Update README.md

README.md

@@ -9,4 +9,309 @@ license: cc-by-nc-nd-4.0
 short_description: Upcoming Flagship LLM series
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# Matrix Lattice — Full Architecture Specification
+**Agentic + Multimodal Frontier MoE Family | Matrix.Corp**
+
+---
+
+## Overview
+
+Matrix Lattice is Matrix.Corp's flagship frontier model family. Designed from the ground up for inference provider deployment (Novita, Hyperbolic, Together, Fireworks, etc.) and accessed via OpenAI-compatible API. Agentic-first, natively multimodal, 1M+ context, MoE architecture keeping active params far below total.
+
+| Model | Total Params | Active Params | Experts | Context | Target Hardware |
+|---|---|---|---|---|---|
+| Lattice-120B | 120B | ~22B active | 64 experts, top-4 | 1M tokens | 4× H100 / 8× p300a |
+| Lattice-430B | 430B | ~38B active | 128 experts, top-4 | 1M tokens | 16× H100 / 28× p300a |
+| Lattice-671B | 671B | ~47B active | 256 experts, top-4 | 1M tokens | 32× H100 / 48× p300a |
+
+---
+
+## Base Lineage
+
+Mixed distillation approach:
+- **DeepSeek-V3 / R1** — MLA attention, MoE routing strategy, math/reasoning capability
+- **Llama 4 Scout/Maverick** — multimodal vision encoder architecture, instruction following, long-context iRoPE scaling
+- **Custom Matrix.Corp additions** — 17 novel modules, lattice routing, agentic infrastructure
+
+---
+
+## Core Public Architectures Used
+
+### 1. Multi-Head Latent Attention (MLA) — DeepSeek-V3
+Compresses KV cache via low-rank projection. At 1M context, standard KV cache is impossible — MLA makes it viable. KV cache reduced by ~90% vs standard MHA.
+
+### 2. Mixture of Experts (MoE) — DeepSeek-V3 Style
+- Shared experts (always active) + routed experts (top-k per token)
+- Fine-grained expert segmentation — more smaller experts vs fewer large ones
+- Load balancing via auxiliary-free strategy (sequence-level bias, no loss penalty)
+- Expert capacity: no token dropping, dynamic overflow routing
+
+### 3. Mixture of Depths (MoD) — Google Research
+Tokens dynamically skip transformer layers based on a learned routing decision. Easy tokens skip up to 50% of layers. Hard tokens (reasoning, code, structured output) use all layers. Net result: ~30% compute reduction at same quality.
+
+### 4. iRoPE / YaRN Scaling — Llama 4 / YaRN paper
+Interleaved NTK-aware RoPE scaling for 1M+ context without positional degradation. Alternating full-attention and sliding window layers. Full attention every 4th layer; sliding window (8K) on intermediate layers.
+
+### 5. Sliding Window Attention — Mistral
+8K sliding window on non-full-attention layers. O(n) memory for most layers, O(n²) only on full-attention layers.
+
+### 6. Speculative Decoding — Google DeepMind
+Each Lattice model ships with a paired draft model (Lattice-120B-Draft at ~4B params). 3–5× inference speedup on provider hardware. Draft model shares embedding weights with main model.
+
+### 7. Multimodal Vision Encoder — Llama 4 / InternVL lineage
+- ViT-based image encoder (6B params, separate from LM)
+- Cross-attention visual tokens injected at every 4th layer
+- Supports: images, video frames, documents, charts, screenshots
+- Patch resolution: 448×448 base, up to 4K via dynamic tiling
+- Audio: separate audio encoder (Whisper-large-v3 lineage) for speech/sound understanding
+
+---
+
+## 17 Custom Modules
+
+### Module 1 — EQ Engine V2
+Upgraded from Zenith's V1. Now tracks emotional arc across the **entire conversation**, not just per-layer.
+- Persistent emotional state vector across turns (GRU with conversation-length memory)
+- 12-emotion classification (expanded from 8)
+- Frustration trajectory prediction — detects escalation before it peaks
+- Per-user emotional baseline calibration (inferred from first 3 turns)
+- Feeds into Persona Stability Enforcer (Module 14)
+- Always FP16, never quantized
+
+### Module 2 — Lattice Router
+Custom MoE routing built specifically for this architecture. Not standard top-k.
+- Hierarchical routing: token → domain cluster → expert group → individual expert
+- Domain clusters: Reasoning, Code, Vision, Language, Agentic, Science, Creative, Safety
+- Experts self-label during training via contrastive specialization loss
+- Router is inspectable at inference — API exposes which expert cluster handled each segment
+- Load-aware routing: aware of current server load, can shift to less-used experts
+
+### Module 3 — Confidence Calibration Head
+Runs in parallel with LM head on every token.
+- Outputs epistemic uncertainty [0–1] per token
+- Aggregated to sentence/paragraph level for API response metadata
+- Trained on calibration data: model rewarded for accurate uncertainty, not just correct answers
+- Exposed via API as `X-Lattice-Confidence` header per response chunk
+- Feeds into Knowledge Boundary Detector (Module 17)
+
+### Module 4 — Native Tool Schema Reasoner
+Not prompt-based function calling. Dedicated architecture.
+- Separate attention heads trained exclusively on tool/API schemas
+- Supports: JSON Schema, OpenAPI 3.x, GraphQL, SQL DDL
+- Schema tokenized as structured graph, not flat text
+- Tool call planner: generates multi-step tool execution plans before first call
+- Parallel tool dispatch: can issue multiple tool calls simultaneously
+- Tool result integrator: dedicated cross-attention for injecting tool results
+
+### Module 5 — Multi-Agent Coordination Layer (MACL)
+Designed for multi-agent systems where multiple Lattice instances talk to each other.
+- Structured agent message format: role, task_id, confidence, partial_result, handoff_request
+- Agent role awareness: knows if it's orchestrator, subagent, critic, or executor
+- Shared scratchpad attention: multiple agents can attend to same working memory
+- Conflict resolution head: when two agents disagree, dedicated reasoning path
+- Exposed via API as `lattice-agent-protocol` extension
+
+### Module 6 — Hierarchical Context Compression Engine (HCCE)
+Makes 1M+ context actually usable, not just theoretically supported.
+- Every 32K tokens: compress to summary embedding + key-fact store
+- Every 128K tokens: meta-summary of summaries
+- Recent 32K: always full resolution
+- Older context: summary + retrievable detail on demand
+- Learned compression: trained to preserve causally important information
+- Compression ratio: ~20:1 on narrative text, ~5:1 on code/structured data
+
+### Module 7 — Structured Output Enforcer (SOE)
+Guaranteed valid structured outputs. Not retry-based.
+- Constrained decoding via token masking against target schema
+- Supports: JSON, YAML, XML, Markdown, CSV, Python, SQL, HTML
+- Zero-shot: give it a Pydantic model or JSON Schema, get guaranteed valid output
+- Partial streaming: streams valid partial JSON as tokens generate
+- Integrated with Tool Schema Reasoner (Module 4) for tool call outputs
+
+### Module 8 — Causal Reasoning Graph (CRG)
+Builds an explicit internal cause-effect graph during generation.
+- Each reasoning step adds nodes + edges to internal graph
+- Graph attention: later reasoning steps attend to causal graph, not just token sequence
+- Detects reasoning loops and contradiction chains
+- Exposed optionally via API as structured reasoning trace
+- Improves performance on multi-hop questions, legal reasoning, scientific causality
+
+### Module 9 — Temporal Awareness Module
+Time is a first-class concept.
+- Dedicated temporal embeddings: absolute dates, relative references ("last week"), durations
+- Timeline builder: constructs event timelines from unstructured text
+- Temporal consistency checker: flags contradictions in event ordering
+- Knowledge cutoff awareness: trained to know what it does and doesn't know about recency
+- Feeds into Knowledge Boundary Detector (Module 17)
+
+### Module 10 — Cross-Lingual Semantic Alignment Layer
+50+ language support with deep semantic alignment, not surface translation.
+- Language-agnostic semantic embedding space
+- Code-switching aware: handles mixed-language inputs naturally
+- Script normalization: handles CJK, Arabic RTL, Devanagari natively at tokenizer level
+- Dialect modeling: distinguishes Brazilian vs European Portuguese, Simplified vs Traditional Chinese
+- Translation quality head: can score its own translation outputs
+
+### Module 11 — Safety Reasoning Module (SRM)
+Auditable, explainable safety — key differentiator for inference providers.
+- Dedicated safety reasoning chain before generation (not post-hoc filtering)
+- Produces explicit safety trace: what risk was considered, what was ruled out, why
+- Granular harm taxonomy: 47 harm categories with confidence scores
+- Provider-configurable: API operators can tune safety thresholds per deployment
+- Audit log: safety decisions logged in structured format for compliance
+- Separate from EQ Engine — safety is logic-based, not emotion-based
+
+### Module 12 — Vision-Language Grounding Module
+Deep integration between visual and language understanding.
+- Object-level grounding: links text references to bounding box regions
+- Chart/diagram interpreter: specialized attention for data visualizations
+- Document layout understanding: OCR + structure (tables, headings, columns)
+- Screenshot-to-code: dedicated pathway for UI → code generation
+- Video temporal grounding: links text references to specific frames
+
+### Module 13 — Long-Horizon Task Planner
+Agentic planning as a first-class capability.
+- Task decomposition head: breaks goals into subtask DAGs
+- Dependency resolver: identifies which subtasks block others
+- Progress tracker: maintains task state across long conversations
+- Replanning trigger: detects when a plan needs revision based on new info
+- Integrates with MACL (Module 5) for distributing tasks across agents
+- Outputs structured task graphs via API
+
+### Module 14 — Persona Stability Enforcer (PSE)
+Maintains consistent identity, tone, and personality across million-token contexts.
+- Persona embedding: operator-defined persona injected as persistent memory
+- Style consistency loss during training: penalizes tone drift
+- Character consistency checker: ensures factual claims about self don't contradict
+- Feeds from EQ Engine V2: adjusts warmth/formality dynamically but within persona bounds
+- Critical for long-running API deployments and character-based applications
+
+### Module 15 — API Telemetry & Observability Hooks
+Built into the model, not bolted on by the provider.
+- Per-token latency profiling embedded in forward pass
+- Expert utilization stats per request
+- Context compression events flagged in stream
+- Confidence + uncertainty exposed per chunk
+- Module activation trace: which of the 17 modules fired for each request
+- All exposed as structured SSE metadata alongside token stream
+
+### Module 16 — Code Intelligence Engine (CIE)
+Goes beyond code completion — full software engineering understanding.
+- AST-aware attention: code parsed to AST, structural tokens injected
+- Multi-file context graph: understands cross-file dependencies
+- Runtime simulation head: predicts execution behavior without running code
+- Bug pattern library: trained on CVE database + common bug taxonomies
+- Test generation: given code, generates comprehensive test suite
+- Integrates with Tool Schema Reasoner for build/exec tool use
+
+### Module 17 — Knowledge Boundary Detector (KBD)
+Knows what it doesn't know.
+- Hallucination risk scorer per claim
+- Sources: Confidence Calibration Head + Temporal Module + retrieval signal
+- Claim classification: known / uncertain / likely-hallucination / outside-training
+- Citation need detector: flags claims that should be sourced
+- Self-consistency checker: runs 3 forward passes on uncertain claims, checks agreement
+- Exposed via API: `X-Lattice-Hallucination-Risk` per response
+
+---
+
+## Hardware & Inference Specs
+
+### Lattice-120B
+| Config | Active Params | VRAM | TPS (est.) |
+|---|---|---|---|
+| BF16 | ~22B | ~240GB | ~35 TPS |
+| INT8 | ~22B | ~120GB | ~70 TPS |
+| INT4 | ~22B | ~60GB | ~130 TPS |
+Target: 4× H100 80GB (INT8) or 8× p300a (INT4)
+
+### Lattice-430B
+| Config | Active Params | VRAM | TPS (est.) |
+|---|---|---|---|
+| BF16 | ~38B | ~860GB | ~18 TPS |
+| INT8 | ~38B | ~430GB | ~38 TPS |
+| INT4 | ~38B | ~215GB | ~72 TPS |
+Target: 8× H100 80GB (INT4) or 28× p300a (INT4)
+
+### Lattice-671B
+| Config | Active Params | VRAM | TPS (est.) |
+|---|---|---|---|
+| BF16 | ~47B | ~1.34TB | ~12 TPS |
+| INT8 | ~47B | ~671GB | ~26 TPS |
+| INT4 | ~47B | ~336GB | ~50 TPS |
+Target: 32× H100 80GB (INT4) or 48× p300a (INT4)
+
+---
+
+## Training Strategy
+
+### Phase 1 — Foundation (all sizes)
+- Mixed distillation from DeepSeek-V3, DeepSeek-R1, Llama 4 Scout/Maverick
+- Data: web text, code, scientific papers, books, multimodal datasets
+- Context: start at 8K, scale to 1M via curriculum
+- MoE load balancing stabilization
+
+### Phase 2 — Module Integration
+- Each of 17 modules trained with task-specific auxiliary losses
+- Module loss weights tuned per module (see training_config.py)
+- Modules frozen in turn as they converge
+
+### Phase 3 — Agentic Fine-tuning
+- Tool use, multi-agent coordination, long-horizon task completion
+- Synthetic agentic trajectories generated by Lattice-120B bootstrapping larger models
+- RLHF / GRPO on agentic task completion + safety
+
+### Phase 4 — Alignment & Safety
+- Safety Reasoning Module fine-tuning on harm taxonomy
+- Constitutional AI-style self-critique
+- Red-team adversarial fine-tuning
+
+---
+
+## API Design (Inference Provider Ready)
+
+OpenAI-compatible with Lattice extensions:
+
+```python
+from openai import OpenAI
+
+client = OpenAI(
+    base_url="https://api.provider.com/v1",
+    api_key="your-key"
+)
+
+response = client.chat.completions.create(
+    model="matrix-lattice-671b",
+    messages=[{"role": "user", "content": "Your prompt"}],
+    tools=[...],  # Native tool schemas
+    extra_body={
+        "lattice": {
+            "expose_confidence": True,
+            "expose_module_trace": False,
+            "expose_reasoning_graph": False,
+            "safety_tier": "standard",  # standard | strict | minimal
+            "persona": "helpful-assistant",
+            "agent_role": "orchestrator"  # orchestrator | subagent | critic
+        }
+    }
+)
+
+# Response includes standard OpenAI fields PLUS:
+# response.lattice.confidence_scores
+# response.lattice.active_modules
+# response.lattice.hallucination_risk
+# response.lattice.expert_clusters_used
+```
+
+---
+
+## Status
+- 🔴 Planned — Architecture specification complete
+- Training infrastructure: TBD
+- Timeline: TBD (depends on compute access at scale)
+
+## HuggingFace
+- `Matrix-Corp/Lattice-120B-V1` (planned)
+- `Matrix-Corp/Lattice-430B-V1` (planned)
+- `Matrix-Corp/Lattice-671B-V1` (planned)
+- Collection: `Matrix-Corp/lattice-v1` (planned)