Architecture for Peak EXecution
Every frontier model today — Mythos, GPT-5.4, Gemini 3.1 Pro — hits the same wall. Not an engineering wall. A fundamental algorithmic one.
Self-attention computes scores between every token pair. Double the context, pay 4× the compute. Jump to 1M tokens, pay 1,000,000× the compute of 1K tokens.
Every token generated requires storing keys and values for the full context. At 256K tokens, a transformer needs 32GB just for the KV cache.
Every conversation starts from scratch. No persistent memory across sessions. For agentic coding over large codebases, this means re-reading the same files thousands of times.
Transformers spend the same compute on "print hello world" as on "debug this race condition across 500k LOC." Massive waste on easy tokens, insufficient on hard ones.
Linux kernel: 30M tokens. Chrome: 40M tokens. No transformer-based model can reason over these natively. The entire premise of AI-assisted large-scale development is blocked.
Chain-of-thought forces intermediate reasoning into tokens — discrete, committed, information-lossy. The full probability distribution collapses to a single token at every step.
Each component targets a specific failure mode of transformers. Together they form a system that scales linearly, reasons continuously, and remembers persistently.
Replaces transformer attention entirely as the primary backbone. Selective state space dynamics maintain a fixed-size hidden state regardless of sequence length. No KV cache. No quadratic blowup. Linear scaling to 10M+ tokens.
KV cache: 32GB → 4GB at 256K tokensRWKV-7 interleaved with Mamba blocks. Parallelizable during training like a transformer, O(1) memory during inference like an RNN. Time-mix layers handle temporal decay dependencies that Mamba's selective scan misses. Truly infinite context length.
Inference memory: constant regardless of ctx lengthBased on Google DeepMind's Titans architecture. A persistent memory network that learns at test time — updating online during inference to accumulate codebase understanding across context resets. Working (32K) + Episodic (2M) + Semantic (64K persistent slots).
Survives context resets · Learns permanentlyMaintains parallel reasoning branches in latent space simultaneously — like human intuition exploring multiple solutions before committing. Each branch is a full hidden-state trajectory. A learned evaluator selects the most promising branch before decoding. Critical for architectural design and multi-step debugging.
N parallel branches · Best path selected before outputBased on Meta's Coconut research. Intermediate reasoning steps stay in continuous embedding space — never committed to discrete tokens. Preserves full probability distributions through reasoning chains. Only the final answer is decoded to tokens, eliminating information loss at every step.
Zero information loss during reasoningA learned confidence predictor gates the number of CLT iterations per token. High confidence (print hello) → 1 iteration. Low confidence (debug race condition) → 64 iterations. Compute scales to problem difficulty automatically. No chain-of-thought prompting needed.
1× to 64× compute · fully automaticMulti-round MoE with four expert tiers: 64 general, 32 specialist (python, systems, security, math, ML, web, formal, NLP), 8 arbitration, 4 meta-cognitive. Three consultation rounds with conflict resolution. Meta-cognitive experts trigger deep thinking when uncertainty detected.
108 experts · domain-specialized · meta-cognitive routingTargeting Claude Mythos on standard benchmarks — while adding capabilities Mythos fundamentally cannot support: 10M+ token contexts and persistent cross-session memory.
How APEX-1's architecture compares to the current frontier on the dimensions that matter for real-world deployment.
| Capability | GPT-5.4 | Gemini 3.1 Pro | Claude Mythos | APEX-1 |
|---|---|---|---|---|
| Attention complexity | O(n²) | O(n²) | O(n²) | O(n) SSM |
| Max practical context | 1M | 1M | 1M | 10M+ |
| RWKV infinite context | ✗ | ✗ | ✗ | ✓ |
| Persistent cross-session memory | ✗ | ✗ | ✗ | ✓ Titans |
| KV cache at 256K tokens | ~32GB | ~32GB | ~32GB | ~4GB |
| Dynamic compute allocation | ✗ | ✗ | Partial (thinking) | ✓ CGR 1×–64× |
| Tree-of-thoughts reasoning | ✗ | ✗ | ✗ | ✓ Native |
| Continuous latent reasoning | ✗ | ✗ | Partial | ✓ CLT |
| Enterprise codebase (10M tok) | ✗ | ✗ | ✗ | ✓ Native |
| SWE-bench Verified | ~85% | ~88% | 93.9% | Target: >91% |
| Open weights | ✗ | ✗ | ✗ | ✓ Planned |
| Training hardware | 10,000+ GPUs | 10,000+ GPUs | 10,000+ GPUs | 16× B300 |