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REVIEW 4 major objections 5 minor 85 references

PHASE-Tree: Modeling Character-State Evolution in Long-Horizon Role-Playing Dialogue

T0 review · 4 major / 5 minor · reviewed 2026-08-10 · deepseek-v4-flash

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Pith's one-line read PHASE-Tree claims that role-playing models can generate from a character's currently evolved state by storing that state in a multi-timescale tree, not a static profile.

desk verdict The benchmark and the semantic-score finding are solid, but the headline character-score gain does not survive the paper's own reference-sensitivity check (Table 9 inverts the ranking). read the letter →

arxiv 2608.06975 v1 pith:JI5T7PQG submitted 2026-08-07 cs.CL cs.AI

Bo Tang , Jianan Yang , Junyi Zhu , Yiquan Wu , Rui Zhao , Zhengyu Yang , Yang Zhang , Feiyu Xiong
show 2 more authors
Zhiyu Li Jiajun Shen
This is my paper · ORCID
classification cs.CLcs.AI
keywords role-playingdialoguecharacter-stateevolutionlong-horizonpersonamodelingLLM-as-judgeevaluationmulti-timescalerepresentationstale-statefailurenext-utterancebenchmark
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

PHASE-Tree is a representation for long-horizon role-playing that stores a character's state as a four-layer tree: an immutable identity root plus persona, session, and moment strata that update on different timescales. The paper's central claim is that this lets a model generate from the character's currently evolved state, avoiding the "stale-state failure" where a character's voice persists but their narrative situation is outdated. To support that, the paper introduces a benchmark that tests whether responses match the state at the current narrative time, and reports that textual provision of the tree outperforms raw profiles, rewritten profiles, static trees, retrieval, and profile-prompt baselines across long-dialogue corpora. The load-bearing mechanism is the update gate: a persona field changes only when evidence spans enough episodes, includes enough high-significance events, and respects a cooldown. If the claim holds, role-playing agents can stay recognizable over long arcs while tracking relationships, mood, and commitments.

What carries the argument

The central object is the PHASE-Tree itself: a four-stratum hierarchy with an immutable identity root (name, gender, backstory) and three mutable strata—persona (slow-moving traits with resistance tiers), session (within-scene learned information, commitments, and attitudes), and moment (transient emotion and intensity). The update rule in Eq. (2) is the gating mechanism: a persona field is updated only if the number of contributing episodes $n_{\mathrm{ep}}(f)$, the number of high-significance evidence entries $n_{\mathrm{high}}(f)$, and the number of episodes since the last update $\Delta_{\mathrm{ep}}(f)$ all clear resistance-dependent thresholds. That gate is what lets core traits like personality change only under season-long evidence while relationships can shift from a single decisive scene. The benchmark LongEvoRoleBench supplies the measuring instrument: eight corpora unified under a next-utterance protocol, with long-dialogue corpora testing cross-episode evolution and short-dialogue corpora serving as within-scene state-tracking checks.

What would settle it

Take the released LongEvoRoleBench responses and score them under the raw character description as the judge reference; if, as in Appendix L.1, PHASE-Tree's long-dialogue character score drops to 2.935 while the raw-profile baseline rises to 3.369, the claimed character-level superiority is an artifact of the chosen judge reference rather than of evolved-state fidelity.

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Extended reading notes

Core claim

The paper discovers that decomposing a character's state by timescale—fixed identity, slow-moving persona, session-level adaptations, and moment-level affect—and gating persona updates by episode count, significance, and cooldown produces generations that align better with the character's current narrative state than either a raw profile or an unstructured rewrite. PHASE-Tree's extracted profiles are used as prompt text, and on the long-dialogue benchmark the full pipeline ranks first in all external textual comparisons, improving character, semantic, and embedding scores over the strongest textual baseline by 19.7%, 12.4%, and 15.1%. The paper also claims that the semantic-score advantage survives swapping judge models and generation backbones, while its own appendix shows the character-score advantage is more sensitive to the judge's reference text.

Load-bearing premise

The evaluation assumes that grading every method against PHASE-Tree's own flattened profile is a fair yardstick for character fidelity; the paper's own reference-swap shows the headline character-score lead flips when the raw character description is used instead.

Editorial extensions

If this is right

  • Role-playing systems can maintain character recognizability across long arcs without freezing a persona, because each editable field is updated locally rather than rewriting the whole profile.
  • Benchmarks for persona fidelity should score against the character state at the current narrative time, not against a static profile, if evolved-state generation is the target.
  • Textual provision of an evolving tree is a practical prompting strategy: it beats retrieval and profile-prompt baselines on long-dialogue semantic and embedding scores.
  • Encoding the same tree into LoRA adapters is more token-efficient but loses the distinctions among tree variants, indicating that the profile-to-LoRA mapping is the bottleneck rather than the tree representation itself.
  • Hand-set resistance thresholds transfer across four different long-dialogue corpora without per-corpus tuning, suggesting that one narrative-pacing prior can govern multiple story domains.

Reading between the lines

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

  • Beyond the paper: the reference-sensitivity result in Appendix L.1 implies that the headline character-score gain is not robust to the judge's profile text; a fair comparison would score all methods against a neutral, method-independent reference.
  • Beyond the paper: because the same model used to build the trees also serves as the primary judge, an independent judge that has never read PHASE-Tree profiles would test whether the semantic-score advantage reflects the state representation or the judge's familiarity with its wording.
  • Beyond the paper: the gating thresholds could be learned from data rather than hand-set; a useful extension would correlate learned thresholds with actual narrative-pacing statistics, such as the number of episodes before relationship changes occur.
  • Beyond the paper: the parametric bottleneck suggests that richer state-conditioned adapters with more capacity might close the gap between the textual and implicit routes, making evolved-state conditioning feasible at fixed prompt cost.
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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 / 5 minor

Summary. The paper proposes PHASE-Tree, a four-stratum character-state representation (immutable identity root; mutable persona, session, and moment layers) with resistance-gated cross-episode evolution, and introduces LongEvoRoleBench, a next-utterance benchmark built from four long-dialogue and four short-dialogue corpora. The central claim is that conditioning generation on the flattened PHASE-Tree state improves evolved-state fidelity, reported as rank-first in 11 of 12 long-dialogue cells against internal variants and all 12 cells against external textual baselines, with long-dialogue character, semantic, and embedding gains of 19.7%, 12.4%, and 15.1%. The paper validates the representation under both explicit textual provision and implicit parametric (profile-to-LoRA) adaptation, with additional human-rating, cross-judge, and cross-backbone analyses.

Significance. If its findings hold, the paper would make a useful contribution: the multi-timescale, field-addressable tree with a deterministic gating rule is a concrete proposal for localized character-state updates, and the benchmark unifies eight existing corpora under a common protocol. The paper ships code, data, and model releases, and it includes a human audit of accepted field updates, a blinded 200-response human study, and a multi-judge/multi-backbone robustness analysis. The semantic-score advantage is reasonably supported: PT vs NR long-dialogue Sem Cohen's d = 0.25 and PT vs ST d = 0.40, with the Sem ranking stable across three judges and four backbones. The character-score superiority, however, is not supported by the evidence: the internal ablation effect is negligible (PT vs NR Char d = 0.01), and the headline Char gain reverses when the judge reference is changed to the raw profile (Appendix L.1, Table 9). The paper's significance therefore rests mainly on the semantic and embedding findings and on the benchmark infrastructure, not on the character-score claim as stated.

major comments (4)
  1. [Appendix L.1, Table 9] The long-dialogue character-score ranking inverts under a neutral judge reference. When the judge's persona reference is switched from the flattened PHASE-Tree profile to the raw character description, RP rises from 2.454 to 3.369 while PT falls from 3.004 to 2.935, reversing the order and erasing the claimed advantage. This directly contradicts the Abstract's headline 'improving character-level ... by 19.7%' and the corresponding claim in Section 5.2. The reference-sensitivity analysis in the appendix is candid, but the main text continues to state the Char gain as a primary result; a revision must either withdraw the Char superiority claim or re-derive it from a reference-neutral evaluation.
  2. [Section 4.3, Appendix G, Table 14] The character-score evaluation is partially circular. The judge is given the flattened PHASE-Tree profile as the persona reference, and this is exactly the conditioning text PT receives (1358 profile tokens on long dialogue, Table 14), while RP, RAG, and PAG condition on different text. PT therefore enjoys a lexical-consistency advantage that is an artifact of the yardstick rather than a measured property of evolved-state fidelity. The same GPT-4.1 model is used for tree extraction and for judging, adding a second channel of self-consistency bias. The Sem and Emb metrics are less sensitive to this issue, but the Char score as implemented does not support the paper's central claim.
  3. [Section 5.2, Table 8] Even under the reference condition most favorable to PT, the internal ablation shows no practical character-score effect: PT vs NR long-dialogue Char Cohen's d = 0.01 with per-cell range [-0.13, 0.08], and PT vs ST d = 0.10. The 19.7% improvement in the Abstract is computed against the external PAG baseline, whose lower score is partly explained by the judge-reference mismatch identified in Appendix L.1. The Sem and Emb gains (d = 0.25-0.40) are credible, but the character-score claim should be either removed from the headline results or replaced with an analysis that uses a reference-neutral profile for all methods.
  4. [Section 3.4, Eq. (2)] The gating mechanism in Eq. (2) depends on manually chosen thresholds (tau_ep, tau_high, tau_cd) and the paper states that learning them automatically is future work. While the thresholds are held fixed across corpora and the human audit (Appendix D, Table 5) shows that accepted updates are evidence-supported, the experiments do not test sensitivity to these threshold values. A brief sensitivity analysis or an explicit argument that the headline results are robust to threshold choice would strengthen the claim that cross-episode evolution, rather than the particular thresholds, drives the Sem gains.
minor comments (5)
  1. [Abstract] The phrase 'on descriptive n=10 PT and NR prompt subsets' reports a very small, descriptive human comparison; the +0.20 Overall difference is not a statistical claim. Please either move this detail to Appendix L.3 or state clearly that it is an anecdotal pilot.
  2. [Appendix C] The sentence 'they were chosen so that core-tier updates require evidence spanning roughly two-thirds of a typical television season of a typical television corpus' is redundant; 'of a typical television corpus' can be deleted.
  3. [Author affiliations] The affiliation 'MemT ensor (Shanghai) Technology' contains an odd space; please fix to 'MemTensor (Shanghai) Technology'.
  4. [Figure 4 caption] The caption contains the fragment 'abl main' in the y-axis labels; this appears to be a LaTeX truncation of 'ablation' and should be corrected.
  5. [Section 3.1, Eq. (1)] The notation 'S persona t' and similar inline subscripts are visually confusing; consider using S^persona_t or a consistently typeset subscript throughout the paper.

Circularity Check

1 steps flagged · score 6.0 of 10

Char headline reduces to the judge reference: PT is scored against its own conditioning text, and Table 9 shows the ranking inverts when the reference is switched to the raw profile.

  1. self definitional [Section 4.3 (Evaluation Metrics), Section 3.5 Eq. (3), Appendix L.1 (Table 9)]
    "The judge evaluates responses using the supplied profile text (Char) or dialogue context (Sem). ... The default judge condition uses the flattened PHASE-Tree profile as the Character Profile for every method."

    By Eq. (3), PT generates from p_theta(y | flatten(S_t), c_t); by Section 4.3, the Char judge scores consistency with 'the supplied profile text,' and Appendix L.1 states that the supplied profile is the flattened PHASE-Tree profile for every method. The Char score is therefore a measure of how well PT reproduces its own conditioning text rather than an independent test of evolved-state fidelity. The paper's own Table 9 demonstrates the dependence: switching the judge reference to the raw character description changes long-dialogue Char from RP 2.454 to 3.369 and PT 3.004 to 2.935, inverting the order and removing the +19.7% margin advertised in the abstract.

full rationale

No load-bearing self-citations or imported uniqueness theorems appear; the evolution gate in Eq. (2) is a deterministic threshold with manually fixed, corpus-agnostic hyperparameters, and the Sem and Emb metrics use dialogue context and ground-truth embeddings rather than the PHASE-Tree serialization, giving the paper substantial independent content. The central circularity is in the character-level evaluation: PT conditions on flatten(S_t) and the Char judge's reference is the same flatten(S_t), so the headline 19.7% Char improvement measures self-consistency with the method's own input. The paper honestly reports the reference-sensitivity analysis, but the acknowledgment does not undo the reduction; it documents it. The use of GPT-4.1 for both tree extraction and judging adds a second self-consistency channel, though that alone would not be formal circularity. Because the long-dialogue Sem advantage persists across judges and backbones and is backed by a human-correlation study, the paper is partially, not wholly, circular; the circularity is concentrated in the Char claim that anchors the abstract's strongest result.

Assumptions & free parameters 2 free parameters · 4 assumptions · 0 invented entities

The central claim rests on hand-set gating thresholds, a psychological grounding assumption, the validity of LLM-as-judge metrics, and several corpus-specific post-processing heuristics. These are disclosed but they are load-bearing: if the thresholds or the judge reference are changed, the ranking changes.

free parameters (2)
  • Resistance-gating thresholds (tau_ep, tau_high, tau_cd) = core 16/6/16, moderate 3/0/3, low 1/0/2 (episodes/high-sig/cooldown)
    Manually set from 'narrative-pacing priors' (Appendix C) and held fixed. They determine whether persona fields ever update, so the cross-episode evolution claim depends on these values.
  • Evidence archive and merge constants = 8-episode medium expiry; 20-entry recent window; >=80% length and >=50% distinctive words preserved for incremental…
    Hand-tuned operational constants in the update pipeline that shape which updates are accepted.
assumptions (4)
  • domain assumption McAdams' distinction between dispositional traits and characteristic adaptations, plus the state-trait affect distinction, justify the four-stratum decomposition
    Invoked in Section 3.1 to motivate the persona/session/moment split; if this psychology does not map to dialogue behavior, the tree structure is arbitrary.
  • domain assumption Ground-truth next utterances in the eight corpora are valid targets for evolved-state generation
    Section 4.1 assumes the original corpus continuations reflect the character's current state, which is plausible but unverified for each instance.
  • domain assumption LLM-as-judge scores are valid measures of profile consistency and contextual coherence
    Sections 4.3 and G rely on 1-5 judge ratings; human validation is limited to 200 responses with r=0.65 and a descriptive n=10 PT/NR subset.
  • ad hoc to paper The six deterministic post-update patches are appropriate for all four long-dialogue corpora
    Appendix D Stage C introduces corpus-specific heuristics for romantic relationships that are applied uniformly; they encode assumptions about relationship tracking.

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Cite this review

Pith. "Pith review of PHASE-Tree: Modeling Character-State Evolution in Long-Horizon Role-Playing Dialogue." pith.science (2026). https://pith.science/paper/JI5T7PQG

@misc{pith2026260806975,
  author       = {Pith},
  title        = {Pith review of: PHASE-Tree: Modeling Character-State Evolution in Long-Horizon Role-Playing Dialogue},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/JI5T7PQG}},
  note         = {Machine review of arXiv:2608.06975}
}
read the original abstract

Long-horizon role-playing demands that characters remain recognizable as they evolve with the narrative. Yet existing work falls short on two fronts: representations are typically static profiles that cannot be updated locally without destabilizing unchanged traits, and benchmarks mainly test persona preservation and memory recall rather than whether a model speaks from a character's currently evolved state. We address both. PHASE-Tree is a multi-timescale character-state tree with an immutable identity root and mutable persona, session, and moment layers, making each mutable field an addressable target for localized within- and cross-episode updates. It conditions generation through explicit textual provision or implicit parametric adaptation. To measure evolved-state generation, we introduce LongEvoRoleBench, which pairs four long-dialogue corpora for cross-episode evolution with four short-dialogue corpora as within-scene state-tracking checks, under a unified next-utterance protocol. On the long-dialogue core, textual PHASE-Tree ranks first in 11 of 12 dataset-metric cells against internal variants and all 12 cells against external textual baselines, improving character-level, semantic, and embedding scores by 19.7%, 12.4%, and 15.1% respectively. In a blinded 200-response study, human ratings correlate with the GPT-4.1 judge (Pearson r= 0.65); on descriptive n= 10 PT and NR prompt subsets, the Overall difference is +0.20. The long-dialogue Sem advantage persists across LLM judges and generation backbones.

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    Stale-romantic decay .Demote “current” romantic partners (boyfriend / girlfriend / partner / etc.) that no longer have recent fresh evidence in the archive

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    Inter-main reciprocity repair.Demote short-lived, unreciprocated romantic claims on a main character, and propagate sustained reciprocal roles to the interacting main when one side asserts a current couple-tier relationship for several consecutive episodes without a matching e...

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    role is name

    Legacy relationship normalization.Normalize bare-name and plural- are legacy formatting so that every entry follows the canonical “role is name” pattern

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    Inverse-pair alignment.A two-pass step that first demotes premature partner roles (e.g., “husband” before an on-screen wedding) and then aligns the partner tier between bidirectional entries

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    Continuity forward-fill.Fill 1-to- N episode regression gaps in main-couple relationships when both sides match before and after the gap, no breakup evidence appears in the archive, and the gap is caused by missing evidence in transitional episodes

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    Core-trait audit(optional, off by default). A periodic, descriptor-level LLM audit ofpersonality and speaking_style at coarse checkpoints (default: every 24 episodes plus the finale); at most one descriptor may change per audit, each supported by at least two high-significance...

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    Do NOT add, infer, or supplement from your own training knowledge

    **USE ONLY THE PROVIDED DATA.** Extract information EXCLUSIVELY from the raw profile JSON. Do NOT add, infer, or supplement from your own training knowledge

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    Do NOT fabricate; null is always better than a hallucination

    **NULL FOR MISSING FIELDS.** If the raw profile contains no information for a field, set its value to null. Do NOT fabricate; null is always better than a hallucination

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    **NO CHARACTER NAME IN VALUES.** The name lives in identity.name; do NOT mention it inside any other value field

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    A single source field may need to be SPLIT across multiple target fields

    **SEMANTIC ANALYSIS -- DO NOT BLINDLY COPY SOURCE FIELDS.** Source profiles may group heterogeneous information under one field; analyze each fact and route it to the single target field whose definition it best matches. A single source field may need to be SPLIT across multip...

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    Job titles only in occupation; personality traits only in personality; relationship details only in relationships; demographics only in demographics

    **STRICT NO-DUPLICATION -- ZERO TOLERANCE.** Each fact appears in exactly ONE field. Job titles only in occupation; personality traits only in personality; relationship details only in relationships; demographics only in demographics

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    Only include species/race when non-human or otherwise distinctive

    **SKIP TRIVIALLY OBVIOUS INFO.** For a human in a real-world setting, do NOT write "human" in demographics. Only include species/race when non-human or otherwise distinctive

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    **OUTPUT LANGUAGE.** Write all value strings in the same language as the input raw profile data; field keys are always English

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    No heavy rewriting or embellishment beyond the source

    **PARAPHRASING TOLERANCE.** Light reorganization only (up to half a sentence of connective phrasing or omission). No heavy rewriting or embellishment beyond the source

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    key: value

    **NO COLONS IN VALUES -- USE NATURAL LANGUAGE.** All value strings must be flowing natural language. No "key: value" formatting inside any value string; use connectors such as "is" / "includes". ## Attribute Tree Structure The tree has four layers: identity, persona, session, ...

  58. [68]

    If it contains catchphrases verbatim , REWRITE

    speaking_style is a DESCRIPTION of how they speak, not raw quotes. If it contains catchphrases verbatim , REWRITE

  59. [69]

    Remove any life event, plot point, power level, cultivation rank, job title, or inferred ability

    behavioral_tendencies describes HABITUAL patterns or skills. Remove any life event, plot point, power level, cultivation rank, job title, or inferred ability

  60. [70]

    ROLE is NAME

    relationships items follow the strict "ROLE is NAME" pattern. Remove narrative verbs and emotional descriptions; if an item is purely an event with no role+name, DELETE it

  61. [71]

    backstory is within the 30 EN words / 40 ZH chars limit and contains no job title or occupation keyword

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    No fact is duplicated across two or more fields; keep it in the most specific field

  63. [73]

    No value string contains a colon used as a key-value separator

  64. [74]

    identity

    demographics / relationships value is set to null if ALL sub-items are null/unknown. ## Output format Output ONLY a valid JSON object. No extra text, explanations, or markdown fences. { "identity": { "name": "...", "gender": "...", "backstory": "..." or null }, "persona": { "s...

  65. [75]

    Your job is ONLY to fill session and moment based on the dialogue

    **PERSONA IS FROZEN.** Do NOT modify or comment on the persona layer. Your job is ONLY to fill session and moment based on the dialogue

  66. [76]

    Only track what THIS character learned, felt, or committed to -- not other speakers

    **ANALYZE FROM THE CHARACTER'S PERSPECTIVE.** The "role" field tells you which character you are analyzing. Only track what THIS character learned, felt, or committed to -- not other speakers

  67. [77]

    Do NOT fabricate

    **USE ONLY DIALOGUE EVIDENCE.** Every field you fill must be grounded in something explicitly said or clearly implied in the dialogue. Do NOT fabricate

  68. [78]

    **BE CONCISE.** Each learned_info item is one short sentence; attitude_shifts values are brief descriptions; emotion is a single word or short phrase

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    Do NOT invent content

    **NULL/EMPTY FOR MISSING INFO.** If the dialogue is too short or simple to extract meaningful changes, return empty lists/objects. Do NOT invent content

  70. [80]

    **OUTPUT LANGUAGE.** Write all values in the SAME language as the persona profile; the required language is specified explicitly in the user message

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    session" and

    **THIRD-PERSON PERSPECTIVE.** You MUST write ALL session and moment content in THIRD PERSON. Never use first-person pronouns to refer to the character being analyzed; use the character's name or third- person pronouns instead. ## Fields to fill ### session (cumulative within t...

  72. [82]

    Analyze ONLY from the specified character's perspective

  73. [83]

    Do NOT use external knowledge about the show's future plot

    Use ONLY evidence from the provided dialogue. Do NOT use external knowledge about the show's future plot

  74. [84]

    I", "my",

    Write in THIRD PERSON. Never use "I", "my", "me"

  75. [85]

    ALL output must be in English

  76. [86]

    low". ## Significance levels - **high**: ALWAYS use

    If the character is barely involved or the scene has no meaningful impact on them, set significance to "low". ## Significance levels - **high**: ALWAYS use "high" when the scene EXPLICITLY shows any of: * a relationship STATUS change (breaking up, getting together, engagement,...

  77. [2026]

    URLhttps://arxiv.org/abs/2604.09212

    arXiv

Pith tools

Reviewed August 10, 2026 · model on record in the stance chip above.