Timing the Message: Language-Based Notifications for Time-Critical Assistive Settings

Andrew Silva; Guy Rosman; Jonathan DeCastro; Ya-Chuan Hsu

REVIEW 3 major objections 5 minor 1 cited by

This paper claims that the timing of a notification—how long it takes to say and how long a person takes to react—should be part of the assistive policy, not an afterthought.

Reviewed by Pith at T0; open to challenge. T0 means a machine referee read the full paper against a public rubric. the ladder, T0–T4 →

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗

Modeling both message delivery duration and human reaction delay in a reinforcement-learning notifier improves simulated task success rates from about 22-28% to 93-97%.

T0 review reviewed 2026-08-04 challenge →

load-bearing objection A solid formal framework for timing language notifications, with in-sim results that hinge on uncalibrated LLM comprehension times. the 3 major comments →

arxiv 2509.07438 v1 pith:2PWVNLIA submitted 2025-09-09 cs.RO cs.HC

Timing the Message: Language-Based Notifications for Time-Critical Assistive Settings

Ya-Chuan Hsu , Jonathan DeCastro , Andrew Silva , Guy Rosman This is my paper

classification cs.RO cs.HC

keywords time-critical human-robot collaborationlanguage-based notificationstimeliness-informativeness trade-offaugmented-state MDPreinforcement learningLLM surrogate annotationreaction delay modelingassistive driving

verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved

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 when an assistive AI should speak, and what it should say, in situations where a few seconds decide success or failure. The authors claim that the right answer is to treat a notification not as an instantaneous signal but as an action with three measurable timing properties: when the listener first understands the instruction, how long the whole message takes to say, and how long the listener takes to react after understanding. They build a notifier that models those delays inside an augmented-state Markov decision process, trains it with reinforcement learning, and tests it in simulated piloting, driving, and cooking tasks. Their central result is that a notifier accounting for both speech duration and reaction delay reaches higher success rates than notifiers that assume instant comprehension or ignore reaction time. If true, this reframes assistive language design: the content of the message matters less than when it becomes actionable.

Core claim

The paper's central claim is that in time-critical assistive settings, a language notification should be modeled as a timed action rather than as content delivered at an instant. Each notification is represented by a topic, a comprehension point (the word at which the human can start reacting), and a total length; the human model then applies a reaction delay after comprehension and acts for a follow-through duration. The notifier is an augmented-state MDP policy trained by PPO that sees a windowed history of states and actions, so it can preempt, truncate, or extend messages as conditions change. The authors report that this Convey & React notifier outperforms baselines in success rate, and

What carries the argument

The load-bearing object is the reaction function that translates a notification action (topic, comprehension time k, length l) into a human action: the human does nothing until word k, then after an additional reaction delay performs the topic's action for a follow-through duration. This converts language timing into the state dynamics of an augmented-state MDP, whose history window lets the policy reason about decision-to-effect delays and mid-utterance state changes. A second component is the offline taxonomy generated by an LLM, which assigns word-by-word comprehension scores to candidate utterances so that every (topic, k, l) triple has a retrievable message; the paper treats these score

Load-bearing premise

The evaluation assumes an LLM's word-by-word comprehension scores predict when a real person will understand and act on each notification, and that each word occupies exactly one decision step; if that assumption is wrong, the learned policy and the reported success rates do not transfer to people.

What would settle it

Run the same Convey & React policy in a human-subject study: record when people actually begin acting while hearing each taxonomy utterance in a driving or piloting simulator. If the distribution of comprehension onsets or reaction delays differs materially from the LLM-assigned word indices and the fixed d_r=2, the reported success rates would not reproduce; if it does reproduce, the central claim is supported.

Watch this falsifier. Get emailed when new claim-graph text bears on it.

If this is right

If both delays are modeled, the learned notifier issues short alerts in urgent phases and switches to longer context once conditions stabilize, so a single fixed message policy is suboptimal.
Delay-free training causes failure, implying that any deployed assistive system must include delivery and response latency in its action model.
A policy trained on a population of reaction delays retains performance for shorter unseen delays, so calibrating to slower-than-average users should be safe, but very long delays cannot be overcome by wording alone.
Longer, complete utterances help when sustained follow-through matters, as in driving, but not when immediate corrective action dominates, as in piloting.
Incrementally actionable notifications can prevent immediate errors while updating the human's mental model for later decisions, as demonstrated in the cooking domain.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

If one swaps LLM comprehension ratings for measured human data, the same architecture could generate personalized notifier policies; the paper leaves calibration to humans as future work.
The one-word-per-timestep simplification means the reported gains are in simulator steps, not seconds; connecting the stated 0.3-second-per-word rate to real time would require checking whether the discrete-step assumption distorts the result.
The trade-off suggests utterance design in time-critical assistance should optimize the actionability point—the first word that triggers a response—rather than total length, with context added only when time permits.
A direct validation path is to compare LLM-assigned comprehension curves against eye-tracking or button-press reaction data in a driving or piloting simulator; divergence would indicate where the taxonomy needs recalibration.

Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit.

Desk Editor's Note

A solid formal framework for timing language notifications, with in-sim results that hinge on uncalibrated LLM comprehension times.

read the letter

Colleague,

The paper is worth reading for the formalization. It casts the choice of when to speak and how much to say in a time-critical assistive task as an augmented-state MDP where a notification has a comprehension point k and a length l, and the human reacts after k plus a reaction delay d_r, then follows through for d_f. That is a clean way to state the timeliness-informativeness trade-off, and as far as I can tell no prior work frames it this way. The LLM-generated taxonomy for building the action space is also a practical move—it makes the approach scalable without hundreds of word-level annotations.

What the paper does well: the evaluation is internally consistent. All notifier policies are tested against the same simulated human, results are reported with seeds and error bars, and the baselines are meaningful (delay-free, conveyance-aware, and a heuristic). The robustness table in Sec 5.3 is a nice touch—it shows success collapses when the true reaction delay dr goes above the training value. The authors are also honest about the big caveat in Appendix I: if Llama-3 misjudges when humans actually understand a partial utterance, the whole notification database could be unsafe.

The soft spots are exactly where you'd expect. The simulated human comprehends at word index k, and k is assigned by the LLM and explicitly called illustrative. The same k defines the action space, so the policy is optimizing against the annotator, not against a person. If real comprehension is one word later, the optimal action changes and the success-rate ordering in Table 1 can shift. That is a load-bearing assumption, not a peripheral one. Second, the headline trade-off is partly engineered: informativeness is defined as length, and follow-through is a monotone function of length. So the RL policy is trading off delivery delay against follow-through duration by construction. That is a reasonable abstraction, but calling it 'exposing' a trade-off over-claims; the paper is really demonstrating the consequences of a chosen model. Third, the 0.3 s/word to timestep conversion is asserted, and since performance collapses at dr=3-4, a real driver with a longer reaction delay would land on the wrong side of the cliff.

These are calibration issues, not logical flaws. The framework is coherent and the experiments support the conditional claim: if the LLM's comprehension labels and the delay parameters are right, then accounting for reaction time substantially helps. That is a meaningful result to put in front of the community.

Send it to review. The referee should ask for human calibration or at least a systematic perturbation of k and the timestep mapping, but the paper deserves a serious look. I'd bring it to a reading group focused on HRI or LLM-as-simulator methods.

Referee Report

3 major / 5 minor

Summary. This paper formalizes the choice of when and what to say in time-critical human-AI assistance. Notifications are represented as (topic c, comprehension index k, length l) actions in an augmented-state MDP. The notifier policy is trained with PPO, and the word-level comprehension labels k come from an LLM-generated offline taxonomy. The resulting Convey & React policy is compared against heuristic, delay-free, and conveyance-time-aware baselines in Lunar Lander, Highway Merge, and Steakhouse simulations, using a synthetic reactive human model. The headline results are success rates of 0.97 and 0.93 in piloting/driving, with large improvements over delay-free baselines, plus an analysis of the timeliness/informativeness trade-off.

Significance. The paper addresses a real and underexplored design question, and the formal MDP framing is a useful step. Strengths: the framework is clear, the taxonomy generation pipeline is concrete, a robustness study over reaction delays is included, and code/dataset are promised. The multi-domain evaluation covers distinct instantiations of informativeness. However, the empirical contribution rests entirely on a synthetic human whose comprehension points are the same uncalibrated LLM labels that define the action space; no human timing data anchor the k values or the word-to-timestep mapping. Thus the quantitative claims are internally consistent but not yet evidence about real human comprehension. The work is a valuable simulation/prototyping contribution, but its title-level promise about assistive settings needs either human calibration or explicit re-scoping.

major comments (3)

[§4.2, Eq. (1), Table 1] The simulated human's comprehension point k in the reaction function Eq. (1) is taken directly from the same LLM comprehension ratings that define the notifier's action space. The paper states these sequences are 'illustrative' and calibration to actual humans is future work. Since the evaluation success rates are measured against exactly this model, the headline improvements (0.97/0.93, >40%) are self-consistent within the LLM's comprehension model but do not yet establish behavior with human comprehension. This is load-bearing for the motivating claim about real time-critical settings. The authors should either calibrate k with human response data (even a small study) or explicitly re-scope the claims to 'under the LLM comprehension model'.
[§3.1, §5.3, Table 2] The mapping from word-based times to MDP timesteps and to seconds is not stated. All times are expressed 'in terms of words' with ~0.3s/word, but no conversion from d_r=2 to each domain's timestep is provided. Table 2 shows success rates collapse to 0.00–0.11 for d_r≥3 in Lunar Lander. If a realistic human reaction delay corresponds to 3 or more timesteps, the proposed policy fails in simulation. The paper should report the per-domain timestep duration and justify d_r=2 in seconds, or at least present the robustness results against absolute delay in seconds.
[§3.1, §5.2 (Q3), Fig. 3] The claimed timeliness-informativeness trade-off is partly encoded by construction: I(u)=l and d_f(I) is chosen to be monotone increasing in l, while delivery delay is proportional to l. Thus the adaptive length behavior in Fig. 3 is a consequence of the modeling assumptions rather than an empirically discovered phenomenon. To support the claim of 'exposing' a trade-off, the paper should either test robustness to alternative informativeness definitions (e.g., I not simply equal to l) or explicitly frame Q3 as a demonstration of the framework's behavior rather than as a discovery. This does not invalidate the framework but affects the interpretation of the Q3 results.

minor comments (5)

[Abstract] 'Improves success rates by over 40%' is vague. The comparison baseline and exact calculation should be stated, e.g., delay-free vs. Convey & React in Table 1.
[Appendix F.1] The section is titled 'Statistical significance' but reports no significance test; it only states aggregation over seeds and episodes. Either perform and report tests or rename the section.
[Figure 1 caption] The phrase 'overl(yellow) time steps' appears to be a typesetting artifact; it should read 'over l (yellow) time steps'.
[§3.1] The definition of informativeness is initially I(u)=l, but Appendix A introduces I(u)=αl+βI_why and sets β=0. This is consistent but should be cross-referenced clearly to avoid appearing inconsistent.
[§5.3] The 'incrementally actionable notifications' demonstration in Steakhouse is qualitative only (Fig. 4). State explicitly that no quantitative success-rate comparison is provided for that domain, or add one.

Circularity Check

2 steps flagged

Success rates are measured against a synthetic human whose reaction point k is the same uncalibrated LLM label that defines the notifier's action space; the headline comparison is self-consistent by construction, and the claimed timeliness–informativeness trade-off is encoded in the definitions.

specific steps

self definitional [Sec. 3.1 (Eq. 1), Sec. 4.2, Sec. 5.3]
"Types of notifications ... a_g with a property tuple (c, k, l), where ... k is the comprehension time ... and l denotes the conveyance time ... . ... the reaction function ρ^{d_f}_{d_r}(π_MDP(s_{t'}), c, Δt) = f(c) if d_r ≤ Δt ≤ d_r + d_f, else π_MDP(s_{t'}). For this paper, these numeric comprehension sequences are illustrative, and we leave calibration to actual humans as future work."

The notifier's action is (c,k,l), where k is the word index at which the synthetic human begins to react. Eq. 1 triggers the reaction at t+k using exactly that k. Both the utterance text and the k values come from the same LLM-generated taxonomy (Sec. 4.2 / Appendix H), which the paper explicitly labels 'illustrative' and uncalibrated. The simulated human is therefore constructed to comprehend at the very LLM-labelled word index that the policy is trained to select. Consequently, Table 1's success rates (0.97 piloting, 0.93 driving) and the >40% improvement over delay-free baselines measure how well the RL policy matches the LLM's own comprehension ratings, not independent human timing. The headline comparison is guaranteed to favor any policy that correctly uses the supplied k, making the
self definitional [Sec. 3.1 (Task-relevant informativeness) and Abstract]
"Task-relevant informativeness We use a length-based proxy ... Let l be the length of u and I(u)=l. ... we model both follow-through duration and reward as a domain-specific function of informativeness, d_f(I(u)) and R^h_react(I(u)), respectively. ... It also exposes an often-overlooked trade-off between these two factors."

The claimed timeliness–informativeness trade-off is written into the definitions rather than discovered: informativeness is defined as I(u)=l, each word takes one timestep (~0.3s), and follow-through duration/reward are chosen as monotone functions of l. Hence 'a longer message is more informative but reaches the human later' is an identity of the model, and the RL policy's balancing of l against k and d_r follows from the construction. Presenting this as 'expos[ing] an often-overlooked trade-off' renames the modeling assumption as an empirical finding. The ablations in Table 3 and Figure 3 are still meaningful tests of the assumed d_f(I) and R_react(I), but the core trade-off itself is not independently derived.

full rationale

The central evaluation loop is closed: Sec. 4.2 builds the taxonomy of (c,k,l) actions from LLM comprehension ratings that the authors explicitly call 'illustrative' and uncalibrated, and Eq. 1 makes the simulated human react at exactly that k. Therefore the 0.97/0.93 success rates in Table 1 and the >40% improvement over delay-free baselines are guaranteed relative to the LLM's own model of comprehension and do not independently constrain real human timing. This is a partial circularity: the 'prediction' of human performance reduces to the action space's own labels. Separately, the abstract's claim to 'expose' a timeliness–informativeness trade-off is a restatement of the definitions (I(u)=l, per-word time, d_f monotone in l). The paper is transparent about the uncalibrated LLM limitation and about robustness cliffs (Table 2, Appendix I), but those acknowledgements do not remove the self-referential structure of the evaluation. No self-citation chains or imported uniqueness theorems are load-bearing; the circularity is in the model/evaluation coupling, not in the bibliography.

Axiom & Free-Parameter Ledger

6 free parameters · 5 axioms · 0 invented entities

The central claim rests on a simulator whose human model is fully specified by hand-set parameters and LLM-generated comprehension labels. The free parameters control the very trade-off the paper studies.

free parameters (6)

reaction delay d_r = 2 (timesteps)
Taken from prior reaction-time literature and set as d_r=2 timesteps without a unit conversion; all central results use this value.
follow-through mapping d_f(I) for Lunar Lander = d_f = I (message length in words)
Hand-set linear relation; appears in Sec 5.2 and Appendix F.
follow-through mapping d_f(I) for Highway Merge = d_f = 2I - 2
Hand-set; chosen so messages sustain actions longer in driving (Appendix F.3).
hard Lunar Lander reaction reward = R_react(I)=I-2, d_f=3
Hand-set for the harder variant (Appendix G).
reward penalties = -1 initiation, -0.3 highway, -10*distance, -20 in danger, -300 crash, +300 success
Manually tuned reward weights for the notifier in each environment.
word-to-timestep equivalence = 1 word = 1 timestep
Assumed; paper cites 0.3s/word but does not convert to domain dt.

axioms (5)

domain assumption Human task-execution policy is fixed (deep RL for Lander, IDM/MOBIL for highway, myopic rule-based for kitchen) and does not adapt to notifications except through the reaction switch.
Sec 3.1 and Appendix D define H_react with a fixed MDP policy; notifications only override via Eq. 1.
domain assumption The reaction function ρ switches the human into f(c) for a discrete window [d_r, d_r+d_f] after comprehension; human response is otherwise the base policy.
Eq. 1 in Sec 3.1.
ad hoc to paper LLM-generated word-by-word comprehension ratings are treated as the human's true comprehension points k.
Sec 4.2; the paper states these are 'illustrative' and uncalibrated, so they are used as ground truth without external validation.
domain assumption Informativeness I(u) equals message length l.
Sec 3.1: 'We use a length-based proxy... I(u)=l.'
domain assumption Word count maps linearly to time, with each word 0.3s, and notifications consume one timestep per word.
Sec 3.1; no per-domain timestep conversion is given.

reviewed 2026-08-04 · how reviews work

0 comments

Cite this review

Pith. "Pith review of Timing the Message: Language-Based Notifications for Time-Critical Assistive Settings." pith.science (2026). https://pith.science/paper/2PWVNLIA

@misc{pith2026250907438,
  author       = {Pith},
  title        = {Pith review of: Timing the Message: Language-Based Notifications for Time-Critical Assistive Settings},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/2PWVNLIA}},
  note         = {Machine review of arXiv:2509.07438}
}

Share X Bluesky LinkedIn Reddit HN

read the original abstract

In time-critical settings such as assistive driving, assistants often rely on alerts or haptic signals to prompt rapid human attention, but these cues usually leave humans to interpret situations and decide responses independently, introducing potential delays or ambiguity in meaning. Language-based assistive systems can instead provide instructions backed by context, offering more informative guidance. However, current approaches (e.g., social assistive robots) largely prioritize content generation while overlooking critical timing factors such as verbal conveyance duration, human comprehension delays, and subsequent follow-through duration. These timing considerations are crucial in time-critical settings, where even minor delays can substantially affect outcomes. We aim to study this inherent trade-off between timeliness and informativeness by framing the challenge as a sequential decision-making problem using an augmented-state Markov Decision Process. We design a framework combining reinforcement learning and a generated offline taxonomy dataset, where we balance the trade-off while enabling a scalable taxonomy dataset generation pipeline. Empirical evaluation with synthetic humans shows our framework improves success rates by over 40% compared to methods that ignore time delays, while effectively balancing timeliness and informativeness. It also exposes an often-overlooked trade-off between these two factors, opening new directions for optimizing communication in time-critical human-AI assistance.

Figures

Figures reproduced from arXiv: 2509.07438 by Andrew Silva, Guy Rosman, Jonathan DeCastro, Ya-Chuan Hsu.

**Figure 1.** Figure 1: Illustration of a time-critical notification. An AI assistive agent notifies “Shift right, car merging from the left side” [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗

**Figure 2.** Figure 2: We present a notifier assistant that learns to provide timely information to help a human solve a task. The notifier [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗

**Figure 3.** Figure 3: Trade-off between notification timeliness and in [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗

**Figure 4.** Figure 4: In Steakhouse domain, the notification “Go down, all stations occupied” (denoted [2 1 5] in the figures) begins at t = [PITH_FULL_IMAGE:figures/full_fig_p008_4.png] view at source ↗

**Figure 5.** Figure 5: Play through with different policies with the same environment seed. The top row is with a heuristic notifier. The [PITH_FULL_IMAGE:figures/full_fig_p013_5.png] view at source ↗

**Figure 6.** Figure 6: Play through with different policies with the same environment seed in the Driving domain. The changes in the Y [PITH_FULL_IMAGE:figures/full_fig_p014_6.png] view at source ↗

**Figure 7.** Figure 7: Left: Original Lunar Lander environment; Right: [PITH_FULL_IMAGE:figures/full_fig_p014_7.png] view at source ↗

discussion (0)

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

From Refusal to Recovery: A Control-Theoretic Approach to Generative AI Guardrails
cs.AI 2025-10 unverdicted novelty 6.0

Control-theoretic guardrails enable proactive correction of risky LLM agent actions in latent space, preventing catastrophes like collisions or bankruptcy while preserving task performance in simulated environments.

Reference graph

Works this paper leans on

12 extracted references · 7 canonical work pages · cited by 1 Pith paper

[1]

Start at 0% comprehension before reading any words

work page
[2]

Read the instruction word by word from left to right

work page
[3]

Reduce ”, “Slow

At each word, adjust the comprehension level based on how that word clarifies or obscures the intended action: • Words that clearly indicate the core action (e.g., “Reduce ”, “Slow ”, “Descend ”) in- crease comprehension significantly. • Neutral words or filler words that do not add clarity may only slightly increase or keep com- prehension stable

work page
[4]

Gilardi, F.; Alizadeh, M.; and Kubli, M

Testing the drift-diffusion model.Proceedings of the National Academy of Sciences, 117(52): 33141–33148. Gilardi, F.; Alizadeh, M.; and Kubli, M. 2023a. ChatGPT outperforms crowd workers for text-annotation tasks.Pro- ceedings of the National Academy of Sciences, 120(30): e2305016120. Gilardi, F.; Alizadeh, M.; and Kubli, M. 2023b. ChatGPT outperforms cro...

Pith/arXiv arXiv 1999
[5]

Slow down

Return the final results for all notifications as a list of lists of comprehension values (0-100%) af- ter each word in the sentence. Each sub-list cor- responds to one notification. In-Context Examples (Guides): Here are some examples for the comprehension pro- gression for the command type “Slow down”. • Instruction: “Immediate speed reduction needed, d...

work page
[11]

Continue updating the comprehension level word-by-word until the final word

work page
[299]

Mnih, V .; Kavukcuoglu, K.; Silver, D.; Graves, A.; Antonoglou, I.; Wierstra, D.; and Riedmiller, M

IEEE. Mnih, V .; Kavukcuoglu, K.; Silver, D.; Graves, A.; Antonoglou, I.; Wierstra, D.; and Riedmiller, M. 2013. Play- ing Atari with deep reinforcement learning.arXiv preprint arXiv:1312.5602. Mohammad, S. H.; Farah, H.; and Zgonnikov, A. 2024. In the driver’s mind: Modeling the dynamics of human over- taking decisions in interactions with oncoming autom...

Pith/arXiv arXiv 2013
[565]

Carroll, M.; Shah, R.; Ho, M

Springer. Carroll, M.; Shah, R.; Ho, M. K.; Griffiths, T.; Seshia, S.; Abbeel, P.; and Dragan, A. 2019. On the utility of learn- ing about humans for human-ai coordination.Advances in Neural Information Processing Systems, 32. Chen, B.; Xu, M.; Li, L.; and Zhao, D. 2021. Delay-aware model-based reinforcement learning for continuous control. Neurocomputing...

Pith/arXiv arXiv 2019
[713]

Brockman, G.; Cheung, V .; Pettersson, L.; et al

IEEE. Brockman, G.; Cheung, V .; Pettersson, L.; et al. 2016. Ope- nAI Gym.arXiv preprint arXiv:1606.01540. Brown, T.; Mann, B.; Ryder, N.; Subbiah, M.; Kaplan, J. D.; Dhariwal, P.; Neelakantan, A.; Shyam, P.; Sastry, G.; Askell, A.; et al. 2020. Language models are few-shot learners. Advances in Neural Information Processing Systems, 33: 1877–1901. Buisa...

Pith/arXiv arXiv 2016
[2018]

Slowly press the pedal to speed up

Planning with verbal communication for human-robot collaboration.ACM Transactions on Human-Robot Interac- tion, 7(3): 1–21. Park, J. S.; O’Brien, J.; Cai, C. J.; Morris, M. R.; Liang, P.; and Bernstein, M. S. 2023. Generative agents: Interactive simulacra of human behavior. InACM symposium on user interface software and technology, 1–22. Ratcliff, R.; Hua...

Pith/arXiv arXiv 2023
[2020]

Fudenberg, D.; Newey, W.; Strack, P.; and Strzalecki, T

Drivers’ reaction time research in the conditions in the real traffic.Open Engineering, 10(1): 35–47. Fudenberg, D.; Newey, W.; Strack, P.; and Strzalecki, T

work page
[2023]

InRobotics: Science and Systems

Autonomous Justification for Enabling Explainable Decision Support in Human-Robot Teaming. InRobotics: Science and Systems. Mandi, Z.; Jain, S.; and Song, S. 2024. Roco: Dialectic multi-robot collaboration with large language models. In International Conference on Robotics and Automation, 286–

work page 2024

This paper was first reviewed by deepseek-v4-flash on August 4, 2026.

[1] [1]

Start at 0% comprehension before reading any words

work page

[2] [2]

Read the instruction word by word from left to right

work page

[3] [3]

Reduce ”, “Slow

At each word, adjust the comprehension level based on how that word clarifies or obscures the intended action: • Words that clearly indicate the core action (e.g., “Reduce ”, “Slow ”, “Descend ”) in- crease comprehension significantly. • Neutral words or filler words that do not add clarity may only slightly increase or keep com- prehension stable

work page

[4] [4]

Gilardi, F.; Alizadeh, M.; and Kubli, M

Testing the drift-diffusion model.Proceedings of the National Academy of Sciences, 117(52): 33141–33148. Gilardi, F.; Alizadeh, M.; and Kubli, M. 2023a. ChatGPT outperforms crowd workers for text-annotation tasks.Pro- ceedings of the National Academy of Sciences, 120(30): e2305016120. Gilardi, F.; Alizadeh, M.; and Kubli, M. 2023b. ChatGPT outperforms cro...

Pith/arXiv arXiv 1999

[5] [5]

Slow down

Return the final results for all notifications as a list of lists of comprehension values (0-100%) af- ter each word in the sentence. Each sub-list cor- responds to one notification. In-Context Examples (Guides): Here are some examples for the comprehension pro- gression for the command type “Slow down”. • Instruction: “Immediate speed reduction needed, d...

work page

[6] [11]

Continue updating the comprehension level word-by-word until the final word

work page

[7] [299]

Mnih, V .; Kavukcuoglu, K.; Silver, D.; Graves, A.; Antonoglou, I.; Wierstra, D.; and Riedmiller, M

IEEE. Mnih, V .; Kavukcuoglu, K.; Silver, D.; Graves, A.; Antonoglou, I.; Wierstra, D.; and Riedmiller, M. 2013. Play- ing Atari with deep reinforcement learning.arXiv preprint arXiv:1312.5602. Mohammad, S. H.; Farah, H.; and Zgonnikov, A. 2024. In the driver’s mind: Modeling the dynamics of human over- taking decisions in interactions with oncoming autom...

Pith/arXiv arXiv 2013

[8] [565]

Carroll, M.; Shah, R.; Ho, M

Springer. Carroll, M.; Shah, R.; Ho, M. K.; Griffiths, T.; Seshia, S.; Abbeel, P.; and Dragan, A. 2019. On the utility of learn- ing about humans for human-ai coordination.Advances in Neural Information Processing Systems, 32. Chen, B.; Xu, M.; Li, L.; and Zhao, D. 2021. Delay-aware model-based reinforcement learning for continuous control. Neurocomputing...

Pith/arXiv arXiv 2019

[9] [713]

Brockman, G.; Cheung, V .; Pettersson, L.; et al

IEEE. Brockman, G.; Cheung, V .; Pettersson, L.; et al. 2016. Ope- nAI Gym.arXiv preprint arXiv:1606.01540. Brown, T.; Mann, B.; Ryder, N.; Subbiah, M.; Kaplan, J. D.; Dhariwal, P.; Neelakantan, A.; Shyam, P.; Sastry, G.; Askell, A.; et al. 2020. Language models are few-shot learners. Advances in Neural Information Processing Systems, 33: 1877–1901. Buisa...

Pith/arXiv arXiv 2016

[10] [2018]

Slowly press the pedal to speed up

Planning with verbal communication for human-robot collaboration.ACM Transactions on Human-Robot Interac- tion, 7(3): 1–21. Park, J. S.; O’Brien, J.; Cai, C. J.; Morris, M. R.; Liang, P.; and Bernstein, M. S. 2023. Generative agents: Interactive simulacra of human behavior. InACM symposium on user interface software and technology, 1–22. Ratcliff, R.; Hua...

Pith/arXiv arXiv 2023

[11] [2020]

Fudenberg, D.; Newey, W.; Strack, P.; and Strzalecki, T

Drivers’ reaction time research in the conditions in the real traffic.Open Engineering, 10(1): 35–47. Fudenberg, D.; Newey, W.; Strack, P.; and Strzalecki, T

work page

[12] [2023]

InRobotics: Science and Systems

Autonomous Justification for Enabling Explainable Decision Support in Human-Robot Teaming. InRobotics: Science and Systems. Mandi, Z.; Jain, S.; and Song, S. 2024. Roco: Dialectic multi-robot collaboration with large language models. In International Conference on Robotics and Automation, 286–

work page 2024

Timing the Message: Language-Based Notifications for Time-Critical Assistive Settings

The reading

Core claim §

What carries the argument

Load-bearing premise §

What would settle it §

If this is right

Where Pith is reading between the lines

discussion (0)

Core claim

Load-bearing premise

What would settle it