# Kimi K3 / DeepSeek V4 Flash 1M Context Reality package

This package implements the five-tier, three-trial corpus requested for the
Kingy.ai investigation. Each byte-identical corpus contains early/middle/end
retrieval facts, distributed needles, a three-hop chain, contradictory policies,
a four-file code dependency, grounded synthesis evidence, and an absent-fact
control.

## Reproduce the corpora

1. Create a Python environment and install `transformers` and `tiktoken`.
2. Set `HF_HOME` to a disposable cache if desired.
3. Run `python generate_corpora.py --output generated`.
4. Verify `generated/manifest.json`. Tokenizer repositories and revisions are
   pinned in `experiment_config.json`.

The manifest's corpus and transparent-serialization counts are exact under the
pinned released tokenizers. They are not proof of what a hosted endpoint accepts.
For Kimi, preflight the exact messages with `/v1/tokenizers/estimate-token-count`;
for both providers, treat a successful response's usage prompt count as the
accepted count.

## Current execution state

No inference call was made on 2026-08-01. DeepSeek's authenticated account check
reported no available balance. Kimi's authenticated console reported a zero
balance, and no usable `MOONSHOT_API_KEY` was available to the runner. All
accuracy, latency, caching, usage, and cost-result fields therefore remain null.

This is a fail-closed state, not a zero score and not evidence that either model
failed the corpus.
