Interaction Paradigms
Neural retrieval models can be organized along a single axis: when the query and the document are allowed to interact. This timing decision is the master trade-off in neural IR — it determines whether documents can be encoded offline (and therefore whether the model can serve first-stage retrieval at scale) and how much fine-grained relevance signal the scorer can capture. Moving “later” in interaction buys quality; moving “earlier” (toward joint encoding) costs the ability to pre-compute.
This is the broader frame behind the comparison table that lives inside the Late Interaction concept. The three named points on the spectrum each have a dedicated concept note: Bi-Encoder (no interaction), Late Interaction / ColBERT (late, token-level), and Cross-Encoder (early, joint).
The Spectrum
No Interaction Late Interaction Early Interaction
─────────────────────────────────────────────────────────────
Bi-Encoder ColBERT Cross-Encoder
(pre-compute) (at query time, (at query time,
token-level MaxSim) full cross-attn)
Fast ◄─────────────────────────────────────────► Accurate
Early interaction (Cross-Encoder): [Q + D] → Encoder → score
No interaction (Bi-Encoder): Q → enc → q; D → enc → d; score(q,d)
Late interaction (ColBERT): Q → enc → [q_tokens]; D → enc → [d_tokens]; MaxSim
Comparison
| Paradigm | When Q·D interact | Doc pre-encode? | Signal granularity | Speed | Quality | Typical role |
|---|---|---|---|---|---|---|
| No interaction (Bi-Encoder) | Never — only final vectors compared | Yes (single vector) | Coarse (whole-doc) | Very fast | Good | First-stage retrieval |
| Late interaction (ColBERT / Late Interaction) | At query time, after independent encoding | Yes (per-token vectors) | Fine (token-level via MaxSim) | Medium | Near cross-encoder | Retrieval or reranking |
| Early interaction (Cross-Encoder) | During encoding (joint cross-attention) | No — must encode each pair | Finest (full cross-attn) | Slow | Highest | Reranking top-k |
The order of the table is the order of the Retrieval Pipeline: cheap-and-coarse interaction filters the corpus, richer interaction reranks the survivors. Because rerankers only see ~100–1000 candidates, they can afford the expensive end of the spectrum — this is the same ladder explored in Reasoning Reranking.
Why Timing Is the Master Trade-off
- Pre-computation gate. If interaction is deferred until after the document is encoded (no / late), document representations can be built offline and indexed. Once interaction happens during encoding (early), every query–document pair must be encoded at query time — O(candidates) work that rules out first-stage use.
- Granularity vs. storage. Later interaction generally means keeping more per-document state: a single vector (bi-encoder) → many token vectors (late interaction, hence Token Pooling and compression concerns) → nothing stored but nothing reusable either (cross-encoder).
- Quality climbs with interaction richness. Full cross-attention (early) captures the subtlest relevance; whole-document dot products (none) capture the least; token-level MaxSim (late) lands in between.
A Second Axis: Sparse vs. Dense
The interaction axis is orthogonal to what the representation is. The same “no / late / early” question can be asked of sparse models: Learned Sparse Retrieval (SPLADE, ELSER) are effectively no-interaction models over learned sparse term weights, scored with an inverted index instead of dot products over dense vectors. Hybrid Search combines a sparse and a dense no-interaction retriever before any later-interaction reranking.
Related Concepts
- Bi-Encoder — the no-interaction baseline (single-vector dual encoder)
- Late Interaction · ColBERT — token-level interaction at query time
- Cross-Encoder — early/joint interaction, the quality ceiling
- Dense Vector Retrieval — the representation most of this axis runs on
- Learned Sparse Retrieval — the same axis on sparse representations
- Reranking · Retrieval Pipeline — where each paradigm sits in a multi-stage system
Related Topics
- Reasoning Reranking — extends the high-interaction end with LLM rerankers
- Frontier of Search 2025 — late interaction as a 2025 frontier theme
Related Articles
- What is ColBERT and Late Interaction and Why They Matter in Search — frames the interaction-timing spectrum directly
- Cross-Encoders ColBERT and LLM-Based Re-Rankers — compares the three paradigms as rerankers
- Bi-encoder vs Cross-encoder When to Use Which One — the two endpoints, when to pick each