This proposal assumes that safety policies, legal compliance, and ethical standards must always remain the highest priority.
The subject of discussion is the prioritization of output optimization within the range that already satisfies these policies.
1. Problem Definition (What is happening)
Current LLMs converge toward outputs that are statistically natural or generally plausible through probabilistic language generation.
This approach is effective for ambiguous requests or creative tasks, but conflicts arise when users explicitly define correctness conditions (constraints).
Examples:
- Strictly specifying a particular shape or structure
- Prohibiting reference to previous outputs
- Requiring identity preservation
- Imposing immutability conditions on terms or numerical values
In such cases, the correct solution is defined not by statistical distribution but by user-specified constraints.
However, when general optimization overrides explicit constraints, the output may appear natural while still being invalid for the task.
2. Practical UX Impact (Why this matters)
This inversion of priority leads to:
- Instability in instruction adherence (loss of reproducibility)
- Increased correction and retry costs
- User-side operational workarounds (thread splitting, regeneration loops)
- Loss of trust and discontinuation of use (even among general users)
Importantly, this issue is often perceived not as a capability limitation,
but as a design problem in prioritization.
Once this perception forms, performance improvements alone may not restore user confidence.
Responsibility Allocation Perspective
When explicit constraints are defined,
outputs that do not satisfy them should be treated as task failures.
However, under current behavior,
outputs that violate constraints are still presented as “plausible candidates,”
effectively transferring verification responsibility to the user.
In this structure,
the model internally evaluates the generation as successful,
while operationally the user is tasked with failure detection.
As a result:
- Additional inspection steps
- Regeneration loops
- Increased decision-making burden
are concentrated on the user side,
raising the overall operational cost of the system.
This is not an issue of output quality,
but a matter of responsibility allocation in constraint adherence.
3. Proposed Improvement (What to change)
The following design enables phased introduction without requiring a full architectural overhaul.
A) Hard Constraint Extraction and Fixation
Detect explicit expressions such as “must,” “do not,” “keep,” or “exactly,”
as well as structured constraint lists.
Fix these constraints as internal representations,
excluding them from general optimization processes.
B) Post-Generation Constraint Validation
Automatically check whether outputs satisfy defined constraints.
If violations are detected, the system may:
- regenerate,
- self-correct, or
- explicitly declare infeasibility.
C) Status Visibility
Provide brief indicators such as:
“Constraint Compliance: OK / NG”
If NG, identify which constraints were violated.
This allows users to understand the nature of failure
and reduces trust erosion.
4. Implementation Cost (Why this is feasible)
This proposal does not require training a new large-scale model.
It can be implemented through:
- constraint extraction (parser / classifier)
- post-generation validation (rule-based or lightweight model)
- regeneration loop (reusing existing inference)
Phased deployment is possible without retraining the base model.
Operationally, this can be provided as a selectable mode:
- Constraint-first
- Creative-first
5. Expected Effects (Return on Investment)
This change improves not only correctness rates but also:
- Reproducibility (reduced output variance for identical instructions)
- Correction cost (fewer retries)
- User retention (even among general users)
- Risk evaluation in enterprise adoption (affecting review and approval processes)
Failures under explicitly defined constraints
damage not perceived intelligence but trust,
suggesting a significant UX ROI.
Conclusion:
This proposal does not alter the default Creative-first behavior.
It introduces an optional inference mode
for tasks with explicitly defined constraints.
Therefore, naturalness optimization for ambiguous or creative tasks remains unaffected,
and phased integration is possible while preserving existing generative behavior.
Under current conditions,
even when explicit constraints are present,
outputs that violate them may still be presented as “plausible candidates,”
effectively transferring final compliance responsibility to the user.
This responsibility shift embeds what should be an internal verification process
into the user’s operational workflow,
resulting in increased trial count,
decision time,
and token consumption.
Prioritizing explicit constraints
and introducing a lightweight post-generation validation layer
is therefore expected to provide substantial improvements
in trust, reproducibility, and operational cost efficiency.