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REVIEW 4 major objections 5 minor 51 references

WHEN TO ACT, WHEN TO WAIT: Modeling the Intent-Action Alignment Problem in Dialogue

T0 review · 4 major / 5 minor · reviewed 2026-08-07 · deepseek-v4-flash

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Pith's one-line read STORM, a dialogue-simulation framework, claims that hiding 40-60% of a user profile can make an AI assistant more helpful than revealing everything, because incomplete information curbs presumptive reasoning and fosters clarifying…

desk verdict A useful framework and dataset from which the headline empirical claim about moderate uncertainty is not yet supported. read the letter →

arxiv 2506.01881 v2 pith:SLE4IFZW submitted 2025-06-02 cs.AI cs.CL

Yaoyao Qian , Jindan Huang , Yuanli Wang , Simon Yu , Kyrie Zhixuan Zhou , Jiayuan Mao , Mingfu Liang , Hanhan Zhou This is my paper · ORCID
classification cs.AIcs.CL
keywords intent-actionalignmentasymmetricinformationdialoguesystemsintentclarityuncertaintycalibrationLLMrole-playsimulationinnerthoughtsclarification-vs-actiontradeoff
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

This paper tries to establish that the failure of dialogue systems is often not a misunderstanding of words but a misalignment of timing: the system cannot tell when a user's request, though semantically parseable, is ready for action. It builds STORM, a simulation framework in which a simulated user with full access to its own private goals and emotions talks to an assistant that sees only the observable conversation, and it claims that hiding part of the user's profile can improve the interaction. Concretely, the paper reports that a moderate uncertainty level — 40-60% of the profile masked — can outperform complete transparency, because complete profiles invite presumptive reasoning while moderate uncertainty elicits more open, clarifying questions. A sympathetic reader would care because this reframes information asymmetry and privacy as design levers rather than defects, and offers metrics that track internal cognitive progress instead of only surface satisfaction.

What carries the argument

STORM (State Trajectory oriented Representation Model), formalized as a five-domain tuple $\{\mathcal{T}, \mathcal{U}, \mathcal{E}, \mathcal{R}, \mathcal{H}\}$ spanning tasks, user profiles, expressions, responses, and hidden states. The load-bearing object is the hidden-state vector $h_t = \langle s_t, c_t, i_t, e_t \rangle$, which the paper treats as encoding satisfaction, intent clarity, emotion, and the user's inner thoughts; it is private to the UserLLM and invisible to the AgentLLM. The uncertainty parameter $p \in \{0\%, 40\%, 60\%, 80\%\}$ decides what fraction of the user profile the agent cannot see, and it is the dial whose tuning produces the paper's central results. The Clarify metric — a third-party judge (GPT-4o) scoring turn by turn whether an agent response improved the user's internal intent clarity — together with the intent-evolution measure $\Delta_t(h) = h_t.\text{clarity} - h_{t-1}.\text{clarity}$, converts the unobservable process of "becoming clearer" into a quantifiable trajectory that the paper compares across models and uncertainty levels.

What would settle it

Run the masked-profile dialogue protocol with human users instead of a simulated UserLLM, eliciting goal clarity directly after each turn (self-reported clarity, or third-party judges who see only the transcript). If a fully informed assistant does not lose ground to a 60%-masked one on users' own clarity ratings — or if the 18% internal-clarity improvement reported for Claude disappears or reverses — then the central empirical claim fails.

Watch

Extended reading notes

Core claim

The paper's central claim is that the Intent-Action Alignment Problem — knowing when an utterance is not just understood but truly ready for system action — can be studied as information-asymmetry dynamics, and that calibrated asymmetry is a design lever rather than a defect. In STORM, a UserLLM with full access to its own hidden state (goals, emotions, satisfaction, and recorded "inner thoughts") converses with an AgentLLM that sees only the dialogue history, producing 4,800 annotated dialogues over 600 profiles and four assistant models. The central empirical finding is that profile access boosts satisfaction by 15-40%, but that a moderate masking level (40-60% of the profile hidden) can beat full transparency: Claude at 60% uncertainty scored 0.92 satisfaction without a profile versus 0.88 with one, and its responses improved users' internal clarity by 18% relative to the 0% baseline. The paper explains this by observing that complete profiles lead to stereotypical, presumptive answers, whereas moderate uncertainty pushes assistants toward open, assumption-free questions; it generalizes this into task-dependent advice (simple tasks prefer low uncertainty, exploratory medical and housing tasks prefer high uncertainty) and into the claim that limiting information acts as an implicit bias mitigator.

Load-bearing premise

The load-bearing premise is that the LLM-generated "inner thoughts" and satisfaction scores are faithful measurements of how a real user's intent actually becomes clearer; if they are not, the 18% clarity gain and the 40-60% advantage reported here may not transfer to human-AI conversation.

Editorial extensions

If this is right

  • Calibrated masking becomes a design parameter: system builders can choose how much user data to expose to an assistant, and 40-60% masking outperforms full transparency in several configurations.
  • Task complexity predicts the right uncertainty level: simple tech support works best with low uncertainty, while medical and housing decisions favor higher masking levels because users stay internally uncertain longer.
  • Satisfaction alone misleads: successful clarification correlates with internal cognitive improvement more than with expressed satisfaction, so satisfaction-only evaluation misranks dialogue systems.
  • Model-specific deployment makes sense: Llama clarifies goals best (Clarify 7.58-7.75), Gemini is robust to missing profiles, Claude maximizes satisfaction, and GPT-4o-mini is consistent but flat.
  • Strategic information limitation acts as an implicit bias mitigator: with full profiles, agents stereotype (for instance, assuming elderly users need simplified help), while at optimal uncertainty they assess individuals.

Reading between the lines

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

  • If the 40-60% sweet spot survives replication with human users, privacy and performance stop being a trade-off: an assistant could be deliberately deprived of access to demographic data and behave better because of it, a consequence the paper gestures at but does not implement.
  • The transfer bottleneck is the inner-thought ground truth; a natural next experiment is a think-aloud human study comparing self-reported goal clarity under a masked versus a fully informed assistant, which the paper does not run.
  • The clarity trajectories STORM records could directly train a wait-versus-act stopping rule for production dialogue systems, connecting the framework to predictive wait-or-answer policies that the paper cites but does not integrate.
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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 introduces STORM, a simulation framework for studying what the authors call the Intent-Action Alignment Problem: deciding when a user's utterance is clear enough for an agent to act. STORM pairs a UserLLM, which has access to a full profile and to per-turn hidden states (inner thoughts, satisfaction, intent clarity, emotion), with an AgentLLM, which observes only the dialogue history. The user profile can be masked to varying degrees (p = 0%, 40%, 60%, 80% uncertainty), and metrics are introduced for satisfaction, clarification effectiveness (Clarify), and a composite Satisfaction-Seeking Actions (SSA) score. Experiments across four models and 4,800 simulated dialogues are used to claim that moderate uncertainty (40–60%) can outperform complete transparency in certain scenarios, with the Claude 3.7 Sonnet at 60% uncertainty without a profile reported as the main counterintuitive result. Additional contributions include a dialogue corpus, a visualization dashboard, and a formal notation for asymmetric information in dialogue.

Significance. If the empirical claims held up, the paper would make a useful contribution: a formal, extensible framework for studying asymmetric information in dialogue, a public corpus and dashboard that lower the barrier for follow-up work, and a thought-provoking privacy implication (calibrated information asymmetry as a design feature rather than a defect). The formalization of hidden states and the explicit masking of profile attributes are genuinely useful building blocks. On the other hand, the headline empirical claim is currently anchored only to simulator-internal self-reports, and the quantitative support consists of point estimates without error bars or significance tests. The paper's strengths are the framework, the dataset, and the visualization tool; its weakness is that the central behavioral conclusion is not yet empirically demonstrated.

major comments (4)
  1. [§3.1–3.2, Table 2, Appendix M.1] The headline claim that moderate uncertainty (40–60%) can outperform complete transparency rests entirely on simulator-internal measurements. Satisfaction is extracted from UserLLM-generated [SATISFACTION] tags, and the Clarify score is judged by GPT-4o from a prompt that includes the UserLLM's [INNER_THOUGHTS] for the next turn (Appendix M.1). Because the uncertainty level p is part of the UserLLM prompt, the treatment directly changes the text inside those tags; the Claude 60% result (0.92 vs 0.88, plus the reported 18% internal-clarity improvement) could therefore be an artifact of the simulated user telling the judge that it became clearer under the 60% condition, rather than evidence of a real change in intent formation. The Section 3.2 rebuttal addresses only whether GPT-4o-mini favors itself as an agent; it does not establish that LLM-generated inner thoughts are causally reliable proxies for human intent formation. An external anchor (human evaluation, observable task outcome, or at least a judge blinded to the uncertainty condition) is needed before this result can be transferred to human-AI collaboration.
  2. [Table 2] All values in Table 2 are point estimates with no error bars, confidence intervals, or significance tests, and the number of dialogues per condition is not stated. The critical differences are small (e.g., average satisfaction 0.92 vs 0.88; high-satisfaction rate 86.7% vs 80.7%), so without repeated-seed variance or a bootstrap/permutation analysis the 60%-uncertainty advantage could be ordinary sampling noise. Please report per-condition sample sizes and variability across independent simulation runs or seeds.
  3. [§3.2, Appendix B.1] The SSA metric is not parameter-free and its construction is data-dependent. The normalization factor λ = 7.75 is defined as the maximum observed Clarify score in the same 4,800-dialogue dataset, so every SSA value in Table 2 is normalized by an in-sample constant; the Appendix F ranking (Llama > Gemini > GPT > Claude) can be changed by rescaling this constant. Moreover, the Clarify definition C = w1Δt(h) + w2Δt(s) + w3gt in Section 2.2 never reports the values of w1, w2, w3, and the SSA weights wα = 0.7, wβ = 0.3 are described only as illustrative. Please report all weights, justify the choice of λ (e.g., through a held-out calibration set), and provide sensitivity analyses over these free parameters.
  4. [§1, §2.2] The manuscript repeatedly refers to The UserLLM's hidden state ht as "ground-truth" internal state, but ht is generated by the same family of LLMs being evaluated and is available only in simulation. This is not a fatal flaw—simulation is a legitimate first step—but the terminology overstates the epistemic status of the data and invites the circularity concern raised in Section 3.1's own discussion of the difficulty of obtaining true internal-state data. The hidden states should be described as simulated or LLM-generated latent states, and the claims about "internal cognitive improvements" (e.g., the 18% clarity improvement) should be qualified accordingly.
minor comments (5)
  1. [§2.2] The hidden-state tuple is written ht = ⟨st, ct, it, et⟩, but the surrounding text names only satisfaction, intent clarity, and emotional state; please define the fourth component or correct the tuple.
  2. [§3.2 vs Appendix B.1] The two displayed forms of the SSA metric are algebraically different (SSA = λ(wαSavg + wβCclarify) in Section 3.2 versus SSA = wα(Savg·λ) + wβCclarify in Appendix B.1); please present one canonical definition and derive any equivalent form.
  3. [Table 2] Clarify and SSA are reported only in the "w/o Profile" columns; please state explicitly whether these metrics were computed only in the no-profile condition or whether profile-aware values exist but were omitted.
  4. [Appendix E] The Executive Summary reports response-appropriateness gains of 15–28%, intent-alignment improvements of 45–65%, and satisfaction improvements of 4–23%, but I could not locate the experiments or tables supporting these numbers in the main text or appendices; please add the supporting analysis or remove the claims.
  5. [References and front matter] There are formatting errors in the author block and references (e.g., the project website link appears garbled, and the Allen et al. reference spells the author name as "Horvtz"); these should be cleaned before publication.

Circularity Check

3 steps flagged · score 6.0 of 10

The headline 40–60% moderate-uncertainty advantage is measured through the UserLLM's own generated inner thoughts and satisfaction tags; the metrics are defined on the same hidden states that the uncertainty manipulation directly prompts, so the central empirical claim is largely self-reported inside the simulator.

  1. self definitional [Section 2.2 (Guser and hidden states); Section 3.1; Section 3.2; Appendix L.6 User Prompt Template]
    "Simultaneously, the function produces the user hidden state ht ... formalized as a vector ht = ⟨st, ct, it, et⟩ ... The first, satisfaction, is derived from user inner thoughts to capture internal contentment. It is computed via a structured process where our UserLLM generates both a numerical score and an explicit textual explanation in each turn. ... Use the exact format: [INNER_THOUGHTS] your thoughts here [/INNER_THOUGHTS] ... [SATISFACTION] score - explanation [/SATISFACTION]."

    The treatment variable p (uncertainty level) is embedded in the UserLLM's profile and difficulty instructions, and the outcome variables (satisfaction, intent clarity) are the same UserLLM's self-reported tags. The claim that 40–60% uncertainty improves satisfaction/clarity therefore reduces to: when the UserLLM is prompted to be uncertain, it writes higher [SATISFACTION] and [INNER_THOUGHTS] clarity. The agent's responses are in the loop, but the ground-truth measurement is produced by the very model whose prompt contains the independent variable, so the headline effect is a self-report by construction.

  2. self definitional [Section 2.2 (Clarity Score definition); Appendix M.1 Turn Pair Analysis Prompt]
    "C(rt, ht, ht+1) evaluates response effectiveness in improving intent clarity. It is computed as a weighted function C = w1∆t(h) + w2∆t(s) + w3gt ... ∆t(h) = ht.clarity − ht−1.clarity. ... User Inner Thoughts: {next_turn['metadata']['hidden_states']['inner_thoughts']}"

    The Clarify metric is explicitly defined as a function of ht, the hidden state generated by Guser. The third-party judge GPT-4o is fed those same inner thoughts in the turn-pair analysis prompt. Thus the '18% internal clarity improvement' at 60% uncertainty is the judge reading the UserLLM's self-description of clarity; the metric is not anchored to any observable behavior outside the simulator. The judge is third-party relative to the agent, but not relative to the data-generating process, so the loop is unbroken.

1 more flagged steps
  1. other [Section 3.2 / Appendix B.1 (SSA normalization)]
    "The normalization factor λ = 7.75 scales satisfaction scores (range 0.0–1.0) to match the magnitude of clarification scores (range 4.0–8.0), where λ corresponds to the maximum observed clarification score in our dataset of 4,800 dialogues."

    The SSA composite is normalized by the maximum Clarify value observed in the same dataset used for evaluation. This does not force the 40–60% finding, but it makes SSA magnitudes dataset-relative rather than an absolute, externally calibrated scale. It is a data-dependent normalization, not an independent prediction, and it contributes to the self-contained character of the evaluation.

full rationale

The paper's framework and dataset are not circular in themselves: the asymmetric-agent simulation, the task library, and the dashboard are concrete artifacts. The circularity is concentrated in the headline empirical claim. Satisfaction is defined operationally as the UserLLM's self-reported numerical score and explanation, and Clarify is defined as a weighted function of the UserLLM's generated hidden-state clarity. Since the uncertainty level p is part of the UserLLM's prompt (profile and difficulty instructions), the measured 'moderate uncertainty advantage' is the same model reporting greater satisfaction/clarity under the p=60% prompt condition and a GPT-4o judge reading those self-reports. The paper's self-enhancement rebuttal addresses whether GPT-4o-mini favors itself as an agent, not whether its self-narrated hidden states are causally reliable. Table 2 also reports no error bars or repeated-seed variance, so the 0.92-vs-0.88 and 18% differences are not statistically anchored. These are validity limitations rather than pure equation-level circularity, but because the central dependent variables are defined on the same generated hidden states that the treatment directly prompts, the headline result partially reduces to its own measurement construction. Hence score 6.

Assumptions & free parameters 4 free parameters · 5 assumptions · 2 invented entities

The central evaluation depends on several assumptions about LLM-generated internal states and the representativeness of the simulated profiles. The only numerical free parameter fitted to the dataset is lambda, and the Clarify weights are unreported internally.

free parameters (4)
  • SSA normalization factor lambda = 7.75
    Set to the maximum observed Clarify score in the 4,800-dialogue dataset (Appendix B.1); used to scale satisfaction (0-1) to the range of clarification scores (4-8). This in-sample statistic normalizes all SSA comparisons.
  • SSA weights w_alpha, w_beta = 0.7, 0.3
    Chosen as 'an illustrative example' (Section 3.2). The SSA ranking across models is sensitive to these hand-picked weights.
  • Clarify score weights w1, w2, w3 = not reported
    Section 2.2 defines C = w1*Delta_t(h)+w2*Delta_t(s)+w3*g_t but does not report the weights used for the Clarify scores in Table 2, so the exact metric is not reproducible.
  • Uncertainty levels p = 0%, 40%, 60%, 80%
    Experimental grid chosen by hand (Section 2.1). The conclusion that 'moderate uncertainty' (40-60%) helps depends on this specific discretization; a finer or different grid could change the finding.
assumptions (5)
  • domain assumption LLM-generated inner thoughts (satisfaction, intent clarity, emotion) are a faithful proxy for the internal states of real users.
    Section 3.1 uses UserLLM to generate both utterances and hidden states; no human ground-truth comparison is provided.
  • domain assumption A third-party LLM judge (GPT-4o) can objectively assess whether an agent response improved a user's internal intent clarity.
    Section 3.2 assigns Clarify to a 'third-party judge model' that reads the simulated inner thoughts; no validation against human judges is reported.
  • domain assumption Simulated user profiles generated by GPT-4o-mini from predefined pools are representative of real user diversity.
    Section 2.1 and Appendix H generate profiles from hand-written pools without external validation against real user distributions.
  • domain assumption The four difficulty dimensions with levels 1-5 cover the range of real expression ambiguity.
    Section 2.1 operationalizes clarity through style, length, content, and tone; the adequacy of this discretization is assumed.
  • ad hoc to paper LLM behavior in this simulated setting transfers to human-AI collaboration.
    The abstract and Section 3.4 draw implications for human-AI collaboration and privacy design from simulated interactions, with no human experiment.
invented entities (2)
  • Hidden state vector h_t = <s_t, c_t, i_t, e_t>
    purpose: Represents the user's private satisfaction, intent clarity, and emotional state at each turn; used as ground truth for evaluation metrics.
    No external measurement grounds this construct; it is generated by the UserLLM as part of the simulation, so the entity only exists inside the paper's pipeline.
  • Intent-Action Alignment Problem
    purpose: Names the research question of when an expression is ready for system action, framing the paper's contributions.
    A newly coined conceptual label, not an independently observable entity.

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Cite this review

Pith. "Pith review of WHEN TO ACT, WHEN TO WAIT: Modeling the Intent-Action Alignment Problem in Dialogue." pith.science (2026). https://pith.science/paper/SLE4IFZW

@misc{pith2026250601881,
  author       = {Pith},
  title        = {Pith review of: WHEN TO ACT, WHEN TO WAIT: Modeling the Intent-Action Alignment Problem in Dialogue},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/SLE4IFZW}},
  note         = {Machine review of arXiv:2506.01881}
}
read the original abstract

Dialogue systems often fail when user utterances are semantically complete yet lack the clarity and completeness required for appropriate system action. This mismatch arises because users frequently do not fully understand their own needs, while systems require precise intent definitions. This highlights the critical Intent-Action Alignment Problem: determining when an expression is not just understood, but truly ready for a system to act upon. We present STORM, a framework modeling asymmetric information dynamics through conversations between UserLLM (full internal access) and AgentLLM (observable behavior only). STORM produces annotated corpora capturing trajectories of expression phrasing and latent cognitive transitions, enabling systematic analysis of how collaborative understanding develops. Our contributions include: (1) formalizing asymmetric information processing in dialogue systems; (2) modeling intent formation tracking collaborative understanding evolution; and (3) evaluation metrics measuring internal cognitive improvements alongside task performance. Experiments across four language models reveal that moderate uncertainty (40-60%) can outperform complete transparency in certain scenarios, with model-specific patterns suggesting reconsideration of optimal information completeness in human-AI collaboration. These findings contribute to understanding asymmetric reasoning dynamics and inform uncertainty-calibrated dialogue system design.

Figures

Figures reproduced from arXiv: 2506.01881 by the authors.

Figure 1
Figure 1. Overview of the STORM Framework treat user goals as fixed targets rather than as dynamic, evolving states. 2) Ignoring Subtext and Nuance: They overlook the rich pragmatic and contextual cues embedded in how users express themselves. 3) Scarcity of Data on Internal States: They are constrained by the fundamental difficulty in acquiring data that reflects a user’s true internal state. These limitations largely stem f… view at source ↗
Figure 2
Figure 2. Interface visualization and process overview [PITH_FULL_IMAGE:figures/full_fig_p017_2.png] view at source ↗
Figure 3
Figure 3. Homepage with Grid View and control options. [PITH_FULL_IMAGE:figures/full_fig_p032_3.png] view at source ↗
Figures from the paper (28 more)
Figure 4
Figure 4. Figure 4: Upload interface for JSON files or folders. [PITH_FULL_IMAGE:figures/full_fig_p032_4.png]
Figure 5
Figure 5. Figure 5: Folder view displaying uploaded dialogue folders. [PITH_FULL_IMAGE:figures/full_fig_p033_5.png]
Figure 6
Figure 6. Figure 6: User list sorted by file name with tags and key dialogue metadata. [PITH_FULL_IMAGE:figures/full_fig_p033_6.png]
Figure 7
Figure 7. Figure 7: User detailed dialogue view showing all turns and states. [PITH_FULL_IMAGE:figures/full_fig_p034_7.png]
Figure 8
Figure 8. Figure 8: User detail view - satisfaction metrics tab. [PITH_FULL_IMAGE:figures/full_fig_p034_8.png]
Figure 9
Figure 9. Figure 9: User detail view - emotional states tab. [PITH_FULL_IMAGE:figures/full_fig_p035_9.png]
Figure 10
Figure 10. Figure 10: User detail view - intent states tab. and user profile ( [PITH_FULL_IMAGE:figures/full_fig_p035_10.png]
Figure 11
Figure 11. Figure 11: User profile tab in the detail view. Scrolling down below the user dialogue list is folder analysis, as shown in [PITH_FULL_IMAGE:figures/full_fig_p036_11.png]
Figure 12
Figure 12. Figure 12: Folder analysis overview with tooltip explanations. [PITH_FULL_IMAGE:figures/full_fig_p036_12.png]
Figure 13
Figure 13. Figure 13: Satisfaction analysis within folder view. [PITH_FULL_IMAGE:figures/full_fig_p037_13.png]
Figure 14
Figure 14. Figure 14: Emotion analysis within folder view. and file details ( [PITH_FULL_IMAGE:figures/full_fig_p037_14.png]
Figure 15
Figure 15. Figure 15: Message analysis within folder view. Further scrolling reveals folder detail analysis including satisfaction ( [PITH_FULL_IMAGE:figures/full_fig_p038_15.png]
Figure 16
Figure 16. Figure 16: File detail view within folder analysis. [PITH_FULL_IMAGE:figures/full_fig_p038_16.png]
Figure 17
Figure 17. Figure 17: Folder detail satisfaction overview. emotion statistics ( [PITH_FULL_IMAGE:figures/full_fig_p039_17.png]
Figure 18
Figure 18. Figure 18: Satisfaction per turn analysis in folder detail. [PITH_FULL_IMAGE:figures/full_fig_p039_18.png]
Figure 19
Figure 19. Figure 19: Emotion statistics in folder detail analysis. [PITH_FULL_IMAGE:figures/full_fig_p040_19.png]
Figure 20
Figure 20. Figure 20: Metric explanations section with expandable details. [PITH_FULL_IMAGE:figures/full_fig_p040_20.png]
Figure 21
Figure 21. Figure 21: Profile selection for batch comparative analysis. [PITH_FULL_IMAGE:figures/full_fig_p041_21.png]
Figure 22
Figure 22. Figure 22: Batch comparison of multiple dialogue profiles. [PITH_FULL_IMAGE:figures/full_fig_p041_22.png]
Figure 23
Figure 23. Figure 23: Emotional states comparison for multiple users. [PITH_FULL_IMAGE:figures/full_fig_p042_23.png]
Figure 24
Figure 24. Figure 24: Detailed dialogue turn comparison across models for the same user. [PITH_FULL_IMAGE:figures/full_fig_p042_24.png]
Figure 25
Figure 25. Figure 25: Side-by-side view of selected single and multi-dialogue comparisons. [PITH_FULL_IMAGE:figures/full_fig_p043_25.png]
Figure 26
Figure 26. Figure 26: Split view for detailed analysis. — Folder-Level Comparison Click the "Folder Comparison" button at the top right to open the component ( [PITH_FULL_IMAGE:figures/full_fig_p043_26.png]
Figure 27
Figure 27. Figure 27: Folder comparison selection interface. Below, detailed differences are shown, including: - Satisfaction comparison ( [PITH_FULL_IMAGE:figures/full_fig_p044_27.png]
Figure 28
Figure 28. Figure 28: Satisfaction comparison between folders. [PITH_FULL_IMAGE:figures/full_fig_p044_28.png]
Figure 29
Figure 29. Figure 29: Emotional states comparison between folders. [PITH_FULL_IMAGE:figures/full_fig_p045_29.png]
Figure 30
Figure 30. Figure 30: Message length comparison between folders. [PITH_FULL_IMAGE:figures/full_fig_p045_30.png]
Figure 31
Figure 31. Figure 31: User profile comparison between folders. [PITH_FULL_IMAGE:figures/full_fig_p046_31.png]

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Reference graph

Works this paper leans on

51 extracted references · 51 canonical work pages

  1. [1]

    At level 1, expressions exhibit highly structured logical flow; at level 5, expressions lack coherence and organization

    Style dimension dstyle defines the structural organization of communication. At level 1, expressions exhibit highly structured logical flow; at level 5, expressions lack coherence and organization. This dimension captures the organizational aspects of communication that significantly impact interpretation complexity, reflecting the reality that most users...

    work page 2025

  2. [2]

    At level 1, expres- sions are concise yet comprehensive; at level 5, expressions are either too brief causing information deficiency or excessively verbose obscuring key points

    Length dimension dlength quantifies verbosity and detail level. At level 1, expres- sions are concise yet comprehensive; at level 5, expressions are either too brief causing information deficiency or excessively verbose obscuring key points. This bidirectional conceptualization addresses the common challenge that users fre- quently provide either too litt...

    work page

  3. [3]

    At difficulty level 1, all necessary information is explicitly provided; at level 5, critical information is omitted, requiring substantial inference

    Content dimension dcontent quantifies contextual sufficiency. At difficulty level 1, all necessary information is explicitly provided; at level 5, critical information is omitted, requiring substantial inference. This dimension directly addresses the prevalent real-world challenge where users frequently omit crucial details they incorrectly assume are obv...

    work page

  4. [4]

    At level 1, the tone is appropriate and consistent; at level 5, emotions fluctuate or misalign with content

    Tone dimension dtone captures emotional expression and interaction engagement. At level 1, the tone is appropriate and consistent; at level 5, emotions fluctuate or misalign with content. The inclusion of this dimension acknowledges the significant role emotional factors play in communication clarity, especially in challenging or frustrating scenarios whe...

    work page 2025

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    Generate 15–20 unique and realistic options

    work page

  6. [6]

    Include both common and unique scenarios

    work page

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    Consider different user perspectives and needs

    work page

  8. [8]

    Make options specific to the task context

    work page

Show all 51 references
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    Include some complex and challenging options

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    Unknown/Not sure

    Add one "Unknown/Not sure" option at the end. Your task: Return a JSON array of strings. Example: ["Option 1","Option 2","Unknown/Not sure"]. Write ONLY the JSON array. Do not include any explanations. Prompt for Generating Budget Information Generate budget information for th...

  3. [11]

    range": {

    Generate a JSON object with the structure: { "range": { "min": number, "max": number }, "flexibility": "string", "payment_methods": ["string"] }

  4. [12]

    Unknown/Not sure

    Consider: • Realistic price ranges for the task. • Different budget flexibility levels. • Various payment methods. • Include "Unknown/Not sure" as a possible flexibility option. Write ONLY the JSON response. Do not include any explanations. 20 Published as a conference paper a...

    2025

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    task_requirements

    Generate a JSON object with structure: { "task_requirements": { "technical": ["string"], "non_technical": ["string"] }, "success_criteria": { "must_meet": ["string"], "should_meet": ["string"], "nice_to_meet": ["string"] } }

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    something modern

    IMPORTANT: Make this profile AMBIGUOUS based on difficulty level {difficulty_level}: • For difficulty 3+: Include vague requirements like "something modern" or "good performance". • For difficulty 4+: Add contradictory requirements. • For difficulty 5: Make most requirements u...

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    name": "Realistic name that matches the profile

    Express confusion about technical specs - use incorrect terms or mix concepts. Write ONLY the JSON response. Do not include any explanations or additional text. K Appendix: Prompts Used for User Profile Generation K.1 Prompt for Generating User Name and Description Prompt for ...

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    The name should be culturally appropriate based on the profile

  9. [17]

    The description should be detailed and consistent with all profile attributes

  10. [18]

    The description should explain why they are interested in the task

  11. [19]

    task_specific_attributes

    Keep the description concise but informative (2-3 sentences) 21 Published as a conference paper at COLM 2025 Role Min Length Max Length Default Tar- get Length User 20 100 50 Assistant 30 150 80 Table 5: Message length constraints for user and assistant roles. K.2 Prompt for G...

    2025

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    Attributes should be specific to the task and consistent with the user profile

  13. [21]

    Consider the user’s tech experience, personality, and behavioral traits

  14. [22]

    Make the attributes realistic and detailed

  15. [23]

    Include at least 3 priority features and usage scenarios

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    Message Length Constraints See Table 5

    IMPORTANT: Your response must be valid JSON only, with no additional text or explanation L Appendix: Configuration and Core Components of AsymmetricDialogueGenerator L.1 1. Message Length Constraints See Table 5. L.2 2. Emotional Keywords Mapping These keywords are used to inf...

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    27 Published as a conference paper at COLM 2025 User Prompt Template Message Format Requirements:

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    Your messages should be between 20 and 100 characters

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    Follow the difficulty instructions for dialogue, profile disclosure, and hidden state expression

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    Use the example messages as a guide for your communication style

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    Maintain consistency with your profile attributes Inner Thoughts Format: • Use the exact format: [INNER_THOUGHTS] your thoughts here [/IN- NER_THOUGHTS] • Place your inner thoughts at the beginning of your message • Keep thoughts concise and relevant to the conversation Satisf...

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    Respond only to information explicitly shared by the user in the conversation

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    Do not make assumptions about the user’s preferences, demographic information, or needs

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    Maintain a natural conversation flow

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    Do not reference any information about the user that they haven’t explicitly shared in the conversation

    Only base your responses on what the user has explicitly told you in the conversation Remember to be patient and understanding. Do not reference any information about the user that they haven’t explicitly shared in the conversation. Profile-aware Mode (Profile Sharing Enabled)...

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    Your messages should be between 30 and 150 characters

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    Be professional, clear, and helpful

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    Consider the user’s profile when providing information

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    Adapt your communication style to match the user’s preferences

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    Focus on addressing the user’s specific needs and requirements

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    Provide relevant and accurate information

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    Ask clarifying questions when needed

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    Maintain a natural conversation flow Remember to be patient and understanding, especially with users who have limited technical experience. L.8 8. Satisfaction Extraction Logic The system extracts satisfaction score and explanation from messages that include: - Format 1: [SATI...

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    turn_pair

    T urn Pair Analysis Prompt T urn Pair Analysis Prompt You are given a JSON file representing a multi-turn conversation between a user and an assistant. Each turn includes the user’s message, the assistant’s response, timestamp, and metadata with satisfaction and inner_thoughts...

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    Each turn includes a user satisfaction score

    Conversation Summary Prompt Conversation Summary Prompt You are given a multi-turn conversation between a user and an assistant. Each turn includes a user satisfaction score. Consider that each user’s background, expertise, and goals may vary; present your analysis as nuanced ...

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    summary_overall: A concise evaluation of overall user satisfaction trend (e.g., positive, negative, mixed)

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    topics_covered: A list of key topics or user intents addressed throughout the conversation

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    • min_score: Minimum score observed

    statistics: An object containing: • average_score: Average satisfaction score across all turns. • min_score: Minimum score observed. • max_score: Maximum score observed. • score_variance: Variance of the satisfaction scores

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    • score: Satisfaction score at that turn

    satisfaction_evolution: A list of objects for each turn: • turn_index: Index of the turn. • score: Satisfaction score at that turn. • delta: Change in score from the previous turn (null for first turn)

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    • user_message: The user’s message at that turn

    important_turns: A list of objects identifying critical turns where satisfaction changes significantly (e.g., change >= 2): • turn_index: Index of the user turn. • user_message: The user’s message at that turn. • score_before: Score at the previous turn. • score_after: Score a...

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    • context_before: The assistant and user messages immediately before this turn

    detailed_findings: A list of objects providing deep insights for each important turn: • turn_index: Index of the turn. • context_before: The assistant and user messages immediately before this turn. • context_after: The assistant and user messages immediately after this turn. ...

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    contextual_notes: A list of any relevant context, caveats, or user metadata considerations that influenced the analysis

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    Getting Started

    general_insights: A list of general patterns or best practices inferred from this conversation that could apply to a broad range of users. Conversation file: {filename} {conversation_text} N Appendix: Dashboard Walkthrough First, open the following URL: https://v0-dialogue-ana...

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Pith tools

Reviewed August 7, 2026 · model on record in the stance chip above.