Future Features and Competitive Evaluation

Future Features and Competitive Evaluation

This page evaluates possible RawCull features after version 3.2.0. It compares the current implementation with representative professional culling products, identifies the most important workflow gaps, and evaluates additional models from Apple’s Core AI model catalog.

The comparison was researched on 1 September 2026. Competitor products and Apple’s model catalog change frequently, so links to primary vendor sources are included. The comparison is based on documented functions and inspection of the RawCull source; it is not a controlled image-quality or speed benchmark.

The term leading applications in this document means representative products with significant professional culling functionality. It is not a market-share ranking.

Executive Recommendation

RawCull should remain a focused, local, explainable culling application. It should not try to become a complete editor, retouching suite, cloud gallery, or delivery platform.

The most valuable next release would combine three features:

1. XMP interoperability with Lightroom Classic and Capture One;
2. face and eye inspection for portraits, events, and group photographs; and
3. an explainable, non-destructive first pass that organizes photographs into Keep, Review, and Likely Reject.

A suitable product description would be:

RawCull First Pass: private, local, and explainable culling with face and eye inspection and seamless XMP handoff.

The first additional Core AI model to evaluate should be EfficientSAM. The RawCull and PhotoAIKit integration already contains an EfficientSAM backend, and the model is much smaller than SAM 3. Object detection and a small vision-language model are reasonable later experiments, but they should not be added until a measured culling problem justifies their download, memory, and maintenance costs.

Product Position

RawCull’s strongest differentiator is not simply that it uses AI. Several competitors also run culling locally. Its differentiator is the combination of:

• fast embedded RAW previews;
• Sony and Nikon camera autofocus-point extraction;
• full-frame, salient-subject, and AF-region sharpness evidence;
• visible focus masks and an explanation of ranking cautions;
• local visual similarity and burst grouping;
• local CLIP semantic search;
• non-destructive rating and review decisions; and
• no requirement to upload photographs for analysis.

This is a credible position: show the photographer why one frame is stronger, and leave the decision with the photographer.

Future automation should preserve that contract. RawCull should not silently delete a file, conceal a low-confidence result, or present a generative explanation as measured fact.

Version 3.0.0 and 3.2.0 Baseline

Version 3.0.0 is sometimes described as the non-AI version. More precisely, it does not require separately downloaded AI models. It still uses Apple Vision feature prints for image similarity and burst grouping, and PhotoAnalysisKit uses Vision and Metal-based analysis. It does not provide CLIP text-to-image search or segmentation-backed Deep Review.

Version 3.2.0 adds the optional Core AI layer:

The normal culling workflow is deliberately shared. Features such as XMP, improved ingest, broader formats, better review queues, and keyboard workflow can therefore benefit both release lines. Model-dependent features should degrade cleanly when no model is present.

Current RawCull Capabilities

Source inspection confirms the following current application behavior:

• catalog discovery for registered Sony ARW and Nikon NEF files;
• concurrent EXIF, dimensions, camera, lens, ISO, aperture, and AF metadata;
• two-tier thumbnail caching plus full-size embedded/developed preview caches;
• AF overlays and GPU-generated focus masks;
• configurable sharpness scoring using full-frame, salient-subject, and AF-region evidence;
• visual grouping of neighboring frames and ranked burst candidates;
• manual comparison, burst workspaces, review/defer state, and manual winners;
• local CLIP image embeddings and text-query embeddings;
• Vision fallback when CLIP is unavailable or disabled;
• optional subject-mask Deep Review;
• reject, neutral keeper, and two- through five-star rating states;
• persisted ratings, analysis artifacts, burst decisions, cache signatures, and settings;
• embedded or developed JPEG export;
• rsync-based copying of tagged or minimum-rated RAW files, with progress and cancellation; and
• memory-pressure monitoring and cache diagnostics.

The application currently stores its rating decisions in RawCull’s own JSON data rather than writing standard XMP sidecars. This protects source files but limits interoperability.

Competitive Products Reviewed

Aftershoot

Aftershoot offers automated, assisted, and manual culling. Its documented workflow includes duplicate grouping, key faces, blur and closed-eye detection, image scores, Selected/Highlight/Maybe/Blur/Closed Eyes groups, Survey Mode, strictness controls, and preserved ratings and labels when exporting to Lightroom Classic or Capture One.

Aftershoot also describes local/offline culling and non-destructive XMP sidecars for RAW photographs. Its 2026 material promotes selection-style learning and culling to a requested yield, although individual functions can have staged availability.

Sources:

• Aftershoot culling guide
• Aftershoot culling FAQ
• Aftershoot technical behavior and XMP
• Aftershoot cull-to-target status

Narrative Select

Narrative Select emphasizes assisted rather than opaque automatic decisions. Its notable functions are synchronized face close-ups, individual face focus and eye-state assessments, scene grouping, first-pass indicators, image sharpness ranking, a people filter, rapid RAW loading, and direct handoff to an editing workflow.

Narrative documents support for Canon CR2/CR3/CRW, Nikon NEF/NRW, Fuji RAF, Sony ARW, Panasonic RW2, Olympus ORF, Pentax PEF, DNG, generic RAW, JPEG, HEIC, and HEIF. It also states that images remain local and are not uploaded for processing unless the user separately opts in to improvement use.

Sources:

• Narrative Select feature overview
• Narrative face assessments
• Narrative Close-Ups panel
• Narrative camera and file compatibility
• How Narrative uses photographs

Adobe Lightroom Classic

Lightroom Classic Assisted Culling can select by subject focus, eye focus, and eyes open. It can reject documents, receipts, probable misfires, and exposure problems, and it can auto-stack by time and visual similarity. Results can be turned into flags, ratings, labels, folders, or collections.

The June 2026 Faces panel presents every detected face together with eye-open and eye-sharpness scores. Lightroom also added catalog-wide exact duplicate detection across folders. This integration is important because it removes the handoff step entirely for existing Lightroom customers.

Sources:

• Lightroom Classic Assisted Culling
• Lightroom Classic June 2026 features
• Lightroom Classic release notes

Imagen

Imagen offers two clear automated policies: retain the best photograph from each similar group, or cull to an exact number or percentage. It can identify duplicates and low-rated images with blur, accidental-capture, or exposure problems. Users review the results before sending selected photographs into Imagen’s editing workflow.

Imagen can show an edited preview before the culling decision, but culling is performed on Imagen Cloud. This is a different product tradeoff from RawCull’s fully local analysis.

Sources:

• Imagen AI culling introduction
• Imagen project and exact-target workflow

FilterPixel

FilterPixel separates fast technical filtering from a genre-aware DeepCull. The documented criteria include blur, focus, expressions, composition, lighting, background, narrative value, brand safety, emotional moments, and peak sports action. It promotes Keep/Review/Reject groups, best-of-burst selection, target counts, and a score plus reason for each choice.

FilterPixel’s public pages observed during this review were inconsistent about whether DeepCull processing is local or cloud-based. RawCull should therefore not use FilterPixel’s processing location as a competitive fact without a new verification. Its genre-aware selection and reason presentation are still useful product references.

Sources:

• FilterPixel product overview
• FilterPixel DeepCull
• FilterPixel workflow

Photo Mechanic

Photo Mechanic remains a useful benchmark for ingest and metadata rather than automatic AI selection. Its ingest can copy in the background, rename files, apply metadata templates, create folders, track copied photographs, and open a contact sheet while copying. It also provides tags, stars, configurable color classes, XMP/IPTC interoperability, variables, and code replacements.

Sources:

• Photo Mechanic ingest
• Photo Mechanic color classes and ratings
• Photo Mechanic terminology and XMP

Capability Matrix

Recommended Roadmap

P0: Release qualification for 3.2.0

Do not add a large new culling feature during the final release window. Finish the contract already presented to users:

1. Run the complete automatic and manual acceptance matrix on representative ARW and NEF catalogs.
2. Verify a clean install, both CLIP downloads, cancellation, removal, relaunch, invalid-model recovery, and Vision fallback on the release candidate.
3. Decide whether Deep Review is a shipped feature, a manual-install preview, or a future feature, and make every UI and documentation statement agree.
4. Correct package-version and model-server documentation drift.
5. Record performance and peak memory for small, medium, and large catalogs.
6. Preserve a non-AI path whose basic culling workflow does not depend on model availability.

At the time of this review, the production code points to the GitHub v2 model manifest and both CLIP descriptors are ready. The README text still says that the server is a non-routable placeholder. SAM 3 remains excluded by the includeSAM3 = false product switch and is blocked for managed redistribution.

The AI dependency-boundary verifier passed. The smoke manifest enumerated 208 unique tests. The smoke run had one failure: the project and lockfile use RawParserKit 1.3.0 while the RawCull README still documents 1.2.9. The observed functional smoke tests passed; the failure was the release-metadata consistency test.

P1: XMP interoperability

This should be the first substantial feature after 3.2.0.

Required behavior

• Read existing XMP sidecars on catalog load.
• Import standard stars, reject flags, pick state, and color labels.
• Optionally import ratings written in-camera.
• Write RawCull decisions to a sidecar without modifying RAW bytes.
• Preserve unrelated XMP namespaces and fields.
• Detect whether the XMP changed since it was read.
• Present a conflict decision instead of overwriting newer metadata.
• Support batch write, cancellation, and atomic replacement.
• Offer explicit Lightroom Classic and Capture One label mappings.
• Provide Write XMP, Reveal in Finder, and Open in editing app actions.

Persistence rule

RawCull’s internal JSON should remain the durable application record. XMP is an interchange projection. That permits RawCull-specific analysis and review state to evolve without placing private schemas into sidecars.

flowchart LR
    RAW["RAW source"] --> READ["Read existing XMP"]
    READ --> MERGE["Merge into RawCull rating state"]
    UI["User culling decisions"] --> STATE["RawCull JSON"]
    STATE --> PROJECT["Project standard stars, flags, and labels"]
    PROJECT --> CONFLICT{"Sidecar unchanged?"}
    CONFLICT -->|yes| WRITE["Atomic XMP write"]
    CONFLICT -->|no| REVIEW["Show conflict and preserve both choices"]

Acceptance criteria

• Round-trip a representative sidecar through Lightroom Classic.
• Round-trip stars and compatible labels through Capture One.
• Prove unrelated IPTC and application-specific fields are unchanged.
• Handle read-only catalogs, missing sidecars, malformed XML, and concurrent external edits.
• Never modify the RAW source while exporting rating metadata.

P1: Face and eye inspection

People-specific assessment is the clearest feature gap against Narrative, Aftershoot, and Lightroom.

Proposed interface

• A face strip beside the loupe and burst comparison.
• One crop for every important face, not only the largest face.
• Synchronized crops across the frames in a burst.
• Per-face sharpness and eye-region sharpness.
• Eye state: Open, Closed, Uncertain, or Not visible.
• Occlusion, profile, small-face, and low-resolution cautions.
• Main-subject versus background-face classification.
• An Everyone acceptable summary for group photographs.
• A shortcut that moves directly between questionable faces.

Eye state must not be reduced to a forced binary value. Sunglasses, profile faces, motion, hair, tiny background faces, and deliberate expression require an uncertain state. Automatic first-pass policy should send uncertainty to Review rather than Likely Reject.

Evidence model

Each face result should record:

• normalized bounding box;
• face identity only within the active catalog or burst, unless the user explicitly enables persistent people grouping;
• face sharpness and eye-region sharpness;
• eye-state result and confidence;
• source fingerprint and analysis descriptor;
• whether the face is considered a main subject; and
• the reasons shown in the UI.

There is no dedicated face/eye-quality model in Apple’s current public Core AI model catalog. This feature therefore needs Apple Vision face observations, possibly a purpose-trained eye-state model converted to Core AI, or both. A general object detector or VLM should not be presented as a reliable substitute without a labeled validation set.

P1: Explainable First Pass

RawCull already produces much of the technical evidence needed for a useful first pass. The first implementation can be a deterministic policy engine; it does not need another neural network.

Output queues

Safety invariants

• Never delete a source photograph.
• Always keep access to every queue.
• Retain at least one candidate per burst unless the user explicitly changes the rule.
• Put low-confidence or conflicting evidence in Review.
• Explain every automatic placement using stored evidence.
• Permit one-click override and undo.
• Re-running with changed policy must not erase manual ratings or winners.
• Persist policy identity so old results are not misrepresented after an algorithm change.

Initial evidence

• burst membership and within-burst rank;
• visual similarity distance;
• full-frame, salient-subject, and AF-region sharpness;
• AF-point containment and distance from the salient subject;
• analysis confidence and existing caution reasons;
• exposure clipping derived from image data rather than histogram appearance alone;
• face and eye results when available; and
• existing manual ratings and winners as hard overrides.

Target yield

After Keep/Review/Reject is trustworthy, add a requested target count or percentage. The target is a policy constraint, not evidence of quality. RawCull should show when reaching the requested number requires inclusion of weaker or lower-confidence frames.

P2: Local preference profiles

RawCull can learn useful preferences by adjusting transparent policy weights before attempting end-to-end personalized AI.

Possible learned values include:

• preferred number of retained frames per burst;
• tolerance for subject motion and deliberate blur;
• relative importance of salient-subject sharpness and camera AF evidence;
• preferred subject size and framing;
• desired delivery percentage;
• treatment of uncertain eyes and background faces; and
• preference for technical perfection versus moment uniqueness.

Profiles should be named, inspectable, exportable, resettable, and scoped by genre when the user wants that behavior. A profile must never silently turn a manual reject into a keeper or replace a manual burst winner.

Suggested first profiles:

• Portrait and group;
• Wedding and event;
• Sports and action;
• Wildlife and birds;
• Landscape and architecture; and
• General/manual assistance.

Genre names should initially select documented weights and rules. Claims such as emotion, storytelling, or peak action require separate labeled evaluation before they become user-visible facts.

P2: Broader format support

The market supports more camera systems than RawCull. A practical expansion order is:

1. Canon CR3;
2. Fujifilm RAF;
3. DNG;
4. JPEG and HEIC/HEIF companions;
5. Panasonic RW2, Olympus ORF, and Pentax PEF.

Generic culling does not have to wait for MakerNote AF support. A format can provide embedded preview, EXIF, sharpness, similarity, ratings, and XMP while reporting that camera AF metadata is unavailable.

The UI should distinguish three states:

• camera AF point available and used;
• camera AF point absent or unsupported, with other evidence available; and
• image decoding or analysis unavailable.

This avoids treating broader format support as inferior or silently assigning a false AF coordinate.

P2: Safer ingest

RawCull should adopt a focused subset of Photo Mechanic’s ingest strengths:

• camera-card detection;
• primary and backup destinations;
• optional checksum verification;
• configurable folder and filename templates;
• preservation of in-camera ratings;
• copy progress and cancellation;
• an ingest receipt containing source, destinations, counts, and failures; and
• optional eject only after successful verification.

Do not erase or format source media. Rich IPTC templates and sports code replacement can remain outside RawCull unless users demonstrate demand.

P3: Exact duplicates and catalog maintenance

Exact byte duplicates across folders are different from visually similar burst frames. They require content hashing, catalog scope, and careful deletion or move policy. This is useful but is closer to digital-asset management than culling. It should follow the workflow features above.

AI Evaluation

Principles for adding another model

The existence of an Apple export recipe does not mean that RawCull should ship the model. Every additional bundle creates:

• a download and storage cost;
• first-use specialization time;
• memory and energy pressure;
• another license and redistribution decision;
• another model fingerprint and cache-compatibility dimension;
• failure, cancellation, update, and removal states;
• a benchmark and regression obligation; and
• user-interface complexity.

A new model is justified only when it solves a measured culling problem better than current Vision, CLIP, segmentation, deterministic analysis, or a small purpose-trained model.

Apple’s Core AI Models repository provides export recipes and Swift runtime utilities for macOS and iOS 27. The current upstream catalog includes language models, diffusion models, Qwen3-VL, CLIP, Depth Anything v3, EDSR, EfficientSAM, PVT v2, SAM 3, YOLOS, audio models, and text encoders. RawCull pins an exact repository revision, so features present on upstream main are not automatically present in the version used by a release.

Candidate model matrix

1. EfficientSAM: strongest next candidate

Apple’s current EfficientSAM recipe describes a 10-million-parameter ViT-Tiny model. It supports a foreground click, box prompt, multiple point queries, and a segment-everything point grid. RawCull already has:

• a CoreAIEfficientSAMBackend dependency;
• an EfficientSAM provider path in RawCullAIIntegration;
• an EfficientSAM model directory;
• a RawCullSegmentationModel.efficientSAM identity; and
• Deep Review code capable of consuming subject masks.

The missing work is primarily product inclusion, model packaging, validation, license/provenance clearance, download-catalog support, and quality evaluation.

Recommended use:

• make EfficientSAM the lightweight Deep Review candidate;
• discover likely foreground subjects with a bounded point grid;
• combine masks with saliency and camera AF points;
• let the user click or box the intended subject when automatic discovery is ambiguous; and
• cache masks using the full model and prompt identity.

Required evaluation:

• portraits, groups, animals, birds, sports, landscapes, and low-light scenes;
• subject selection success rather than generic segmentation metrics alone;
• mask placement and boundary quality;
• AF-point containment stability;
• detail-score stability inside the selected mask;
• false selection of background objects;
• latency, peak memory, first-use specialization, cache size, and energy; and
• quality comparison against SAM 3 on the same labeled cases.

EfficientSAM does not understand a text target. The UI must describe point-grid or user-prompted selection accurately and must not imply SAM 3-style language grounding.

2. SAM 3: premium text-guided Deep Review

SAM 3 remains valuable when a photographer wants to specify bird, face, player, or another text target. Apple’s recipe describes an 848-million- parameter gated model. The capability is stronger than EfficientSAM’s point prompts, but the size, gated access, and redistribution review make it a poor mandatory dependency.

Recommended policy:

• keep Vision and ordinary burst review fully functional without it;
• treat SAM 3 as an optional advanced model;
• make its managed download visible only after legal and provenance clearance;
• require explicit license acceptance if the final legal review requires it;
• compare its added winner-selection value with EfficientSAM, not only mask appearance; and
• avoid advertising Deep Review as turnkey while normal users cannot obtain the model through the app.

3. Dedicated face and eye model: needed but not in the catalog

The most important new AI capability is face/eye quality, yet Apple’s public Core AI model catalog currently contains no dedicated model for:

• eyes open versus closed;
• eye-region sharpness;
• facial-expression suitability;
• gaze or camera engagement;
• group-photo all-faces acceptance; or
• intentional versus accidental eye closure.

RawCull should first evaluate Vision face rectangles and landmarks combined with the existing sharpness analyzer. If eye-state classification remains insufficient, a small purpose-trained classifier is a better fit than adding a general 2B vision-language model.

Such a classifier needs consented or properly licensed training/evaluation data, coverage across skin tones and ages, glasses and sunglasses, profiles, occlusions, makeup, low light, motion, and small faces, plus explicit uncertainty calibration. Accuracy must be reported per condition, not only as a single aggregate percentage.

4. YOLOS: possible object evidence

Apple provides YOLOS Tiny at approximately 6.5 million parameters and YOLOS Base at approximately 127 million parameters, together with an object-detection runtime product. A detector could provide:

• person, animal, vehicle, or sports-object boxes;
• subject occupancy and edge-cutoff cautions;
• possible ball-in-frame evidence for selected sports; and
• a subject ROI when saliency is ambiguous.

Limitations:

• a general object vocabulary does not equal photographic importance;
• detection confidence does not measure focus, expression, composition, or peak action;
• it cannot replace a dedicated face/eye assessment; and
• unsupported subjects could bias the first-pass policy.

If evaluated, begin with YOLOS Tiny and use detections as optional evidence, never as an automatic reject condition.

5. Qwen3-VL: later research only

Apple’s current upstream main contains a Core AI export path for Qwen3-VL-2B-Instruct with a 448-pixel vision encoder, token embedding, text decoder, tokenizer, and bundle metadata. This capability may be newer than the exact coreai-models revision pinned by RawCull.

Potential experiments:

• create searchable catalog captions;
• summarize a burst’s visible differences;
• suggest scene or genre labels;
• identify possible emotional or peak-action moments; and
• turn structured evidence into accessible natural language.

Risks:

• generative claims can hallucinate details or intent;
• a 448-pixel view may miss the technical detail used for focus decisions;
• per-image inference across a large catalog may be too slow or memory-heavy;
• captions add a new private persistent-data category;
• a VLM can sound more certain than its evidence; and
• the model and tokenizer create a much larger distribution obligation.

Any VLM result must be labeled as a suggestion. It must not override measured sharpness, face results, user ratings, or manual winners. A small deterministic formatter remains preferable for explanations such as “subject sharpness was higher and the AF point was inside the selected mask.”

6. Depth Anything v3: limited incremental value

Depth Anything v3 Small predicts monocular depth and confidence. It might help measure subject separation, background complexity, foreground obstructions, or depth-layer composition.

However, RawCull already has saliency and optional segmentation. Depth is not itself a culling-quality measure, and incorrect monocular depth can create confident but irrelevant rankings. Evaluate it only against a labeled feature such as “background distraction” or “subject separation.” Do not add it merely because a Core AI recipe exists.

7. EDSR: never use reconstructed detail for sharpness scoring

EDSR can enlarge a low-resolution preview, but super-resolution creates an estimate of detail rather than evidence from the source. It must not feed:

• sharpness scoring;
• AF-region scoring;
• focus masks;
• eye sharpness;
• burst winner selection; or
• any label presented as source-image quality.

An optional display-only enhancement could be considered, but RawCull can already extract embedded previews or develop the RAW. That makes EDSR a low priority even for presentation.

Model Admission Gates

Every new model should pass all gates below before it appears in production Settings.

Product gate

• Name the user problem and the decision improved by the model.
• Define a non-model baseline.
• Demonstrate incremental value on representative RawCull catalogs.
• Confirm that the feature remains understandable and reversible.

Quality gate

• Use a versioned, labeled evaluation set.
• Separate technical metrics from subjective photographer preference.
• Record false-positive and false-negative costs.
• Calibrate an uncertain state where applicable.
• Compare results across supported cameras, genres, and difficult conditions.
• Require human review of ranking changes, not only tensor parity.

Conversion and identity gate

• Pin the exact upstream revision and weight checksum.
• Record the exact Core AI exporter and PhotoAIKit revisions.
• Verify reference/Core AI parity using the real preprocessing path.
• Fingerprint the complete runtime bundle and tokenizer/resources.
• Include preprocessing, normalization, dimensions, and output interpretation in the backend descriptor.
• Invalidate only incompatible cached artifacts after a model change.

Performance gate

• Measure cold specialization and warm inference separately.
• Record median and tail latency per image.
• Record peak resident memory and memory-pressure behavior.
• Test bounded concurrency and cancellation.
• Measure a realistic 500-, 2,000-, and 10,000-image workflow where relevant.
• Confirm that UI interaction and thumbnail loading remain responsive.

Distribution gate

• Complete license and redistribution review for weights and converted assets.
• Preserve model card, notices, upstream revision, export command, and hashes.
• Verify the archive produced by Managed Background Assets.
• Test download, cancellation, validation, relaunch, update, removal, and insufficient-storage behavior.
• Do not confuse technical success with permission to redistribute.

Presentation gate

• State whether a result is measured, inferred, or generated.
• Show confidence and meaningful uncertainty.
• Do not hide unavailable models behind unexplained disabled controls.
• Provide fallback behavior and recovery steps.
• Maintain VoiceOver descriptions for progress, result, confidence, and error states.

Proposed AI Model Roadmap

This ordering solves product needs rather than maximizing the number of models shown in Settings.

Features Not Recommended

RawCull should defer or reject the following unless its product scope changes:

• RAW editing profiles and automatic editing;
• retouching and generative removal;
• cloud galleries, proofing, print sales, and delivery;
• diffusion-based image generation;
• destructive automatic rejection or deletion;
• an LLM used only to rewrite deterministic evidence in friendlier words;
• super-resolution used as quality evidence;
• persistent face recognition by default; and
• a large model marketplace without a specific culling purpose for every model.

Aftershoot and Imagen are developing broad cull-edit-retouch-deliver systems. Competing on their complete scope would consume resources without strengthening RawCull’s most defensible advantages.

Success Measures

Future features should be judged by user outcomes rather than by model count. Useful measures include:

• time from catalog open to completed first pass;
• time spent zooming into faces;
• percentage of automatic placements the photographer changes;
• missed-keeper rate in Likely Reject;
• number of unresolved Review images;
• agreement with manually chosen burst winners;
• XMP round-trip success rate;
• catalog formats and cameras admitted successfully;
• peak memory and cancellation latency; and
• percentage of workflows completed without leaving RawCull before handoff.

For automatic culling, missed keepers matter more than a superficially high overall accuracy score. The default policy should be conservative enough that uncertain photographs remain visible in Review.

Recommended Next Design Work

Before implementation, prepare three focused specifications:

1. an XMP mapping and conflict-resolution specification;
2. a face/eye evidence schema and labeled acceptance matrix; and
3. a First Pass policy document defining queue invariants, reasons, target yield, overrides, persistence identity, and failure behavior.

EfficientSAM can be evaluated in parallel because its backend boundary already exists. It should not delay XMP or the deterministic First Pass, and a new model should never be required merely to display existing RawCull evidence clearly.