Engineering / research
Align and serve multiple modalities
Trace modality encoding, alignment, missing inputs, fusion, and evaluation through practical multimodal training and serving pipelines.
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Module 01 research
Align and serve multiple modalities
Trace modality encoding, alignment, missing inputs, fusion, and evaluation through practical multimodal training and serving pipelines.
- 01
Count encoder features and language-model slots before admitting media
Trace modality-specific representation lengths through a declared connector and reserve space for formatting and generation without assuming one universal visual-token count.
32 min - 02
A tensor batch is aligned only when its identities and temporal windows match
Validate paired asset keys, transformations, time bases, and split lineage before treating a batch diagonal as cross-modal supervision.
32 min - 03
Contrastive negatives and temperature define a comparison task, not automatic semantic truth
Derive a normalized cross-modal comparison loss and identify how duplicate positives, batch composition, and temperature affect its supervision.
34 min - 04
Connector compatibility requires sequence, width, ownership, and mask contracts
Distinguish feature projection from learned resampling and cross-attention, then verify tensor shapes and visibility rules before model execution.
34 min - 05
Missing-media behavior depends on the conditioning pattern, not a generic dropout promise
Define presence masks, artificial dropout distributions, valid null paths, and evaluation cohorts before claiming robustness to absent modalities.
33 min - 06
Resolution and packing change token geometry, masking, and actual kernel work
Count variable-resolution patch grids and distinguish logical within-image attention from padding, dense packed scores, and block-aware implementation costs.
34 min - 07
A multimodal conversation is an ordered media-and-token contract
Preserve roles, media identity, feature expansion, training labels, and padding through the exact checkpoint-compatible processor and template.
33 min - 08
Grounding requires evidence-sensitive task tests beyond fluent captions
Use matched image-text contrasts, valid media interventions, and attribution controls to distinguish semantic evidence use from language priors and presentation artifacts.
35 min - 09
Preprocessing is part of the learned model and the evidence coordinate system
Version decoding, geometry, scaling, normalization, tokenization, and cache identity so the same media retains a reproducible model meaning.
34 min - 10
Admit and test multimodal requests against representation-aware resource and failure contracts
Account for decoder cache, encoder peaks, preprocessing, request cohorts, and declared degraded paths without turning a toy capacity estimate into a measured latency guarantee.
35 min