Matrix-Corp/Matrix-Lattice

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.

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, taskid, confidence, partialresult, 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

Target: 4× H100 80GB (INT8) or 8× p300a (INT4)

Lattice-430B

Target: 8× H100 80GB (INT4) or 28× p300a (INT4)

Lattice-671B

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:

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)