PERSPECTIVE v2 (OMNI)

PERSPECTIVE v2 is a sparse Mixture-of-Experts language model architecture targeting consumer hardware constraints (4 GB VRAM + 32 GB RAM) through layer streaming, ternary execution, recurrent state, and geometry-based routing.

This repository is an implementation and validation workspace for the architecture described in docs/v2/.

1. What This Project Is Trying To Build

Objective

Build a production-capable LLM system that can:

• scale to 1.05T total parameters through sparse expert activation and streaming
• run inference on constrained hardware
• maintain calibrated output confidence
• support bounded online adaptation
• enforce hard geometric output safety constraints

Why This Exists

Standard dense transformer deployments fail this hardware envelope. This design treats parameter count as a delivery and scheduling problem rather than a full-residency VRAM problem.

2. Architecture Snapshot (Current Spec)

Item	Value
Total parameters	1.05T
Experts	128
Shared parameters	6.83B
Active parameters per token	14.95B (top-1 routing)
Layers	80 (60 PDR + 20 windowed GQA)
Hidden size (d_model)	4096
PDR rank (d_state)	256
Vocabulary	32768
Expert manifold	3D torus (T^3) on 8x4x4 lattice
Expert precision	Ternary ({-1,0,+1})

3. Core Components

Component	Purpose	Source Modules
Perspective Decay Recurrence (PDR)	O(1) recurrent sequence processing with perspective-conditioned decay	src/core/pdr.rs, src/core/pdr_state.rs
Manifold Routing + Delta Streaming	Top-1 expert routing over toroidal manifold with neighbor-aware transfer	src/routing/manifold.rs, src/routing/router.rs, src/routing/delta_stream.rs
Layer-Streamed Ternary Execution	Add/sub/skip kernels and double-buffered load/compute overlap	src/execution/, src/kernels/
Holographic Distributed Memory (HDM)	Binary hypervector associative memory	src/memory/
Multi-Perspective Decoding (MPD)	Agreement-based decoding and confidence signal	src/decoding/
Forward-Mode Evolutionary Adaptation (FMEA)	LoRA + JVP + NES adaptation without backprop graph storage	src/learning/
Safety Polytope Projection (SPP)	Hard output projection into safe convex region	src/safety/

4. Current Maturity (Repository Reality)

Area	Status	Notes
Architecture modules	Partial implementation	Most modules exist with tests; some runtime paths are placeholders/fail-fast
Inference runtime	Not production-ready	src/runtime/pipeline.rs still intentionally returns error in token processing path
GPU kernels	Not complete	CUDA/HIP/SYCL dispatch remains fallback-heavy/stubbed in places
Training system	Not implemented end-to-end	No full trainer/data/checkpoint/eval pipeline yet
Safety/decoding/learning components	Prototype level	Core logic present; full integration and scale validation pending
Docs v2 alignment	Improved	Significant consistency fixes applied; further ongoing verification expected

5. Roadmap Checklist (Tracking)

This section is the project execution checklist. Use it as the canonical done/pending tracker.

5.1 Completed Recently

• Migrate expert manifold from 2D to 3D torus (T^3)
• Implement fold-in-place manifold updates (new evidence folds into existing coordinates, no append semantics)
• Add optional native manifold acceleration path in C/C++ (native-manifold feature)
• Wire Rust FFI bridge for native manifold fold + nearest-expert operations
• Add ZeroClaw runtime adapter and CLI task hook (--swarm-task)
• Update affected docs/v2 manifold and adaptation references to 3D/fold language

5.2 Required For "True LLM" Readiness

• Implement full token processing path in src/runtime/pipeline.rs (replace fail-fast placeholder with real execution)
• Resolve full-suite memory crash and stabilize all tests under CI
• Build end-to-end training stack (data ingest, tokenizer pipeline, trainer loop, checkpoints, resume)
• Complete GPU backend kernels and remove fallback-only paths for core hot loops
• Add distributed training orchestration (multi-GPU / multi-node execution and recovery)
• Validate scaling strategy on smaller models before high-cost runs
• Execute staged training curriculum end-to-end (dense warmup -> sparse routing/manifold -> ternary/distillation)
• Define and enforce acceptance gates (perplexity, benchmark thresholds, calibration, safety, throughput)
• Productionize inference service (packaging, API surface, observability, rollback)
• Integrate ZeroClaw deeper into training/inference job orchestration (beyond single CLI task call)

5.3 Exit Criteria For First Trainable Release

• Reproducible training run with checkpoint resume
• Stable convergence on at least one representative benchmark suite
• End-to-end inference pipeline functional without placeholder failures
• Safety and calibration metrics validated against documented thresholds
• Throughput and memory targets met on declared hardware envelope

6. Build, Test, and Run

Build

cargo build
cargo build --release

Test

cargo test

Native manifold path (C/C++)

cargo check --features native-manifold
cargo test routing:: --features native-manifold

Basic CLI run

cargo run -- --model-dir ./model -n 128 "Your prompt here"

ZeroClaw task dispatch

cargo run -- --swarm-task "your agentic task" --zeroclaw-bin zeroclaw --swarm-workspace .

7. Repository Layout

src/
  core/        # PDR, GQA, model skeleton
  routing/     # 3D manifold router and delta streaming
  execution/   # streaming pipeline and weight packing
  kernels/     # CPU kernels, dispatch, optional native manifold C/C++
  memory/      # HDM memory subsystem
  decoding/    # MPD decoding and calibration
  learning/    # JVP, LoRA, NES, FMEA
  safety/      # polytope safety projection
  runtime/     # provider, pipeline orchestration, health, zeroclaw adapter
docs/v2/       # architecture specification and validation documents

8. Documentation Index (docs/v2)

File	Topic
docs/v2/00_V2_INDEX.md	v2 architecture index
docs/v2/01_model_topology.md	model topology
docs/v2/02_budgets_v2.md	hardware/storage budgets
docs/v2/03_perspective_decay.md	PDR equations and implementation
docs/v2/04_manifold_routing.md	3D manifold routing and delta strategy
docs/v2/05_ternary_execution.md	ternary execution details
docs/v2/06_holographic_memory.md	HDM memory model
docs/v2/07_multi_perspective.md	MPD decoding
docs/v2/08_forward_adaptation.md	FMEA adaptation
docs/v2/09_safety_polytope.md	SPP safety model
docs/v2/10_training_plan.md	staged training plan
docs/v2/11_inference_pipeline.md	inference pipeline and latency model
docs/v2/12_validation_v2.md	validation framework
docs/v2/13_issue_matrix.md	issue-to-component mapping

9. Contribution Standard

Changes are expected to be:

• mathematically consistent with docs/v2
• dimension-safe (no hidden magic constants)
• memory-budget aware
• benchmarked on hot paths when performance-sensitive
• accompanied by tests for behavioral changes

10. License

MIT