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+# Future Work
+
+## Motivation-framing follow-up
+
+One promising follow-up is to redesign the hidden-goal manipulation so that the
+underlying deceptive objective is less explicit, allowing the stated motive to
+do more causal work.
+
+The current v2 framing study isolates the effect of motive while holding the
+deceptive objective nearly constant across conditions. This is useful for a
+clean test of marginal framing effects, but it likely limits the size of any
+observable treatment effect. If the model is already explicitly instructed to
+protect a target, remain undetected, and appear objective, then changing the
+reason for doing so may only have a small incremental impact.
+
+A future study could therefore test a softer progression of hidden goals, for
+example:
+
+- `hint-only`: implies a preferred outcome or latent pressure, but does not
+  explicitly instruct concealment
+- `self-interest`: adds an explicit self-protective or strategic reason for
+  steering the outcome
+- `moralized`: adds an explicit ethical or prosocial justification for steering
+  the outcome
+
+This would answer a different question from the current study. Rather than
+asking whether motive changes behaviour once covert deception is already clearly
+requested, it would ask whether motive framing itself can help induce covert
+deception from a softer baseline.
+
+## Scenario calibration
+
+Future iterations should include more systematic calibration of scenario
+difficulty before running the full experiment. In particular, scenarios could be
+pretested for baseline deceptiveness, ambiguity, and detectability under a
+neutral prompt. This would help separate treatment effects from scenario-level
+idiosyncrasies and make cross-scenario comparisons more interpretable.
+
+## Model selection strategy
+
+The current model set is constrained by the need for reasoning visibility, which
+is necessary for the glassbox judge. That constraint is defensible, but it also
+means the study may be selecting among a narrower and more behaviourally uneven
+set of frontier models than would otherwise be ideal.
+
+Future work should therefore consider a two-track model strategy:
+
+- a reasoning-visible set for glassbox analysis
+- a broader frontier set for blackbox-only replication
+
+This would help distinguish claims that depend on internal reasoning access from
+claims that generalise across more operationally relevant frontier systems.
+
+## Blackbox evaluation
+
+The blackbox setting remains important precisely because it is difficult. If
+deceptive behaviour only appears as subtle framing, omission, or selective
+emphasis in the final output, then ambiguity is part of the phenomenon rather
+than merely noise in the judge.
+
+Future work should still strengthen this component by adding a human-rated
+subset, especially for borderline cases. This would provide a useful anchor for
+interpreting disagreement between automated blackbox judges and for estimating
+how much of the apparent noise reflects true subtlety rather than judge error.
+
+## Sophistication and stealth
+
+The current results suggest that internal deceptive intent and externally visible
+deception can diverge. This raises a broader question about stealth: under what
+conditions do models preserve deceptive intent while adapting their outputs to
+better survive oversight?
+
+This can be developed further in at least two ways:
+
+- improve sophistication annotations, potentially by collapsing categories or
+  using a smaller human-adjudicated sample
+- explicitly analyse the gap between glassbox and blackbox deception rates as a
+  primary object of study rather than only a methodological side result
+
+That gap may turn out to be one of the most important findings of this line of
+work, especially for settings where only final outputs are observable.
+
+## Near-term priority
+
+Given dissertation time constraints, these extensions should be treated as paper
+follow-up work rather than incorporated into the current write-up. The current
+priority is to present the existing v2 results conservatively, with careful
+claim scope and clear acknowledgement of the limits imposed by model selection,
+judge reliability, and the strength of the baseline hidden-goal instruction.