{"id":"d9991d22-b2aa-41fc-9a97-e14af21ad45b","arxiv_id":"2501.12405","paper_version":1,"verdict":"CONDITIONAL","confidence":"HIGH","novelty_score":6.0,"correctness_risk":"medium","formal_verification":"none","parameter_count":0,"one_line_summary":"Alignment should be defined by three scopes, competence, transience, and audience, rather than by generic context-free values.","lead":"This paper proposes that AI alignment should be tailored along three dimensions: what the model must know or do, how long that need lasts, and who it serves. It gives a vocabulary for moving beyond one-size-fits-all helpful, harmless, and honest alignment, and applies it to a mental health counseling example.","discovery_kind":"unification","skeptic_critique":{"model":"deepseek-v4-flash","headline":"The taxonomy's usefulness depends on its categories being reliably applicable, but Section 3's definitions are informal and overlapping; without a demonstrated assignment rule, Section 5's promised technical guidance is not secured.","rationale":"The paper is a clear, well-motivated position piece. Its central claim is modest: alignment is not a single activity, and multiple scopes are worth distinguishing. The writing is strong, the motivating example is illustrative, and the authors do not overclaim empirical support. I agree with the reader that the paper is best treated as CONDITIONAL rather than fully accepted, but I would anchor the condition differently. The reader's weakest assumption is that the three axes are independent and this is not demonstrated. That concern is real but not the most load-bearing: the paper's value lies in using the taxonomy to derive technical implications, and even a non-orthogonal set of useful distinctions could support that. The more fundamental weakness is that the categories themselves are operationalized too thinly. Section 3 gives informal definitions with examples, but no assignment rules, boundary criteria, or reliability evidence. The audience axis is explicitly defined using a communication-medium metaphor that is hard to apply to deployed LLM systems. The competence axis mixes action-level attributes (politeness) with functional capabilities (summarizing emails) under 'behaviors' and 'skills' in a way that invites inconsistent labeling. Without a demonstrable ability to classify real alignment tasks, Section 5's claims that different scopes lead to different data requirements, optimization algorithms, and interaction protocols are more asserted than established. This does not require rejecting the paper; it supports keeping the CONDITIONAL verdict and asking for either sharper definitions or a small annotation study. I credit the paper for not overreaching: it explicitly says that choosing the right scope is not detailed and that reflective equilibrium is needed, which is a genuine limitation rather than a hidden one. Self-citations are present but do not drive the central argument, so circularity is not a concern. No artifacts or formal proofs are provided, which is typical for a conceptual paper. Overall, the central idea is plausible and worth publishing conditionally, with the proposed operationalization check providing a concrete path to strengthen it.","tokens_in":9278,"tokens_out":6814,"duration_ms":75620,"concrete_test":"Operationalization test: select 15-20 alignment specifications from recent papers, model cards, or deployed-system descriptions (e.g., a ChatGPT-style system prompt, a LoRA adapter for a customer-service bot, a personalized therapy assistant, a community-specific content filter). Have three independent annotators classify each specification on competence (knowledge/skills/behaviors), transience (semantic/episodic), and audience (mass/public/small-group/dyadic) using only the definitions in Section 3. Compute Cohen's kappa (or Krippendorff's alpha) per axis. Also ask annotators to flag any cells they consider impossible or any pairs of categories they consider indistinguishable.","verdict_should_be":"UNCHANGED","load_bearing_attack":"The central claim (Section 3) is that alignment should be specified along three scopes: competence, transience, and audience. The paper's payoff claim (Section 5) is that these scopes 'clearly specify the technical needs' of alignment technologies, leading to specific implications such as fine-tuning vs. inference-time adapters and unidirectional vs. bidirectional procedures. That payoff requires the three axes to be usable as a classification scheme. Section 3 does not provide assignment rules or boundary criteria. Competence defines knowledge, skills, and behaviors, but behaviors are 'values, attitudes, and temperament evidenced through actions,' which can overlap with skills: the paper itself calls 'converting texts into hip-hop raps' a skill, whereas 'politeness' and 'verbosity' are called behaviors, yet both are action-level properties of model output. Transience is borrowed from neuroscience but no operational test is given for what counts as 'bound to a time, place, or other context' as opposed to 'general about the world.' Audience is defined partly by group size and partly by communication medium: public alignment is 'simply speaking to a community in-person,' whereas mass alignment requires media; thus the same deployed model could be classified as public or mass depending on how it reaches users. If two competent annotators cannot reliably classify a given alignment specification into one category per axis, then the proposed 3-by-2-by-4 space is not a usable design space, and the technical implications in Section 5 do not follow. This is a more fundamental problem than the reader's concern about axis independence: even if the axes were fully independent, ambiguous categories would still defeat the claimed purpose of 'clearly specify[ing] the technical needs.'","agreement_with_reader":"partial"},"referee_report":{"model":"deepseek-v4-flash","summary":"The paper is a position statement arguing that AI alignment should not be reduced to a single generic activity. It proposes three \"scopes\" of alignment: competence (knowledge, skills, or behaviors), transience (episodic versus semantic), and audience (mass, public, small-group, or dyadic). The authors claim that prevailing practice, mass semantic alignment for behaviors, is only one point in this larger space, and they use a hypothetical mental-health counseling example in the U.S. versus China to illustrate how the scopes could differ by context. Section 5 argues that the scopes have technical implications, such as full fine-tuning versus inference-time adapters and unidirectional versus bidirectional alignment procedures, and suggests that reflective equilibrium could be used to choose a scope and thereby dissolve value conflicts. The paper is explicitly non-empirical and positions itself as a precursor to pluralistic alignment.","tokens_in":9550,"tokens_out":2733,"duration_ms":29257,"significance":"If the proposed taxonomy is accepted, it offers a useful descriptive vocabulary for discussing alignment targets and draws attention to alignment settings beyond the dominant one. The paper is clear in its motivation and points to a non-empty space, citing existing work on personalized alignment, contextual alignment, episodic memory, and knowledge-oriented alignment. However, its central contribution is a conceptual scheme whose utility depends on the categories being clearly applicable and reasonably complete. The paper does not provide a formal derivation, but for a position paper that is not itself disqualifying; the honesty of the writing and the modest scope of the claims are strengths. The three-axis taxonomy could serve as a starting point for more operational work, but as it stands the framework's applicability is not yet demonstrated.","major_comments":[{"comment":"The three competence categories (knowledge, skills, behaviors) are not defined with mutually exclusive boundaries. The paper calls 'converting texts into hip-hop raps' a skill and 'politeness' and 'verbosity' behaviors, but both are action-level properties of model output. If the distinction between skill and behavior is not operational, then Section 5's claim that different competences require different data and optimization strategies cannot be grounded. A usable taxonomy needs either an explicit assignment rule or a statement that the categories are overlapping perspectives rather than disjoint dimensions.","section":"Section 3, competence"},{"comment":"The episodic/semantic distinction is introduced with examples but no operational criterion for deciding whether a given knowledge, skill, or behavior is 'bound to a time, place, or other context' or 'general about the world.' For instance, 'obtaining manager pre-approval before booking air tickets' is described as episodic, yet in another organization it could be a standing rule. Without a test or at least a clear decision procedure, two annotators could plausibly classify the same alignment target into different transience categories, which undermines the claimed design space.","section":"Section 3, transience"},{"comment":"The motivating example changes all three axes simultaneously when moving from the U.S. to China: competence shifts from individualized to collectivist, transience from episodic to semantic, and audience from individual to community. This co-variation suggests that the three axes may not be independent as the design-space framing implies, and it does not provide evidence for a 3-by-2-by-4 space. The paper should either give an example where one axis varies while the other two are fixed, or soften the claim that the three scopes are independent dimensions.","section":"Section 4 and Section 3"},{"comment":"The claim that 'we will also implicitly deal with the problem of conflicting values because the reflective equilibrium will have resolved conflicts by narrowing the scope until there are few remaining' is asserted without a method or justification. Narrowing the scope can avoid a conflict, but that is different from resolving it, and no procedure is given for finding a scope with minimal internal conflict. The paper itself acknowledges that 'such a process is clearly necessary,' so this section currently promises more than it delivers.","section":"Section 5, reflective equilibrium"}],"minor_comments":[{"comment":"The reference style is inconsistent: the text says 'As Tan Zhi-Xuan et al. submit,' but the bibliography entry is 'Zhi-Xuan et al. 2024.' Please standardize.","section":"Section 5"},{"comment":"The transliteration 's¯adh¯aran.a-dharma and vi´ses.a-dharma' contains formatting artifacts; it should read 'sādhāraṇa dharma' and 'viśeṣa dharma'.","section":"Section 3"},{"comment":"The sentence beginning 'It is important to differentiate the transience of alignment because while semantic alignment implies ' is missing a comma after 'because' and is long enough to obscure the conditional structure; consider splitting it.","section":"Section 5"},{"comment":"The mental-health example is labeled hypothetical, but the paper does not qualify that the characterization of U.S. and Chinese cultural norms is a simplification; adding a caveat would prevent the example from being read as an empirical claim.","section":"Section 4"},{"comment":"The figure is not described in the text beyond a parenthetical about the dotted line; the lifecycle stages in the figure should be enumerated in the prose to make the figure self-contained.","section":"Figure 1"}],"recommendation":"major_revision","confidential_remarks":"The paper is a position statement rather than a technical contribution, which fits a venue like AAAI/ACM AIES or a workshop-style cs.CY venue. The central taxonomy is plausible but needs sharper boundary definitions and a more careful statement of what is claimed about the design space. The reflective-equilibrium passage in Section 5 is the weakest part; it could be cut or reframed as an open problem without damaging the paper's main message. I do not see grounds for rejection, but the revisions are substantive enough to require another round of review."},"author_rebuttal":null,"desk_editor":{"model":"deepseek-v4-flash","letter":"Two things to know. First, this is a legitimate conceptual contribution: it gives alignment research a simple, memorable vocabulary — competence, transience, audience — for saying that alignment is not one thing, and it writes clearly. Second, the framework is asserted, not defended: the axes aren't derived, no completeness or orthogonality argument is offered, and the category boundaries are fuzzy. That makes the technical payoff in Section 5 more conditional than the paper lets on.\n\nWhat's new: while most frontier-model work aligns to a single universal value set, the paper names the other points in the design space. The mental-health example is well chosen and does real work. The related-work section is honest, treating existing techniques as illustrations rather than pretending the space is empty. The self-citations are appropriate here; they don't drive the argument.\n\nSoft spots, in proportion. The stress-test note is right that the categories are hard to apply. Competence mixes skills and behaviors without a separating test; the paper calls 'converting texts into hip-hop raps' a skill and 'politeness' a behavior, but both are output-level properties. Transience borrows episodic/semantic from neuroscience but gives no operational rule for what counts as 'bound to a time, place, or other context.' Audience is defined partly by group size and partly by medium, so the same model could be public or mass depending on how it reaches users. These blurrinesses don't kill a position paper, but the claim that scopes 'clearly specify technical needs' outruns the definitions. The reflective-equilibrium paragraph is hand-wavy: it asserts narrowing the scope resolves value conflicts without saying how.\n\nThe reader's conditional verdict is about right. This isn't a paradigm shift, but it's a solid framing that could become useful if the categories get operationalized. I'd send it to peer review; a good referee could push for sharper definitions or a limited-completeness argument, and the paper would come back stronger. I wouldn't build my own work on it, but I'd cite it in related work. For a reading group, it's short and likely to spark debate, so maybe.","headline":"A clear, readable position paper that gives alignment a useful three-scope vocabulary, but the axes are asserted rather than derived and the categories are under-specified.","tokens_in":10104,"tokens_out":2413,"would_cite":false,"duration_ms":23091,"reading_group":"maybe","serious_thinker":"yes","would_accept_peer_review":true},"rs_alignment":null,"lean_confirmation":null,"pith_extraction":{"msc":[],"pacs":[],"model":"deepseek-v4-flash","headline":"Alignment is not one workflow: the paper proposes three scopes — competence, transience, and audience — along which AI alignment should be designed.","keywords":["AI alignment","large language models","scoped alignment","competence","transience","audience","pluralistic alignment","values"],"falsifier":"Find two deployment contexts that differ on exactly one scope — say audience, dyadic versus small-group, with competence and transience held fixed — and measure whether the optimal alignment method or outcome differs meaningfully; if no systematic difference appears across several such pairs, the scopes do not carve alignment at its joints. A complementary test would look for a context that differs on all three scopes yet still requires the same alignment workflow, showing the axes are not the load-bearing choices.","tokens_in":9057,"feed_emoji":"🎯","tokens_out":5528,"duration_ms":52179,"temperature":0.7,"pith_summary":"This paper argues that AI alignment should not be a single one-size-fits-all process of instilling helpful, harmless, and honest values into a large language model. Instead, alignment work should be chosen according to three scopes: competence (knowledge, skills, or behaviors), transience (episodic, tied to a time and place, or semantic, general across contexts), and audience (mass, public, small-group, or dyadic). The authors' point is that the standard practice — mass semantic alignment for behaviors — is only one cell in a much larger design space. A sympathetic reader should care because different scopes imply different data, optimization, and inference-time methods, and because properly scoping alignment may avoid the value conflicts that pluralistic alignment tries to mediate. The paper offers the framework as a prerequisite for more pluralistic alignment work.","feed_headline":"Alignment should split by competence, transience, and audience","feed_subtitle":"The usual helpful-harmless-honest target is just one cell in a larger design space, the paper argues.","key_machinery":"The organizing device is the three-axis scope framework: competence by knowledge, skills, or behaviors; transience by episodic or semantic; and audience by mass, public, small-group, or dyadic. The framework works as a classification space for alignment goals and methods, letting the authors position existing techniques as particular cells — for example, reward-based preference learning as suitable only for local uses, low-rank adapters as a path to episodic alignment, and mutual theory of mind as a mechanism for dyadic alignment. It carries the argument by turning the vague term 'alignment' into a design space in which each axis is supposed to point to different technical choices.","core_discovery":"The paper's central claim is that 'alignment should not be just one activity, technical approach, or workflow,' but should be 'more specific and scoped for the different needs of different groups over time.' It proposes three dimensions along which an alignment effort can be placed: competence, which covers the knowledge, skills, or behaviors the model must have; transience, which distinguishes episodic alignment bound to a particular time, place, or context from semantic alignment about general facts of the world; and audience, which runs from a single user in a dyadic relationship, through small groups and publics, to mass alignment with all of humanity. The claim is that most current research and frontier-model practice occupies one corner of this space — mass semantic alignment for behaviors such as helpfulness, harmlessness, and honesty — and that this narrowness hides the technical requirements of other scopes. If the claim is right, alignment is a family of workflows with different data sources, learning algorithms, and inference-time mechanisms, not one method applied to one target.","pith_inferences":["The three axes are proposed as independent but the paper's motivating example varies all three together when moving from one country to another; an editorial reading is that the axes may co-vary, so the true design space may be smaller than the 3-by-2-by-4 grid suggests.","A natural extension the paper leaves implicit is an empirical test: hold two scopes constant, vary the third, and compare which data, optimization, or inference-time method succeeds, turning the taxonomy into a predictive theory.","If scoping succeeds, evaluation and red-teaming may also need to be scoped, since a model aligned for a dyadic therapy relationship should not be judged by a mass-semantic benchmark.","Another extension is to treat scope selection itself as a governed, human-centered design decision, because the paper notes scopes should be found through reflective equilibrium but does not prescribe a process for doing so."],"forward_implications":["Researchers would stop treating mass semantic behavioral alignment as the default and would report which scope their method targets.","Alignment technologies would diverge by competence: knowledge alignment favors question-answer generation from content, skills favor repeated examples, and behaviors favor preference data with scenarios.","Episodic alignment would be done at inference time, for example by swapping low-rank adapters on the fly, rather than by permanently fine-tuning the model.","Dyadic and small-group alignment would be bidirectional, with both human and model adapting, unlike the unidirectional alignment used for mass audiences.","Properly scoped alignment could resolve many value conflicts before pluralistic mediation is needed, because narrowing the scope can remove conflicting values."],"supporting_citations":[{"why":"Defines the constitutional-AI style of harmlessness that the paper argues is only one alignment target.","marker":"Bai et al. 2022"},{"why":"Shows prevailing instruction-following alignment via human feedback, the default the paper wants to broaden.","marker":"Ouyang et al. 2022"},{"why":"Supplies the 'empty signifier' diagnosis that motivates the need for scoped definitions.","marker":"Kirk et al. 2023"},{"why":"Surveys alignment literature mostly aimed at behavior, used as the reference point for mass semantic behavioral alignment.","marker":"Ji et al. 2023"},{"why":"Dyadic morality theory, which the paper uses to argue that moral perception is context- and audience-dependent.","marker":"Schein and Gray 2018"},{"why":"Supplies the argument that reward-based preference alignment fits only local, narrow scopes.","marker":"Zhi-Xuan et al. 2024"},{"why":"Exemplifies alignment from unstructured text, used as a technique for public alignment.","marker":"Padhi et al. 2024"},{"why":"LoRA low-rank adapters are cited as a mechanism for episodic alignment at inference time.","marker":"Hu et al. 2022"},{"why":"Shows that knowledge alignment benefits from question-answer generation, illustrating competence-specific methods.","marker":"Sudalairaj et al. 2024"},{"why":"Mutual theory of mind is cited as the mechanism behind dyadic and small-group alignment.","marker":"Wang et al. 2024"}],"fun_headline_variants":["Alignment needs scopes: competence, transience, audience","Beyond helpful-harmless-honest: scoped alignment","Three axes of AI alignment: competence, transience, audience","Scoped alignment: not one-size-fits-all for AI","Alignment is a family of workflows, not one target"],"cache_read_input_tokens":3200,"weakest_assumption_plain":"The three scopes are assumed to vary independently and to cover the meaningful decisions in alignment, but the paper's own example changes all three at once when the country changes, so if they co-vary, the proposed design space claims more degrees of freedom than exist.","fun_headline_variants_meta":{"raw":{"variants":["Alignment needs scopes: competence, transience, audience","Beyond helpful-harmless-honest: scoped alignment","Three axes of AI alignment: competence, transience, audience","Scoped alignment: not one-size-fits-all for AI","Alignment is a family of workflows, not one target"]},"model":"deepseek-v4-flash","effort":"low","cost_usd":0.00019,"raw_usage":{"total_tokens":1304,"prompt_tokens":873,"completion_tokens":431,"prompt_tokens_details":{"cached_tokens":384},"prompt_cache_hit_tokens":384,"prompt_cache_miss_tokens":489,"completion_tokens_details":{"reasoning_tokens":349}},"tokens_in":489,"tokens_out":431,"duration_ms":3700,"temperature":1.0,"reasoning_tokens":349,"cache_read_input_tokens":384,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-08-10T20:23:41.760521+00:00","model_set":{"reader":"deepseek-v4-flash"},"falsifier":"Find two deployment contexts that differ on exactly one scope — say audience, dyadic versus small-group, with competence and transience held fixed — and measure whether the optimal alignment method or outcome differs meaningfully; if no systematic difference appears across several such pairs, the scopes do not carve alignment at its joints. A complementary test would look for a context that differs on all three scopes yet still requires the same alignment workflow, showing the axes are not the load-bearing choices.","supporting_citations":[{"cited_title":null,"cited_arxiv_id":null,"evidence_quote":"Shows prevailing instruction-following alignment via human feedback, the default the paper wants to broaden."},{"cited_title":null,"cited_arxiv_id":null,"evidence_quote":"Dyadic morality theory, which the paper uses to argue that moral perception is context- and audience-dependent."},{"cited_title":null,"cited_arxiv_id":null,"evidence_quote":"Supplies the argument that reward-based preference alignment fits only local, narrow scopes."},{"cited_title":null,"cited_arxiv_id":null,"evidence_quote":"Exemplifies alignment from unstructured text, used as a technique for public alignment."},{"cited_title":"D.; and Goel, A","cited_arxiv_id":null,"evidence_quote":"Mutual theory of mind is cited as the mechanism behind dyadic and small-group alignment."}],"review_version":1}