data/docs/match_vector_sampling_policy.md

# 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:

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:

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

Order shorthand:

entity / property / qualifier / value

Examples:

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:

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:

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:

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:

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:

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:

00n0
00n1
0010
0011

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

5. Dataloader rule

For each row:

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:

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.