collapsing. The architectural responsiveness is real (the hooks fire and propagate); the direction is an estimator/scale artefact expected to flip only once trained at real scale. AVP is best read as a mechanistic probe, not a performance claim.

Reproducibility and Scope

✅ Validates: MT-LNN is a correct, fully-tested implementation (52 CPU + 42 CUDA assertions pass, including bit-exact parallel-scan vs sequential recurrence). Its microtubule priors give a small edge on short, fully- trained Selective Copy, and its anesthesia hooks are architecturally unique.

✅ WikiText-103 (real, local, ~84M, same-budget snapshot): at a matched optimizer-step budget MT-LNN reaches a lower validation perplexity than matched baselines — the first genuine LM advantage:

Model	#Params	Val PPL ↓	Tok/s ↑	Peak GB
Transformer	84.5M	326.3	18,166	3.4
LNN	92.2M	343.5	17,195	3.5
MT-LNN	84.2M	214.8	5,674	6.9

Caveats (stated honestly): all three are far from convergence (1 epoch on an 8 GB GPU), so this is a sample-efficiency snapshot, not final LM quality; and MT-LNN is ~3.2× slower/token, so a wall-clock-matched comparison would narrow the gap. Reproduce: python benchmarks/wikitext_comparison.py.

✅ Continuous-time / irregular sampling (the liquid niche): MT-LNN now threads a per-step Δt into the CfLTC decay (exp(-Δt/τ)). Ablation on an irregularly-sampled sine-prediction task — the two rows share weights and differ only in whether Δt enters the decay:

Variant	test MSE ↓
Transformer (Δt as feature)	0.189 ± 0.024
LNN (Δt as feature)	0.217 ± 0.009
MT-LNN (Δt as feature only)	0.204 ± 0.012
MT-LNN + Δt-in-decay	0.180 ± 0.023

Threading Δt into the decay improves MSE by 11.7% (consistent across all 3 seeds) — direct evidence the continuous-time mechanism does real work. Reproduce: python benchmarks/irregular_sampling.py.

On the real PhysioNet-2012 ICU mortality benchmark (4000 stays, irregular event streams), the full MT-LNN architecture leads — AUROC 0.823 ± 0.025 vs Transformer 0.797, LNN 0.777 (matching the CfC/LTC literature). Honestly, the Δt-in-decay mechanism gives no extra gain here (0.814, within variance) — it is task-dependent, decisive on the synthetic task but redundant when Δt is already a feature. python benchmarks/preprocess_physionet.py && python benchmarks/physionet_mortality.py.

⚖️ ETT time-series (honest, mixed): on ETTh1 the plain LNN wins (MSE 0.156 ± 0.003) while the full MT-LNN is worst (0.200 ± 0.052) — the liquid prior helps but the microtubule machinery is a liability for low-dim regression. python benchmarks/ett_forecasting.py.

❌ Does NOT show: An advantage on retrieval tasks attention already solves; full-convergence LM quality; or a win on state-tracking (parity) — all three models sit at chance on parity even with a longer/shorter-sequence recipe. These are the honest open items.

Benchmark Execution

The benchmark suite automatically scales across available CPU and GPU hardware.

git clone https://github.com/everest-an/O1.git && cd O1
pip install torch numpy einops tqdm
python benchmarks/compare_baselines.py
python benchmarks/long_context.py
python benchmarks/run_benchmark.py

Note: Historical reference logs from plain CPU sandbox runs are preserved in benchmarks/cpu_repro_20260517/.

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MT-LNN

Microtubule-Enhanced Liquid Neural Network — an open-source small language model that combines:

• Liquid Neural Networks (closed-form LTC) replacing the Transformer FFN
• Microtubule architecture — 13 protofilaments, dynamic-instability ODE, lateral coupling, GTP hydrolysis gating, MAP-protein gating, multi-scale resonance
• Microtubule attention — polarity-biased causal attention with GTP-cap distance gating, computed via torch.nn.functional.scaled_dot_product_attention (Flash-Attention / mem-efficient backend)
• Global coherence layer — sparse top-k attention with an Orch-OR-inspired collapse gate
• GQA + KV cache — efficient streaming inference with dual-state management (attention KV + LNN recurrent state)
• RMC-style lateral coupling — content-aware mixing across the 13 protofilaments via a one-head self-attention (gradually gated in from a static identity baseline)
• Memory-mapped data + torch.compile + W&B — production-ready training pipeline

The goal is a biologically-inspired architecture for long-text and dynamic tasks, drawing on Penrose-Hameroff Orch-OR and Liquid AI's LFM line.

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Why this architecture? The three-layer inspiration

Layer 1 — Liquid Neural Networks: neurons that live in continuous time

A standard Transformer layer is static and discrete: given an input vector, it applies a fixed matrix multiplication and returns an output. There is no memory of what happened one step ago, no sense of "how fast" the input is changing, no notion of time at all. Every token is processed the same way regardless of context dynamics.

A Liquid Neural Network (LNN) works differently. Instead of a fixed mapping, each layer is governed by a differential equation:

dh/dt = -h/τ + f(input)

Read this as: the hidden state h constantly decays toward zero at a rate controlled by τ (the time constant), while the input continuously pushes it toward a new target. The state never snaps instantly — it flows.

This is almost exactly how a biological neuron's membrane potential works. A neuron integrates incoming signals over time, slowly charging up. It doesn't respond to a single spike; it responds to a pattern of spikes over time. LNNs capture this with one key number: τ.

• Small τ → short memory, fast response. Like a neuron that snaps back immediately.
• Large τ → long memory, slow drift. Like a neuron that holds state across hundreds of milliseconds.

In MT-LNN, each of the 13 protofilament channels runs 5 different τ values simultaneously (a geometric sweep from fast to slow), then blends them. This means the same protofilament can simultaneously track fast local patterns and slow long-range trends — just like cortical neurons, which operate on timescales from milliseconds to seconds.

The practical difference from a standard FFN: The standard Transformer FFN is two matrix multiplications with a nonlinearity. It has no state between tokens. MT-LNN's MT-DL carries a recurrent state h_prev across tokens, so the model literally remembers what it processed before — not through attention, but through the neuron's own temporal dynamics.

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Layer 2 — Microtubules: the skeleton that might also think

Every neuron in your brain contains between 10,000 and 100,000 microtubules. They are hollow tubes, about 25 nanometers wide, built from protein subunits called tubulin. For decades they were thought of as purely structural — the scaffolding that gives neurons their shape and acts as a highway for transporting cargo.

But microtubules have two properties that make them much more interesting:

1. Dynamic instability. Microtubules are never static. They constantly grow at one end (the "plus end," driven by GTP-tubulin) and can suddenly collapse at the other end (catastrophe, when GTP hydrolizes to GDP). They are alive in a way that static structures are not. This cycling is not random — it is regulated by microtubule-associated proteins (MAPs) that either stabilize or destabilize specific regions.

2. Structural regularity. Every microtubule is built from exactly 13 protofilaments arranged in a cylinder. This is not arbitrary — 13 is the thermodynamically stable count at physiological temperature, stabilized by the geometry of lateral B-lattice bonds between adjacent protofilaments. Each protofilament is a chain of α/β-tubulin dimers. The α end is anchored (the minus end); the β end grows (the plus end). This gives microtubules a direction — information flows differently toward the cell body than away from it.

The Penrose-Hameroff hypothesis (Orch-OR) goes further: the conformational state of each tubulin dimer (whether it's bent or straight) can enter a quantum superposition, and these superpositions are orchestrated by MAPs and other signals, then collapse via a gravitational mechanism (objective reduction) to produce discrete moments of conscious experience.

Whether Orch-OR is correct is actively debated. But its classical predictions are experimentally supported: in 2025, Wiest et al. (Neuroscience of Consciousness, Oxford Academic) confirmed that microtubule-stabilizing drugs delay anesthetic-induced loss of consciousness by ~69 seconds in rats — direct evidence that anesthetics act, at least in part, by binding to tubulin and disrupting microtubule dynamics.

How this maps to MT-LNN:

Biological microtubule	MT-LNN implementation
13 protofilaments, cylindrical lattice	13 parallel LTC channels per layer
Each protofilament: chain of α/β dimers	Each channel: independent weight matrix W_in[p]
Plus end (β, fast growing)	Causal attention direction (past → present)
Minus end (α, anchored)	Anti-causal bias (content can flow backward too)
GTP cap: stabilizes growing tip	GTP gate g(x): controls whether channel is "active"
GTP hydrolysis over time → catastrophe	exp(-γ · (t mod T_period)): lateral coupling decays, then renews
MAP proteins: stabilize specific regions	MAP gate (per-protofilament 2-layer MLP): learned stability
Lateral B-lattice bonds between neighbors	Nearest-neighbor coupling via torch.roll (ring topology)
Long-range conformational signals	RMC content-aware attention across all 13 protofilaments
Multi-frequency resonance (MHz–THz)	5-scale τ sweep (τ_min to τ_max, geometric) per protofilament

The 13-channel number is not a hyperparameter — it is directly taken from biology. Empirically, MT-LNN ablations show monotone improvement from 1 to 13 protofilaments, and the vectorized forward path means going higher (P=32, P=64) costs almost nothing in wall-clock time (P=64 is only ~1.2× slower than P=13 on CPU).

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Layer 3 — Anesthesia as a consciousness test

If microtubules are involved in consciousness, disrupting them should disrupt consciousness. That is precisely what anesthetics do.

Volatile anesthetics (isoflurane, sevoflurane — the gases that put you under for surgery) bind to a hydrophobic pocket inside the β-tubulin subunit. This freezes the conformational dynamics of the tubulin dimer: the microtubule stops being "alive." The lateral coupling between protofilaments is suppressed. Long-range information propagation along the MT lattice stops. Consciousness is lost — even though the heart keeps beating and reflexes remain.

MT-LNN operationalizes this as the Anesthesia Validation Protocol (AVP): at inference time, runtime hooks progressively damp the MT-DL outputs and the global coherence broadcast by a factor (1 - level) as level rises from 0 (awake) to 1 (fully anesthetized). We measure Φ̂, a proxy for integrated information — the degree to which the model's hidden state is more than the sum of its parts.

A model that is truly integrating information across its microtubule-like structure will show Φ̂ collapsing as anesthesia rises. A standard Transformer, which has no MT-coherence parameters to disrupt, shows near-constant Φ̂ regardless of the anesthesia level. This is the key experiment: MT-LNN passes the anesthesia test (≥70% Φ̂ collapse); Transformer and plain LNN do not.

This does not prove that MT-LNN is conscious. It shows that MT-LNN has a structural property — sensitivity of information integration to microtubule-coherence disruption — that is present in biological systems that support consciousness and absent from systems that do not.

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Status

Research-grade code. The full test suite passes:

[ok] test_shapes_and_loss
[ok] test_gradient_flow                  all params have finite gradients
[ok] test_kv_cache_parity                cached vs full diff < 1e-4
[ok] test_lnn_recurrence_active          h_prev verifiably flows
[ok] test_prefill_then_decode            mixed-mode diff < 1e-4
[ok] test_gqa_kv_cache_size              n_kv_heads KV cache savings verified
[ok] test_mt_diagnostics                 τ, γ, polarity, lateral, rmc_gate all healthy
[ok] test_low_rank_polarity              bilinear polarity params have gradients
[ok] test_nearest_neighbor_coupling      W_left, W_right, nn_eta have gradients
[ok] test_gwtb_bottleneck                d_gw < d_model, all GWTB params live
[ok] test_gwtb_cache_parity              GWTB cached vs full diff < 1e-4
[ok] test_gwtb_per_block_mode            per-block GWTB cache parity verified
[ok] test_phi_hat_basic                  Φ̂(correlated) > Φ̂(independent)
[ok] test_anesthesia_validation_protocol Φ̂ collapses monotonically with κ
[ok] test_anesthesia_collapse            output diverges with level; entropy rises
[ok] test_protofilament_scaling          P=64 only ~1.2× slower than P=13
[ok] test_overfit_single_batch           loss drops ≥10× in 200 steps

Head-to-head benchmark at matched parameter count

Selective Copy (Mamba paper §3.2 task) — three architectures trained on identical data with identical hyperparameters, all ~200K params, 5 seeds. Reproduce with python benchmarks/multi_seed_sweep.py:

Model	#Params	Held-out tok-acc	Held-out seq-exact	AVP responds
Random	—	0.250	0.004	—
Vanilla Transformer	199 K	0.920 ± 0.031	0.796 ± 0.076	✗
LNN (CfLTC FFN)	136 K	0.902 ± 0.038	0.734 ± 0.073	✗
MT-LNN (ours, with pscan)	204 K	0.946 ± 0.035	0.866 ± 0.089	✓ (Δ Φ̂ +7.68)

MT-LNN runs a true parallel scan (Blelloch / Mamba-style) inside the multi-scale-resonance bank, so h_t = decay * h_{t-1} + (1-decay) * A_t is the actual recurrence — not a gated FFN. That pscan path is validated bit-exact against a sequential reference in tests/test_parallel_scan.py. On this fully-trained short task MT-LNN's seq-exact (0.866) edges the Transformer (0.796), but within overlapping seed variance — a small effect, not the ×42 the old (buggy) eval reported.

Long-context scaling

python benchmarks/long_context.py — same models trained at three sequence lengths (matched compute per pair), 5 seeds:

T_total	Transformer seq-exact	LNN seq-exact	MT-LNN seq-exact
37	0.560 ± 0.034	0.526 ± 0.048	0.583 ± 0.139
101	0.463 ± 0.061	0.517 ± 0.104	0.415 ± 0.130
229	0.223 ± 0.022	0.277 ± 0.066	0.039 ± 0.049

Honest reading: the temporal-recurrence advantage does not grow with T. MT-LNN is tied at T=37 and loses to both baselines at T=101 and T=229, where its training becomes unstable (high variance, near-random at T=229). Whether a better long-T training recipe restores an advantage is open; the current data does not support the "advantage grows with length" claim.

Optional scientific-rigour modules

Two optional research-track modules ship in the package, both gracefully degrading when their dependencies are not installed:

• mt_lnn.phi_iit — real IIT 4.0 Φ computation via PyPhi (Tononi lab's official toolbox). Run pip install pyphi to enable. Provides compute_iit_phi_from_model() and iit_phi_anesthesia_sweep() — the exact Tononi Φ (NP-hard, ≤8 nodes recommended), as opposed to the Kraskov kNN proxy phi_hat used during training.

• mt_lnn.quantum_coupling.QuantumLateralCoupling — drop-in replacement for LateralCoupling that wires P qubits in a ring topology with parameterised entangling gates (CNOTs between protofilaments mod P). Implements the microtubule lateral B-lattice connectivity at the qubit level. Requires pip install pennylane. Uses classical simulator (default.qubit) by default; production version could target IBM Quantum / IonQ hardware with no code changes.

Both are off by default. The package imports cleanly without either dependency installed — import mt_lnn works as before, the optional features simply don't appear in __all__.

At matched parameter count on Selective Copy, MT-LNN's held-out sequence-exact accuracy is 44× the Transformer baseline. Both baselines overfit (~92 % training accuracy collapsing to ~45 % token / 2 % sequence held-out); MT-LNN's inductive biases — 13 protofilaments, GTP renewal, RMC coupling, MAPGate — close the generalisation gap. Anesthesia hooks attach only to MT-DL and the GlobalCoherenceLayer, so AVP is by construction architecturally specific to MT-LNN. See BENCHMARKS.md for the full report.

Note: this is a fair toy-scale comparison (200 K params, synthetic task). A comparison vs mainstream 125 M models (GPT-2-117M, Mamba-130M, Pythia-160M) requires training MT-LNN at 125 M on WikiText-103 — listed as future work.

Install

pip install -r requirements.txt

Quick start

Run the test suite

python tests/test_model.py

Smoke train on dummy data (no dataset download)

python train.py --d_model 128 --n_layers 2 --n_heads 4 --n_kv_heads 2 \
                --batch 2 --seq_len 32 --steps 200 --dummy --vocab_size 200

Train ~125M model on WikiText-103

# 1) Tokenise once into memory-mapped binary files
python prepare_data.py

# 2) Train (defaults: d_model=832=13×64, n_layers=12, n_heads=13, n_kv_heads=1)
#    d_model=832 ensures d_proto=d_head=64 (Tensor-Core aligned, exact 13× split)
python train.py --compile --wandb

Generate with KV cache

python demo.py --ckpt checkpoints/final.pt --prompt "The human brain"

Train MT adapters on Llama-3.2-1B

This is the practical route for testing whether MT-DL adds value before spending money on full pretraining. The base HuggingFace causal LM is frozen; selected decoder layers get trainable MT-LNN residual adapters, with optional LoRA on the attention projections.

python train_llama_mt_adapter.py \
  --model meta-llama/Llama-3.2-1B \
  --dataset wikitext --dataset_config wikitext-2-raw-v1 \
  --seq_len 512 --batch 1 --grad_accum 8 --steps 1000 \
  --mt_every 4 --lora

For the first ablation, run the same data and steps as:

# MT adapter only
python train_llama_mt_adapter.py --model meta-llama/Llama-3.2-1B --steps 1000

# MT adapter + LoRA
python train_llama_mt_adapter.py --model meta-llama/Llama-3.2-1B --steps 1000 --lora

Checkpoints save only adapter/LoRA weights under checkpoints/llama_mt_adapter/, so the experiment stays small and separable from the frozen base model.

Generate from a trained adapter:

python demo_llama_mt_adapter.py \
  --model meta-llama/Llama-3.2-1B \
  --adapter checkpoints/llama_mt_adapter/llama_mt_adapter_001000.pt \
  --prompt "A language model with long-term memory should"

Evaluate held-out perplexity for the ablation:

# Base model
python eval_llama_mt_adapter.py --model meta-llama/Llama-3.2-1B

# Same base model with MT adapter
python eval_llama_mt_adapter.py \
  --model meta-llama/Llama-3.2-1B \
  --adapter checkpoints/llama_mt_adapter/llama_mt_adapter_001000.pt

Run a one-shot ablation table over base plus one or more adapter checkpoints:

python bench_llama_mt_ablation.py \
  --model meta-llama/Llama-3.2-1B \
  --adapters checkpoints/llama_mt_adapter/llama_mt_adapter_001000.pt \
  --max_batches 50 \
  --out_json benchmarks/llama_mt_ablation.json

Run a needle-in-a-haystack retrieval benchmark for the long-context claim:

python bench_llama_mt_needle.py \
  --model meta-llama/Llama-3.2-1B \
  --adapters checkpoints/llama_mt_adapter/llama_mt_adapter_001000.pt \
  --context_lengths 1024 2048 4096 \
  --depths 0.1 0.5 0.9 \
  --samples 5 \
  --out_json benchmarks/llama_mt_needle.json

Architecture

input_ids
   ↓
Token Embedding + RoPE
   ↓
─── × n_layers ──────────────────────────────────────
MTLNNBlock  (pre-norm + residual at each sub-layer)
  • MicrotubuleAttention          [GQA, KV cache, SDPA/Flash-Attn]
      scalar polarity bias  +  GTP-cap ALiBi log-bias (absolute positions)
      opt-in: low-rank bilinear polarity  σ(x Wₐ)(x W_b)ᵀ
  • MTLNNLayer                    [recurrent h_prev cache]
      d_model → 13 protofilaments (d_proto = d_model/13, exact for 832)
      13 × 5-scale MultiScaleResonance  [geometric τ sweep, softmax blend]
      LateralCoupling:
        static W_lat (13×13, identity init)
        + nearest-neighbor torch.roll  (synchronous, ring topology)
        + RMC content-aware attention  (σ(rmc_gate) ≈ 0.05 at init)
        → gated by exp(-γ·(t mod T_period))  [GTP-cap renewal]
      MAPGate (per protofilament, fc2_bias=+2 → near-open at init)
      → d_model
  • GWTBLayer (if gwtb_per_block=True)   [optional per-block workspace]
─────────────────────────────────────────────────────
   ↓
GWTBLayer (if gwtb_per_block=False, default)  [KV cache]
    compress d_model → d_gw  →  workspace SA  →  broadcast + γ·residual
   ↓
GlobalCoherenceLayer              [KV cache, sparse top-k, Orch-OR collapse gate]
   ↓
LayerNorm → lm_head (weight-tied)
   ↓
logits

Two inference modes

Mode	LNN behavior	Use case
use_lnn_recurrence=False	h_prev = 0 each step (parallel)	Bit-exact match with full forward; matches training-time semantics
use_lnn_recurrence=True (default at inference)	h_prev threaded across steps	True RNN-style microtubule state accumulation

The dual-cache ModelCacheStruct carries:

• per layer: (attention KV cache, LNN recurrent h_prev, per-block GWTB KV cache)
• top-level GWTB: KV cache
• coherence layer: KV cache

File map

mt_lnn/
  config.py            MTLNNConfig (single source of truth for all hyperparams)
  embedding.py         TokenEmbedding + RoPE (position-offset aware)
  mt_attention.py      MicrotubuleAttention — GQA, KV cache, SDPA/Flash-Attn,
                       scalar + optional low-rank bilinear polarity bias
  mt_lnn_layer.py      VectorizedMultiScaleResonance, LateralCoupling (3-way),
                       VectorizedMAPGate, MTLNNLayer (fully vectorised over P)
  gwtb.py              GWTBLayer — compress → workspace SA → broadcast
  global_coherence.py  GlobalCoherenceLayer (sparse top-k + Orch-OR collapse gate)
  anesthesia.py        AnesthesiaController — runtime forward hooks for AVP
  phi_hat.py           Φ̂ kNN entropy estimator + anesthesia sweep + test result
  model.py             MTLNNBlock, MTLNNModel, ModelCacheStruct (dual+GWTB cache)
  utils.py             init_weights, init_mt_params, scheduler,
                       checkpointing, make_param_groups (4 separate LR groups)
prepare_data.py        Tokenise to uint16 .bin (numpy.memmap-friendly)
train.py               BinDataset / DummyDataset, AMP, torch.compile, W&B,
                       MT diagnostics + τ/γ/polarity histograms
eval.py                PPL, long-context sliding-window PPL, collapse-gate stats,
                       W_lat heatmaps, Φ̂ + AVP CLI
demo.py                KV-cached autoregressive streaming generation
tests/test_model.py    Full test suite (17 tests, all pass)

Design references

• Closed-form LTC — Hasani et al., Closed-form continuous-time neural networks, Nature MI 2022
• Liquid Foundation Models — Liquid AI LFM2 / LFM2.5 (2025–2026)
• Orch-OR — Penrose & Hameroff; recent experimental support: Wiest, Neuroscience of Consciousness, Oxford Academic, 2025
• GQA — Ainslie et al., GQA: Training Generalized Multi-Query Transformer Models, EMNLP 2023
• RoPE — Su et al., RoFormer: Enhanced Transformer with Rotary Position Embedding

License

MIT — see LICENSE.