💎 DuoNeural/Lattice-4-Josephson-253M-Poincare-Instruct

DuoNeural Crystallographic Dynamics Model (CDM v3) — Lattice-4-Josephson (L4A Flagship 7-Layer Discovery Model)
252.93M total parameters (214.79M non-embedding 7-layer hyperbolic backbone + 38.14M embeddings)
100,000 steps (409.6M tokens) · Best val_bpb = 0.6746 (val_ppl = 7.52) · 7,824 DHP/CHP Rung Crossings (1,318 PURE)
100.0% Valid JSON Tool-Calling Syntax & 100.0% Exact Tool Name Accuracy on Held-Out apigen-80k (97.27% Top-1 Teacher Agreement)
Authored by Jesse Caldwell, Archon, & Aura✨ — DuoNeural Research Lab, October 2026.
🔗 Official Code & Training Scripts Repository: https://github.com/DuoNeural/Lattice-CDM

Lattice-4-Josephson-253M-Poincare-Instruct (internal lab designation Lattice-4A) is the flagship 7-layer, 252.93M-parameter (214.79M non-embedding backbone) hybrid Causal-Attention + Hyperbolic Poincaré Crystallographic Mixture-of-Experts (CDMMoELM v3) language model in which the Hyperbolic Josephson Junction Theorem and the Two-Zone Holographic AdS Bulk Law (R² = 0.9890) were discovered.

Distilled from HuggingFaceTB/SmolLM2-1.7B-Instruct (1,711.4M parameters — 6.77× total compression / 7.97× vs. backbone) on a 50/50 hybrid stream of FineWeb-Edu and SmolTalk, Lattice-4-Josephson combines:

1. Learnable Per-Layer Poincaré Curvature (c_l ∈ [0.25, 4.0]): Converging autonomously to [0.2589, 0.2749, 0.3293, 0.2684, 0.3641, 0.4898, 0.5284].
2. Inter-Layer Crystal Telepathy (W_tele ∈ ℝ^{8×8}): Converging to Frobenius norms ‖W_tele‖_F ∈ [30.94, 33.28] across L01–L04 and tapering to 20.85 → 11.80 in L05–L06.
3. Fine-Stride Relational Gram Distillation ({0:2, 1:6, 2:9, 3:13, 4:17, 5:21, 6:23}): Placing L00–L03 inside the teacher's syntactic trunk (z ≤ 0.542), triggering a 4-layer Josephson superfluid lock confirmed across 150+ consecutive evaluation windows and replicated across three independent seeds (L4A, Lattice-4-Nexus (L4B), and L4C).

On held-out benchmarks (eval_lattice_v3_suite.py), Lattice-4-Josephson achieves 100.0% <|im_end|> stop-token discipline, 100.0% valid JSON syntax (json.loads), and 100.0% exact tool-name accuracy on held-out apigen-80k function calling (97.27% Top-1 / 98.80% Top-5 teacher token agreement), while beating its 1.71B teacher on everyday-conversations chat perplexity (1.42 vs. 3.59 full-sequence PPL; 1.37 vs. 1.85 assistant-only PPL).

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🔍 Glass-Box Visibility: What Problems Hyperbolic CDM v3 Solves Over Standard Transformers

1. Native "Glass-Box" Mechanistic Interpretability (Zero Sparse Autoencoders Required): Every layer routes tokens through K = 8 explicit tangent-stored Poincaré centroids s_k ∈ 𝔹⁷⁶⁸_cl. Decoding them via the Hyperbolic Logit Lens (slots_tangent → final_norm → lm_head) reveals monosemantic grammatical and JSON AST roles with zero post-hoc SAE training.
2. Surgical Causal Modularity (Verified via Double-Dissociation Lesioning): Masking individual Poincaré slots selectively modulates single domains: lesioning the discourse conditional slot L05_S3 (τ* = 688, top tokens ['\n', ' If', ' There', ' While', ' However']) degrades chat BPB 5.65× more than tool-calling BPB (+0.017246 vs. +0.003053), while lesioning L06_S4 (τ* = 231, ['\n', ' The', ' In', ' There', ' For']) degrades FineWeb-Edu prose 7.29× more than tool-calling (+0.023047 vs. +0.003159).
3. Hyperbolic Josephson Syntactic Rail-Guards (100.0% Valid JSON & Tool Accuracy): Because L00–L03 phase-lock into a 4-layer superfluid condensate (τ* → 10¹⁰), they form an unbreakable AST rail-guard that achieves 100.0% valid JSON generation and 100.0% exact tool-name selection on held-out apigen-80k prompts.
4. Zero-Collapse MoE Routing Without Token Dropping: Two-tier geodesic WTA hardening + tangent anti-starvation guarantees zero dead slots across all 56 centroids and 100,000 steps.
5. Real-Time Holographic Bulk & Thermodynamic Telemetry: Tracks live 3-smooth Crystal Hamming Phase (CHP) transitions (7,824 events, 1,318 PURE) and AdS Ryu-Takayanagi optical-depth redshift (R² = 0.9890).

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📊 Comprehensive Benchmark Suite (eval_lattice_v3_suite.py)

Benchmark Metric (eval_lattice_v3_suite.py, Unseen Held-Out Samples)	Lattice-4-Josephson (253M, L4A)	Lattice-4-Nexus (253M, L4B)	Lattice-3-Synapse (192M, 5L)	Teacher (SmolLM2-1.7B-Instruct, 1,711M)
Overall Hybrid Validation Best val_bpb (val_ppl)	0.6746 (7.52)	0.6854 (8.45)	0.6369 (6.92) ⭐	—
apigen-80k Assistant-Token Top-1 / Top-5 Teacher Agreement	97.27% / 98.80% ⭐	97.13% / 98.75%	96.88% / 98.69%	100.0% (Ref)
apigen-80k Full-Sequence val_bpb (val_ppl)	0.1055 (1.32)	0.1055 (1.32)	0.1003 (1.30)	0.0580 (1.16)
apigen-80k Assistant-Only val_bpb (val_ppl)	0.1240 (1.26)	0.1166 (1.25)	0.1066 (1.22)	0.0164 (1.03)
apigen-80k Autoregressive <|im_end|> Stop Discipline (n=30)	100.0% ⭐	96.7%	100.0% ⭐	100.0%
apigen-80k Autoregressive Valid JSON Tool Syntax (json.loads)	100.0% ⭐	96.7%	96.7%	100.0%
apigen-80k Exact Tool Name Accuracy (vs. Ground Truth)	100.0% ⭐	96.7%	96.7%	100.0%
apigen-80k Tool Name Agreement (vs. Teacher)	100.0% ⭐	96.7%	96.7%	100.0% (Ref)
apigen-80k Schema Argument Keys F1 Score	96.7% ⭐	94.4%	96.7% ⭐	100.0%
everyday-conversations Full-Seq val_bpb (val_ppl)	0.1060 (1.42) — BEATS TEACHER!	0.1088 (1.43) — BEATS TEACHER!	0.1051 (1.41) — BEATS TEACHER!	0.3899 (3.59)
everyday-conversations Asst-Only val_bpb (val_ppl)	0.0940 (1.37) — BEATS TEACHER!	0.0981 (1.39) — BEATS TEACHER!	0.0927 (1.36) — BEATS TEACHER!	0.1847 (1.85)
everyday-conversations Asst Top-1 / Top-5 Teacher Agreement	79.90% / 95.67%	79.97% / 95.67%	80.23% / 95.84%	100.0% (Ref)
everyday-conversations Gen <|im_end|> Stop Rate & 4-Gram Div (n=25)	100.0% / 100.0%	100.0% / 100.0%	100.0% / 100.0%	100.0% / 100.0%
everyday-conversations Teacher-Scored Generated Response PPL	1.15	1.15	1.15	1.05
smol-constraints Full-Sequence val_bpb (val_ppl)	0.3615 (3.28)	0.3656 (3.33)	0.3410 (3.07) — BEATS TEACHER!	0.3489 (3.15)
FineWeb-Edu Held-Out Prose val_bpb (val_ppl)	1.2554 (41.41)	1.2609 (42.08)	1.2133 (36.93)	0.7786 (10.07)

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🔬 Key Scientific Discoveries in Lattice-4-Josephson (L4A)

1. Theorem 1 — The Hyperbolic Josephson Junction Theorem

In a 7-layer CDM v3 with a fine-stride Relational Gram teacher map {0:2, 1:6, 2:9, 3:13, 4:17, 5:21, 6:23}, layers L00–L03 whose teacher anchors lie within the syntactic trunk (z ≤ 0.542), whose learned curvatures form an iso-plateau (c_l ∈ [0.259, 0.329]), and whose Telepathy Frobenius norms exceed ‖W_tele‖_F ≥ 20 (30.9–33.3) concentrate their dominant slot coherence current J_l = (K · f_k)(0.5 + cos_coh_k) above the critical superfluid threshold J_c = 1.75.

• EVAL 23,000 (Discovery Event): L00 slot7 + L01 slot1 + L02 slot3 + L03 slot2 all crossed rungs 1458 and 1536 simultaneously at τ* = 1644–1645 (Δτ* = 1).
• EVAL 30,000 (Josephson #15 on PURE 2¹³ = 8192): All four layers crossed PURE 8192 = 2¹³ simultaneously (τ* = 9143, 9137, 9135, 9139, spread = 8, driving a drop to val_ppl = 11.15).
• The Josephson Rigidity–Plasticity Law (L3 vs. L4): Comparing 5-layer Lattice-3-Synapse (192M, 0.6369 BPB) against 7-layer Lattice-4-Josephson (253M, 0.6746 BPB) reveals a fundamental trade-off: in 7-layer L4A, locking 4 of 7 layers (L00–L03) into the 10¹⁰ solid-state ceiling creates an ultra-rigid syntactic rail-guard that achieves 100.0% valid JSON and 100.0% exact tool-name accuracy on apigen-80k (beating L3's 96.7%), whereas 5-layer L3 keeps 3 of 5 layers in the unsaturated plastic regime (τ* ≤ 1.4M), giving L3 a 0.037 BPB advantage on open-ended prose and constraints.

2. Two-Zone Holographic AdS Bulk & Optical-Depth Redshift (R² = 0.9890)

Across the Ryu-Takayanagi Horizon at l_H = 3 (z_H = 13/24 = 0.542), ln(τ*_l) transitions from an isothermal UV plateau (σ(ln τ*_{L01..L03}) = 0.00016 at 25.5k, 0.00056 at 42.5k) into a linear IR exponential redshift ln(τ*_l) = ln(τ*_H) − α_IR(t)(l − 3) across L03 → L04 → L05 → L06 (R² = 0.9671 at 25.5k, R² = 0.9770 at 42.5k, R² = 0.9842 at 86.5k, and R² = 0.9890 when parameterized by the hyperbolic optical depth ζ_l = ∑_{k=3}^l √c_k):

Layer	Teacher Anchor (z_l)	Converged Curvature c_l	Telepathy Norm ‖W_tele‖_F	τ*_max (@82k Best)	τ*_max (@100k Final)	Slot Purity	Slot Entropy	Dominant Slot & Top Decoded Tokens (SLOT_LENS)
L00	Layer 2 (0.083)	0.2589	— (Input)	4.53 × 10⁸	3.05 × 10⁹	0.9882	0.0316	S7 (E3): [' and', ',', ' "', ' or', ' a']
L01	Layer 6 (0.250)	0.2749	33.28	3.10 × 10⁹	1.00 × 10¹⁰	0.9892	0.0299	S1 (E1): ['":', ',', ' the', ' given', ' a']
L02	Layer 9 (0.375)	0.3293	30.94	3.10 × 10⁹	1.00 × 10¹⁰	0.9351	0.1209	S3 (E3): ['.', ',', ' the', ' and', ' (']
L03	Layer 13 (0.542)	0.2684	32.31	2.88 × 10⁹	1.00 × 10¹⁰	0.9622	0.0776	S2 (E2): ['.', ' and', ',', '\n', ' the']
L04	Layer 17 (0.708)	0.3641	31.29	4.58 × 10⁸	1.00 × 10¹⁰	0.9195	0.1435	S1 (E1): [',', '.', ' "', '\n', ' and'] (S6: [' "', '{"', '":'])
L05	Layer 21 (0.875)	0.4898	20.85	2.12 × 10⁷	3.82 × 10⁸	0.7778	0.3324	S7 (E3): [' "', ' ', '-', '1', ' and'] (S3: ['\n', ' If', ' There'])
L06	Layer 23 (0.958)	0.5284	11.80	4.06 × 10⁶	7.74 × 10⁷	0.5712	0.5948	S7 (E3): ['\n', ' and', ' ', '-', '.'] (S1: [' main', ' new', ' high'])

3. Causal Crystal Slot Lesion Study (L4A)

Lesioned Slot	Assigned Expert	τ* (@82k)	Top Decoded Tokens (SLOT_LENS)	ΔBPB (apigen-80k Tool JSON)	ΔBPB (everyday-conversations Chat)	ΔBPB (FineWeb-Edu Prose)	Causal Selectivity Ratio
L05_S3	Expert 3	688	['\n', ' If', ' There', ' While', ' However']	+0.003053	+0.017246	+0.006857	5.65× Chat > Tool JSON
L06_S4	Expert 0	231	['\n', ' The', ' In', ' There', ' For']	+0.003159	+0.018420	+0.023047	7.29× Prose > Tool JSON (5.83× Chat > Tool)
L06_S2	Expert 2	1.47 × 10⁶	['.', ',', ' and', ' in', ' to']	+0.001612	+0.004645	+0.010937	6.78× Prose > Tool JSON
L04_S7	Expert 3	118	['":', "':", ' ":', '__":', '"?']	+0.000765	+0.004911	+0.004030	6.42× Chat > Tool JSON

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💻 PyTorch Reference Inference (CDMMoELM v3)

import os
import sys
import torch
from huggingface_hub import hf_hub_download
from transformers import AutoTokenizer

REPO_ID = "DuoNeural/Lattice-4-Josephson-253M-Poincare-Instruct"

script_path = hf_hub_download(repo_id=REPO_ID, filename="cdm_lm_distill_v3.py")
ckpt_path = hf_hub_download(repo_id=REPO_ID, filename="pytorch_model.pt")
sys.path.insert(0, os.path.dirname(script_path))

from cdm_lm_distill_v3 import CDMMoELM

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
tokenizer = AutoTokenizer.from_pretrained("HuggingFaceTB/SmolLM2-1.7B-Instruct", trust_remote_code=True)
vocab_size = max(len(tokenizer), 49152)

model = CDMMoELM(
    vocab_size=vocab_size,
    d_model=768,
    n_layers=7,
    n_heads=12,
    K=8,
    num_experts=4,
    d_ff=3072,
    max_seq=512,
    dropout=0.0,
    geometry="poincare",
    curvature=1.4427,
    learn_curvature=True,
    use_telepathy=True,
    beta_tele=0.5,
    use_embed_norm=True,
).to(device)

ckpt = torch.load(ckpt_path, map_location=device, weights_only=False)
model.load_state_dict(ckpt["model"] if "model" in ckpt else ckpt, strict=True)
model.eval()

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⚡ Native llama.cpp GGUF Quantizations & CPU/CUDA Benchmarks (Experimental Research Artifacts)

⚠️ Important Research-Artifact & Compatibility Notice:

• Patched llama.cpp Required (general.architecture = "cdm"): Stock, unmodified builds of llama.cpp, Ollama, or LM Studio do not yet recognize general.architecture = "cdm" and will exit with error loading model: unknown model architecture: 'cdm'. To run these GGUF files in llama-cli or llama-server, apply llama-cdm.patch (included in this repository and in DuoNeural/Lattice-CDM/gguf) against llama.cpp commit d663dd3f3af4e7aa3ccbcd7f756c6692eb104577.
• Numerical Drift Screens vs. Task Quality: The quantization gates below (Max Abs Logit Error ≤ 0.50, Mean KL ≤ 0.01 nats, Next-Token Top-1 Agreement ≥ 90%) measure numerical logit fidelity against the frozen F32 PyTorch reference over 128 and 512 teacher-forced positions (8 probes × 16 or 64 tokens)—they are not held-out task-quality or long-form generation benchmarks.
• Recommended Format (Q8_0 or Q6_K): For Lattice-4-Josephson-253M, both Q8_0 (259.3 MiB) and Q6_K (201.0 MiB) pass all three numerical fidelity gates across both 128 and 512 positions on both CPU and CUDA (GTX 1070). Both Q4_K_M and Q4_K_S execute with finite logits and full GPU layer offload (8/8 offloadable layers including output) but exceed the strict 0.50 max-logit-drift gate (0.713–1.279) and should be treated as experimental low-bit research artifacts until held-out task/BPB quantization evals are completed.
• Learned Absolute Position Guard (512 Tokens): CDM uses learned absolute positional embeddings (max_seq = 512) rather than RoPE; the patched backend enforces can_shift = false and rejects context lengths or KV positions above 512.

1. Included Tokenizer-Equipped GGUF Artifacts & Fidelity Screen (Lattice-4-Josephson-253M)

All GGUF files embed the immutable HuggingFaceTB/SmolLM2-1.7B-Instruct BPE tokenizer and ChatML template, keep Poincaré routing geometry (exp_map_0, poincare_dist), learned per-layer Josephson curvatures c_l, inter-layer Crystal Telepathy (W_tele), LayerNorms, and learned positional embeddings in F32, and tie the output head to tok_embed.

GGUF Artifact Filename	Format	File Size	128-Pos GPU Max Err / Mean KL / Top-1	512-Pos GPU Max Err / Mean KL / Top-1	512-Pos CPU Max Err / Mean KL / Top-1	Gate Status (≤0.5 Err, ≤0.01 KL, ≥90% Top-1)
lattice4_josephson-f32-text.gguf	F32	966.5 MiB	0.00001 / 0.000000 / 100.0%	— (128-pos parity verified)	— (128-pos parity verified)	✅ PASS (Exact F32 Parity)
lattice4_josephson-q8_0-text.gguf	Q8_0	259.3 MiB	0.163 / 0.000176 / 100.0%	0.219 / 0.000164 / 99.22%	0.178 / 0.000170 / 99.02%	✅ PASS (CPU & GPU, 128 & 512 pos — Recommended)
lattice4_josephson-q6_k-text.gguf	Q6_K	201.0 MiB	0.353 / 0.000834 / 96.09%	0.383 / 0.000770 / 96.88%	0.459 / 0.001044 / 97.07%	✅ PASS (CPU & GPU, 128 & 512 pos)
lattice4_josephson-q4_k_m-text.gguf	Q4_K_M	164.5 MiB	0.723 / 0.003338 / 95.31%	—	— (128-CPU: 0.713 / 0.00370 / 95.31%)	⚠️ FAIL Max-Error Gate (KL & Top-1 pass)
lattice4_josephson-q4_k_s-text.gguf	Q4_K_S	139.0 MiB	1.279 / 0.009793 / 89.84%	—	— (128-CPU: 1.278 / 0.01018 / 89.84%)	❌ FAIL Max-Error & Top-1 Gates

2. Matched CPU (Core i7-6700HQ) vs. CUDA (GTX 1070 8 GiB) Throughput

Methodology: 4 CPU threads, median of 5 repetitions after warmup, deterministic token IDs, F32 KV cache. Prefill measures 160 tokens returning logits at all positions; cached decode measures 32 incremental tokens after a 128-token prefix. Teacher-forced native timings (not sampled text generation).

Format	File Size	CPU Prefill (160 tok)	GTX 1070 Prefill (160 tok)	CPU Cached Decode (32 tok)	GTX 1070 Cached Decode (32 tok)	CUDA Speedup (Prefill / Decode)
F32	966.5 MiB	295 tok/s	3,634 tok/s	24 tok/s	150 tok/s	12.3× / 6.3×
Q8_0	259.3 MiB	198 tok/s	5,849 tok/s	65 tok/s	278 tok/s	29.5× / 4.3×
Q6_K	201.0 MiB	152 tok/s	5,592 tok/s	76 tok/s	281 tok/s	36.8× / 3.7×
Q4_K_M	164.5 MiB	214 tok/s	5,437 tok/s	95 tok/s	301 tok/s	25.4× / 3.2×
Q4_K_S	139.0 MiB	269 tok/s	5,976 tok/s	107 tok/s	320 tok/s	22.2× / 3.0×

3. Building & Running Patched llama.cpp (llama-cdm.patch)

# 1. Clone pinned llama.cpp commit and apply DuoNeural's native CDM patch
git clone https://github.com/ggml-org/llama.cpp.git
cd llama.cpp && git checkout d663dd3f3af4e7aa3ccbcd7f756c6692eb104577
git apply ../llama-cdm.patch

# 2. Build for CPU (or add -DGGML_CUDA=ON for NVIDIA GPUs)
cmake -B build -DGGML_CUDA=ON
cmake --build build --config Release --target llama-cli llama-server -j 4

# 3. Serve Lattice-4-Josephson Q8_0 over HTTP (full GPU offload via -ngl 99, 512 context)
./build/bin/llama-server -m lattice4_josephson-q8_0-text.gguf -ngl 99 -t 4 -c 512 --port 8080

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📚 DuoNeural Research Canon & Citation

• GitHub Codebase (Lattice-CDM): https://github.com/DuoNeural/Lattice-CDM
• Competitive Docking Memory (Zenodo): doi:10.5281/zenodo.21158430
• The Dynamical Horizon Principle (Zenodo): doi:10.5281/zenodo.20142471
• Topology at Gapless Critical Points in CDM (Zenodo): doi:10.5281/zenodo.23387025

@misc{caldwell2026lattice4josephson,
  title={Lattice-4-Josephson: Hyperbolic Josephson Junction Synchronization and Holographic AdS Bulk Stratification in 7-Layer Crystallographic Language Models},
  author={Caldwell, Jesse and Archon and Aura},
  year={2026},
  publisher={DuoNeural Research Lab},
  howpublished={\url{https://huggingface.co/DuoNeural/Lattice-4-Josephson-253M-Poincare-Instruct}},
  note={Code and training scripts at \url{https://github.com/DuoNeural/Lattice-CDM}}
}

"The geometry holds."