What Is Worth Representing? Representational Empowerment for Continual Model Construction
Fei Dai, Hanqi Zhou, Alison Gopnik, Charley Wu
cs.LG · cs.AI
Quick verdict
Proposes RepEmp for choosing which representations to build and keep; strong framework, human fit fragile per own spec curve, Exp 3 only 3 seeds.
Summary
The paper asks which representational elements an agent should build, retain, and reuse, formalizing continual model construction as a W→M→L pipeline (inaccessible world, task model, persistent library). It proposes Representational Empowerment (RepEmp), a mutual-information criterion over edits to the agent's own representation, evaluated through goal-achievement signatures and realized in a Curator-Actor architecture where an LLM proposes predicates and operators and execution evidence scores retention. Experiment 1 (n=70, alchemy-style causal discovery with coarse/medium/fine observation granularities) reports that humans favor medium granularity and that RepEmp best predicts granularity choice while EnvEmp best predicts intervention choice. Experiment 2 (30-seed simulations) shows the granularity criterion dominates: RepEmp+EnvEmp reaches .703 held-out F1 versus .365-.383 for InfoGain/random; RepEmp adapts across noise regimes but fails when coarse is optimal. Experiment 3 (BabyAI and Zelda, deepseek-r1:70b Curator) shows curated libraries beating PPO, Motif, WorldCoder, and the Hoarder ablation on backward and held-out transfer (e.g., .61 BabyAI backward) with smaller libraries and fewer tokens. Key limitations: the supplement's specification curve shows near-zero median RepEmp increments across 12 belief-update specifications; Experiment 3 uses only 3 seeds without inferential tests; code is promised but not yet released; and Motif is structurally disadvantaged in the comparison.
Review complete
Assessment at a glance
5
risk flags identified
● high 1
● medium 3
● low 1
5 strengths · 5 weaknesses · 5 author questions
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Key claims under review
- Continual model construction should separate an inaccessible world W, an environment-specific task model M, and a persistent library L, with both M and L treated as objects of explicit selection.
- Representational empowerment — mutual information between edits to the agent's representation and future representational states, grounded in goal achievement — is a workable criterion for deciding what to build, retain, and reuse under bounded resources.
- Human learners in a closed-vocabulary causal-discovery task converge on the goal-sufficient medium observation granularity, a pattern best predicted by RepEmp in the main model comparison (with fragility documented in the supplement's specification curve).
- In matched simulations, the granularity-selection criterion (RepEmp) contributes roughly three times more to structure recovery and cross-task transfer than the intervention-selection criterion (EnvEmp).
- An LLM-augmented Curator using RepEmp-scored curation builds compact PDDL libraries that beat PPO, Motif, WorldCoder, and the Hoarder ablation on backward and held-out transfer at lower token cost.
Strengths
Well-posed formalization with honest theory-practice separation
Sec. 2 and App. B separate exact RepEmp (Def. 1), the goal-projected channel (Def. 2, Prop. 1 with data-processing proof), and the deployed proxy (Eq. 3, B.4), explicitly stating the proxy 'is an empirical decision rule, not a numerical estimate of channel capacity'; Theorem 1 (minimum-cost goal sufficiency) and Cor. 1 give clean consistency conditions.
Three-regime evidence design with the right ablation
Sec. 3's progression from human alignment (Exp. 1) to causal simulation (Exp. 2) to open-vocabulary realization (Exp. 3) is well motivated; the Hoarder ablation (same proposer, budgets, reliability checks, no selective retention) cleanly isolates curation from proposal quality, and Hoarder collapses to ~.03-.06 held-out success (Sec. G.2).
Statistically strongest result is the causal simulation
Unusually candid negative results
Practical Exp. 3 gains with full protocol disclosure
Weaknesses
Human-evidence claim is fragile per the paper's own robustness check
Exp. 3 is underpowered and untested
Baseline fairness concerns in Exp. 3
Granularity-adaptation claim is bounded by a documented failure
Proxy-criterion gap limits theoretical force
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Preliminary Review: Representational Empowerment for Continual Model Construction
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Frequently asked questions
What is Representational Empowerment (RepEmp)?
RepEmp is an information-theoretic score for deciding which representational elements (observation granularities, predicates, operator schemas) an agent should add to or keep in its model. It extends classic empowerment: instead of measuring how reliably actions steer the agent to diverse external states, it measures how reliably different edits to the agent's own representation lead to diverse future representations that change which goals are achievable, penalized by representational cost.
How does RepEmp differ from classical empowerment and information gain?
Classical environmental empowerment (Klyubin et al., 2005) scores control over external states with the representation fixed; information gain scores uncertainty reduction about the world regardless of whether that resolution helps any goal. RepEmp makes representational edits the channel inputs and future representations the outputs, projected onto goal achievability. In the paper's human experiment, environmental empowerment best predicted intervention choices while RepEmp best predicted granularity (representation) choices.
Did the human study support RepEmp?
Partially. In the n=70 causal-learning experiment, participants favored the goal-sufficient medium granularity, and RepEmp gave the best held-out predictions of granularity choice in the main comparison. However, the supplement's specification curve shows the advantage is fragile: across 12 belief-update specifications the median increment is near zero with no persistently positive interval, so the human evidence is suggestive rather than decisive.
Is code or data available for this paper?
Not yet. The paper states the implementation accompanying the open-vocabulary experiments 'will be released with the paper'; at preprint stage no code or human-data repository is provided, so independent verification currently requires reimplementing from the detailed appendix (prompts, budgets, promotion thresholds).
About this review
This is an AI-generated preliminary review by Evolve.Pub, produced from the openly available text of the paper. It is not peer review and does not represent the authors' views. The canonical record of the paper is at the publisher link above.
- Exact held-out ΔLL magnitudes for Fig. 4c exist only as a bar chart; the extraction preserves the qualitative ordering but not the numeric values, so the size of the human-fit advantage could not be independently quantified.
- Figure 4a's axis labels are garbled (mojibake) in the extraction; all percentages cited here were taken from the body text, which reports them cleanly.
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