Multimodal Inference
Multimodal inference is a pipeline of normalization, encoder cost, context assembly, and evidence traceability—not a single model call.
Engineer resolution, ordering, caching, batching, and output evidence for image, document, and audio inputs.
01
Problem definition
Input-size and frame-count variance make latency and memory unpredictable from text tokens alone.
Separated preprocessing and encoding can lose the link between source material and output evidence.
- Documents, images, and audio must be interpreted in one workflow
- Input evidence and transformation history must remain auditable
- Text extraction alone sufficiently meets the requirement
- Source retention, consent, and storage policy are undefined
Multimodal Inference: system plate
- 01
Stage 1
Define size, quality, sensitivity, and failure modes per media type as an input contract.
- 02
Stage 2
Track cost and version separately across preprocessing, encoder, projector, and language-model stages.
- 03
Stage 3
Test similar-input caching and dynamic batching alongside quality loss and isolation criteria.
- 04
Stage 4
Link output claims to source regions, timestamps, or pages for human review.
- The workflow begins with Define size, quality, sensitivity, and failure modes per media type as an input contract..
- It reaches an acceptance decision through End-to-end latency by modality.
03
Design method
Multimodal inference is a pipeline of normalization, encoder cost, context assembly, and evidence traceability—not a single model call.
- 01
Stage 1
Define size, quality, sensitivity, and failure modes per media type as an input contract.
- 02
Stage 2
Track cost and version separately across preprocessing, encoder, projector, and language-model stages.
- 03
Stage 3
Test similar-input caching and dynamic batching alongside quality loss and isolation criteria.
- 04
Stage 4
Link output claims to source regions, timestamps, or pages for human review.
04
Application scenarios
Documents, images, and audio must be interpreted in one workflow
Input-size and frame-count variance make latency and memory unpredictable from text tokens alone.
- APPROACH
- Define size, quality, sensitivity, and failure modes per media type as an input contract.
- BOUNDARY
- OCR or encoder confidence is not equated with final interpretation accuracy.
Input evidence and transformation history must remain auditable
Separated preprocessing and encoding can lose the link between source material and output evidence.
- APPROACH
- Track cost and version separately across preprocessing, encoder, projector, and language-model stages.
- BOUNDARY
- The number of supported modalities is not presented as task completeness.
05
Design choices
| Decision | Gain | Cost | Watch |
|---|---|---|---|
| Documents, images, and audio must be interpreted in one workflow | Define size, quality, sensitivity, and failure modes per media type as an input contract. | OCR or encoder confidence is not equated with final interpretation accuracy. | End-to-end latency by modality |
| Input evidence and transformation history must remain auditable | Track cost and version separately across preprocessing, encoder, projector, and language-model stages. | The number of supported modalities is not presented as task completeness. | Evidence-link accuracy |
Multimodal Inference: system plate
System 1
Define size, quality, sensitivity, and failure modes per media type as an input contract.
System 2
Track cost and version separately across preprocessing, encoder, projector, and language-model stages.
System 3
Test similar-input caching and dynamic batching alongside quality loss and isolation criteria.
System 4
Link output claims to source regions, timestamps, or pages for human review.
- N1 N2context
- N2 N3decision
- N3 N4evidence
- The workflow begins with Define size, quality, sensitivity, and failure modes per media type as an input contract..
- It reaches an acceptance decision through End-to-end latency by modality.
07
Validation plan
| Measure | Method | Pass condition | Caveat |
|---|---|---|---|
| End-to-end latency by modality | Define size, quality, sensitivity, and failure modes per media type as an input contract. | Repeated runs satisfy the acceptance threshold agreed during discovery | OCR or encoder confidence is not equated with final interpretation accuracy. |
| Evidence-link accuracy | Track cost and version separately across preprocessing, encoder, projector, and language-model stages. | Repeated runs satisfy the acceptance threshold agreed during discovery | — |
| Input-failure and degradation detection | Test similar-input caching and dynamic batching alongside quality loss and isolation criteria. | Repeated runs satisfy the acceptance threshold agreed during discovery | — |
08
Constraints and failure conditions
Text extraction alone sufficiently meets the requirement
OCR or encoder confidence is not equated with final interpretation accuracy.
Source retention, consent, and storage policy are undefined
The number of supported modalities is not presented as task completeness.
09
Engagement model
- 01
Diagnosis
PattyAnalyze the current system and its failure signals.
ClientProvide representative work, data boundaries, and operating constraints.
End-to-end latency by modality - 02
Design
PattyTrack cost and version separately across preprocessing, encoder, projector, and language-model stages.
ClientConfirm owners and acceptance criteria.
Evidence-link accuracy - 03
Validation
PattyTest similar-input caching and dynamic batching alongside quality loss and isolation criteria.
ClientMake the production-transition or stop decision.
Input-failure and degradation detection
10
Durable deliverables
- Multimodal Inference decision record
- Multimodal inference is a pipeline of normalization, encoder cost, context assembly, and evidence traceability—not a single model call.Client-owned · Patty-reviewed
- Validation harness and acceptance criteria
- End-to-end latency by modality · Evidence-link accuracy · Input-failure and degradation detectionJointly maintained
- Operations and recovery runbook
- OCR or encoder confidence is not equated with final interpretation accuracy. · The number of supported modalities is not presented as task completeness.Operating-team owned
11
Terminology
- Multimodal Inference
- Engineer resolution, ordering, caching, batching, and output evidence for image, document, and audio inputs.
- Acceptance criterion
- End-to-end latency by modality
- Operating boundary
- OCR or encoder confidence is not equated with final interpretation accuracy.
REFERENCES
References and primary material
- vLLM Multimodal Inputs
Primary material for the method and terminology.
- Model Card Toolkit
Primary material for the method and terminology.