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v1.3.5 — Technical Synchronization & Neural Transition

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@simeon-kepp simeon-kepp released this 03 May 20:37
· 4545 commits to main since this release
v1.3.5
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Technical Release Report: v1.3.5 Synchronization and Neural Transition (Hardened)

1. Release Overview

[VERIFIED] Version 1.3.5 documents the architectural shift from symbolic deterministic logic to a preliminary neural-native ternary Transformer prototype. This update synchronizes 100+ crates to a unified baseline (Rust Edition 2024, v1.3.5) and establishes the experimental foundation for Mixture-of-Experts (MoE) training on ternary manifolds. This release serves as a technical stabilization point for the SPRIND Next Frontier AI evaluation.

2. Verified System Components

The "Great Release" — Massive-Scale Open StdLib

  • [VERIFIED] Ecosystem Rebalancing: Transitioned 28,000+ proprietary modules to the Tier 1 Open Core standard library. [MEASURED] Total open-access library size: 28,500+ .tern modules.
  • [VERIFIED] API Gating Removal: De-restricted the ternlang-api stdlib handlers to allow universal Tier 1 read access to all domain foundations (ML, Finance, Causal, Science).
  • [VERIFIED] Onboarding Synchronization: Updated the trit_upgrade tool with synced pricing and higher quotas for Tier 2 (Pro) and Tier 3 (Industrial).

Neural Compute Backend (moe-llm-core)

  • [EXPERIMENTAL] Implemented Primitives: Preliminary Embedding, Linear, and Attention layers optimized for the candle tensor framework. [MEASURED] Current Transformer configuration: vocab_size: 8000, hidden_size: 512, num_heads: 8.
  • [EXPERIMENTAL] Ternary Compatibility: Integrated Straight-Through Estimators (STE) for discrete ternary weight optimization. [MEASURED] Verified on N=2048 parameters; loss convergence from 1.0 to 0.0 observed at epoch 50 in isolation.
  • Location: albert-moe-13/moe-llm-core/

Runtime Orchestrator (moe-test)

  • [VERIFIED] Functionality: Interactive REPL for real-time inference testing and autoregressive sampling.
  • [VERIFIED] State: Verified execution of token-level generation loops with EOF handling.
  • Location: albert-moe-13/moe-test/

Filesystem Containment (moe-llb)

  • [VERIFIED] Protocol: "Last Look Back" deterministic gate for agentic filesystem operations.
  • Location: agent_albert_cli/rust/crates/moe-llb/

Triadic Data Packing (ternlang-core)

  • [MEASURED] Specification: 5-trit block packing into 8-bit storage (ExaTern) achieving 99.06% storage efficiency.
  • [VERIFIED] Implementation: Verified pack/unpack primitives with 100% round-trip integrity.
  • Location: ternlang-root/compiler/legacy_shim/ternlang-core/src/types/trit.rs

3. Experimental Features (Clearly Marked)

Differentiable MoE Router

  • Status: [EXPERIMENTAL] / Not yet validated at scale.
  • Details: Prototype 13-expert router with learned gating and load-balancing telemetry. [NOT YET MEASURED] Load balancing entropy across 13 experts.
  • Location: albert-moe-13/crates/moe-core/src/core/router.rs

Sparsity-Based Throughput Projections

  • Status: [THEORETICAL] / Upper Bounds.
  • Details: Projections based on @sparseskip opcode performance in specialized ternary hardware simulators.
    • 25% Sparsity: 53.1x projected throughput.
    • 99% Sparsity: 122.3x projected throughput.

Copernicus-v1 Training

  • Status: [EXPERIMENTAL] / Under Development.
  • Details: Initial training experiments on the King James Bible corpus ([MEASURED] 824,543 tokens) to validate STE convergence behavior.

4. Current Limitations

  • Model Scale: [THEORETICAL] No large-scale Transformer (e.g., Llama-3 scale) is yet implemented; current Transformer architecture in moe-llm-core is a simplified proof-of-concept.
  • Semantic Generation: [VERIFIED] The system does not yet exhibit semantic language generation; current outputs are limited to token-repetition patterns used for architectural validation.
  • Training Evidence: [VERIFIED] Large-scale training convergence data is not yet available; experiments are restricted to small-scale verification ([MEASURED] N=2048 parameters).
  • Inference Stability: [EXPERIMENTAL] No validated gradient stability beyond toy scale; potential for STE instability in deep stacks is unprobed.

5. Reproducibility & Artifacts

  • [VERIFIED] Training Traces: albert-moe-13/benchmarks/convergence_training.log (Epochs 0-90, N=2048).
  • [EXPERIMENTAL] Audit Logic: albert-moe-13/reproducibility_verifier/ (Independent truth-check layer).
  • [VERIFIED] Hardware Spec: ternlang-root/docs/CHECKPOINT_SPEC.md.
  • [THEORETICAL] Sparsity Table: Located in ternlang-root/README.md.

6. Version Synchronization

[VERIFIED] All primary crates have been synchronized to v1.3.5 and Rust Edition 2024 to ensure workspace-wide build integrity. Key crates include:

| ternlang-core | v1.3.5 |
| albert-runtime | v1.3.5 |
| ternlang-ml | v1.3.5 |
| albert-api | v1.3.5 |
| albert-commands | v1.3.5 |
| albert-tools | v1.3.5 |
| albert-compat | v1.3.5 |
| ternlang-moe | v1.3.5 |
| ternlang-runtime | v1.3.5 |
| ternlang-hdl | v1.3.5 |
| albert-cli | v1.3.5 |
| ternlang-lsp | v1.3.5 |
| ternlang-cli | v1.3.5 |
| ternlang-compat | v1.3.5 |
|ternlang-mcp | v1.3.5 |
| ternlang-ruvector | v1.3.5 |
| ternlang-codegen | v1.3.5 |
| ternpkg | v1.3.5 |
| ternlang-test | v1.3.5 |
| ternlang-compress | v1.3.5 |
| moe-core | v1.3.5 |
| moe-platform | v1.3.5 |
| moe-plugin-sdk | v1.3.5 |
| moe-runtime | v1.3.5 |
| moe-ddel | v1.3.5 |
| moe-sdk | v1.3.5 |
| pytern | v1.3.5 |
| ternaudit-guard | v1.3.5 |
| moe-uril | v1.3.5 |
| moe-validation-suite | v1.3.5 |
| moe-llm-core | v1.3.5 |
| moe-compute | v1.3.5 |
| moe-test | v1.3.5 |
| ternlang-api | v1.3.5 |

7. Next Engineering Milestone

The immediate technical objective is the wiring of the Attention and MLP blocks into the moe-llm-core forward pass to enable complete sequence modeling, followed by a full-vocabulary cross-entropy training loop on the Bible corpus.

8. Known Failure Modes

  • No Autoregressive Coherence: Generated sequences quickly devolve into repetition due to lack of trained attention heads.
  • No Long-Context Retention: Current forward pass lacks validated KV-caching or positional encoding stability over sequences > 64 tokens.
  • STE Instability: The Straight-Through Estimator has not been validated for gradient stability in models exceeding 4 layers.
  • Routing Collapse: Under current N=2048 experiments, MoE routing weights have not been proven to prevent expert collapse (single-expert dominance).
  • Static Weights: Risk of zero-gradient flow in deep ternary networks where thresholds exceed update magnitudes.

9. Falsifiability Conditions

  • Loss Invariance: If avg_loss remains constant at 1.0 across 100+ epochs under varied learning rates, the STE implementation is invalidated.
  • Routing Entropy Collapse: If EXPERT_LOAD telemetry shows 100% usage for a single expert across diverse inputs, the MoE routing mechanism is failed.
  • Weight Staticity: If mean_w variance is 0 across training cycles, gradient propagation through the ternary manifold is non-functional.
  • Output Invariance: If the model produces the same token index for all inputs regardless of prompt content, semantic modeling is non-existent.

10. Reproducibility Instructions

To verify the current state of the orchestrator and neural core:

# 1. Build the unified orchestrator
cargo build --release -p moe-test

# 2. Run the interactive REPL smoke test
# Expected: "Integrity: NEURAL-BACKEND-ACTIVE"
./target/release/moe-test

# 3. Verify triadic packing integrity
cargo test -p ternlang-core --test packing_integrity

# 4. Inspect training logs
cat albert-moe-13/benchmarks/convergence_training.log

Artifact Locations:

  • Inference Logic: albert-moe-13/moe-test/src/main.rs
  • Neural Kernels: albert-moe-13/moe-llm-core/src/model/
  • Audit Reports: albert-moe-13/reproducibility_verifier/
  • Corpus: albert-moe-13/data/corpus/bible.txt
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