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license: mit
language:
- en
task_categories:
- text-generation
- question-answering
pretty_name: Risk-Routed KV Exact-Recall Benchmark
size_categories:
- n<1K
---
# Risk-Routed KV Exact-Recall Benchmark
This dataset contains controlled synthetic exact-recall examples used to evaluate **risk-routed heterogeneous KV memory** policies for long-context Transformer inference.
The benchmark is designed for testing whether a model can retrieve exact strings from long contexts under different KV-cache policies:
- **Full KV**
- **Uniform low-bit Quantized KV**
- **Risk-routed heterogeneous KV**, where exact-critical spans stay in Full KV and background context is quantized
## Tasks
1. `single_needle`: retrieve a secret marker from a long filler context.
2. `multi_needle`: retrieve the marker associated with a queried label such as BLUE.
3. `kv_retrieval`: retrieve a key-value record such as a city access code.
4. `code_string`: retrieve the exact string returned by a small code block.
5. `date_negation`: retrieve the approved final date while ignoring a negated distractor.
## Schema
Each JSONL row has:
```json
{
"id": "single_needle-8192w-0",
"task": "single_needle",
"context_words_target": 8192,
"context": "...long context...",
"query": "Which marker was labeled as the secret marker?",
"gold": "MK53-4897X",
"distractors": ["ZX17-2044Q", "ALPHA-77K2"],
"choices": ["MK53-4897X", "ZX17-2044Q", "ALPHA-77K2"],
"exact_spans": ["MK53-4897X"],
"source": "synthetic_controlled_exact_recall"
}
```
## Intended evaluation
For forced-choice evaluation, compute answer sequence NLL for each candidate in `choices` after the context and query. The prediction is correct if the `gold` answer has the lowest NLL.
## Related code
https://github.com/Ahmet2001/-risk-routed-heterogeneous-kv-memory
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