# Match-vector based hard negative policy

This file defines how fact-retrieval training should consume the enriched InfoSeek fact maps.

## 1. Source fields

For each InfoSeek numeric/time-style training row:

```text
positive = selected_positive_fact_ids
negative metadata = hard_negative_meta_v2[fact_id]
```

Do not rely on the old negative type label alone. The type label is useful as provenance/debug metadata, but the training sampler should use `match_vector`.

## 2. Match vector

Each hard negative fact has:

```json
{
  "entity": true/false,
  "property": true/false,
  "qualifier": true/false/null,
  "value": true/false/null
}
```

Order shorthand:

```text
entity / property / qualifier / value
```

Examples:

```text
11n0 = same entity, same property, no qualifier constraint, wrong value
1110 = same entity, same property, same qualifier, wrong value
10n1 = same entity, different property, same value
01n0 = different entity, same property, wrong value
```

`null` means the dimension is not applicable or cannot be judged. It is not the same as `false`.

## 3. Positive-like negatives

The following vectors must not be used as negatives:

```text
1111
11n1
```

These mean entity/property/value all match the selected positive, so they are likely duplicate positives or unresolved aliases.

Current sanity after vector recomputation:

```text
val:   missing_vector = 0, positive_like_negative = 0
train: missing_vector = 0, positive_like_negative = 0
```

## 4. Training negative priority

Recommended sampling priority:

### Tier A: strongest hard negatives

Same entity and same property, but wrong value or wrong qualifier:

```text
1110
11n0
1100
1101
```

These directly teach the fact retriever not to select a fact just because the entity and relation are right.

### Tier B: same entity, different property

Same subject entity, but the requested property is different:

```text
10n1
10n0
10nn
1001
1010
1011
```

These are useful because the visual entity is correct, but the retrieved fact answers the wrong question.

### Tier C: same property, different entity

Same or similar property, but from another subject:

```text
01n0
0100
0110
01n1
0101
0111
```

These teach the model not to retrieve a globally plausible property fact from the wrong entity.

### Tier D: mixed/easy negatives

Everything else:

```text
00n0
00n1
0010
0011
```

Use only as filler if there are not enough Tier A/B/C negatives.

## 5. Dataloader rule

For each row:

```text
positive_ids = selected_positive_fact_ids

negative_ids =
  sample Tier A first
  then Tier B
  then Tier C
  then Tier D if needed
```

The sampler should keep the vector metadata, because later codebook analysis will compute:

```text
same_code_prefix_len(positive_fact, negative_fact)
```

This will be added after fact embeddings/codebooks are built.

## 6. Important caveat

The existing type labels may still contain noisy names such as `same_entity_property_matched_wrong_value` even when the final `match_vector.property` is `false`.

That is expected after the correction. Training must follow `match_vector`, not the legacy label.