# Ttt E2e Long Context

> Enable long-context modeling via test-time training with meta-learning. Inner loop continues training on context, compressing information into weights rather than KV cache, outer loop optimizes initialization—maintaining full-attention quality with RNN-like constant inference latency across 8K-128K token contexts.

- Skill: `adu2021/ttt-e2e-long-context` (Agent Skill)
- Install (CLI): `npx skillmds@latest add adu2021/ttt-e2e-long-context`
- Raw SKILL.md: https://api.skillmd.com/api/skills/adu2021/ttt-e2e-long-context/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: AI & ML
- License: MIT
- Author: adu2021 (https://skillmd.com/u/adu2021)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/adu2021/ttt-e2e-long-context

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## Overview

Reformulates long-context as continual learning problem solved at test time.

## Core Technique

**Meta-Learning for Test Time:**

```python
# Inner loop: compress context into weights
for token in context:
    gradient = compute_gradient_on_token(model, token)
    model.weights += gradient  # Compress context

# Outer loop: optimize initialization
# Treat inner loop as differentiable step
```

## Performance

- Full-attention quality across context lengths
- 2.7× faster than attention at 128K
- Constant-time inference

## References

- Test-time meta-learning
- Weight compression of context
- End-to-end optimization