cs.HCSep 14, 2026
SaveFrom Momentary Emotion Inference to Sustained Emotion Support: Evaluating a Companion Agent in a Longitudinal Study
Organizations: School of Information Sciences, University of Illinois Urbana-Champaign, Champaign, Illinois, United States · College of Information, University of Maryland, College Park, Maryland, United States · University of Illinois Urbana-Champaign, Champaign, Illinois, United States · Department of Computer Science, Florida State University, Tallahassee, Florida, United States
Abstract
Sustained emotional support is a long-horizon interaction task closely tied to human well-being. Recent research demonstrates generative agents' capacity for momentary emotional support, yet how these capabilities sustain support over time remains unclear. To examine this challenge, we deployed PAIR, a theory-based emotion-regulation companion, with 19 participants for 14 days. Across 1,093 sessions, we paired emotion estimates with self-reports before and after guidance and analyzed logs and interviews. Estimates corresponded more closely to self-reported valence and dominance than arousal. Guided conversations were followed by higher valence and state-dependent arousal changes. Participants felt understood through contextual exploration and emotional acknowledgment, acting on guidance suited to their needs and constraints. Perceived helpfulness of guided conversation significantly increased over time. Our findings link memory updates and retained corrections to cross-session personalization, informing future emotional support tools that adapt to evolving needs, learn from prior outcomes, and preserve user control over memory.
Figures & tables
Figure 1. PAIR supports users across everyday emotional moments over 14 days. (1) Users can check in whenever something happens and share the experience through speech or text. (2) The companion draws on what it has learned about the user, including persona, calibration, and prior conversations, to understand how they are feeling and choose a fitting regulation strategy. (3) It then guides the user through the moment in either voice or chat, helping them move toward a more manageable next step with more support. A three-stage illustration of a user's experience with \sysname{} over a 14-day deployment. A user checks in after an emotional event and shares what happened through speech or text. The companion uses information about the user and prior interactions to interpret the emotion and choose a regulation strategy. It then provides guidance through voice or chat, helping the user identify a concrete next step and feel more supported.
| Inference | Guidance | Continuity | ||||||
|---|---|---|---|---|---|---|---|---|
| System | Emo. | State. | Supp. | Cond. | Mem. | Adapt. | Schedule | What the system does |
| MindfulDiary (CHI’24) ( Kim et al., 2024a ) | ○ | ○ | 28 d, on demand | Conversational journaling with emotion summaries for patients and clinicians. | ||||
| Socrates 2.0 (JMIR MH’25) ( Held et al., 2025 ) | ✓ | 28 d, on demand | Socratic cognitive reappraisal for depression and anxiety. | |||||
| Therabot (NEJM AI’25) ( Heinz et al., 2025 ) | ○ | ○ | ○ | 4 wk daily; 4 wk on demand | Generative mental health support using expert-trained dialogue and conversation history. | |||
| ComPeer (UIST’24) ( Liu et al., 2024 ) | ✓ | ✓ | ○ | 7 + 7 d, proactive | Proactive peer support from a persona and conversation history. | |||
| VITA (THRI’25) ( Spitale et al., 2025 ) | ✓ | ✓ | ○ | ✓ | ○ | ✓ | 28 d 1/week | Robotic well-being coaching through positive-psychology exercises. |
Table 1. Comparison of selected longitudinal language-model-based emotional support systems. Icons: ✓ yes, ○ partial, blank = not reported. Emo. = emotion regulation as the target; State. = explicit state representation; Supp. = multiple support strategies; Cond. = state-conditioned strategy selection; Mem. = cross-session user memory; Adapt. = memory updated from subsequent user feedback; Schedule = deployment duration and interaction frequency.
Figure 2. Prompt evaluation pipeline. (a) Four emotion-inference prompts with incremental reasoning scaffolding are compared on (b) 30 scenarios pairing personas with lived-experience events, through (c, d) blind pairwise bracket tournaments by three human raters, (e) yielding two top performers with complementary strengths, both deployed in PAIR. A five-panel diagram showing four prompt designs, the construction of 30 persona and event scenarios, a blind pairwise prompt-evaluation bracket, the raters' scoring interface with three rating dimensions, and the two winning prompts selected for deployment.
Figure 3. Event structure and emotional mixing were associated with prompt preference in the 30-scenario formative set, while preferences were more evenly divided for personal-history use and goal clarity. Event structure and emotional mixing largely overlapped, and the deployed router uses event structure alone. Bars give the number of scenarios in which each prompt was preferred by majority vote based on the raters’ rankings; the right column reports the winning share. A butterfly chart with eight rows grouped into four scenario features. Scenarios won by Cog extend left in green, those won by Combined extend right in orange. For event structure and emotional mixing the long bar switches sides between the feature's two levels; for personal-history use and goal clarity both levels show short, near-equal bars on either side.
| Cog (appraisal-based) | Combined (integrative) | |
|---|---|---|
| Best suited for | Single-component events (14/17): discrete incidents, isolated conflicts, and single anticipatory states | Multi-component events (12/13): stacked stressors, chained events, body–mind interactions, and multi-stage experiences |
| Primary strength | Focused, person-centered reasoning. Follows one clear causal chain. | Preserves multiple emotional and causal components. Captures bodily and layered experiences. |
| Failure mode when mismatched | Omits relevant components, especially bodily signals and layered emotions. | Introduces unsupported links to the persona or past experience. |
Table 2. Complementary strengths and failure modes of the two selected prompts.
Figure 4. User interaction flow and guidance logic in PAIR. (a) The user recounts an everyday event through speech or text. (b) The system perceives the user’s current emotional state using one of two theory-grounded reasoning scaffolds, selects a regulation strategy based on the event’s intensity and controllability, and generates an internal guidance plan. (c) The plan is delivered through a brief guided conversation with coordinated multimodal cues. After guidance, the system produces a post-guidance estimate from the original event, the initial estimate, and the conversation transcript. A three-panel diagram showing the user interaction flow and core guidance logic in \sysname{}. Panel (a) shows a user describing an everyday event through speech or text. Panel (b) shows the system perceiving the user's current emotional state, selecting among four regulation-strategy families based on intensity and controllability, and generating an internal guidance plan. Panel (c) shows the plan delivered through a short guided conversation with multimodal cues, followed by a post-guidance estimate produced from the original event, the initial emotion estimate, and the conversation transcript.
Figure 5. Study procedure. Day 0: researcher-led onboarding with consent and app setup, baseline surveys, the personalization interview, and a calibration and practice exercise. Days 1–14: three types of daily touchpoints (morning check-in, just-in-time logging, evening reflection), each pairing the system’s affective estimates with SAM self-ratings before and after the guided conversation. Day 14+: post-study questionnaire and semi-structured interview. A timeline diagram showing the three study phases: Day 0 onboarding, the 14-day daily companion phase with its per-session step sequence, and the post-study session.
Figure 6. Exit ratings of perceived emotional understanding. In-the-moment understanding item had the highest mean rating, while emotional nuance recognition had the lowest. Boxes show the distribution of participant ratings, grey points are individual participants, and diamonds with error bars show means with 95% confidence intervals. Items were rated on a 7-point scale (1 = strongly disagree, 7 = strongly agree). Horizontal box plot with three rows, one per questionnaire item, on a one-to-seven scale. All three boxes sit between five and six. The in-the-moment item has the highest mean and the tightest spread.
| Dimension / Estimator | MAE | Signed err. | ||
| Initial | Valence | 1.196 | .669*** | |
| Dominance | 1.299 | .538*** | ||
| Arousal | 1.651 | .236*** | ||
| Post-guidance | Valence | 1.115 | .614*** | |
| Dominance | 1.189 | .495*** | ||
| Arousal | 1.963 | .175*** |
Table 3. Estimation accuracy against unanchored self-report on the 9-point SAM scale. Initial estimates are evaluated against before-guidance ratings and post-guidance estimates against after-guidance ratings. The bottom three rows compare initial valence estimators across all narrations, with Claude Opus 4.8 used for both LLM conditions. Signed error is computed as estimate minus self-report, so positive values indicate overestimation. All correlations are significant at (***).
Figure 7. Exit ratings of perceived emotion-regulation support. Relevant emotional guidance and support for emotional coping received the highest mean ratings (both ), while improved emotional self-understanding received the lowest ( ). Boxes show participant rating distributions, grey points show individual participants, and diamonds with error bars show means with 95% confidence intervals. Items were rated on a 7-point scale (1 = strongly disagree, 7 = strongly agree). A horizontal box plot with six emotion-regulation support items on a 1–7 self-rating scale. From top to bottom, the mean ratings are 4.63 for improved emotional self-understanding, 5.26 for relevant emotional guidance, 4.84 for actionable emotion-management ideas, 4.79 for a new perspective, 5.26 for emotional coping, and 4.89 for everyday emotion management. Grey points show individual participant ratings, boxes show their distributions, and black diamonds with error bars show means with 95% confidence intervals.
Figure 8. Guided conversations were followed by state-dependent affective movement. (A) Arrows connect clustered mean self-reported valence and arousal before guidance to the corresponding means after guidance; labels show the number of sessions in each cluster. Anxious and dejected trajectories generally moved toward higher valence, with arousal decreasing for anxious states and increasing for dejected states. Calm trajectories also became more activated, while excited trajectories changed least. (B) Example session from each starting-state quadrant illustrates these directions by connecting the narrated event and guidance with the self-reported state after guidance. A two-part figure plotting valence horizontally and arousal vertically. Panel A divides the rating space into anxious or agitated, excited or energized, dejected or flat, and calm or content quadrants. Fifteen arrows connect the mean state before guidance, shown by a circle, to the mean state after guidance for clusters of nearby starting coordinates; each arrow is labelled with its session count. Most anxious arrows point right and downward, dejected and calm arrows point right and upward, and excited arrows are shorter and vary in direction. Panel B contains four example trajectories, one per quadrant. Each example shows a dashed circle for the state before guidance, a solid circle for the state after guidance, an arrow between them, and text boxes summarizing the event, guidance, and post-guidance experience.
Figure 9. Perceived understanding was associated with within-session valence change, and participant corrections identified recurring system mismatches. (A) Mean valence change (after minus before) by understanding rating. Bars show means, error bars show 95% confidence intervals, and labels show session counts; higher ratings corresponded to greater valence gains ( , 95% CI ). (B) The 44 correction texts by coded category, each paired with a representative correction. The four system-attributable categories contained 37 corrections, representing 3.4% of all sessions; the remaining seven indicated that the participant was unable to evaluate the system. A two-panel figure. Panel A is a bar chart of mean valence change after versus before guidance across understanding ratings from 1 to 7. Bars for ratings 1 to 3 extend below zero, while bars for ratings 4 to 7 extend above zero. Error bars show 95% confidence intervals, and labels above the bars show 2, 6, 15, 21, 575, 377, and 97 sessions. Panel B is a horizontal bar chart of 44 correction texts grouped into emotion misread (15, 34%), context confusion (8, 18%), memory failure (8, 18%), strategy mismatch (6, 14%), and unable to evaluate (7, 16%). Each category includes a representative participant correction. A bracket groups the first four categories as 37 corrections attributable to the system, or 3.4% of all sessions.
Figure 10. Exit ratings of perceived personalization and ease of use over the 14-day deployment. Feeling increasingly known and ease of use received higher mean ratings than personalized emotion estimates. Items were rated on a 7-point scale (1 = strongly disagree, 7 = strongly agree). Horizontal box plot with three rows on a one-to-seven scale. Learning and ease of use have means near five and a half. Personalization has the lowest mean.
Figure 11. Four design implications for sustained emotional support, organized around evolving support goals, guidance continuity, and user-controlled memory. Each implication connects its rationale to a system requirement. A four-column table presents design focuses, design implications, rationales, and system requirements. Evolving support goals motivate aligning listening, advice, and session completion with current needs. Guidance continuity motivates adapting recommendations to prior attempts, outcomes, and constraints. User-controlled memory motivates two implications: making memory sources visible and corrections persistent, and giving users control over stored details, retention periods, and contextual reuse.
Appendix figures & tables7 assets
Supplementary material from the paper’s appendix.
Appendix
Figure 12. Runtime orchestration for a single session. An event narration and the persisted user context enter a router that counts interacting emotional components and sends the narration to the cognitive-appraisal scaffold or, where several components interact, to the combined scaffold. Both produce one unified output: a dimensional emotion estimate with a matching SAM reading, emotion labels, appraised intensity, controllability, and situation phase, from which the regulation strategy and all delivery parameters are derived. The guided conversation then advances one plan step per turn along the exploration, comforting, and action arc until it closes. The scaffold selected by the router then re-runs over the event, the estimate, and the transcript to produce a post-guidance estimate. A nightly pass folds themes, calibration trend, corrections, and technique outcomes back into the user context. A left-to-right flow diagram of one session. Event narration and a persistent user context block feed a router, which branches on whether the narration contains multiple interacting emotional components and selects one of two appraisal scaffolds. Both scaffolds converge on a unified output box holding the emotion estimate and a strategy and delivery box. A guided conversation loop follows, cycling companion turns until a close condition is met, and its transcript feeds a post-guidance estimate. A dashed return path labelled nightly memory update runs from the end of the pipeline back to the user context.
| Prompt | #1 | #2 | #3 | #4 | Mean rank |
|---|---|---|---|---|---|
| Cog | 39 | 26 | 19 | 6 | 1.91 |
| Combined | 34 | 31 | 8 | 17 | 2.09 |
| Neuro | 13 | 12 | 51 | 14 | 2.73 |
| Baseline | 4 | 21 | 12 | 53 | 3.27 |
Table 4. Ranking distribution of the four emotion-inference prompts across 90 scenario-rater rankings (30 scenarios 3 raters, overall-preference dimension). Each cell counts how often a prompt placed at that rank in the blind bracket tournament; lower mean rank is better. Results were consistent across the outcome-plausibility and reasoning-fidelity dimensions, which are omitted for brevity.
| Characteristic | (%) or ( SD ) |
|---|---|
| Demographics | |
| Age in years | 25.1 (5.1) |
| Age range | 18–33 |
| Woman | 13 (68%) |
| Man | 6 (32%) |
| Asian | 15 (79%) |
Table 5. Participant characteristics from the onboarding questionnaires ( ). AI familiarity was rated on a 5-point scale from not familiar at all to extremely familiar. The three ERQ-S expressive-suppression items were rated on a 7-point scale from strongly disagree to strongly agree; the composite is their mean (Cronbach’s ).
| Source | Data |
|---|---|
| Onboarding (Day 0) | Demographics; AI background; ERQ suppression; persona interview responses; calibration narration + SAM ratings |
| Self-report (per session) | Before/after SAM ratings (valence, arousal, dominance); three feedback items; understanding rating + correction text if 4; rotating-question responses |
| System logs (per session) | Narration text and modality; initial and post-guidance affective estimates (PAD, SAM, emotion labels, intensity); selected regulation strategy; per-turn conversational stage and support-strategy labels; full transcript with timestamps; delivery-channel state (color, pulse speed, expression sequence, speech on/off, language); prompt version and persona snapshot |
| Engagement telemetry | App opens; just-in-time logging frequency and timing; missed sessions; voice vs. text ratio; slider interaction dynamics |
| Post-study (Day 14+) | 12-item questionnaire; semi-structured interview recording |
Table 6. Data collected per session and per study phase.
Figure 13. Self-reported affect before and after guided conversation. Boxes show session-level distributions, grey points show each participant’s mean, and diamonds with error bars show the session-weighted mean across sessions with 95% participant-bootstrap confidence intervals. denotes mean within-session change and the rank-biserial correlation from participant-level Wilcoxon signed-rank tests. All three contrasts remained significant after Holm correction ( ). Six horizontal box plots on a 1–9 rating scale show valence, arousal, and dominance before and after guidance. For each dimension, the after-guidance distribution is shifted to the right of the before-guidance distribution. Grey points show participant means, and black diamonds with confidence intervals show session-level means. Labels report mean increases of 0.53 for valence, 0.33 for arousal, and 0.47 for dominance, with rank-biserial correlations of .99, .87, and 1.00, respectively.
| Rating | SM | CC | RM | AD | |||
|---|---|---|---|---|---|---|---|
| Helpfulness | 5.03 | 5.16 | 5.01 | 5.00 | 3.00 | .391 | .395 |
| Feasibility | 5.28 | 5.18 | 5.20 | 5.04 | 2.98 | .395 | .395 |
| Willingness | 5.24 | 5.09 | 5.09 | 5.10 | 3.72 | .293 | .395 |
Table 7. Mean immediate feedback on 7-point scales by selected regulation strategy. SM denotes situation modification ( ), CC cognitive change ( ), RM response modulation ( ), and AD attentional deployment ( ). Means are unadjusted session averages. Test statistics refer to the strategy term in the mixed-effects models.
| (SD) | Day-continuous model | Week-binned model | |||||
| Measure | Overall | Week 1 | Week 2 | [95% CI] | [95% CI] | ||
| Counts (negative binomial) | |||||||
| Sessions per day | 4.13 (1.06) | 4.16 (1.13) | 4.10 (1.00) | [ , ] | .963 | [ , ] | .747 |
| Conversation turns | 10.02 (2.80) | 10.23 (2.93) | 9.87 (2.66) | [ , ] | .091 | [ , ] | .109 |
| Log-transformed (lengths in characters, in SAM points with log1p) | |||||||
| Narration length | 59.5 (105.9) | 69.9 (140.7) | 48.6 (45.0) | [ , ] | [ , ] | ||
Table 8. Longitudinal models for the nine session measures across the 14-day deployment. (SD) are unadjusted means over all sessions and within Week 1 (days 1–7) and Week 2 (days 8–14). is the per-day slope from the day-continuous mixed-effects model (reported ), and is the Week 2 minus Week 1 contrast from the week-binned model. Estimates for count and log-transformed outcomes are on the link scale.
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