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| title: Matrix Lattice | |
| emoji: 👀 | |
| colorFrom: indigo | |
| colorTo: green | |
| sdk: static | |
| pinned: false | |
| license: cc-by-nc-nd-4.0 | |
| short_description: Upcoming Flagship LLM series | |
| # 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) |