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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-Confidenceheader 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-protocolextension
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-Riskper 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:
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)