--- title: Matrix Lattice emoji: šŸ‘€ colorFrom: indigo colorTo: green sdk: static pinned: false license: cc-by-nc-nd-4.0 short_description: Upcoming Flagship LLM series --- # Matrix Lattice ā€” 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 | 4Ɨ H100 / 8Ɨ p300a | | Lattice-430B | 430B | ~38B active | 128 experts, top-4 | 1M tokens | 16Ɨ H100 / 28Ɨ p300a | | Lattice-671B | 671B | ~47B active | 256 experts, top-4 | 1M tokens | 32Ɨ H100 / 48Ɨ p300a | --- ## Base Lineage Mixed distillation approach: - **DeepSeek-V3 / R1** ā€” MLA attention, MoE routing strategy, math/reasoning capability - **Llama 4 Scout/Maverick** ā€” multimodal vision encoder architecture, instruction following, long-context iRoPE scaling - **Custom Matrix.Corp additions** ā€” 17 novel modules, lattice routing, agentic infrastructure --- ## Core Public Architectures Used ### 1. Multi-Head Latent Attention (MLA) ā€” DeepSeek-V3 Compresses KV cache via low-rank projection. At 1M context, standard KV cache is impossible ā€” MLA makes it viable. KV cache reduced by ~90% vs standard MHA. ### 2. Mixture of Experts (MoE) ā€” DeepSeek-V3 Style - Shared experts (always active) + routed experts (top-k per token) - Fine-grained expert segmentation ā€” 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) ā€” 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 ā€” 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 ā€” Mistral 8K sliding window on non-full-attention layers. O(n) memory for most layers, O(nĀ²) only on full-attention layers. ### 6. Speculative Decoding ā€” Google DeepMind Each Lattice model ships with a paired draft model (Lattice-120B-Draft at ~4B params). 3ā€“5Ɨ inference speedup on provider hardware. Draft model shares embedding weights with main model. ### 7. Multimodal Vision Encoder ā€” 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: 448Ɨ448 base, up to 4K via dynamic tiling - Audio: separate audio encoder (Whisper-large-v3 lineage) for speech/sound understanding --- ## 17 Custom Modules ### Module 1 ā€” 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 ā€” 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 ā€” Lattice Router Custom MoE routing built specifically for this architecture. Not standard top-k. - Hierarchical routing: token ā†’ domain cluster ā†’ expert group ā†’ 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 ā€” API exposes which expert cluster handled each segment - Load-aware routing: aware of current server load, can shift to less-used experts ### Module 3 ā€” Confidence Calibration Head Runs in parallel with LM head on every token. - Outputs epistemic uncertainty [0ā€“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 ā€” 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 ā€” 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 ā€” 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 ā€” 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 ā€” 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 ā€” 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 ā€” 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 ā€” Safety Reasoning Module (SRM) Auditable, explainable safety ā€” 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 ā€” safety is logic-based, not emotion-based ### Module 12 ā€” 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 ā†’ code generation - Video temporal grounding: links text references to specific frames ### Module 13 ā€” 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 ā€” 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 ā€” 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 ā€” Code Intelligence Engine (CIE) Goes beyond code completion ā€” 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 ā€” 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: 4Ɨ H100 80GB (INT8) or 8Ɨ 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: 8Ɨ H100 80GB (INT4) or 28Ɨ 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: 32Ɨ H100 80GB (INT4) or 48Ɨ p300a (INT4) --- ## Training Strategy ### Phase 1 ā€” 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 ā€” 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 ā€” 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 ā€” 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 - šŸ”“ Planned ā€” 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)