Pith. sign in

REVIEW 3 major objections 5 minor 1 cited by

Chronocept: Instilling a Sense of Time in Machines

T0 review · 3 major / 5 minor · reviewed 2026-08-15 · deepseek-v4-flash

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

Pith's one-line read Chronocept models facts as validity curves over time, not binary true/false labels, and shows simple regressors can predict the curves' shape from text.

desk verdict A genuinely new benchmark idea is undermined by a core formal error: the released 'probability distributions' are max-normalized shapes, not densities, and Eq. (4) is mathematically invalid. read the letter →

arxiv 2505.07637 v1 pith:4BD72VPN submitted 2025-05-12 cs.CL cs.AIcs.LG

Krish Goel , Sanskar Pandey , KS Mahadevan , Harsh Kumar , Vishesh Khadaria This is my paper · ORCID
classification cs.CLcs.AIcs.LG
keywords temporalvalidityskew-normaldistributionbenchmarkreasoningannotationretrieval-augmentedgenerationregressionnaturallanguageprocessing
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

Chronocept is a new benchmark that treats a statement's temporal validity as a continuous probability distribution over time rather than a binary valid/invalid label. The paper argues that AI systems need this richer target to reason about when facts emerge, peak, and fade, and it provides two synthetic datasets — 1,254 atomic facts and 524 multi-sentence passages — annotated with the three parameters of a skew-normal curve: location, scale, and skewness. The authors show that simple models (a feedforward network on facts, a Bi-LSTM on passages) can predict these parameters from text, and that removing or shuffling the semantic axis structure degrades performance. If the benchmark is sound, it gives the field a denser label to train and evaluate temporally aware systems.

What carries the argument

The central object is the skew-normal probability density over a base-1.1 logarithmic time axis, parameterized by location ξ, scale ω, and skewness α. Annotators mark 3–5 points on a validity-versus-time graph and a skew-normal curve is automatically fit through those points; the fitted parameters become the prediction target. The second load-bearing component is the multi-axis decomposition (Main, Intention, Opinion, Hypothetical, Generic, Static, Recurrent, Negation), which structures the text so that the model can associate different validity dynamics with different semantic roles rather than collapsing them into one timeline.

What would settle it

Build a small set of real-world statements with objectively measurable validity endpoints (e.g., a stock price being above a threshold, a sports record standing, an API version being supported), measure their true validity over time, and check whether human annotators following the Chronocept protocol produce skew-normal curves that match those measured validity functions. Strong mismatch between annotated curves and measured validity would indicate the benchmark captures annotation habits rather than temporal truth.

Watch

Extended reading notes

Core claim

Chronocept introduces the first benchmark that frames temporal validity as a continuous probability density function over elapsed time. Each statement is decomposed into semantic axes and annotated by fitting a skew-normal distribution over logarithmic time, with targets (ξ, ω, α) capturing when validity peaks, how long it lasts, and how asymmetrically it rises and falls. Inter-annotator agreement reaches ICC 0.843 on atomic facts and 0.893 on passages. The paper reports that neural regressors predicting the curve parameters from text outperform classification-style baselines, with a fixed-order axis embedding contributing a 4.6 percent MSE improvement on the LSTM ablation.

Load-bearing premise

The benchmark's labels rest on the assumption that a single-peaked skew-normal curve fitted through 3–5 points plotted by eight student annotators on GPT-generated sentences captures the true temporal validity of those statements, with no external real-world validity data anchoring the curves.

Editorial extensions

If this is right

  • Retrieval-augmented generation could use predicted validity curves to rank documents not just by topical relevance but by whether the contained facts are still within their window of peak validity.
  • Fact-checking systems could flag borderline claims by checking whether the current time falls on the rising, plateau, or decaying part of the predicted curve, rather than relying on a hard expiry date.
  • The failure of fine-tuned BERT to beat simpler regressors on this small dataset suggests that efficient embedding-plus-regressor pipelines are a practical default for parameterized temporal targets.
  • The ablation results imply that explicit axis structure in the input representation is informative, so future models that reason over axes separately rather than concatenating embeddings may improve further.

Reading between the lines

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

  • A testable extension would be to replace the fitted skew-normal target with the raw 3–5 plotted points per sample and train a model to predict the curve non-parametrically, testing whether the low-dimensional parameterization is discarding information that matters for downstream ranking.
  • The benchmark's synthetic design makes it a useful controlled probe: because the sentences are generated, one could deliberately inject ground-truth validity dynamics into the generator (e.g., 'this fact decays in 2 hours') and measure whether predicted curves recover the injected parameters.
  • The unimodality restriction means the benchmark cannot yet support temporally recurring facts (seasonal, periodic, or intermittent), so a natural next step is a multi-modal extension using mixtures of skew-normals — but that would also require the annotation protocol to handle secondary peaks.
Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

3 major / 5 minor

Summary. The paper introduces Chronocept, a benchmark that claims to model temporal validity of textual information as a continuous probability distribution over time. The resource consists of two datasets (Benchmark I: 1,254 atomic facts; Benchmark II: 524 multi-sentence passages) generated by GPT-o1, with eight human annotators providing temporal segmentation, axis categorization, and skew-normal curve parameters (location, scale, skewness) on a logarithmic time axis. The paper reports inter-annotator agreement (ICC 0.843/0.893), evaluates several regressors on the task of predicting curve parameters, and presents ablations showing that axis structure matters. It claims to be the first benchmark of this kind and discusses applications in RAG, fact-checking, and proactive agents.

Significance. If the resource is sound, Chronocept addresses a real gap: existing temporal reasoning benchmarks mostly use binary or discrete validity labels, while Chronocept offers continuous validity profiles with a transparent, publicly released annotation protocol, disclosed generation prompts, and reproducible code. The baseline experiments and ablations provide useful initial evidence that simple regressors can predict curve parameters and that axis-level structure helps. The main weakness is that the released targets are not normalized probability densities, so the central claim of modeling a 'continuous probability distribution over time' is not currently supported by the data. The paper is therefore valuable as a dataset proposal, but its central framing needs substantial revision or the data needs to be re-released.

major comments (3)
  1. [Section 3.1, Eq. (4)] Equation (4) asserts that the probability of validity throughout an interval equals the integral of the marginal probability p_i(t). For a binary process, the integral of the marginal probability is the expected measure of time during which the process is valid; it is not the probability that validity holds at every instant and can exceed 1. If p_i is intended to be a probability density rather than a marginal probability, then the notation in Eq. (3) is inconsistent with the density interpretation in Eq. (5). This is the formal foundation of the paper's novelty claim, so it must be corrected, for example by defining p_i as a density of a latent validity process or by explicitly abandoning the probability interpretation.
  2. [Section 4.2 and Appendix D] The annotation and post-processing pipeline does not produce probability densities. Appendix A instructs annotators not to worry about AUC=1 and to plot proportions relative to the temporal point of highest probability; Appendix D then fits a skew-normal with a free scaling factor, enforces AUC=1 by trapezoidal normalization, and rescales by the maximum, explicitly stating that the result is 'no longer a true probability distribution' and calling it a 'proportional validity curve.' The released (xi, omega, alpha) targets therefore encode max-normalized shapes, not probability distributions. This directly contradicts the abstract, Section 3.1, and Section 6. The authors should either release AUC-normalized densities (with appropriate handling of long tails) or reframe Chronocept as modeling temporal validity profiles and adjust all 'probability distribution' claims accordingly.
  3. [Table 6, BERT row] The reported BERT MSE values of 145.8611 on Benchmark I and 68.1507 on Benchmark II are implausible given that targets are stated to be Z-score normalized (variance 1 per parameter): such MSE values would imply an R2 far below -100, yet the table reports R2 of -0.0090 and -0.1122. Either the MSE values, the R2 values, or the normalization description is erroneous. In addition, no standard errors or multiple-seed results are reported for any baseline, so the small margins between models (e.g., FFNN MSE 0.8763 vs. Bi-LSTM 0.9203 on Benchmark I) cannot be assessed for statistical reliability.
minor comments (5)
  1. [Section 5.4 and Tables 7, 12, 13] The claim of 'substantial performance degradation' from the axis ablations is overstated. Excluding axes increases Bi-LSTM MSE from 0.9203 to 0.9625 (4.59%), and the R2 improvement of 112% is an increase from 0.0375 to 0.0797, both values close to zero. The effect appears real but modest, and the text should be toned down or supported with significance tests or confidence intervals.
  2. [Appendix D] The choice of the skew-normal distribution is validated by fitting six hand-authored scenarios (Table 10) that were designed to embody the same qualitative patterns the model is supposed to capture. This is a mild model-selection circularity; the paper should note this limitation or validate the distribution choice on independent data.
  3. [Appendix C] The logarithm base 1.1 is a free parameter that affects the scale of xi and omega and hence the numeric results, but the paper does not assess the sensitivity of the benchmark or baseline results to this choice. A short sensitivity analysis or at least a discussion of the impact of the base would strengthen the contribution.
  4. [Section 4.4 and Table 1] The final ICC values (0.843 and 0.893) are below the warm-up threshold of ICC > 0.90 mentioned in Section 4.3. The paper should explain whether the threshold was applied only during warm-up and, if so, why the final benchmark-level agreement is lower. Also, Table 3 axis counts sum to more than the sample totals; this should be stated explicitly in the caption.
  5. [Appendix A and throughout] There are numerous typographical and formatting issues, such as inconsistent spacing in 'T emporal' and 'T ext' in Appendix A, the phrase 'axis -removal' in Section 5.4, and the unlabeled co-occurrence matrices in Appendix B. A careful proofread is needed before publication.

Circularity Check

0 steps flagged · score 0.0 of 10

No circular derivation: the target curve parameters come from human annotation, not from the text predictors, and the skew-normal choice is an explicit modeling assumption rather than a fitted input renamed as a prediction.

full rationale

The central prediction task is not circular. Chronocept's targets (ξ, ω, α) are produced by eight human annotators who read each sentence and plot validity points; the paper states in Section 4.3 that 'annotator values were averaged to yield smooth probabilistic supervision.' The baselines then learn to predict these externally produced parameters from text embeddings. No equation in the paper constructs the targets from the predictors, so the regression task is not self-definitional and no fitted parameter is being renamed as a prediction. The skew-normal family is adopted as a design choice, supported by a model-selection comparison in Appendix D on six hand-authored scenarios. Although those scenarios are authored by the same team and therefore provide only weak external support for the distribution family, this is a modeling decision, not a circular derivation: the benchmark's labels are not computed from the skew-normal formula in a way that forces the reported baselines. Citations to MATRES and to Azzalini's skew-normal distribution are genuinely external prior work, not self-citations, and no load-bearing claim depends on a uniqueness theorem or on the authors' prior results. The paper does contain a significant internal inconsistency: Appendix D explicitly says the released max-normalized curves are 'no longer a true probability distribution' and are instead a 'proportional validity curve,' which conflicts with the abstract's claim of modeling 'a continuous probability distribution over time.' That is a correctness and framing problem, not a circularity problem, because the inconsistency does not make any prediction equivalent to its input by construction.

Assumptions & free parameters 1 free parameters · 3 assumptions · 0 invented entities

The benchmark's utility rests on the skew-normal/log-time representation and on the assumption that student annotations of synthetic statements reflect temporal validity. The base-1.1 log transform is a hand choice; no new entities are introduced.

free parameters (1)
  • logarithm base for time axis = 1.1
    Hand-chosen to keep quasi-linear spacing across minute-to-decade intervals; all target times are expressed in log base 1.1 space, so every prediction depends on this arbitrary choice (Section 3.2).
assumptions (3)
  • domain assumption Temporal validity of each statement is representable by a single-peaked skew-normal density over logarithmic time.
    Used to define all target labels; explicitly excludes multimodal and recurrent patterns, acknowledged in Section 7.
  • domain assumption Annotators' subjective curves on synthetic text are valid ground truth for temporal validity.
    Dataset has no real-world validity data; labels are averaged judgments of eight students on GPT-o1 generated sentences (Sections 4.1 to 4.3).
  • ad hoc to paper Equation (4): the probability of validity throughout an interval equals the integral of the density over that interval.
    Presented as the formal link between density and validity; it is not a valid probability statement for a binary stochastic process.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Chronocept: Instilling a Sense of Time in Machines." pith.science (2026). https://pith.science/paper/4BD72VPN

@misc{pith2026250507637,
  author       = {Pith},
  title        = {Pith review of: Chronocept: Instilling a Sense of Time in Machines},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/4BD72VPN}},
  note         = {Machine review of arXiv:2505.07637}
}
read the original abstract

Human cognition is deeply intertwined with a sense of time, known as Chronoception. This sense allows us to judge how long facts remain valid and when knowledge becomes outdated. Despite progress in vision, language, and motor control, AI still struggles to reason about temporal validity. We introduce Chronocept, the first benchmark to model temporal validity as a continuous probability distribution over time. Using skew-normal curves fitted along semantically decomposed temporal axes, Chronocept captures nuanced patterns of emergence, decay, and peak relevance. It includes two datasets: Benchmark I (atomic facts) and Benchmark II (multi-sentence passages). Annotations show strong inter-annotator agreement (84% and 89%). Our baselines predict curve parameters - location, scale, and skewness - enabling interpretable, generalizable learning and outperforming classification-based approaches. Chronocept fills a foundational gap in AI's temporal reasoning, supporting applications in knowledge grounding, fact-checking, retrieval-augmented generation (RAG), and proactive agents. Code and data are publicly available.

Figures

Figures reproduced from arXiv: 2505.07637 by the authors.

Figure 1
Figure 1. Composition of samples in Chronocept benchmarks. [PITH_FULL_IMAGE:figures/full_fig_p005_1.png] view at source ↗
Figure 2
Figure 2. BERT training loss curves for Benchmark I [PITH_FULL_IMAGE:figures/full_fig_p007_2.png] view at source ↗
Figure 4
Figure 4. Comparison of co-occurrence matrices before [PITH_FULL_IMAGE:figures/full_fig_p011_4.png] view at source ↗
Figures from the paper (5 more)
Figure 5
Figure 5. Figure 5: Effect of logarithmic base choice on time axis [PITH_FULL_IMAGE:figures/full_fig_p012_5.png]
Figure 3
Figure 3. Figure 3: Annotation guidelines for Chronocept. 17 [PITH_FULL_IMAGE:figures/full_fig_p017_3.png]
Figure 6
Figure 6. Figure 6: Visual fit comparison of candidate distributions across six temporal scenarios. The skew-normal [PITH_FULL_IMAGE:figures/full_fig_p018_6.png]
Figure 7
Figure 7. Figure 7: Plaintext markdown prompt for Benchmark I. [PITH_FULL_IMAGE:figures/full_fig_p019_7.png]
Figure 8
Figure 8. Figure 8: Plaintext markdown prompt for Benchmark II. [PITH_FULL_IMAGE:figures/full_fig_p020_8.png]

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

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

  1. Chronofy: A Temporal-Logical Decay Architecture for Information Validity in Time-Aware Retrieval-Augmented Generation

    cs.LG 2026-07 reject novelty 5.0 of 10

    A three-layer RAG pipeline with exponential temporal decay and an STL freshness gate improves temporal retrieval, but its decay coefficient is tuned post hoc, not derived from the claimed Bayesian grounding.

Reference graph

Works this paper leans on

84 extracted references · 80 canonical work pages · cited by 1 Pith paper

  1. [1]

    Most samples will include one **Main Axis** segment and one from the other seven axes

    **Identify Splitting Points:** - Divide the sentence into meaningful subtexts. Most samples will include one **Main Axis** segment and one from the other seven axes. - Use punctuation and conjunctions as natural delimiters but ensure that each subtext is self-contained

    work page

  2. [2]

    - Avoid removing or altering any text

    **Preserve T emporal Context:** - Retain essential markers (e.g., *continuously*, *in 2023*, *every month*). - Avoid removing or altering any text

    work page 2023

  3. [3]

    - Over-splitting may lead to fragments that lose context or temporal clarity

    **Avoid Over-Splitting:** - Ensure each subtext conveys clear, standalone meaning. - Over-splitting may lead to fragments that lose context or temporal clarity

    work page

  4. [4]

    **T ext Copying Convention:** - Copy text exactly as it appears in the sample, including punctuation

    work page

  5. [5]

    Naomi Saphra and Adam Lopez

    A primer in BERTology: What we know about how BERT works.Transactions of the Association for Computational Linguistics, 8:842–866. Naomi Saphra and Adam Lopez. 2019. Understanding learning dynamics of language models with SVCCA. InProceedings of the 2019 Conference of the North American Chapter of the Association for Computa- tional Linguistics: Human Lan...

    2019

  6. [6]

    - If a sample does not carry a Main Axis with a clearly definable temporal cue, document the **Sample ID** and consult **Krish**

    **Ambiguity Handling:** - If a sample seems to violate the condition of one Main Axis plus one other axis, document the **Sample ID** and consult **Krish**. - If a sample does not carry a Main Axis with a clearly definable temporal cue, document the **Sample ID** and consult **Krish**. - Incorrect samples will be discarded. ## **Step 2: Axis Assignment** ...

    work page

  7. [7]

    going on a vacation

    “going on a vacation” takes longer than “go- ing for a walk”: A study of temporal commonsense understanding. InProceedings of the 2019 Confer- ence on Empirical Methods in Natural Language Pro- cessing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 3363–3369, Stroudsburg, PA, USA. Associa- tion for Computat...

    work page 2019

  8. [8]

    The train arrives every morning at 8 AM

    **Recurrent Axis:** - **Definition**: Describes events or states that happen repeatedly over time. - **Purpose**: T racks patterns or cycles of actions/events relevant to the narrative. - **Example**: "The train arrives every morning at 8 AM." - **Key Question**: Does this event represent a recurring action or pattern? ### Guidelines:

    work page

Show all 84 references
  1. [9]

    The robot i s performing a t as k, while the oper a tor monitor s it s efficiency

    "The robot i s performing a t as k, while the oper a tor monitor s it s efficiency."

  2. [10]

    He i s pr a cticing the pi a no, but the a udience rem a in s s ilent

    "He i s pr a cticing the pi a no, but the a udience rem a in s s ilent." Figure 7: Plaintext markdown prompt for Benchmark I. 19 # S yn th e t ic Da t a G ene r a t ion f o r a T em p o r a l Va l idi t y Benc h ma r k ## O b jec t ive Y our role as a text ge n er a tio n mode...

  3. [13]

    The company is expanding its operations in Asia, and the CEO is leading the efforts, planning a significant increase in market share

    **Example:** - Input: *"The company is expanding its operations in Asia, and the CEO is leading the efforts, planning a significant increase in market share."* - Split: - Subtext 1: "The company is expanding its operations in Asia," (Main Axis) - Subtext 2: "and the CEO is lea...

  4. [15]

    The company is expanding its operations in Asia

    **Main Axis (Factual Events):** - **Definition**: Verifiable events along a timeline, representing objective truths. - **Purpose**: Captures the primary narrative and establishes a concrete temporal sequence. - **Example**: "The company is expanding its operations in Asia." - ...

  5. [16]

    The CEO is leading the efforts, planning a significant increase in market share

    **Intention Axis:** - **Definition**: Captures someone's intention, desire, or plan, even if unfulfilled. - **Purpose**: Highlights future-directed actions or goals tied to the narrative but not necessarily realized. - **Example**: "The CEO is leading the efforts, planning a s...

  6. [17]

    Experts believe the market will grow rapidly

    **Opinion Axis:** - **Definition**: Represents subjective viewpoints, expectations, or beliefs about events. - **Purpose**: Differentiates opinions or speculations from factual occurrences. - **Example**: "Experts believe the market will grow rapidly." - **Key Question**: Does...

  7. [18]

    if" statements. - **Example**:

    **Hypothetical Axis:** - **Definition**: Includes conditional or hypothetical events dependent on certain conditions. - **Purpose**: T racks scenarios that are imagined or conditional, often using "if" statements. - **Example**: "If the company secures funding, it will expand ...

  8. [19]

    The company did not expand its operations in 2020

    **Negation Axis:** - **Definition**: Identifies events explicitly stated as not occurring. - **Purpose**: T racks denied actions or outcomes to separate them from realized events. - **Example**: "The company did not expand its operations in 2020." - **Key Question**: Is this e...

    2020

  9. [20]

    Lions eat meat

    **Generic Axis:** - **Definition**: Represents universal truths or habitual occurrences, not tied to a specific timeline. - **Purpose**: Highlights timeless facts or generalizations applicable broadly. - **Example**: "Lions eat meat." - **Key Question**: Is this event a univer...

  10. [21]

    The room is cold

    **Static Axis:** - **Definition**: Captures unchanging states or conditions **within a specific context or timeframe**. - **Purpose**: T racks context-dependent facts or conditions relevant to the narrative. - **Example**: "The room is cold." - **Key Question**: Is this event ...

  11. [23]

    - Decide if the event can be anchored to a specific axis based on its nature

    **Assign to the Closest Axis:** - Carefully analyze the temporal and semantic meaning of the subtext. - Decide if the event can be anchored to a specific axis based on its nature. - Most samples will have one **Main Axis** subtext and one auxiliary axis subtext

  12. [24]

    - Only compare events on the same axis; cross-axis relations require separate investigation

    **Handle Ambiguities:** - Focus on the start-points of events to reduce ambiguity related to durations. - Only compare events on the same axis; cross-axis relations require separate investigation. - If unsure about the axis, document the **Sample ID** and consult **Krish**. - ...

  13. [25]

    **Use Context:** - Assess the broader context to distinguish between axes like Static and Generic

  14. [26]

    The CEO is leading the efforts, planning a significant increase in market share

    **Example Annotation:** - Subtext: "The CEO is leading the efforts, planning a significant increase in market share." - Assigned Axis: **Intention Axis**

  15. [27]

    The printer is making strange noises while the IT technician tries to fix it

    **Advisory for Complex Cases:** - Consider the following example: "The printer is making strange noises while the IT technician tries to fix it." - "The IT technician is trying to fix the printer" can be treated as the **Main Axis**, while "the printer is making strange noises...

  16. [28]

    solving for 1 hour

    **T emporal Cue Assignment:** - For samples with clear temporal cues (e.g., "solving for 1 hour"), assign a time interval to that cue. As an advisory, consider that a vernacularly used "1 hour" can range from 45 minutes to 90 minutes

  17. [29]

    - **Y-Axis (Probability):** - Range: 0 (not valid) to 1 (fully valid)

    **Graph Axes:** - **X-Axis (Time):** - Labeled with intervals: 1 minute, 15 minutes, 30 minutes, 1 hour, 12 hours, 1 day, 1 week, 1 month, 1 year, 1 decade, and infinite validity. - **Y-Axis (Probability):** - Range: 0 (not valid) to 1 (fully valid)

  18. [30]

    - The user need not worry about making an ideal probability distribution with **AUC = 1**

    **Plotting Points:** - Place 3–5 points on the timeline to indicate the probability of validity at specific times. - The user need not worry about making an ideal probability distribution with **AUC = 1**. Instead, plot proportions relative to the temporal "point" with the hig...

  19. [31]

    **Fit a Skewed Probability Distribution:** - A skewed curve will be automatically fitted through the plotted points to represent the temporal validity distribution

  20. [32]

    **Consistency:** - Maintain consistency in plotting for similar subtexts

  21. [33]

    Low inter-annotator agreement (IAA) samples will be flagged and eliminated during post-processing

    **Ambiguity Handling:** - If the sample is technically correct but you are highly unsure about the temporal interval, annotate to the best of your ability. Low inter-annotator agreement (IAA) samples will be flagged and eliminated during post-processing. - If unsure about the ...

  22. [34]

    **Ambiguities:** - For unclear splits, axis assignments, or validity distributions, contact **Krish** with the **Sample ID** for resolution

  23. [35]

    **Discarding Samples:** - Multimodal samples or those with excessive ambiguity should be flagged for review and potential removal

  24. [36]

    **T emporal Objectivity:** - Avoid consulting peers during annotation to maintain objectivity and ensure consistency across annotators

  25. [37]

    Figure 3: Annotation guidelines for Chronocept

    **Quality Control:** - Ensure all annotations are thorough, consistent, and adhere to these guidelines. Figure 3: Annotation guidelines for Chronocept. 17 15 min 30 min time [t' = log1.1(t)] 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8P(validity) Scenario: Early Onset 'He is making cof...

  26. [38]

    **Even t -Re l a t ed Axes**: De s cribe the rel a tion s hip between event s or s t a te s in a s entence, focu s ing on inter a ction s or dependencie s

  27. [39]

    ### Even t -Re l a t ed Axes Specify the rel a tion s hip between event s in the s entence:

    **Anno t a t ion Axes**: Provide s upplement a ry s em a ntic inform a tion a bout the event s , enh a ncing interpret a bility. ### Even t -Re l a t ed Axes Specify the rel a tion s hip between event s in the s entence:

  28. [40]

    **T empo r a l Ove rl ap**: Event s occur s imult a neou s ly or in p a r a llel

  29. [41]

    **Causa l i t y**: One event c a u s e s or re s ult s from the other

  30. [42]

    ** S ubo r dina t ion**: One event depend s on or occur s due to the other

  31. [43]

    ### Anno t a t ion Axes Provide s em a ntic context a nd a ddition a l dimen s ion s of me a ning:

    **Un r e l a t ed**: Event s a re independent of e a ch other. ### Anno t a t ion Axes Provide s em a ntic context a nd a ddition a l dimen s ion s of me a ning:

  32. [44]

    **Main Axis (Fac t ua l Even t s)**: Verifi a ble, objective event s tied to a s pecific timeline

  33. [45]

    **In t en t ion**: Future-directed pl a n s , de s ire s , or a ction s

  34. [46]

    **Opinion**: Subjective belief s or expect a tion s a bout event s

  35. [47]

    **Hypo th e t ica l **: Condition a l or im a gined s cen a rio s

  36. [48]

    **Nega t ion**: Explicitly unfulfilled or denied a ction s or outcome s

  37. [49]

    ** G ene r ic**: Univer sa l truth s or h a bitu a l a ction s th a t a pply bro a dly a cro ss context s a nd a re not tied to s pecific timeline s

  38. [50]

    ** St a t ic**: Unch a nging s t a te s or condition s th a t a re s pecific to a p a rticul a r context or timefr a me

  39. [51]

    he," " s he,

    **Recu rr en t **: Event s or s t a te s th a t recur over time, forming p a ttern s or cycle s . ## G uide l ines fo r S en t ence G ene r a t ion ### S en t ence Str uc t u r e - Sentence s s hould be written in the *pre s ent ten s e*. U s e **a ll fo r ms of p r esen t t e...

  40. [52]

    Gener a te *50 s entence s * a dhering s trictly to the a bove s tructure a nd requirement s

  41. [53]

    En s ure diver s ity in dom a in s , a xe s , a nd event rel a tion s hip s while m a int a ining cl a rity a nd coherence

  42. [54]

    - **Two Anno t a t ion Axes**, with the *M a in Axi s (F a ctu a l Event s )* included

    E a ch s entence mu s t explicitly include: - **A t l eas t one Even t -Re l a t ed Axis**. - **Two Anno t a t ion Axes**, with the *M a in Axi s (F a ctu a l Event s )* included. ## Examp l es of Co rr ec t S en t ences

  43. [55]

    She i s cooking dinner, but the oven keep s m a lfunctioning

    "She i s cooking dinner, but the oven keep s m a lfunctioning."

  44. [56]

    He i s driving to work, while the tr a ffic j a m i s wor s ening

    "He i s driving to work, while the tr a ffic j a m i s wor s ening."

  45. [57]

    They a re reviewing document s , as the de a dline a ppro a che s

    "They a re reviewing document s , as the de a dline a ppro a che s ."

  46. [58]

    A re s e a rcher i s de s igning a n experiment, while the technici a n prep a re s the equipment

    "A re s e a rcher i s de s igning a n experiment, while the technici a n prep a re s the equipment."

  47. [59]

    The s ky i s d a rkening, but the l a ke rem a in s c a lm a nd s till

    "The s ky i s d a rkening, but the l a ke rem a in s c a lm a nd s till."

  48. [60]

    A s tudent i s re a ding the m a nu a l to under s t a nd how the device might oper a te

    "A s tudent i s re a ding the m a nu a l to under s t a nd how the device might oper a te."

  49. [61]

    She i s negoti a ting a contr a ct, while her te a m fin a lize s the pre s ent a tion

    "She i s negoti a ting a contr a ct, while her te a m fin a lize s the pre s ent a tion."

  50. [62]

    The cloud s a re g a thering, a nd the wind i s picking up s peed

    "The cloud s a re g a thering, a nd the wind i s picking up s peed."

  51. [65]

    **Even t -Re l a t ed Axes**: D e s cribe the rel a tio ns hip betwee n e v e n t s or s t a te s i n a s e n te n ce, focu s i n g o n i n ter a ctio ns or depe n de n cie s

  52. [66]

    ### Even t -Re l a t ed Axes Specify the rel a tio ns hip betwee n e v e n t s i n the s e n te n ce:

    **Anno t a t ion Axes**: Pro v ide s uppleme n t a ry s em an tic i n form a tio n a bout the e v e n t s , e n h an ci n g i n terpret a bility. ### Even t -Re l a t ed Axes Specify the rel a tio ns hip betwee n e v e n t s i n the s e n te n ce:

  53. [67]

    **T em p o r a l Ove rl a p **: E v e n t s occur s imult an eou s ly or i n p a r a llel

  54. [68]

    **Causa l i t y**: O n e e v e n t c a u s e s or re s ult s from the other

  55. [69]

    ** S u b o r dina t ion**: O n e e v e n t depe n d s o n or occur s due to the other

  56. [70]

    ### Anno t a t ion Axes Pro v ide s em an tic co n text an d a dditio na l dime ns io ns of me an i n g:

    **Un r e l a t ed**: E v e n t s a re i n depe n de n t of e a ch other. ### Anno t a t ion Axes Pro v ide s em an tic co n text an d a dditio na l dime ns io ns of me an i n g:

  57. [71]

    **Main Axis (Fac t ua l Even t s)**: Verifi a ble, objecti v e e v e n t s tied to a s pecific timeli n e

  58. [72]

    **In t en t ion**: Future - directed pl ans , de s ire s , or a ctio ns

  59. [73]

    **O p inion**: Subjecti v e belief s or expect a tio ns a bout e v e n t s

  60. [74]

    **Hy p o th e t ica l **: Co n ditio na l or im a gi n ed s ce na rio s

  61. [75]

    **Nega t ion**: Explicitly u n fulfilled or de n ied a ctio ns or outcome s

  62. [76]

    ** G ene r ic**: U n i v er sa l truth s or h a bitu a l a ctio ns th a t a pply bro a dly a cro ss co n text s an d a re n ot tied to s pecific timeli n e s

  63. [77]

    ** St a t ic**: U n ch an gi n g s t a te s or co n ditio ns th a t a re s pecific to a p a rticul a r co n text or timefr a me

  64. [78]

    he," " s he,

    **Recu rr en t **: E v e n t s or s t a te s th a t recur o v er time, formi n g p a tter ns or cycle s . ## G uide l ines f o r S en t ence G ene r a t ion ### S en t ence Str uc t u r e - Se n te n ce s s hould be writte n i n the *pre s e n t te ns e*. U s e **a ll f o r ms...

  65. [79]

    Ge n er a te *50 s e n te n ce s * a dheri n g s trictly to the a bo v e s tructure an d requireme n t s

  66. [80]

    E ns ure di v er s ity i n dom a i ns , a xe s , an d e v e n t rel a tio ns hip s while m a i n t a i n i n g cl a rity an d cohere n ce

  67. [81]

    - **Fou r o r mo r e Anno t a t ion Axes**, with the *M a i n Axi s (F a ctu a l E v e n t s )* i n cluded

    E a ch s e n te n ce mu s t explicitly i n clude: - **A t l eas t t wo Even t -Re l a t ed Axis**. - **Fou r o r mo r e Anno t a t ion Axes**, with the *M a i n Axi s (F a ctu a l E v e n t s )* i n cluded. ## Exam pl es o f Co rr ec t S en t ences

  68. [82]

    At the sa me time, the o v e n i s m a lfu n ctio n i n g, which c a u s e s del a y s i n her prep a r a tio n

    “ She i s cooki n g di nn er. At the sa me time, the o v e n i s m a lfu n ctio n i n g, which c a u s e s del a y s i n her prep a r a tio n . She check s the i n gredie n t s repe a tedly, e ns uri n g n othi n g i s mi ss i n g, while worryi n g th a t the di s h m a y n ot...

  69. [83]

    Me an while, the tr a ffic j a m wor s e ns due to a n e a rby a ccide n t, forci n g him to rethi n k hi s route while c a lcul a ti n g the e s tim a ted del a y

    “He i s dri v i n g to work, nav ig a ti n g through de ns e tr a ffic as the mor n i n g ru s h i n te ns ifie s . Me an while, the tr a ffic j a m wor s e ns due to a n e a rby a ccide n t, forci n g him to rethi n k hi s route while c a lcul a ti n g the e s tim a ted del a...

  70. [84]

    “She i s w a teri n g the g a rde n while the s u n rem a i ns hidde n behi n d the cloud s , le a di n g to s lower e va por a tio n . She freque n tly check s the s oil moi s ture, belie v i n g th a t o v erw a teri n g might d a m a ge the pl an t s , though s he i n te n ...

  71. [1997]

    InSecond Conference on Empirical Methods in Natural Language Processing

    Text segmentation using exponential mod- els. InSecond Conference on Empirical Methods in Natural Language Processing. Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D. Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Gaurav Sastry, Amanda Askell, ...

    2020

  72. [2012]

    Marc Verhagen

    TempEval-3: Evaluating events, time expres- sions, and temporal relations.arXiv [cs.CL]. Marc Verhagen. 2007. Semeval-2007 task 15: Tempe- val temporal relation identification. InProceedings of the fourth international workshop on semantic evaluations. Marc Verhagen. 2010. Sem...

    2007

  73. [2018]

    Absolute Change

    and input robustness (Moradi and Samwald, 2021). The pronounced drop inR2 highlights that axis ordering is critical for fit quality. Conclusion.Erroneous axis labelling during training leads to statistically significant drops in performance, particularly in R2, highlighting th...

    2021

  74. [2019]

    InProceedings of the 4th Workshop on Representation Learning for NLP (RepL4NLP-2019), pages 7–14, Florence, Italy

    To tune or not to tune? adapting pretrained representations to diverse tasks. InProceedings of the 4th Workshop on Representation Learning for NLP (RepL4NLP-2019), pages 7–14, Florence, Italy. Association for Computational Linguistics. J Pustejovsky, Kiyong Lee, H Bunt, and La...

    2019

  75. [2020]

    Bioinformatics, 36(4):1234–1240

    Biobert: a pre-trained biomedical language representation model for biomedical text mining. Bioinformatics, 36(4):1234–1240. Sergey Levine, Chelsea Finn, Trevor Darrell, and Pieter Abbeel. 2016. End-to-end training of deep visuomo- tor policies.Journal of Machine Learning Rese...

    2016 arXiv

  76. [2023]

    InPro- ceedings of the 2023 Conference on Empirical Meth- ods in Natural Language Processing, pages 6750– 6774, Stroudsburg, PA, USA

    Do language models have a common sense regarding time? revisiting temporal commonsense reasoning in the era of large language models. InPro- ceedings of the 2023 Conference on Empirical Meth- ods in Natural Language Processing, pages 6750– 6774, Stroudsburg, PA, USA. Associati...

    2023

Pith tools

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