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Architecture for Peak EXecution
Post-Transformer • State Space • Infinite Context
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
| Problem | Transformer | APEX-1 |
|---|---|---|
| Attention complexity | O(n²) — breaks at >1M tokens | O(n) — linear forever |
| Enterprise codebase (10M tokens) | Impossible or astronomically expensive | Native, first-class |
| Memory per token | Grows with sequence length (KV cache) | Constant — fixed state size |
| Reasoning | Discrete token-space CoT | Continuous latent thought space |
| Cross-session memory | None — stateless | Persistent semantic memory |
| Compute per problem | Fixed regardless of difficulty | Dynamic — 1× to 64× auto-allocated |
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
| Spec | Value |
|---|---|
| Total parameters | ~600B |
| Active params/token (base) | ~20B |
| Active params/token (deep think) | up to ~80B |
| Effective context | 10M+ tokens |
| Working memory | 32K full-fidelity |
| Episodic memory | 2M tokens compressed |
| Semantic memory | 64K persistent slots (survives context resets) |
| Training hardware | 16× NVIDIA B300 (Blackwell Ultra) |
Target Benchmarks vs Claude Mythos
| Benchmark | Mythos Preview | APEX-1 Target |
|---|---|---|
| SWE-bench Verified | 93.9% | >91% |
| SWE-bench Pro | 77.8% | >72% |
| Terminal-Bench 2.0 | 82.0% | >78% |
| GPQA Diamond | 94.6% | >90% |
| HLE (with tools) | 64.7% | >62% |
| USAMO 2026 | 97.6% | >85% |
| 10M Token Codebase | ❌ Not supported | ✅ Native |
| Cross-session memory | ❌ Stateless | ✅ Persistent |
Repository Structure
| Repo | Description |
|---|---|
APEX-THE-NEXT-GEN/apex1-architecture |
Full architecture spec, PyTorch modules |
APEX-THE-NEXT-GEN/apex1-configs |
Training configs for all stages |
APEX-THE-NEXT-GEN/apex1-data |
Data pipeline scripts and dataset cards |
APEX-THE-NEXT-GEN/apex1-evals |
Evaluation harness and benchmark results |
APEX-THE-NEXT-GEN/apex1-showcase |
Interactive demo space |
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
"Transformers conquered language. APEX-1 conquers scale."