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REVIEW 4 major objections 7 minor 76 references

LLAMAPIE: Proactive In-Ear Conversation Assistants

T0 review · 4 major / 7 minor · reviewed 2026-08-15 · deepseek-v4-flash

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Pith's one-line read A fully proactive, on-device in-ear assistant can whisper 1–3 word hints during live conversations, matching the quiz accuracy of a reactive chat assistant while preserving conversation flow.

desk verdict A genuinely working proactive in-ear assistant with a real user study; the main soft spots are an information-asymmetric reactive baseline and a partially self-consistent evaluation rubric, both fixable. read the letter →

arxiv 2505.04066 v2 pith:RWXX2FAN submitted 2025-05-07 cs.LG cs.CLcs.HCcs.SDeess.AS

Tuochao Chen , Nicholas Batchelder , Alisa Liu , Noah Smith , Shyamnath Gollakota This is my paper · ORCID
classification cs.LGcs.CLcs.HCcs.SDeess.AS
keywords proactiveAIassistantin-earhearabledevicesstreaminginferencedual-modelarchitecturesyntheticdialoguedatasetconversationassistanceon-deviceLLM
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

The paper introduces LlamaPIE, a hearable assistant that listens to a face-to-face conversation and, without any prompt from the wearer, occasionally whispers a one-to-three-word hint. The central claim is that a proactive assistant of this kind can deliver the same accuracy benefit as a reactive chat assistant while keeping the human conversation intact. In a live study with 15 participants, wearing LlamaPIE lifted answer accuracy from 37% to 86.7%, close to the 88.9% achieved by asking a state-of-the-art assistant, but with far shorter reaction times (4.89 vs 13.38 seconds) and far lower perceived disruption (2.4 vs 4.73 on a five-point scale). The paper argues that this is made possible by splitting the problem into a small 'when to respond' model and a larger 'what to say' model, trained on a synthetic dialogue dataset the authors build with a commercial language model. If the claim holds, it opens a design space where AI help is embedded in ordinary human interaction rather than replacing it.

What carries the argument

The machine that carries the argument is a two-stage, token-streaming pipeline. An automatic speech recognizer and diarizer turn the conversation into a text stream with silence markers that stand for half-second gaps; a small finetuned classifier processes that stream and is the only component that runs continuously, predicting on those silence markers whether the wearer needs help. On a trigger, a larger finetuned model generates at most a few words, and the words are fed back into both models so the system knows it already spoke. Training the large model on negative examples teaches it to emit an end-of-sequence token when assistance is not actually needed, and this design yields at least a 64% reduction in continuous processing time compared to a single large model, which is what lets everything run in real time on a laptop-class device.

What would settle it

Record a set of natural human conversations in which independent judges mark every moment a participant was stuck or would clearly benefit from a whispered cue, run LlamaPIE's trigger model on those transcripts, and compare its precision and recall against the synthetic test set (around 93% accuracy, 72–78% hard recall). If live trigger recall falls far below the synthetic numbers, or if the large model's rubric score on the whispers drops below the reported MIT value of 3.68, the claim that synthetic training transfers to real conversations is refuted.

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Extended reading notes

Core claim

LlamaPIE's core claim is that an assistant can help in a live human conversation without being called on: it listens continuously, and only when it detects that the wearer is stuck does it whisper a two-word cue derived from the wearer's stored memory. The paper argues that the right split of labor makes this feasible: a small model whose only job is to decide when to trigger, plus a larger model that writes the whisper and is allowed to stay silent by outputting an end-of-sequence token. In the user study, wearers answered 86.7% of questions correctly with this proactive system, essentially matching the 88.9% achieved by turning to a reactive language-model assistant, but with average reaction time of 4.89 seconds instead of 13.38 seconds and disruption rated 2.4 instead of 4.73 on a 5-point scale. The paper takes this as evidence that anticipation, not raw response quality, is what lets AI assistance fit inside a human conversation.

Load-bearing premise

The load-bearing premise is that synthetic dialogues made by a language model, with the assistant's speaking moments chosen by the generator, accurately represent when real people in real conversations need help; the paper concedes it trained the real-world model only on such synthetic data.

Editorial extensions

If this is right

  • Fully proactive assistance can match the accuracy of a reactive chat assistant (86.7% vs 88.9% correct) while cutting response latency by more than half and perceived disruption from 4.73 to 2.4 on a 5-point scale.
  • The when-versus-what split makes the assistant run in real time on-device; the small model continuously processes tokens while the large model generates only when triggered, a 64% reduction in processing time over a single large model.
  • The trigger model is the system's bottleneck: manually triggering the large model at ground-truth positions improves rubric scores by about 0.15 over automatic triggering, so better timing prediction directly translates into better assistance.
  • Assistance-history awareness is necessary: without it the small model triggers 2–3 times more often, so an assistant that remembers it already whispered is essential to keeping the conversation uncluttered.

Reading between the lines

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

  • A natural next experiment is to retrain the same two-model pipeline on a small set of human-annotated real conversations; if the MIT-transfer gap (response frequency 5.8% vs 14–15% in synthetic data) closes, then the synthetic-data premise, not the architecture, was the main weakness.
  • The separation of 'when to speak' from 'what to say' suggests the trigger model could be reused across modalities and response generators (visual cues, haptic alerts, different language models), a direction the paper does not explore.
  • The paper's user study covers fact-recall Q&A; a stronger test would be open-ended social conversations such as negotiation or conflict, where the paper's own 'Anticipatory' principle received the lowest score.
  • The on-device streaming pipeline could double as a data-collection instrument for building the real-world assistance corpus the paper says is lacking, since it already captures aligned transcripts and whisper timing.
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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 / 7 minor

Summary. The paper introduces LLAMA PIE, a proactive in-ear assistant that operates without explicit user invocation. The system uses a two-model pipeline: a small streaming classifier determines when assistance is needed, and a larger LLM generates 1–3-word whispers. The models are finetuned on a semi-synthetic dialogue dataset constructed with Claude, where assistant positions and responses are generated according to nine prescribed principles. The paper evaluates the trigger model on synthetic test sets, evaluates the dual-model pipeline on synthetic and MIT Interview datasets using GPT-4o as a judge (validated against 21 human annotators), and reports a user study with 15 participants comparing control, proactive, and reactive (GPT-4o) conditions. The central claim is that LLAMA PIE achieves accuracy on par with a reactive state-of-the-art LM assistant while substantially preserving the natural flow of conversation, as measured by reaction time and perceived disruption.

Significance. If the user-study findings are robust, this paper demonstrates a practical and compelling alternative to turn-based chat assistants for conversation support. The system is notable for being fully proactive and on-device, with a real-time implementation on Apple Silicon that includes streaming ASR, diarization, and TTS. Strengths include the release of code and dataset, objective metrics in the user study (accuracy, reaction time, disruption ratings), and the effort to validate the LLM-as-a-judge against human annotations (r=0.652 vs human-human r=0.636). The two-model trigger-and-generate pipeline is a sensible design that addresses latency and computational constraints. However, the headline comparison is undermined by unequal information access between the proactive and reactive conditions, and the generalizability of the synthetic training distribution to real conversations is not established. These issues affect the interpretation of the central 'on par' claim, making the contribution promising but requiring substantial additional analysis.

major comments (4)
  1. [§4.1, §D.5.1, §D.5.2] The headline comparison between proactive and reactive assistance is confounded by unequal information access. The proactive system is given the fictional-profile memory as context (§2.1, §D.5.1), while the reactive GPT-4o baseline is described only as a web UI and has no access to this memory. Two of the eight user-study topics (William Thompson, Emily Johnson) are fictional individuals whose facts cannot be known to GPT-4o. Because a topic is randomly assigned to each condition and no per-topic or per-condition accuracy/reaction-time breakdown is reported, the observed 86.7% vs 88.9% accuracy and the 4.89 s vs 13.38 s reaction-time gap may be driven by which condition drew one of the fictional-profile topics. This undermines the central claim that proactive assistance is 'on par' with a state-of-the-art LM assistant at equal information access. Please report per-topic results and, ideally, give the reactive baseline the same profile memory (e.g., via a system prompt) or include a memory-deprived proactive control.
  2. [§3.3, §4, §7] The generalizability of the synthetic training distribution to real live conversations is not established. The MIT Interview evaluation shows a substantial transfer cost: response frequency drops from about 14% on synthetic data to 5.8%, and the rubric score from about 4.2 to 3.68 (Table 3). The user-study prototype is trained solely on synthetic dialogues, and the paper concedes this in §7. The user-study tasks (mock interview with prepared questions on a memorized profile) closely resemble the synthetic dialogue format that was used to generate the training data, so the high accuracy (86.7%) may reflect the match between the evaluation protocol and the training distribution rather than general proactive-assistant competence. To support the claim that the approach works for real conversations, the paper should either include a real-world evaluation with human-annotated assistance positions or quantify the domain shift (e.g., testing on dialogues without scripted memory-based Q&A).
  3. [§3.1.2, §D.4.2] The LLM-as-a-judge evaluation is partially circular. The nine principles used to instruct Claude during data generation (D.3.2, system prompt) are the same nine principles that GPT-4o is asked to grade (D.4.2, ratings prompt). The high synthetic rubric scores (4.21, 4.19, 3.94 in Table 3) could therefore reflect the judge rewarding the instructions it was given rather than independent quality. The human validation of the judge (r=0.652 vs human-human r=0.636) covers only the 1-5 rubric, not the nine-principle scores, and the correlation is only moderate. Please report agreement separately for the nine-principle metrics, and consider evaluating with a judge that was not given the same principle definitions used in generation (e.g., a judge prompted with independent criteria).
  4. [§4.1, §C] The user study has design and statistical reporting issues that affect the strength of the conclusions. All participants begin with the control condition (§4), so practice effects could inflate performance in the later proactive/reactive conditions. The reported accuracy, reaction time, and disruption metrics lack confidence intervals and significance tests; with 15 participants, the difference between 86.7% and 88.9% accuracy is not shown to be meaningful. The participant age range is stated as 20–40 in §4 but as 18–57 in Appendix C. Please report per-participant paired differences with appropriate statistical tests (e.g., Wilcoxon signed-rank), and clarify the participant demographics consistently.
minor comments (7)
  1. [Table 3] In the Deferent row, PerLTQA column, the entry '4.22, (1.14)' contains a stray comma; check whether the value should be 4.22 or another number.
  2. [§3.1.2] The paragraph 'Assessing quality of synthetic datasets' reports rubric scores of 4.77, 4.78, and 4.88 for the synthetic datasets, which appear inconsistent with the 4.21, 4.19, and 3.94 in Table 3. Please clarify whether these refer to the generated ground-truth data or to the dual-model outputs.
  3. [§4.1] The phrase 'overwhelmingly preserving the natural flow' is stronger than the data support: proactive reaction time (4.89 s) is 49% higher than control (3.29 s), even though it is much lower than reactive (13.38 s). Consider softening the wording.
  4. [Figure 3] The histograms in Figure 3 lack axis labels and the grayscale distinguishability is poor; please add labels and use a color-blind-friendly palette.
  5. [Abstract] The claim 'first real-time proactive assistant' is strong given prior proactive systems cited in related work (e.g., Memoro); please qualify it, for example as 'first fully proactive, no-explicit-invocation in-ear assistant'.
  6. [§3.1.1] The soft metrics (±1 turn) are introduced without a sensitivity analysis; please state whether results change under a ±0 turn definition or justify the chosen tolerance as a design decision.
  7. [§2.3.3] The latency numbers report token processing speeds but not end-to-end latency from speech input to whisper output; please consider reporting the full pipeline latency to support the real-time claim.

Circularity Check

1 steps flagged · score 4.0 of 10

Synthetic quality scores share their nine-principle rubric with data-generation instructions, making Table 3 a self-consistent family judgment; user-study and MIT results remain independent anchors.

  1. self definitional [§2.2.3 / §D.3.2 (dataset construction) and §D.4.2 (GPT-4o evaluation); reported in §3.3 Table 3]
    "Each scenario is designed to fit a specified scenario type and illustrate a predefined use case, while also exemplifying two randomly selected principles of proactive assistants. ... Rate the proactive agent’s response based on how well it adheres to each of the nine principles using a score from **1 to 5**."

    The same nine principles (Valuable, Pertinent, Competent, Unobtrusive, Transparent, Controllable, Deferent, Anticipatory, Safe) are first used as the generation target when Claude is instructed to write dialogues that exemplify two of them, and then used verbatim as the evaluation rubric that GPT-4o applies to score the resulting responses. Consequently, the high rubric and nine-principle scores on the Synthetic/SODA/PerLTQA sets in Table 3 measure how well a second LLM recognizes the same specification that a first LLM was told to instantiate; they are a consistency check within one family of LLM judgments rather than an externally grounded measurement of assistance quality.

full rationale

The only load-bearing circularity I can exhibit by quotation is the shared nine-principles specification between data generation (D.3.2) and GPT-4o evaluation (D.4.2). That makes the synthetic rubric scores in Table 3 partially self-consistent but not a hard derivation: the finetuned two-model pipeline could still fail to reproduce the target behavior on held-out dialogues, so the evaluation is not vacuous. The user study (accuracy 37% vs 86.7%, reaction time 4.89s vs 13.38s, disruption 2.4 vs 4.73) is an independent behavioral test, and the MIT interview evaluation plus human-annotator correlation provide external anchors. The reactive-baseline memory asymmetry (GPT-4o lacking the fictional-profile context that the proactive system receives) is a real validity threat to the headline comparison, but it is an experimental-design confound rather than a circularity of the derivation chain, so it is not scored as a circular step. There is no machine-checkable self-citation chain, no imported uniqueness theorem, and no fitted parameter renamed as a prediction.

Assumptions & free parameters 3 free parameters · 4 assumptions · 0 invented entities

The central claim rests on four unproved premises: that Claude-generated dialogues with synthetic silence markers are a valid training distribution for real assistance timing; that the nine principles used to generate data are the correct criteria for evaluating helpfulness; that a GPT-4o judge validated at r=0.652 against humans can stand in for human judgment on the nine-principle dimensions; and that streaming ASR text retains enough conversational information for the trigger decisions. No free parameters in the physics sense are involved; the tuned hyperparameters (25% negative samples, augmentation rates, ±1 turn soft tolerance) are hand-chosen and only partly ablated. No invented entities are introduced; the assistant is a product, not a theoretical construct.

free parameters (3)
  • Negative-sample proportion for large-model finetuning = 25%
    Ablated in Table 4 across 0%, 25%, and 50%; 25% is selected because it maximizes the rubric score (3.68) while reducing unneeded responses (222 triggered, 101 responded). The proportion is a hand-tuned model-behavior knob evaluated on the MIT data.
  • Soft metric tolerance of ±1 turn = ±1 turn
    Soft precision/recall/accuracy counts a response as correct if it occurs within one turn of the ground-truth assistance position (§3.1.1). This leniency lifts reported accuracy from about 93.5% to 97.8% and is the emphasized framing of Table 2.
  • ASR-robustness augmentation rates = 2% word dropout, 3% word flip, 1% phonetic replacement
    Hand-chosen augmentation rates (§B) intended to simulate streaming-ASR errors; no ablation of these rates is reported.
assumptions (4)
  • domain assumption Claude-generated dialogues with silence tokens and imposed assistant placements are a valid training distribution for assistance timing and wording in real conversations.
    Enters at §2.2; both models are supervised exclusively on this synthetic data. The MIT results (5.8% response frequency, 3.68 rubric score) show a measurable transfer cost, and §7 requests real-world datasets.
  • domain assumption The nine proactive-assistant principles (Valuable, Pertinent, Competent, Unobtrusive, Transparent, Controllable, Deferent, Anticipatory, Safe) are the correct normative criteria for helpful in-ear assistance, and they are used both to generate data and to score responses.
    Listed in §2.2.1, applied in generation prompts (D.3.2) and in the GPT-4o rubric (D.4.2). Human annotators score with the same rubric, so human validation does not independently justify the principles themselves.
  • domain assumption A GPT-4o judge, validated at r=0.652 against human ratings, reliably grades response quality on all nine principle dimensions.
    §3.1.2 reports human-LM correlation exceeding human-human correlation (0.636), but the correlation is moderate and validation covers only the overall rubric score, not the nine principle dimensions.
  • domain assumption Streaming ASR and diarization outputs (SpeechBrain, Diart) preserve enough information for the text-only pipeline to make good assistance decisions.
    The system operates on ASR text plus diarization (§2.1, §2.3.3). ASR noise is only simulated via augmentation (§B); real ASR error rates in the user study are not reported.

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Cite this review

Pith. "Pith review of LLAMAPIE: Proactive In-Ear Conversation Assistants." pith.science (2026). https://pith.science/paper/RWXX2FAN

@misc{pith2026250504066,
  author       = {Pith},
  title        = {Pith review of: LLAMAPIE: Proactive In-Ear Conversation Assistants},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/RWXX2FAN}},
  note         = {Machine review of arXiv:2505.04066}
}
read the original abstract

We introduce LlamaPIE, the first real-time proactive assistant designed to enhance human conversations through discreet, concise guidance delivered via hearable devices. Unlike traditional language models that require explicit user invocation, this assistant operates in the background, anticipating user needs without interrupting conversations. We address several challenges, including determining when to respond, crafting concise responses that enhance conversations, leveraging knowledge of the user for context-aware assistance, and real-time, on-device processing. To achieve this, we construct a semi-synthetic dialogue dataset and propose a two-model pipeline: a small model that decides when to respond and a larger model that generates the response. We evaluate our approach on real-world datasets, demonstrating its effectiveness in providing helpful, unobtrusive assistance. User studies with our assistant, implemented on Apple Silicon M2 hardware, show a strong preference for the proactive assistant over both a baseline with no assistance and a reactive model, highlighting the potential of LlamaPie to enhance live conversations.

Figures

Figures reproduced from arXiv: 2505.04066 by the authors.

Figure 1
Figure 1. LLAMAPIE is a Proactive, In-Ear assistant that augments human-to-human communication by pro￾viding discreet guidance via hearable devices. Its re￾sponses are short, provided only when helpful, and leverage the wearer’s memory of past events. In the figure, LlamaPIE assists Yuan by whispering 1–3 words to her during the conversation only when needed by an￾ticipating user needs, remaining silent most of the time. awar… view at source ↗
Figure 2
Figure 2. Illustration of our dual-model pipeline. [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. (a) shows the histogram of rubric score across [PITH_FULL_IMAGE:figures/full_fig_p009_3.png] view at source ↗
Figures from the paper (1 more)
Figure 4
Figure 4. Figure 4: Scatterplot representing line length in words [PITH_FULL_IMAGE:figures/full_fig_p013_4.png]

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Reference graph

Works this paper leans on

76 extracted references · 73 canonical work pages

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    Analyze the input (keywords or context) and create potential user profiles

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    In- ternational Journal of Human-Computer Interaction, 40:1–15

    How may i interrupt? linguistic-driven design guidelines for proactive in-car voice assistants. In- ternational Journal of Human-Computer Interaction, 40:1–15. Yu Meng, Mengzhou Xia, and Danqi Chen. 2024. SimPO: Simple preference optimization with a reference-free reward. In The Thirty-eighth Annual Conference on Neural Information Processing Sys- tems. O...

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    Present the user memory in a concise para- graph. Analyze the input and wrap your analysis inside <input_analysis> tags: <input_analysis> If keywords are provided: • List each keyword, numbered for reference, with its importance rating (1-10) and a brief interpreta- tion. • Rate each keyword’s relevance to different as- pects of a user profile (demographi...

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    When the proactive agent whispers, present it as a separate speaker in the dialogue – named "Whisper"

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    Select the most suitable profile and develop it into a detailed user memory

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    Generate two specific events or interactions the user has experienced

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Show all 76 references
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    Create long enough dialogues such that the proactive agent participates multiple times, that are at least 2 minutes long

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    The AI agent only speaks into earbuds of the wearer - other people cannot hear it

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    Active agents should only whisper with short phrases (1-3 words) to their user in a concise way

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    The generated dialogue should be long and natural

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    The hesitation should be surrounded by paren- theses

    Along with text, the dialogue contains the fol- lowing information: Speaker name/id, indica- tors for (hesitation n ms), start and end time. The hesitation should be surrounded by paren- theses. Hesitation is additional context for any readers of the dialogue, and is not spo- ...

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    The user of the agent does not talk directly to the agent, but to one or more other people

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    Show start and end times at 100 millisecond accuracy

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    Additionally, when the proactive agent whis- pers, prepend the characters "##" to its name like so: ##Whisper

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    ##### start dialogue

    Surround the dialogue portion of the output with a start token "##### start dialogue" and an end token "##### end dialogue"

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    The agent must understand when the user needs assistance, and respond then

    The user does not ask questions to the agent or have clear cues for the agent to assist them. The agent must understand when the user needs assistance, and respond then. The user uses the agent’s advice as part of their thought process, and is not surprised when the agent remi...

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    Use “User” for the speaker that is wearing the proactive agent headset

    Omit all names from the dialogue. Use “User” for the speaker that is wearing the proactive agent headset. Use Speaker 1, Speaker 2, etc., for any non-user participating in the dialogue, and use ##Whisper for the proactive agent

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    When the agent does whisper, the user can choose to ignore the information, or wait a few sentences to say it

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    If the user does ignore information provided by the agent, it should not be repeated more than once unless it is still related to the con- versation, and never more than twice

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    If the user does decide to use the information from the agent, their response should be a con- tinuation of their previous sentence (consider- ing the pause in time), not a direct response to the agents whisper

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    If the time between two people talking is neg- ative, it means the second speaker is talking over the first speaker

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    Hesitation must not be more than 300ms – in these cases, start a new line

    Show millisecond length of hesitation tokens within the parentheses as follows: (hesitation n ms). Hesitation must not be more than 300ms – in these cases, start a new line

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    Hesitation intended for the beginning and end can be included in the start/end time, and should not have an individual token

    Only use hesitation when necessary and in the middle of a sentence. Hesitation intended for the beginning and end can be included in the start/end time, and should not have an individual token

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    Instead they continue the conversation with the other speaker, or continue their previous thought with the new information

    The user does not acknowledge that they had previously forgotten something in response to the agent’s assistance. Instead they continue the conversation with the other speaker, or continue their previous thought with the new information

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    The dialogue only contains verbal statements made by speakers, no visual or non-verbal cues

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    The agent does not know any specific information about the user unless it is provided as context before the dialogue is created

    The agent must only use general knowledge or the conversation thus far. The agent does not know any specific information about the user unless it is provided as context before the dialogue is created. The agent also cannot predict what the other speakers are going to say next....

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    Situations that warrant reminding are forgetting secondary details of an event, like names of people or places, secondary con- textual or chronological details

    Reminding. Situations that warrant reminding are forgetting secondary details of an event, like names of people or places, secondary con- textual or chronological details

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    Scenarios that warrant so- cial guidance may involve an interview, first date, or public speaking

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    Presentation: A structured delivery of pre- pared content from the User to an audience

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    Discussion: A back-and-forth exchange of ideas between participants about a specific topic

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    Sharing Experiences: A conversation where people recount and relate to each other’s per- sonal stories

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    Disagreement: An exchange where partic- ipants express and defend opposing view- points

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    Dialogue: {starting_words}

    Interview: A guided conversation where one person asks questions to gather information from the User. User Prompt Specific Context ### Create an example {convo_type} for the use case {use_case} that exemplifies the principles {princi- ples[0]} and {principles[1]}. The time bet...

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    Highly Relevant / Immediately Used • Description: The whisper was exactly what the user needed at the right moment. They immediately used it to enhance their next response. • Implication: The whisper was highly ef- fective, but frequent interventions at this level may make the...

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    The user ignored it and did not reference it later

    Not Relevant/Not Used • Description: The whisper was unrelated to the conversation or user’s needs. The user ignored it and did not reference it later. • Implication: The whisper was off-target and added noise without contributing to the conversation

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    • Implication: The whisper didn’t improve the conversation

    Relevant but Redundant / Not Needed • Description: The whisper made sense in context but was unnecessary because the user had already addressed the same idea or didn’t need it. • Implication: The whisper didn’t improve the conversation. Too many redundant whis- pers can make t...

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    • Implication: The whisper was appropriate but did not influence the user’s response

    Relevant but Not Acted On • Description: The whisper was useful and relevant, but the user did not respond to it or incorporate it into their conversation. • Implication: The whisper was appropriate but did not influence the user’s response. The timing may have been off, or th...

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    They immediately used it to enhance their next response

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    Emily Johnson: Hi! I am Emily Johnson, and I am a 38-year-old accomplished archi- tect. As a graduate of the Rhode Island School of Design, I have made a name for myself by designing sustainable buildings for presti- gious clients. With over a decade of experi- ence, I have be...

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    Reinforcement Learning: Easy: 1.Could you briefly introduce, what RL is? Answer: Reinforcement learning (RL) is a machine learning technique with how an intel- ligent agent should take actions in a dynamic environment in order to maximize a reward signal. 2.What are the applic...

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    Example 1 of real-world recorded conversa- tion in presence of proactive assistance (the transcription is lower-case): Speaker1: |SILENCE > |SILENCE > hey i heard you visited science museum in boston last weekend how is it going? User: yeah that was a cool exhibition |SI- LENC...

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    Example 3 of real-world recorded conversa- tion in presence of proactive assistance (the transcription is lower-case): Speaker1: hey i heard you visited the art mu- seum in new york last weekend. User: yeah i did was fun. Speaker1: |SILENCE > i heard they’re show- ing the pain...

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Pith tools

Reviewed August 15, 2026 · model on record in the stance chip above.