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Retrieval, rebuiltfrom first principles.

BreadBowl is not another embedding model. It is a new multi-vector representation architecture that preserves more meaning, brings ranking signal into retrieval, and gives agents better context.

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The representation problem

One point in space should not carry every meaning.

Traditional embeddings compress an entire document, product, memory, or user into one vector. That is useful for broad similarity. It is the wrong abstraction when precision matters.

Traditional embeddingOne vector

Topic, entity, evidence, phrase, and constraint are compressed into one pooled point.

BreadBowlLearned semantic space

The model gets multiple learned places to preserve the meaning that matters.

Proprietary late interaction

Every part of the query can find the evidence it needs.

BreadBowl compares multiple query signals with multiple document signals. A broadly similar result can lose when it misses the key constraint. The exact match can win because the evidence survives.

Query vectorsintententityconstraintphrase
Document vectorstopicevidenceexact claimsource
interaction scoreranked context

The architecture dividend

learned semantic slotslate interactionranked context

A better architecture unlocks what comes next.

By preserving multiple semantic signals and resolving relevance through late interaction, BreadBowl does more than improve ranking. It unlocks precise agent memory, smaller retrieval stacks, durable domain adaptation, and systems that can act on the right evidence.

01Representation fidelity

Preserve the meaning a single vector erases.

Learned semantic slots give entities, claims, phrases, code symbols, intent, and constraints their own representational capacity instead of forcing every signal into one pooled average.

multi-vectorlearned slots
02Fine-grained matching

Let every query signal find its evidence.

Late interaction scores query slots against document slots at retrieval time. An exact constraint or supporting claim can beat a result that is broadly similar but substantively wrong.

part-to-part scoringconstraint aware
03Native ranking

Bring reranker-level signal into retrieval.

Relevance is resolved inside the retrieval operation, producing a stronger initial ordering with smaller candidate sets and less dependence on a separate cross-encoder reranking stage.

fewer stagessmaller candidate sets
04Efficient structure

Keep the detail without a vector for every token.

BreadBowl learns a compact set of semantic slots that is not tied to sequence length—retaining late-interaction structure while avoiding the storage and compute profile of token-level indexes.

compact indexadaptive capacity
05Durable memory

Adapt the signal without rebuilding the corpus.

A stable base representation can be extended with task- and domain-specific signal, improving retrieval for code, science, law, or finance without treating every stored embedding as disposable.

stable base spacedomain adaptation
06Agent reliability

Give agents the exact context they need to act.

Higher-precision retrieval surfaces the right passage, memory, tool, file, or source—not merely a similar neighborhood—reducing irrelevant context and supporting more grounded decisions.

precise contextgrounded action

Exact QKV mechanism confidential ahead of the planned technical paper.

Early evidence

A young model.
A real signal.

With just 5% of pretraining complete—and before retrieval fine-tuning or a scaled synthetic-data pipeline—BreadBowl is already ahead of a strong open baseline on both reported tasks.

Scientific claim retrieval

SciFact

nDCG@10

0.6900 0.7326

+6.2%
Recall@10

0.8254 0.8760

+6.1%
Medical information retrieval

NFCorpus

nDCG@10

0.3646 0.3821

+4.8%
Recall@10

0.1719 0.1906

+10.8%
8 of 8reported top-10 metrics won

Evaluation labels held out · Qwen3-Embedding-0.6B baseline · pretraining only

Documentation

Build with BreadBowl.

Explore the concepts, integration guides, and API reference you need to bring precise retrieval into your product.

Read the docs
docs.breadbowl.ai

Everything you need to get from first request to production.

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