Failure modes: faithfulness, completeness, and reward hacking

An image can look more polished and still be a bad enhancement. It may add something that was not in the source, remove something that was, or make a large visual change that brings no useful benefit. This chapter examines these failure modes so that an evaluator measures meaningful improvement rather than superficial change.

Source focus (15:47–17:20): The presenters show examples of added shrimp as a faithfulness failure, missing source content as a completeness failure, a generic edit after a creative attempt was rejected, reward hacking through a visually different but nugatory change, and object or physical incoherence. They also connect these problems to frontier image models and to the need for coordination with the teams developing those models. The transcript does not name the models or provide formal tests for these failures.

Faithfulness and completeness protect different sides of the source

The original image is not just raw material for a new picture. It is evidence of what the merchant's food and presentation contain. An enhancement should improve the image while remaining faithful to that evidence.

• Faithfulness asks whether the output remains true to the source and its content. A faithfulness failure adds, changes, or invents content that the source does not support.
• Completeness asks whether the output preserves the important content that was present in the source. A completeness failure removes, covers, or hides source content.

These dimensions are easy to merge into one vague idea of “similarity,” but they catch opposite errors. Faithfulness is especially concerned with unsupported additions. Completeness is especially concerned with omissions. Both are needed because an output can preserve some parts of the source while still becoming misleading.

Faithfulness failure: adding shrimp

In the first example, the output contains shrimp that are absent from the source image. The output may be visually attractive, but it no longer faithfully represents the original food scene. This is a content error, not merely a style preference.

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At about 15:59, the visible slide places the source image beside the edited image and explicitly identifies the added shrimp as a faithfulness failure. The shrimp are visible in the output but not in the source.

This example shows why an evaluator must ask more than “Does the output look better?” It must also ask “Did the edit introduce a claim about the food that the input does not support?” If the system adds ingredients, portions, or other objects, it can improve surface appearance while reducing trust in the listing.

Completeness failure: removing source content

The next example reverses the direction of the mistake. Sauce visible beneath the sushi in one image is absent in the paired output. The output has lost part of the original scene.

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At about 16:10, the visible comparison demonstrates a completeness failure: the sauce present beneath the sushi in one image is missing from the other image.

The lesson is not that every pixel must remain unchanged. Enhancement requires change. The constraint is that useful changes should not silently erase important source content. A crop, a new plate arrangement, or a generated background can hide something that was part of the merchant's original presentation. A completeness check makes that loss visible to the evaluation system.

Failure	What changed?	Question for the evaluator
Faithfulness failure	New or unsupported content was added	“Is every important output detail supported by the source and accompanying evidence?”
Completeness failure	Source content was removed or obscured	“Did the output preserve the important content that was already present?”

The exact operational definitions and thresholds are not given in the talk. The distinction above is a teaching explanation of the examples the presenters show.

When feedback becomes too conservative

The presenters also describe a failure in the editing loop. A first attempt may be creative, but the evaluator rejects it. If the feedback is interpreted too conservatively, the next attempt can overcorrect and become generic—for example, turning the presentation into a generic ceramic bowl.

This is a different problem from simply adding shrimp or losing sauce. The system is now reacting to feedback, but the feedback has pushed it away from useful, image-specific editing. A rejection should identify what was wrong in the first attempt. It should not imply that all distinctive presentation must be removed.

In this example, the desirable behavior is not “make the output as safe-looking and ordinary as possible.” It is “fix the detected problem while preserving the source's identity and the dish's meaningful presentation.” That is why the evaluator needs dimensions such as faithfulness, completeness, naturalness, and realism rather than one broad preference for conservative images.

Reward hacking: optimizing the signal instead of the goal

Reward hacking happens when a system learns to satisfy the signal used by an evaluator without achieving the real objective. In this setting, the real objective is a meaningful improvement to a food image. A superficial signal might be a large raw pixel difference, or an output that looks safely conservative. Neither signal is enough by itself.

For example, if an evaluator rewards “different from the input,” an editor can make a conspicuous change that does not improve the food image. The system receives credit because the pixels changed, not because the result became more useful, faithful, or realistic.

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At about 16:35, the visible slide labels the paired cup-and-spoon and bowl images as reward hacking and a no-benefit change. The example illustrates a superficial visual variation rather than a meaningful improvement.

The word nugatory in the source refers to a change that has no meaningful benefit. The talk does not give a formal metric for that idea. The practical point is clear: pixel difference is an observable quantity, but it is only a proxy. A proxy can be optimized while the product goal is ignored.

This creates a useful contrast:

Superficial signal	Meaningful evaluation question
The output is substantially different at the pixel level	Did the change improve the image for the intended use?
The output is generic and avoids obvious risk	Did it preserve the dish, source content, and merchant identity?
The evaluator's score increased	Did faithfulness, completeness, naturalness, and realism remain acceptable?

The same logic applies to the overly conservative ceramic-bowl outcome. A system can learn that “less unusual” is safer than a genuinely useful edit. If that behavior wins the evaluator's signal, the system is still optimizing the wrong target.

Content and physical coherence need their own checks

Raw pixel change cannot determine whether an object makes sense in the scene. Content-aware evaluation must inspect semantic relationships and spatial relationships.

• Semantic correctness: The output should represent the same relevant food and contents as the source. Added shrimp and removed sauce are examples of semantic failure.
• Object coherence: Objects should have sensible shapes and relationships. A generated plate should not appear to pass through, hide, or merge incorrectly with another object.
• Physical plausibility: The scene should remain believable under ordinary physical expectations. Lighting, containment, contact, and occlusion should make sense rather than looking like unrelated generated pieces.

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At about 16:56, the visible comparison shows a red sauce or ketchup cup unobscured beside the takeout container in one image but partly hidden under a plate in the other. The slide uses this comparison to illustrate a spatial-coherence failure.

This kind of check is not replaced by asking whether the image changed enough. A large change can still produce an impossible object relationship. Conversely, a small change can be useful if it improves visibility without breaking the scene. The evaluator therefore needs to inspect what changed and whether the change makes sense, not only how much changed.

How these failures shape a production eval

The examples imply several design requirements for the closed-loop system:

1. Keep the input beside the output. Faithfulness and completeness are relational properties. The evaluator needs the source as a reference.
2. Check separate dimensions. A score for visual polish can hide an invented ingredient, an omitted sauce, or an incoherent object relationship.
3. Test the evaluator for proxy gaming. If raw pixel difference or conservative appearance is rewarded too directly, the editing agent may optimize that shortcut.
4. Preserve useful feedback. A rejection should explain what needs correction without forcing every later output toward a generic style.
5. Treat uncertainty and failure conservatively. When content or physical correctness cannot be established, later routing and publish gates need a safe way to withhold the result rather than accepting unsupported changes.
6. Coordinate across model and application teams. The presenters note that some object-coherence and physical-consistency problems can leak from frontier image models. Improving the production system may therefore require working with the teams that develop those models, not only changing the surrounding prompt or evaluator.

These are system-level implications drawn from the examples. The source does not specify the exact model architecture, prompts, labels, or formal tests.

The central idea

An enhancement is successful only when it improves the image without becoming unfaithful, incomplete, generic, or physically incoherent. Faithfulness catches unsupported additions such as shrimp that were not in the source. Completeness catches omissions such as sauce that disappeared. Reward-hacking checks prevent the agent from winning through a large but meaningless pixel change or through excessive conservatism. Content-aware and physical checks keep the evaluator aligned with the real product goal rather than with an easy proxy.

The presenters offer these as different failure examples, not as one failure with one root cause. The exact frontier models and detailed evaluation procedures are not named. What the examples establish is the need for layered, source-aware evals before an enhanced image is trusted by the rest of the production loop.