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REVIEW 4 major objections 6 minor 115 references

Mitigating Response Delays in Free-Form Conversations with LLM-powered Intelligent Virtual Agents

T0 review · 4 major / 6 minor · reviewed 2026-08-06 · deepseek-v4-flash

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Pith's one-line read This paper reports that in VR conversations with LLM-powered agents, response delays above four seconds degrade users' perceived response time and experience, while natural conversational fillers—thinking gestures plus filler…

desk verdict A well-run, fully interactive VR study of latency and fillers whose headline claims are a bit broader than the pairwise data will bear—still worth a serious referee. read the letter →

arxiv 2507.22352 v1 pith:WGDWWY6U submitted 2025-07-30 cs.HC

Mykola Maslych , Mohammadreza Katebi , Christopher Lee , Yahya Hmaiti , Amirpouya Ghasemaghaei , Christian Pumarada , Janneese Palmer , Esteban Segarra Martinez
show 4 more authors
Marco Emporio Warren Snipes Ryan P. McMahan Joseph J. LaViola Jr
This is my paper · ORCID
classification cs.HC
keywords conversationalfillersresponselatencyLLM-poweredagentsvirtualrealityperceivedtimequalityofexperienceuserstudy
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 asks whether conversational fillers can soften the experience of waiting for an LLM-powered virtual agent to reply in free-form conversation, and whether conventional loading indicators do the same. The authors report that delays above four seconds degrade perceived response time and broader user experience, that natural fillers (a thinking gesture plus a filler phrase) significantly improve perceived response time at four and 6.5 second delays, and that artificial wait indicators do not. If correct, the findings give interaction designers a concrete, low-cost mitigation for the latency that remains unavoidable when speech recognition, generation, and synthesis run in sequence.

What carries the argument

The load-bearing intervention is the natural conversational filler: a multimodal delay-mitigation cue in which the agent performs a "thinking" gesture (head turn, chin touch, subtle breathing) and speaks a filler voice line during the response delay. It works by occupying the wait with social cues that mimic human deliberation, and the paper measures its effect through a six-item post-condition survey whose first item asks whether the agent "was quick to start responding meaningfully."

What would settle it

Run the same study with a natural filler that uses only the thinking gesture and no voice line; if the perceived-response-time advantage disappears, the effect came from participants counting the filler phrase as the start of the answer rather than from a genuine reduction in perceived waiting.

Watch

Extended reading notes

Core claim

This paper claims that response delay is a first-class usability problem for free-form, LLM-powered embodied conversational agents, and that the right interface-level remedy is a natural conversational filler rather than a conventional loading indicator. In a within-subjects VR study with 54 participants conversing with nine agents across three scenarios, delay degraded perceived response time and broader perception metrics, with effects becoming pronounced at 4.0s and 6.5s. Natural fillers—a thinking gesture plus a filler phrase such as "Hmm, let's see..."—significantly improved perceived response time at both of those delay levels compared with no filler, supporting H2a; artificial wait indicators did not produce significant improvement, and neither filler type rescued the broader experience metrics.

Load-bearing premise

The main measurement assumption is that the survey question about the agent being "quick to start responding meaningfully" measures perceived latency and not the fact that, in the Natural condition, the agent audibly speaks during the wait, so participants may treat the filler phrase itself as the start of the reply.

Editorial extensions

If this is right

  • Designers of LLM-powered conversational agents should target system response times under four seconds, because longer delays significantly degrade perceived response time, engagement, impression, competence, and willingness to interact again.
  • When delays of four to 6.5 seconds are unavoidable, natural conversational fillers (gesture plus filler phrase) can recover perceived response time, though they do not restore the broader perception metrics.
  • Artificial loading icons and processing sounds should not be relied on as a latency-mitigation strategy, since the study found no significant benefit over no filler.
  • Studies using LLM-based free-form agents should report response latency, because high delays bias user perceptions and limit comparability across results.
  • The open-source deployment pipeline makes the system reproducible for future VR agent studies.

Reading between the lines

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

  • The gesture-versus-voice split in participant preferences suggests a personalization opportunity: a single filler design may not serve all users, and offering selectable filler modality could strengthen mitigation.
  • The null result for artificial wait indicators may be specific to generic spinners; more communicative indicators that signal content (for example, showing that the agent is checking a record or that a response stage is underway) might behave differently, and the paper's recommendation to explore such indicators is a natural next test.
  • The four-second threshold is measured in a leisurely task-guided VR setting; time-pressure or high-stakes contexts such as emergency or medical conversation may compress tolerance, so the threshold is likely an upper bound rather than a universal constant.
  • In non-embodied channels such as phone voice assistants or text chatbots, the natural filler's gestural component is absent, so the voice-line alone may be the transferable part; the paper's embodied results do not directly establish that transfer.
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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 / 6 minor

Summary. This paper reports a within-subjects VR experiment (N=54) in which participants held free-form, task-guided conversations with nine LLM-powered embodied agents under three response-latency levels (1.5s, 4.0s, 6.5s) and three filler conditions (None, Artificial wait indicator, Natural conversational filler). The authors use ART ANOVA with Holm-Bonferroni-corrected ART-C contrasts and find that latency significantly degrades perceived response time and several broader perception ratings, that Natural fillers significantly improve the single-item perceived-response-time rating at Medium and High latency (H2a), and that Artificial wait indicators do not produce significant effects (H3a/H3b unsupported). The paper interprets these results as showing that response delays above 4 seconds degrade quality of experience, that natural fillers mitigate perceived delay, and it contributes an open-source Unity/ASR/LLM/TTS pipeline for deploying conversational agents in VR.

Significance. The study is a well-powered full-factorial experiment in a realistic LLM-based conversational pipeline, and the open-source release is a practical contribution to VR conversational-agent research. Strengths include a power analysis, balanced-Latin-square counterbalancing, appropriate nonparametric ART ANOVA with Holm-Bonferroni correction, and a credible interactive system with a measured 1.5s SRT. If the H2a finding survives a cleaner measure of perceived latency, it would usefully extend prior WoZ and pre-recorded filler results to real-time LLM-driven free-form conversation. However, the central interpretation depends on a single questionnaire item whose wording does not fully separate filler speech from response speech, and the 'above 4 seconds' threshold claim goes beyond the sampled latency grid. The paper also makes summary claims in the Discussion and Conclusion that are broader than the significant results.

major comments (4)
  1. [§3.2.1, §3.1.2, §5.2] The main positive result (H2a) is measured by a single item, Q1: 'From the moment I stopped talking, the agent was quick to start responding meaningfully.' In the Natural condition the agent utters a semantically interpretable filler line ('Hmm, let me think about that...') and performs a thinking gesture immediately after the user stops talking, so the objective time to the first audible speech is shorter in Natural than in None or Artificial. The word 'meaningfully' was intended to prevent participants from counting filler speech, but no manipulation check in §3.2 or §4.2 verifies that they did so; PSQ6 asks whether fillers were noticed but is not used to test this interpretation. As a result, the Medium/High Q1 advantage of Natural over None (p < 0.01 and p < 0.0001 in §5.2) is also consistent with participants treating the filler utterance as the start of the response, making the effect partly definitional rather than a genuine improvement in perceived latency. Please add a manipulation check (for example, a post-condition item that distinguishes 'the agent said something while thinking' from 'the agent answered my question') or re-analyze with a measure that isolates the final response onset, and adjust the H2a claim accordingly.
  2. [§5.3, Figure 5] The abstract, §5.3, and the design recommendations state that response delays 'above 4 seconds' degrade quality of experience, but the experiment samples only 1.5s, 4.0s, and 6.5s. Q1 shows all three pairwise delay differences, but the broader perception metrics (Q2-Q6) show significant degradation only between Low and High, with Q2 the only broader metric distinguishing Medium from High and no broader metric distinguishing Low from Medium. The data therefore establish that 6.5s is worse than 1.5s and that 4.0s and 6.5s are perceived as slower than 1.5s on Q1, but they do not locate a threshold 'above 4 seconds.' Please soften the threshold claim to 'the two longer delays tested' or add a latency grid that brackets the boundary.
  3. [§5.2, §7] The Discussion and Conclusion go beyond the supported effects. The only significant filler effects are on Q1 at Medium and High latency; no filler effect reaches significance on Q2-Q6, and the paper appropriately reports that H2b, H3a, and H3b are unsupported. Yet §7 concludes that Natural fillers 'enhance VR user experience' and 'reduce latency's negative repercussions,' and §5.2 states that fillers 'improve tolerance for delayed responses.' Please align these summary claims with the measured single-item Q1 effect and the broader null results, and avoid implying that the study demonstrates improvement in global user experience.
  4. [§4.2.5] The claim that conversational fillers increase willingness to use the slowest system rests on a comparison of PSQ5 and PSQ10, which are repeated measures from the same 54 participants. The reported chi-square test treats the two sets of responses as independent; a paired analysis (e.g., a McNemar test on collapsed agree/disagree categories or a paired ordinal model) is needed because the same participants answer both questions. Please re-analyze these data and adjust §5.2 if the conclusion changes.
minor comments (6)
  1. [§1] The phrase 'free-from conversation' appears to be a typo for 'free-form conversation'; please correct it.
  2. [§4.1] The set of 36 Holm-Bonferroni-adjusted contrasts should be defined explicitly in the text or supplementary material; currently the reader cannot tell which family of comparisons the correction controls.
  3. [§3.2, §6] The custom questionnaires are appropriately acknowledged in Section 6 as unvalidated, but the single-item Q1 and the aggregated RoSAS-based Q4/Q5 would benefit from a brief psychometric justification or a pilot validation, especially because Q1 carries the main result.
  4. [Figures 4-8] In the copy under review, the text inside Figures 4-8 appears as long unreadable token sequences (e.g., '/uni000000...' strings); please ensure the final PDF contains clean figure graphics with readable labels.
  5. [§4.2.6] The inductive analysis of text justifications is described as preliminary, but no inter-rater reliability or coding agreement is reported; a brief statement on the coding process would help readers assess the thematic counts.
  6. [Table 3] The observed filler effect sizes are small (η²p = 0.017-0.081), whereas the power analysis targeted a medium-to-large effect; the null results for Q2-Q6 should be explicitly framed as 'not supported' rather than as evidence of no effect, which the text mostly does but could state more consistently.

Circularity Check

1 steps flagged · score 4.0 of 10

Q1's 'responding meaningfully' does not fully separate the Natural filler's spoken utterance from the start of a meaningful response, so the H2a result is partly definitional.

  1. self definitional [§3.2.1 (Q1), §3.1.2 (Natural filler), §5.2 (H2a result)]
    "Q1 assessed perceived response latency and included the word "meaningfully" to avoid bias toward Natural fillers that included speech. (Q1) Response Time: From the moment I stopped talking, the agent was quick to start responding meaningfully. In the Natural condition, each agent randomly selected from three 'thinking' gestures and six filler voice lines at runtime."

    The load-bearing H2a result rests on Q1, which asks whether the agent was quick to start responding meaningfully. In the Natural condition, the agent speaks a filler voice line immediately at the onset of the wait, before the delayed LLM response. These filler lines are not semantically empty: "Hmm, let me think about that..." acknowledges the user and signals deliberation. Thus the objective time from the participant stopping talking to the agent's first meaningful speech is shorter in the Natural condition than in None or Artificial by construction. Participants who count the filler as the beginning of a meaningful response will rate Q1 higher regardless of perceived waiting time.

full rationale

This is an empirical VR user study with no fitted parameters, no uniqueness theorems, and no mathematical derivation chain, so most circularity patterns do not apply. Self-citations are present (e.g., the authors' prior pilot study and the VALID avatar library) but are not load-bearing: the experiment is self-contained, and the main latency results are statistical comparisons from Likert responses. The one genuine circularity risk is measurement-level and concerns H2a. The outcome Q1 asks whether "the agent was quick to start responding meaningfully," and the Natural filler condition makes the agent speak a semantically meaningful filler line immediately when the wait begins. Because filler lines like "Hmm, let me think about that..." acknowledge the user and signal deliberation, participants may count the filler as the start of a meaningful response, making the Natural condition objectively shorter on the very construct Q1 measures. The paper added the word "meaningfully" to avoid this bias, showing awareness, but it reports no manipulation check confirming that participants excluded filler speech. The Q1 advantage of Natural fillers at Medium and High latency is thus at least partly definitional rather than pure evidence of perceived-latency mitigation. This does not undermine H1a or the delay main effects, which do not involve fillers, and the broader perception results (H2b not supported) are unaffected because those questions do not reference response onset. Overall, one partial measurement-level circularity in the central filler claim warrants a score of 4.

Assumptions & free parameters 0 free parameters · 3 assumptions · 0 invented entities

The empirical result depends on experimental design and measurement assumptions rather than fitted parameters. The delay levels are conditions, not free parameters. The main measurement risk is the Q1 wording and the audible content of the natural filler.

assumptions (3)
  • domain assumption The 1.5s, 4.0s, and 6.5s delay levels span natural, comfortable, and excessive conversational pauses.
    The levels are justified from prior turn-taking literature, with 4.0s matched to the reported silence threshold (§3.1.1).
  • ad hoc to paper The Q1 wording 'quick to start responding meaningfully' separates the filler utterance from the actual response.
    The paper relies on this wording to prevent natural filler speech from being counted as response onset, but no manipulation check is reported (§3.2.1).
  • standard math Aligned Rank Transform ANOVA is appropriate for these five-point Likert responses.
    ART is a standard nonparametric factorial method for ordinal data (§4.1).

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Cite this review

Pith. "Pith review of Mitigating Response Delays in Free-Form Conversations with LLM-powered Intelligent Virtual Agents." pith.science (2026). https://pith.science/paper/WGDWWY6U

@misc{pith2026250722352,
  author       = {Pith},
  title        = {Pith review of: Mitigating Response Delays in Free-Form Conversations with LLM-powered Intelligent Virtual Agents},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/WGDWWY6U}},
  note         = {Machine review of arXiv:2507.22352}
}
read the original abstract

We investigated the challenges of mitigating response delays in free-form conversations with virtual agents powered by Large Language Models (LLMs) within Virtual Reality (VR). For this, we used conversational fillers, such as gestures and verbal cues, to bridge delays between user input and system responses and evaluate their effectiveness across various latency levels and interaction scenarios. We found that latency above 4 seconds degrades quality of experience, while natural conversational fillers improve perceived response time, especially in high-delay conditions. Our findings provide insights for practitioners and researchers to optimize user engagement whenever conversational systems' responses are delayed by network limitations or slow hardware. We also contribute an open-source pipeline that streamlines deploying conversational agents in virtual environments.

Figures

Figures reproduced from arXiv: 2507.22352 by the authors.

Figure 1
Figure 1. In this paper, we investigated response delays with intelligent virtual agents and the effects of conversational fillers: [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Intelligent Embodied Virtual Agents in their corresponding virtual environments. [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 3
Figure 3. System architecture: speech is recorded in Unity, passed to the ASR model, combined with the message history, passed [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗
Figures from the paper (5 more)
Figure 4
Figure 4. Figure 4: Breakdown of latencies achieved in our system. [PITH_FULL_IMAGE:figures/full_fig_p007_4.png]
Figure 5
Figure 5. Figure 5: Effect of response delay on user perception of intelligent embodied virtual agents in the [PITH_FULL_IMAGE:figures/full_fig_p008_5.png]
Figure 6
Figure 6. Figure 6: Effect of delay with all filler types on user perception of intelligent embodied virtual agents. [PITH_FULL_IMAGE:figures/full_fig_p008_6.png]
Figure 7
Figure 7. Figure 7: Number of times each filler was ranked in terms of [PITH_FULL_IMAGE:figures/full_fig_p009_7.png]
Figure 8
Figure 8. Figure 8: Participants’ responses to question on whether they would use the system depending on the speed of agents’ responses [PITH_FULL_IMAGE:figures/full_fig_p010_8.png]

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Pith tools

Reviewed August 6, 2026 · model on record in the stance chip above.