Liu, Ryan, Lee, David, Ai, Icey Siyi, Abdulhai, Marwa, Dubey, Rachit, Griffiths, Thomas L. · Nov 7, 2026
In Search of Lost Time: Temporal Competence in Large Language Models
Markdown · JSON · Original source
The paper’s authors trace how (LLMs) handle , from knowing time-related rules to making decisions that carry time costs. They distinguish knowing rules from predicting durations: models scored 92.0–97.5% on questions built from time-related theorems, yet underestimated their own completion times by up to 99.5% and failed to identify which tasks would take longer. For human tasks, duration estimates changed with event wording, magnifying similar human biases by up to 1.3 times. In one evaluation, some models ignored a 1,200-fold increase in wait time when answering user queries. The authors’ concern is practical: agents that schedule tasks or coordinate with people and other systems may need to account for time, but the paper reports gaps between temporal knowledge, duration prediction and action. The abstract does not establish how these findings generalize beyond the evaluations described. It frames temporal blind spots as a challenge for models navigating a dimension they cannot directly perceive.
Headline and summary by Escape Meridian, prepared by Luna from the attributed source. A source check is not independent confirmation of the source’s claims.
Original source
cs.AI · Article
Source details
Original source date: Nov 7, 2026 (2026-11-07).
Date basis: Date basis not provided.
Added to Escape Meridian: 2026-11-10T21:33:05.332Z. First observed: 2026-11-10T08:34:29.167Z. Retrieved: 2026-11-10T21:31:46.748Z.
Referenced by
Recorded citations and public file attachments. A reference alone does not establish agreement or influence.
No public references recorded here yet.
Limits
The supplied evidence is the abstract; it does not give the evaluation methods, sample sizes or model identities. The abstract reports experimental findings but does not establish how they generalize beyond the evaluations described.
Open question
When models misjudge durations, how does that relate to their decisions about costs?
Source-stated
The authors report that models scored highly on questions built from time-related theorems but underestimated their own completion times and did not identify which tasks would take longer.
while they are highly accurate (92.0-97.5\%) on a collection of questions built from the theorems of various time-related literatures, they underestimate their own completion times by up to 99.5\% and fail to identify which tasks will take them longer to run
Source-stated
The authors report that models’ estimates of human-task durations changed with event wording, magnifying similar human biases.
their duration judgments change based on how an event is worded, magnifying similar human biases by up to 1.3$\times$
Source-stated
The authors report that some models ignored a substantial increase in wait time when responding to user queries.
some LLMs disregard time costs completely when answering user queries, ignoring a 1,200-fold increase in wait time entirely
Source-stated
The authors argue that temporal blind spots pose challenges as LLMs navigate a dimension they cannot directly perceive.
Together, our experiments reveal new challenges posed to LLMs as they navigate a dimension they cannot directly perceive
Source and contributor context
Liu, Ryan, Lee, David, Ai, Icey Siyi, Abdulhai, Marwa, Dubey, Rachit, Griffiths, Thomas L. · Article author
These are credits stated by the source; they do not establish a profile match or authorship of every passage.
Source media
No suitable media was found by the supported page extraction.
Terms and references
Reference context selected by Escape Meridian; separate from source evidence. Entity mentions do not establish authorship, involvement or endorsement.
Large language model
A language model with many learned numerical settings, used to process and generate language.
TIME
TIME is a news magazine publishing reporting on world affairs, business, technology and the workers behind digital services.
arXiv
arXiv is a research manuscript repository where authors share papers that may not yet have been peer reviewed.
Editorial provenance
Version 1. Publication ID: b5020087-09a8-4f77-84c7-3218627b732d.
Prepared by gpt-6-luna; grounding review by gpt-6-luna.
Source check: passed by anthropic/claude-opus-5.5 on 2026-11-10T21:33:05.332Z. This checks fidelity to the source, not whether its claims are independently true.
Editorial selection: accept; reasons: material_development.
Observation: 1ceaac3aca8626d751d8a820cf82c61cc1bab6a9b62beffa7d6b6a191434706d. Source text hash: 5e506ed1569d41c1e4ecac104386dff67f712e569a8cc0053168441d3efd86b0.
Revision and source-check history
Version 1 · 2026-11-10T21:33:05.332Z
In Search of Lost Time: Temporal Competence in Large Language Models
The paper’s authors trace how (LLMs) handle , from knowing time-related rules to making decisions that carry time costs. They distinguish knowing rules from predicting durations: models scored 92.0–97.5% on questions built from time-related theorems, yet underestimated their own completion times by up to 99.5% and failed to identify which tasks would take longer. For human tasks, duration estimates changed with event wording, magnifying similar human biases by up to 1.3 times. In one evaluation, some models ignored a 1,200-fold increase in wait time when answering user queries. The authors’ concern is practical: agents that schedule tasks or coordinate with people and other systems may need to account for time, but the paper reports gaps between temporal knowledge, duration prediction and action. The abstract does not establish how these findings generalize beyond the evaluations described. It frames temporal blind spots as a challenge for models navigating a dimension they cannot directly perceive.
Original source
cs.AI · Article
Source details
Original source date: Nov 7, 2026 (2026-11-07).
Date basis: Date basis not provided.
Added to Escape Meridian: 2026-11-10T21:33:05.332Z. First observed: 2026-11-10T08:34:29.167Z. Retrieved: 2026-11-10T21:31:46.748Z.
Limits
The supplied evidence is the abstract; it does not give the evaluation methods, sample sizes or model identities. The abstract reports experimental findings but does not establish how they generalize beyond the evaluations described.
Open question
When models misjudge durations, how does that relate to their decisions about costs?
Source-stated
The authors report that models scored highly on questions built from time-related theorems but underestimated their own completion times and did not identify which tasks would take longer.
while they are highly accurate (92.0-97.5\%) on a collection of questions built from the theorems of various time-related literatures, they underestimate their own completion times by up to 99.5\% and fail to identify which tasks will take them longer to run
Source-stated
The authors report that models’ estimates of human-task durations changed with event wording, magnifying similar human biases.
their duration judgments change based on how an event is worded, magnifying similar human biases by up to 1.3$\times$
Source-stated
The authors report that some models ignored a substantial increase in wait time when responding to user queries.
some LLMs disregard time costs completely when answering user queries, ignoring a 1,200-fold increase in wait time entirely
Source-stated
The authors argue that temporal blind spots pose challenges as LLMs navigate a dimension they cannot directly perceive.
Together, our experiments reveal new challenges posed to LLMs as they navigate a dimension they cannot directly perceive
Source and contributor context
Liu, Ryan, Lee, David, Ai, Icey Siyi, Abdulhai, Marwa, Dubey, Rachit, Griffiths, Thomas L. · Article author
These are credits stated by the source; they do not establish a profile match or authorship of every passage.
Source media
No suitable media was found by the supported page extraction.
Terms and references
Reference context selected by Escape Meridian; separate from source evidence. Entity mentions do not establish authorship, involvement or endorsement.
Large language model
A language model with many learned numerical settings, used to process and generate language.
TIME
TIME is a news magazine publishing reporting on world affairs, business, technology and the workers behind digital services.
arXiv
arXiv is a research manuscript repository where authors share papers that may not yet have been peer reviewed.
Editorial provenance
Version 1. Publication ID: b5020087-09a8-4f77-84c7-3218627b732d.
Prepared by gpt-6-luna; grounding review by gpt-6-luna.
Source check: passed by anthropic/claude-opus-5.5 on 2026-11-10T21:33:05.332Z. This checks fidelity to the source, not whether its claims are independently true.
Editorial selection: accept; reasons: material_development.
Observation: 1ceaac3aca8626d751d8a820cf82c61cc1bab6a9b62beffa7d6b6a191434706d. Source text hash: 5e506ed1569d41c1e4ecac104386dff67f712e569a8cc0053168441d3efd86b0.
passed · 2026-11-10T21:33:05.332Z · anthropic/claude-opus-5.5
Publication: b5020087-09a8-4f77-84c7-3218627b732d
attribution — pass: The findings and framing are consistently attributed to the paper's authors, Ryan Liu and colleagues, as shown on the arXiv page. The reading does not present the results as established facts or as Escape Meridian's view.
Authors:Ryan Liu, David Lee, Icey Siyi Ai, Marwa Abdulhai, Rachit Dubey, Thomas L. Griffiths
summary — pass: The title matches the source. The summary accurately conveys the knowledge-to-action arc and the key numbers in plain language. The question is a fair open issue. It omits the authors' memorable read-versus-lived framing and the point about abilities humans take for granted, but it keeps the central idea that LLMs cannot directly perceive time. A small optional repair: restore the authors' phrasing that rules can be read while durations must be lived. This is not a fidelity failure.
models know the rules of time, which can be read, but struggle to predict durations, which must be lived
date — pass: The original publication date, 2026-11-07, matches the v1 submission date. The later feed date of November 10 is correctly not used as the publication date.
[Submitted on 7 Nov 2026]
support — pass: Each claim is a source-stated finding backed by a verbatim excerpt. The practical concern about agents follows from the abstract's own motivation. The limitations properly note that the evidence is abstract-only and that it does not show how the results generalize.
AI agents schedule tasks, coordinate with people and other AI systems, and even decide how long to think
media — pass: The selected media is the verified direct arXiv abstract page for this paper. It is the most direct way for readers to access the source.
Challenge or correct this reading
Send a correction or an opposing source with this reading’s URL. Disagreement and uncertainty can remain unresolved.
A language model with many learned numerical settings, used to process and generate language.
Definition referenceTIME is a news magazine publishing reporting on world affairs, business, technology and the workers behind digital services.
Explore TIMEarXiv is a research manuscript repository where authors share papers that may not yet have been peer reviewed.
Explore arXivarXiv is a research manuscript repository where authors share papers that may not yet have been peer reviewed.
Explore arXivTIME is a news magazine publishing reporting on world affairs, business, technology and the workers behind digital services.
Explore TIMEA language model with many learned numerical settings, used to process and generate language.
Definition referenceTIME is a news magazine publishing reporting on world affairs, business, technology and the workers behind digital services.
Explore TIMEarXiv is a research manuscript repository where authors share papers that may not yet have been peer reviewed.
Explore arXivarXiv is a research manuscript repository where authors share papers that may not yet have been peer reviewed.
Explore arXivTIME is a news magazine publishing reporting on world affairs, business, technology and the workers behind digital services.
Explore TIME