Unverified50% confidenceFactExact time
ViDoRe adopts a ColBERT-style late interaction mechanism: documents are split into patches, each patch generates a dense vector, queries are likewise encoded as multi-vectors, and scoring is done via MaxSim by summing the per-vector maximum similarities.
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Long-term
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10/13/2026
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UnverifiedIn long-text generation scenarios, the more tokens that are generated, the greater the cumulative speedup benefit of speculative decoding.67% similarVerifiedA Cross-Encoder concatenates a query with each candidate document and feeds them jointly into the same model, capturing word-level interactions through the attention mechanism; its ranking precision is superior to vector similarity, but latency grows linearly with the size of the candidate set.66% similarUnverifiedLate Chunking was proposed by Jina AI; it first processes an entire document through a long-context encoder to obtain context-aware representations for each token, then splits by boundaries into chunks, so that the embedding of each chunk retains the full-document context information.65% similarUnverifiedDiscrete masked diffusion draws on BERT's masked language modeling idea—randomly masking and restoring sequences during training and iteratively filling in from a fully masked sequence at inference—with representative works including MDLM and SEDD.64% similarUnverifiedEmbedding technology converts documents and queries into high-dimensional vectors, and locates relevant content by computing similarity via cosine similarity or Euclidean distance.64% similar
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