MATRIX.CORP — FRONTIER SERIES

MATRIX
LATTICE

Agentic · Multimodal · 1M+ Context · MoE · API-First

120B / 430B / 671B ~22–47B ACTIVE PARAMS 17 CUSTOM MODULES DEEPSEEK-V3 + LLAMA 4 LINEAGE INFERENCE PROVIDER READY OPENAI-COMPATIBLE API MLA ATTENTION MIXTURE OF DEPTHS SPECULATIVE DECODING

Model Family

Three Tiers, One Architecture

Lattice — Entry

120B

~22B active params · 64 experts · top-4

CONTEXT1M tokens

EXPERTS64 routed + 2 shared

HARDWARE4× H100 / 8× p300a

INT4 VRAM~60GB

TPS (INT4)~130

STATUS🔴 PLANNED

Lattice — Pro

430B

~38B active params · 128 experts · top-4

CONTEXT1M tokens

EXPERTS128 routed + 4 shared

HARDWARE8× H100 / 28× p300a

INT4 VRAM~215GB

TPS (INT4)~72

STATUS🔴 PLANNED

Lattice — Max

671B

~47B active params · 256 experts · top-4

CONTEXT1M tokens

EXPERTS256 routed + 8 shared

HARDWARE32× H100 / 48× p300a

INT4 VRAM~336GB

TPS (INT4)~50

STATUS🔴 PLANNED

Foundation

Public Architectures Integrated

Multi-Head Latent Attention (MLA)

DeepSeek-V3 · KV cache compressed ~90% via
low-rank projection · Essential for 1M context

Mixture of Experts (MoE)

DeepSeek-V3 style · Fine-grained expert segmentation
Auxiliary-free load balancing · No token dropping

Mixture of Depths (MoD)

Google Research · Tokens skip up to 50% of layers
~30% compute reduction at same quality

iRoPE / YaRN Scaling

Llama 4 + YaRN · NTK-aware RoPE for 1M+ context
Full attention every 4th layer · 8K sliding window

Speculative Decoding

Paired draft model per tier (~4B params each)
3–5× inference speedup · Shared embedding weights

Multimodal Vision Encoder

Llama 4 / InternVL lineage · ViT 6B params
Images, video, documents, charts · 4K via tiling

Audio Encoder

Whisper-large-v3 lineage · Speech + sound understanding
Cross-attention injected into LM backbone

Sliding Window Attention

Mistral · 8K window on non-full-attention layers
O(n) memory for most layers of the network

Custom Architecture

17 Custom Modules

EQ V2

MODULE 01

EQ Engine V2

Conversation-arc emotional tracking via persistent GRU.
12-emotion classification. Frustration trajectory
prediction. Per-user baseline calibration (3 turns).

CORE

MODULE 02

Lattice Router

Hierarchical MoE routing: token → domain cluster →
expert group → expert. 8 domain clusters.
Experts self-label. Load-aware dispatch.

API

MODULE 03

Confidence Calibration Head

Parallel to LM head. Epistemic uncertainty [0–1]
per token. Aggregated per sentence. Exposed via
X-Lattice-Confidence header in streaming API.

AGENTIC

MODULE 04

Native Tool Schema Reasoner

Dedicated attention heads for JSON Schema, OpenAPI,
GraphQL, SQL DDL. Tool call planner generates
multi-step plans. Parallel tool dispatch.

AGENTIC

MODULE 05

Multi-Agent Coordination Layer

Structured agent message protocol. Role awareness:
orchestrator / subagent / critic / executor.
Shared scratchpad attention. Conflict resolution head.

CONTEXT

MODULE 06

Hierarchical Context Compression

Every 32K tokens compressed to summary + key-facts.
Meta-summary at 128K. Recent 32K always full-res.
~20:1 narrative · ~5:1 code compression ratio.

OUTPUT

MODULE 07

Structured Output Enforcer

Constrained decoding via token masking. Guaranteed
valid JSON, YAML, XML, Python, SQL, HTML.
Partial streaming of valid JSON as tokens generate.

REASON

MODULE 08

Causal Reasoning Graph

Builds explicit cause-effect graph during generation.
Graph attention on reasoning steps. Detects loops
and contradiction chains. Optional API trace output.

TIME

MODULE 09

Temporal Awareness Module

Dedicated temporal embeddings for absolute dates,
relative references, durations. Timeline builder.
Temporal consistency checker for event ordering.

LANG

MODULE 10

Cross-Lingual Alignment Layer

50+ languages. Language-agnostic semantic space.
Code-switching aware. CJK, Arabic RTL, Devanagari
native. Dialect modeling. Self-scoring translation head.

SAFETY

MODULE 11

Safety Reasoning Module

Explicit safety chain before generation, not post-hoc.
47 harm categories with confidence scores.
Provider-configurable tiers. Structured audit log.

VISION

MODULE 12

Vision-Language Grounding

Object-level text-to-region grounding. Chart/diagram
interpreter. Document layout understanding.
Screenshot-to-code. Video temporal grounding.

AGENTIC

MODULE 13

Long-Horizon Task Planner

Task decomposition into DAGs. Dependency resolver.
Progress tracker across long sessions. Replanning
trigger. Integrates with MACL for multi-agent tasks.

PERSONA

MODULE 14

Persona Stability Enforcer

Operator-defined persona as persistent embedding.
Style consistency loss during training. Factual
self-consistency checker. EQ-aware tone modulation.

API

MODULE 15

API Telemetry & Observability

Per-token latency, expert utilization, compression events,
confidence, module activation trace — all exposed as
structured SSE metadata alongside token stream.

CODE

MODULE 16

Code Intelligence Engine

AST-aware attention. Multi-file dependency graph.
Runtime simulation head. CVE bug pattern library.
Test generation. Build/exec tool integration.

TRUST

MODULE 17

Knowledge Boundary Detector

Hallucination risk scorer per claim. Claim classification:
known / uncertain / hallucination-risk / out-of-training.
3-pass self-consistency check on uncertain outputs.

Performance

Estimated Inference Throughput

LATTICE-120B

BF16~35 TPS

INT8~70 TPS

INT4~130 TPS

LATTICE-430B

BF16~18 TPS

INT8~38 TPS

INT4~72 TPS

LATTICE-671B

BF16~12 TPS

INT8~26 TPS

INT4~50 TPS

Integration

OpenAI-Compatible API

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": "..."}],
    tools=[...],
    extra_body={
        "lattice": {
            "expose_confidence": True,         # X-Lattice-Confidence per chunk
            "expose_reasoning_graph": False,  # Causal graph trace
            "expose_module_trace": True,     # Which modules fired
            "safety_tier": "standard",      # standard | strict | minimal
            "agent_role": "orchestrator",   # orchestrator | subagent | critic
            "persona": "helpful-assistant"  # Persona Stability Enforcer
        }
    }
)

# Response extensions:
# response.lattice.confidence_scores
# response.lattice.active_modules
# response.lattice.hallucination_risk
# response.lattice.expert_clusters_used

Training Plan

Four-Phase Training Strategy

PHASE 01

Foundation

Mixed distillation from DeepSeek-V3, R1, Llama 4. Web + code + science + multimodal. Context curriculum 8K→1M.

PHASE 02

Module Integration

All 17 modules trained with auxiliary losses. Frozen in sequence as each converges.

PHASE 03

Agentic SFT

Tool use, MACL, long-horizon planning. Synthetic agentic trajectories. GRPO on task completion.

PHASE 04

Alignment

Safety module fine-tuning. Constitutional AI self-critique. Red-team adversarial tuning.

MATRIX LATTICE

MATRIX
LATTICE