APEX

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What is APEX-1?

APEX-1 is a novel post-transformer architecture built from the ground up to overcome the fundamental limitations that cap current frontier models including Claude Mythos, GPT-5.4, and Gemini 3.1 Pro.

The core insight: transformers are fundamentally broken at scale. Quadratic attention means analyzing a 10M token enterprise codebase costs ~100× more than a 1M token one. APEX-1 replaces this with a hybrid SSM architecture that scales linearly — processing 10M tokens costs the same as 1M.

The Problem With Transformers

Architecture: 7 Novel Components

1. 🌊 Mamba-2 SSM Core

The backbone. Replaces transformer attention with selective state space layers. O(n) complexity, constant memory — no KV cache explosion at scale. Reduces KV cache from 32GB to 4GB at 256K tokens versus full-attention transformers. Scales to 10M+ tokens on the same hardware that chokes transformers at 200K.

2. 🦅 RWKV-7 "Goose" Time-Mix Layers

RWKV combines efficient parallelizable training of transformers with efficient RNN inference — linear time, constant space, no KV cache, infinite context length. Interleaved with Mamba-2 blocks for complementary sequence modeling — Mamba handles selection, RWKV handles time-decay dependencies.

3. 🧠 Titans Persistent Memory Module

Neural long-term memory that learns at test time. Based on Google DeepMind's Titans architecture — a persistent memory that updates online during inference, accumulating knowledge about a codebase across context resets. No other model has this. For agentic coding over massive repos, this is the difference between amnesia and genuine understanding.

4. 🌳 Tree-of-Thoughts Branching Engine

Human brains don't think linearly — they explore branches, backtrack, and converge. APEX-1's ToT engine maintains parallel thought trees in latent space during generation, evaluating multiple reasoning paths simultaneously before committing to output tokens. Critical for multi-step debugging and architectural decisions.

5. ⚡ Continuous Latent Thinking (CLT)

Reasoning in embedding space, not token space. Based on Meta's Coconut research — intermediate reasoning steps never get committed to discrete tokens, preserving full representational richness. The model "thinks" with full floating-point precision, only decoding the final answer.

6. 🎯 Confidence-Gated Recurrence (CGR)

Dynamic compute allocation per token difficulty. Easy tokens (print hello world) get 1 compute unit. Hard tokens (debug this race condition across 50k LOC) get 64× automatically. No manual chain-of-thought prompting needed — the architecture handles it.

7. 🔀 Dynamic Expert Orchestration (DEO)

Multi-round MoE with 108 experts across 4 tiers: general (64), specialist (32 — python, systems, security, math, etc.), arbitration (8), meta-cognitive (4). Three rounds of consultation per token with conflict resolution and domain routing.

Scale

Target Benchmarks vs Claude Mythos

Repository Structure

Key Papers Informing APEX-1

• Mamba-2 — "Transformers are SSMs" (Dao & Gu, 2024)
• RWKV-7 "Goose" — "Expressive Dynamic State Evolution" (Peng et al., 2025)
• Titans — "Learning to Memorize at Test Time" (Behrouz et al., Google DeepMind, 2024)
• Coconut/CLT — "Training LLMs to Reason in Continuous Latent Space" (Hao et al., Meta, 2024)
• RWKV-X — "Sparse Attention + Recurrent Memory for 1M Token Decoding" (2025)
• Tree of Thoughts — "Deliberate Problem Solving with LLMs" (Yao et al., 2023)
• GRPO — "DeepSeekMath" (Shao et al., 2024)
• SWE-RL — "Advancing LLM Reasoning via RL on Open-Source Repos" (Meta, 2025)

Status

Active Research — Architecture implementation in progress. Training begins Q2 2026.

Follow this org for:

• Architecture paper (coming soon)
• 100B prototype weights
• Training logs and benchmark results
• Demo spaces