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arxiv: 2605.15812 · v1 · pith:6SN5TYPLnew · submitted 2026-05-15 · 💻 cs.HC · cs.AI

Toward Natural and Companionable Virtual Agents via Cross-Temporal Emotional Modeling

Feier Qin , Xiao Li , Yi Zheng , Haibin Huang , Hanyao Wang , Xiaoyu Wang , Yan Lu , Yuan Zhang This is my paper

Pith reviewed 2026-05-20 19:01 UTC · model grok-4.3

classification 💻 cs.HC cs.AI
keywords cross-temporal emotion modelingvirtual companion agentsemotional state modelinglong-term interactionsconversational agentsnaturalnesscoherenceemotional harmony
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The pith

Cross-Temporal Emotion Modeling creates a closed loop that lets virtual agents evolve emotional states from past interactions and feedback to improve long-term naturalness.

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

Current conversational agents often feel episodic and inauthentic because their behaviors do not influence an internal emotional state and emotions do not shape future actions. Cross-Temporal Emotion Modeling addresses this by linking long-term behavioral history to immediate emotional expressions through a continuous cycle of updates from experiences and user input. If effective, this approach would allow agents to reflect on shared history and anticipate user responses, fostering more authentic companionship over extended periods. The authors test this by building Auri, an agent on a messaging platform, and observing better ratings for naturalness and coherence after three weeks of use.

Core claim

The paper presents Cross-Temporal Emotion Modeling (CTEM) as a framework that establishes a closed loop: past experiences update an evolving emotional state, this state conditions immediate interactions, and user feedback revises both memory and emotional state to enable reflection and anticipation. When implemented in the Auri agent, this modeling leads to measurable improvements in perceived naturalness, coherence, and emotional harmony during a 21-day in-the-wild study.

What carries the argument

Cross-Temporal Emotion Modeling (CTEM), a framework that connects an agent's long-term memory and evolving emotional state to shape each interaction while incorporating user feedback to update both.

Load-bearing premise

The 21-day in-the-wild study with Auri on an instant-messaging platform provides a valid measure of long-term companion-like naturalness and emotional harmony without major confounds such as novelty effects, self-selection bias, or lack of blinded controls.

What would settle it

A follow-up study in which users rate emotional harmony and coherence the same for CTEM agents and standard agents after the first week of use would show the framework does not produce sustained gains.

Figures

Figures reproduced from arXiv: 2605.15812 by Feier Qin, Haibin Huang, Hanyao Wang, Xiao Li, Xiaoyu Wang, Yan Lu, Yi Zheng, Yuan Zhang.

Figure 1
Figure 1. Figure 1: We present Auri, a lightweight companion agent designed to foster long-term emotional connections through cross-temporal emotional modeling. Auri is able to deliver contextually coherent and emotionally resonant interactions over time. Abstract Recent advances in foundation models have enabled conversational agents that aim for sustained companionship rather than mere task completion. Yet most still remain… view at source ↗
Figure 2
Figure 2. Figure 2: System overview of Auri, showing the CTEM framework with front-end and back-end components. [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Examples of cross-temporal interaction under CTEM across two scenarios. BGI refers to Behavior [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Mean ratings of trait contributions to perceived [PITH_FULL_IMAGE:figures/full_fig_p009_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Distribution of usage contexts across groups in [PITH_FULL_IMAGE:figures/full_fig_p009_5.png] view at source ↗
read the original abstract

Recent advances in foundation models have enabled conversational agents that aim for sustained companionship rather than mere task completion. Yet most still remain unable to support natural, long-term companion-like interactions, resulting in experiences that feel episodic and inauthentic. We argue that current agents overlooked cross-temporal modeling of agents' social behaviors and internal emotions: generated behaviors rarely influence an agent's emotional state, and emotional states seldom shape subsequent behaviors. We present Cross-Temporal Emotion Modeling (CTEM), a framework that links long-term behavioral history to moment-to-moment emotional expression. CTEM establishes a closed loop where past experiences update an evolving emotional state; this state conditions immediate interactions; and user feedback continually revises both memory and emotional state, enabling reflection and anticipation. We instantiate CTEM as Auri, a companion agent on an instant-messaging platform, and report a 21-day in-the-wild study showing that CTEM shows improvements in perceived naturalness, coherence, and emotional harmony.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The manuscript proposes Cross-Temporal Emotion Modeling (CTEM), a framework that links long-term behavioral history to moment-to-moment emotional expression in virtual agents via a closed loop: past experiences update an evolving emotional state, which conditions immediate interactions, and user feedback revises both memory and emotional state to enable reflection and anticipation. CTEM is instantiated as Auri, a companion agent on an instant-messaging platform, and evaluated in a 21-day in-the-wild study that reports improvements in perceived naturalness, coherence, and emotional harmony.

Significance. If the empirical claims hold after addressing validation gaps, CTEM could meaningfully advance HCI research on sustained companionship in conversational agents by explicitly modeling cross-temporal emotional dynamics, a dimension often missing in current foundation-model systems. The closed-loop structure provides a clear conceptual contribution that could guide future agent architectures.

major comments (2)
  1. [Abstract] Abstract: the claim that the 21-day study 'shows improvements' in naturalness, coherence, and emotional harmony supplies no participant numbers, metrics, statistical tests, control conditions, or exclusion criteria, so it is impossible to verify whether the gains are supported by the data or attributable to the CTEM closed loop.
  2. [Evaluation] User study description: without a control condition, pre/post baseline, or statistical isolation of the emotional-modeling component, improvements cannot be confidently attributed to CTEM rather than confounds such as novelty effects or self-selection bias; this directly undermines the central claim that the cross-temporal loop produces the observed outcomes.
minor comments (2)
  1. [Framework] Provide pseudocode or a diagram for the exact state-update and feedback-revision rules in CTEM to make the framework reproducible.
  2. [Evaluation] Clarify how 'emotional harmony' was operationalized in the study questionnaire and whether inter-rater reliability was assessed.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive and detailed feedback. The comments highlight important issues regarding the presentation of our empirical results and the strength of causal claims. We address each point below and indicate the revisions we will make to the manuscript.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the claim that the 21-day study 'shows improvements' in naturalness, coherence, and emotional harmony supplies no participant numbers, metrics, statistical tests, control conditions, or exclusion criteria, so it is impossible to verify whether the gains are supported by the data or attributable to the CTEM closed loop.

    Authors: We agree that the abstract is too concise and does not supply the quantitative details needed for immediate verification. The Evaluation section of the manuscript reports the full study protocol, including participant numbers, the specific metrics and scales employed, the statistical tests performed, and exclusion criteria. To resolve this, we will revise the abstract to concisely include these elements (participant count, key metrics, statistical outcomes) while preserving its length constraints. This change will allow readers to assess the reported improvements without first consulting the full text. revision: yes

  2. Referee: [Evaluation] User study description: without a control condition, pre/post baseline, or statistical isolation of the emotional-modeling component, improvements cannot be confidently attributed to CTEM rather than confounds such as novelty effects or self-selection bias; this directly undermines the central claim that the cross-temporal loop produces the observed outcomes.

    Authors: We acknowledge that the absence of a control condition limits strong causal attribution to the CTEM closed loop specifically, and that factors such as novelty or self-selection could contribute to the observed changes. The study was intentionally designed as a 21-day in-the-wild deployment to examine sustained, real-world companion interactions rather than a short-term lab experiment. Pre- and post-study questionnaires were administered to capture longitudinal shifts. In revision we will (1) add an explicit Limitations subsection that discusses potential confounds and the exploratory nature of the design, (2) moderate the language in the abstract and conclusion from “shows improvements” to “indicates improvements consistent with” the CTEM framework, and (3) outline directions for future controlled studies that could isolate the emotional-modeling component. These revisions address the concern without altering the core contribution of the closed-loop architecture. revision: partial

Circularity Check

0 steps flagged

No circularity: CTEM is a conceptual framework with independent empirical evaluation

full rationale

The paper presents CTEM as a modeling framework that establishes a closed loop between past experiences, evolving emotional state, immediate interactions, and user feedback. This is instantiated as Auri and supported by a 21-day in-the-wild study reporting improvements in naturalness, coherence, and emotional harmony. No equations, fitted parameters renamed as predictions, self-citations, or uniqueness theorems are described that would reduce the framework or results to inputs by construction. The derivation chain is self-contained as a proposal plus separate user study, with no load-bearing steps that collapse into self-definition or fitted data.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 1 invented entities

Based solely on the abstract, the central claim rests on the introduction of an evolving emotional state as a modeling construct and the assumption that a closed behavioral-emotional-feedback loop produces measurable improvements; no explicit free parameters or prior axioms are stated.

axioms (1)
  • domain assumption Past behaviors and user feedback can be used to maintain and update an internal emotional state that then shapes future agent responses
    This is the core premise of the CTEM framework described in the abstract.
invented entities (1)
  • evolving emotional state no independent evidence
    purpose: To link long-term behavioral history to moment-to-moment emotional expression and enable reflection and anticipation
    New modeling construct introduced by CTEM; no independent evidence or falsifiable prediction outside the framework is mentioned in the abstract.

pith-pipeline@v0.9.0 · 5712 in / 1519 out tokens · 61781 ms · 2026-05-20T19:01:30.157927+00:00 · methodology

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