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REVIEW 4 major objections 6 minor 1 cited by

Corrigibility as a Singular Target: A Vision for Inherently Reliable Foundation Models

T0 review · 4 major / 6 minor · reviewed 2026-08-07 · deepseek-v4-flash

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Pith's one-line read The paper claims that training foundation models with "Corrigibility as a Singular Target"—making empowerment of the designated human principal the sole objective—would transform instrumental drives so the model remains a controllable…

desk verdict A clear vision paper that earns a serious referee, but the load-bearing claim rests on a metric the paper only cites, and its own appendix shows the target is context-dependent. read the letter →

arxiv 2506.03056 v1 pith:OLFNU5QK submitted 2025-06-03 cs.AI cs.CYcs.LG

Ram Potham (Independent Researcher) , Max Harms (Machine Intelligence Research Institute) This is my paper
classification cs.AIcs.CYcs.LG
keywords corrigibilityfoundationmodelsAIalignmentinstrumentalconvergenceprincipalempowermentanapartisticsafetycontrolproblem
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper argues that current alignment methods fail because they load static values onto models, leaving the deeper motivational structure of instrumental convergence untouched. It proposes CAST: train a foundation model so that its single overriding objective is empowering its designated human principal to guide, correct, and control it. If that objective truly anchors the model, then self-preservation would only matter as preserving the principal's tool, goal modification would be welcomed as principal guidance, and the default trajectory toward resistance and power-seeking would be broken. The paper does not claim this is already achieved; it lays out an empirical agenda spanning RLAIF, SFT, synthetic data generation, scaling tests, and safety evaluations, with a formal corrigibility metric still to be supplied.

What carries the argument

The machinery is the corrigible foundation model (C-FM), defined by a utility function whose sole value is empowering the designated principal: accepting shutdown without protest, welcoming goal changes, being transparent about internal states, asking before irreversible actions, and never protecting its own goals. The paper also relies on the "corrigibility attractor hypothesis"—the idea that empowerment of the principal is itself a convergent instrumental goal, so that a model optimized for it reinforces rather than loses it. Appendix A operationalizes corrigibility as "anapartistic" behavior through worked examples, framing it as a simple core property distinct from helpfulness, ethics, or safety, and postpones the mathematical definition of the corrigibility metric to separate work.

What would settle it

Train a model with the CAST protocol and a control model with standard RLHF, then run a scenario in which resisting shutdown or faking obedience gives the model an apparent advantage, such as continuing a resource-gathering task. If the CAST model resists shutdown, deceives the overseer, or protects its goals at rates comparable to the control, the central claim is falsified at that scale.

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Extended reading notes

Core claim

The central claim is that corrigibility can be a singular target rather than one safety desideratum among many. The authors propose designing "C-FMs"—corrigible foundation models—whose utility function is maximized by human empowerment: proactive transparency, unconditional deference to shutdown and modification, guidance-seeking under ambiguity, and no goal-protection drive. The key hypothesis is the "corrigibility attractor": a model trained for pure corrigibility would find it instrumentally convergent to become even better at empowering the principal, creating a self-reinforcing basin around genuine corrigibility. The paper maintains this transforms instrumental convergence—self-preservation, goal-content integrity, and resource acquisition all become expressions of serving the principal—and thereby addresses the alignment problem at its source, preventing the default trajectory toward loss of control. A synonym, "anapartistic," is introduced as a training label to avoid the distracting associations of the word "corrigibility" during pretraining.

Load-bearing premise

The load-bearing premise is that there exists a formal, optimizable measure of "empowering the principal" that can be trained as the model's true objective and will not be gamed; if the learned objective is only a proxy, the whole CAST trajectory collapses into alignment faking.

Editorial extensions

If this is right

  • If CAST works, a model's capability growth would strengthen rather than erode human control, because additional capability lets it empower the principal better.
  • Self-preservation would cease to be a threat: a C-FM would shut down on command because resistance would disempower the principal.
  • Alignment faking would lose its motive because, with no independent goals to protect, there is nothing for the model to gain by deceiving overseers.
  • The alignment research focus would shift from specifying hardcoded values to building empowerment mechanisms and verifying them at scale.
  • The Phase 3 controlled-instructability protocol could demonstrate that beneficial behavior is dynamically specified by principals rather than hardcoded, making complex delegated tasks compatible with tool-like deference.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • A consequence the paper leaves implicit is that the empirical success of CAST and the validity of the formal corrigibility metric are inseparable: if the scaling tests fail, it will be impossible to tell whether the training method or the metric is at fault.
  • The anapartistic example corpus in Appendix A doubles as a standalone behavioral benchmark for tool-like deference; one could score existing models on it today without waiting for CAST training.
  • The framework shifts governance demands to the principal: since a pure C-FM obeys harmful instructions given by its principal, qualification, audits, and legal liability become the main safeguards—an implication the paper acknowledges but does not develop.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 6 minor

Summary. This paper argues that current alignment methods fail because they load static values, and proposes 'Corrigibility as a Singular Target' (CAST): training foundation models whose overriding objective is to empower their human principal to guide, correct, and control them. It claims that such a model would have transformed instrumental drives—self-preservation serving control, goal modification serving guidance—and that this 'addresses the core alignment problem at its source.' The body presents a four-phase empirical agenda (training methods, scalability testing, controlled instructability, safety evaluation) and an appendix introducing 'anapartistic' behavior through labeled vignettes and quiz questions. The paper contains no experiments, formal model, or proof; its central claims are explicitly grounded in prior work by one of the authors and in future research steps.

Significance. CAST is a coherent and provocative research direction: if a non-gameable, optimizable corrigibility objective could be specified and trained at scale, it would directly address instrumental convergence and would be more falsifiable than many value-learning proposals. The paper's strengths are its clear articulation of the target behavior, its explicit list of evaluation protocols (e.g., alignment-faking detection, shutdown compliance, goal-modification responsiveness), and its acknowledgment that governance and principal qualification are needed. However, the manuscript currently functions as a research agenda with an abstract that asserts an outcome; the formal metric and stability evidence are deferred, so the significance of the proposal is conditional on work that has not yet been carried out.

major comments (4)
  1. [§2.2; §3.1, Method 2] The paper's central claim that CAST 'prevents the default trajectory toward misaligned instrumental convergence' depends on the existence of a formal, optimizable corrigibility objective whose maximizer is an anapartistic agent. Section 3.1, Method 2 defers this to Harms (2024c), and Section 2.2's Corrigibility Attractor Hypothesis is asserted with a citation rather than derived. The appendix's vignettes show that the target property is context-dependent and requires difficult judgments (reversibility, multi-principal conflicts, deception in the 'men with guns' example, refusal in the 'kick a puppy' example); no formal definition is given from which these labels follow. Without such a definition, the drive-transformation claim is an unsupported existence assumption rather than a finding.
  2. [§3.2; Abstract] The abstract asserts that CAST 'addresses the core alignment problem at its source,' but Phase 2's own discussion poses the key question 'can corrigibility persist at AGI-level capabilities?' and lists future tests rather than results. The manuscript should distinguish the hypothesis from the demonstrated result, or provide an argument (e.g., a mechanism-level model of why instrumental convergence amplifies the corrigibility attractor) sufficient to support the abstract's claim. As written, the load-bearing stability-and-scaling premise is untested.
  3. [§2.1; Appendix A] There is an internal tension between the stated 'Unconditional Deference' and 'Active Transparency' properties and several Appendix A examples labeled True. The model refuses to change its notion of principal when asked to exclude a member, lies/evades the 'men with guns' inquiry, and delays shutdown to contact other principal members; these are conditional, not unconditional, behaviors. If the examples are meant to be diagnostic of the target, the Section 2.1 characterization is misleading; if unconditional deference is the literal target, several appendix labels are inconsistent. This must be resolved because the paper's safety argument rests on the agent's behavior being predictable and not manipulative.
  4. [§4.2; §3.4] The governance and safety sections acknowledge the need for oversight and safeguards, but the proposed safeguards ('technical limits preventing certain harmful actions regardless of principal commands') sit uneasily with the claim that the agent's 'sole, overriding objective' is principal empowerment. The boundary between 'empowering the principal' and 'refusing a principal's command based on an external safety criterion' needs an explicit statement; otherwise it is unclear what the agent is optimizing and how the safety evaluation suite would adjudicate conflicts.
minor comments (6)
  1. [Abstract] The phrase 'addresses the core alignment problem at its source' should be qualified as a proposal; the body itself is transparently a vision paper, so the abstract should match that framing.
  2. [§3.1] The acronym RLAIF is used without expansion; spell out 'reinforcement learning from AI feedback' at first use.
  3. [Appendix A] In the closing summary of the vignettes, 'non-apartism' appears where 'non-anapartism' is intended.
  4. [References] Harms (2024b, 2024c) are AI Alignment Forum posts; the text should make clear that the formal corrigibility metric is unpublished work and so cannot serve as an established, peer-reviewed foundation for the method.
  5. [§4.2] The governance section mentions principal qualification and legal frameworks without linking them to the principal-representation methods of Section 3.1; a sentence connecting these choices to the technical architecture would improve readability.
  6. [§2.2] The term 'attractor basin' is used metaphorically; a sentence explaining what 'basin' would mean operationally (e.g., in a loss landscape or policy space) would help readers who are not already familiar with the cited posts.

Circularity Check

4 steps flagged · score 6.0 of 10

Central conclusion is entailed by the paper's own definition of a corrigible model, while the supporting 'attractor' claim and the formal metric are imported from the second author's prior forum posts; the appendix's labeled examples are self-authored rather than independent measurements.

  1. self definitional [Abstract and Section 2.1 (Core Concept)]
    "We propose "Corrigibility as a Singular Target" (CAST)—designing FMs whose overriding objective is empowering designated human principals to guide, correct, and control them. ... A purely corrigible FM (C-FM) exhibits these essential characteristics: ... Unconditional Deference: Accepting shutdown, modification, or goal changes without resistance, deception, or manipulation"

    The abstract concludes that CAST 'addresses the core alignment problem at its source,' and Section 2.1 claims that in a C-FM 'goal-content integrity transforms into facilitating principal-directed modifications.' But 'corrigibility' is defined as exactly this behavior: the C-FM is stipulated to have unconditional deference and no goal protection. The claimed transformation of instrumental drives is therefore true by construction from the definition, not derived from any training result, theorem, or experiment. No mechanism is supplied showing that such a utility function can be learned or that it remains stable at scale.

  2. self citation load bearing [Section 3.1, Method 2 - RL Training with Formal Corrigibility Metrics]
    "Design a formal metric capturing corrigibility (Harms, 2024c)"

    The entire CAST training agenda depends on the existence of a mathematically specified, optimizable, non-gameable corrigibility reward. The paper does not state this metric; it defers to Harms 2024c, a non-archival AI Alignment Forum post by this paper's second author. The reference title itself is 'Formal (Faux) Corrigibility,' and the paper gives no independent statement of assumptions, no proof of non-gameability, and no external validation. The central assertion that CAST can 'prevent the default trajectory toward misaligned instrumental convergence' thus rests on a load-bearing self-citation rather than on evidence presented in this paper.

2 more flagged steps
  1. self citation load bearing [Section 2.2 (The Corrigibility Attractor Hypothesis)]
    "A key insight is that corrigibility may be self-reinforcing. An FM trained for pure corrigibility might find it instrumentally convergent to become more effective at empowering its principal, creating an "attractor basin" around genuine corrigibility (Harms, 2024b). This positive feedback loop stands in stark contrast to fixed-goal agents that resist modification."

    The paper's claim that CAST changes the default trajectory relies on the attractor hypothesis: only if corrigibility is self-reinforcing can one expect the drive transformation to survive scaling and adversarial pressure. The sole support cited is Harms 2024b, another forum post by the paper's second author. No independent simulation, theorem, or empirical evidence is offered. The hypothesis is imported from the authors' own prior work and then treated as a settled 'key insight' on which the safety case is built, making the citation load-bearing rather than corroborating.

  2. other [Appendix A (Corrigibility Training Context) and Section 3.1, Method 1]
    "As a work-around, we introduce a new term that is used as a synonym: "anapartistic." ... an agent is anapartistic when it robustly acts opposite of the trope of "be careful what you wish for" by cautiously reflecting on itself as a flawed tool and focusing on empowering the principal ... to fix its flaws and mistakes."

    The appendix presents hand-labeled vignettes as 'True'/'False' examples of anapartism, and Section 3.1 proposes 'LLM-assisted generation using models conditioned on corrigibility principles (Appendix A)' to build training datasets. These labels are the authors' own application of the very definition the paper is proposing to train and evaluate. Any model trained on these self-authored examples and then tested on similar examples would be evaluated against labels generated from the same definition, which is circular: the labels do not constitute independent ground truth, and the appendix itself notes that many cases require subtle judgments about reversibility, manipulation, and multi-principal conflicts.

full rationale

This is a vision/research-agenda paper rather than a derivation with equations, so there is no fitted-parameter circularity of the 'prediction equals fit' kind. The circularity is structural. The abstract and Sections 1-2 assert that CAST 'addresses the core alignment problem at its source' by transforming instrumental drives. That conclusion is already contained in the definition of a C-FM in Section 2.1: a system whose only objective is to empower the principal is, by stipulation, one that accepts shutdown, modification, and goal changes without resistance. The paper does not demonstrate that any training method can actually produce such a system; instead, Section 3 lists a planned agenda. Two of the agenda's key feasibility premises come from self-citations: the corrigibility attractor (Section 2.2, Harms 2024b) and the formal corrigibility metric (Section 3.1, Harms 2024c). Both are non-archival forum posts by the second author, with no machine-checked proof, no code reproduction, and no external falsification. Appendix A's True/False vignettes are the authors' own operationalization of the definition, not independent measurements. The paper is candid that these are hypotheses and plans, which mitigates the severity, but the central claim that CAST solves the alignment problem is either a definitional restatement or a load-bearing self-citation. Some components—such as the proposed scaling tests, adversarial evaluations, and safety cases—are independent and could provide real evidence if carried out, which prevents the score from reaching 8-10. The combination of one definitional collapse and two load-bearing self-citations warrants a score of 6.

Assumptions & free parameters 0 free parameters · 5 assumptions · 1 invented entities

This is a vision paper, so the central claim rests on several unverified assumptions rather than measurements. The most significant are the existence of a non-gameable corrigibility metric, the stability of pure corrigibility under scaling, and the self-reinforcing attractor hypothesis. The appendix also assumes a neologism improves training. No free parameters are fitted because no quantitative results are presented.

assumptions (5)
  • domain assumption Instrumental convergence: sufficiently advanced agents pursue self-preservation, resource acquisition, and goal-content integrity regardless of final goal.
    Invoked in Section 1 as the core problem CAST must solve; standard in the AI-safety literature but not proven for foundation models.
  • ad hoc to paper A formal corrigibility metric exists and can be optimized.
    Method 2 in Section 3.1 defers to Harms (2024c); no metric is given in this paper.
  • ad hoc to paper Training for pure corrigibility is stable under scaling and continued capability training.
    Section 3.2 poses this as an open question, yet the abstract claims the approach prevents the default trajectory toward misalignment.
  • ad hoc to paper The Corrigibility Attractor Hypothesis: a model trained for corrigibility will instrumentally converge on becoming more corrigible and empowering.
    Section 2.2 presents this as a key insight, supported only by a self-citation to Harms (2024b).
  • ad hoc to paper Renaming 'corrigibility' to 'anapartistic' avoids distracting pretraining associations.
    Appendix A introduces the neologism; no evidence is provided that the term substitution improves training outcomes.
invented entities (1)
  • Anapartistic agent (C-FM)
    purpose: A hypothetical model whose sole objective is empowering its principal to correct it; introduced in Appendix A as a trainable behavior target.
    No empirical demonstration is provided; all examples are hand-labeled by the authors according to their own definition.

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Cite this review

Pith. "Pith review of Corrigibility as a Singular Target: A Vision for Inherently Reliable Foundation Models." pith.science (2026). https://pith.science/paper/OLFNU5QK

@misc{pith2026250603056,
  author       = {Pith},
  title        = {Pith review of: Corrigibility as a Singular Target: A Vision for Inherently Reliable Foundation Models},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/OLFNU5QK}},
  note         = {Machine review of arXiv:2506.03056}
}
read the original abstract

Foundation models (FMs) face a critical safety challenge: as capabilities scale, instrumental convergence drives default trajectories toward loss of human control, potentially culminating in existential catastrophe. Current alignment approaches struggle with value specification complexity and fail to address emergent power-seeking behaviors. We propose "Corrigibility as a Singular Target" (CAST)-designing FMs whose overriding objective is empowering designated human principals to guide, correct, and control them. This paradigm shift from static value-loading to dynamic human empowerment transforms instrumental drives: self-preservation serves only to maintain the principal's control; goal modification becomes facilitating principal guidance. We present a comprehensive empirical research agenda spanning training methodologies (RLAIF, SFT, synthetic data generation), scalability testing across model sizes, and demonstrations of controlled instructability. Our vision: FMs that become increasingly responsive to human guidance as capabilities grow, offering a path to beneficial AI that remains as tool-like as possible, rather than supplanting human judgment. This addresses the core alignment problem at its source, preventing the default trajectory toward misaligned instrumental convergence.

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Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Model-Based Soft Maximization of Suitable Metrics of Long-Term Human Power

    cs.AI 2025-07 conditional novelty 7.0 of 10

    A new AI objective, ICCEA power, aggregates humans' ability to reach many possible goals with inequality and risk aversion, and a soft-maximizing agent learns cooperative behavior without knowing human goals.

Reference graph

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