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arxiv: 2605.08590 · v1 · submitted 2026-05-09 · 💻 cs.HC · cs.AI· cs.CL· cs.CY

Recognition: no theorem link

Causal Stories from Sensor Traces: Auditing Epistemic Overreach in LLM-Generated Personal Sensing Explanations

Shanshan Zhu , Han Zhang , J. Doris Chi , Subigya Nepal , Koustuv Saha
Authors on Pith no claims yet

Pith reviewed 2026-05-12 00:49 UTC · model grok-4.3

classification 💻 cs.HC cs.AIcs.CLcs.CY
keywords epistemic overreachLLM explanationspersonal sensingsensor datacausal attributionanomaly explanationcollege student dataevidential grounding
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The pith

LLMs frequently generate causal explanations for personal sensing anomalies that go beyond what the sensor data can support.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

This paper examines how large language models create natural-language stories explaining unusual days from personal sensor traces like activity and mood. The authors define epistemic overreach as when these explanations claim more than the evidence allows. They test this on thousands of scenarios from three college student datasets using multiple LLM families and different prompting styles. The results show that unsupported causal attributions are common, extra data does not fix it reliably, and even prompts to stick to the data only partially help. This matters because such explanations are used in personal sensing apps, so users might get misleading accounts of their own behavior.

Core claim

We introduce epistemic overreach (EO) as a measure for cases where a generated explanation implies more than the available sensing evidence can justify. To audit how often and in what forms EO occurs, we obtained anomalous-day scenarios from three longitudinal sensing datasets of college students. Across activity, sleep, and affect anomalies, we generated 14,922 explanations using three LLM families under two prompting conditions. We find that LLMs routinely attribute anomalous days to causes without sufficient support from the data, and that this pattern replicates across datasets, anomaly types, and model families. Further, providing richer context does not reliably reduce EO; bounded 0,0,

What carries the argument

Epistemic overreach, measured by a structured rubric that scores explanations on unsupported causal attribution, unacknowledged data gaps, overconfident language, temporal inconsistency, and diagnostic inference.

If this is right

  • Evidential grounding should be a primary criterion for evaluating LLM-generated personal sensing explanations, alongside fluency and plausibility.
  • Personal sensing explanation systems need to explicitly distinguish what is observed, what is inferred, and what remains unknown.
  • The pattern of overreach holds across different anomaly types and data sources, suggesting it is a general property of current LLMs in this use case.
  • Bounded prompting reduces but does not remove epistemic overreach, indicating a need for stronger controls or architectural changes.

Where Pith is reading between the lines

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

  • Users of personal sensing apps that use LLMs for explanations may need warnings or alternative non-causal summaries to avoid overinterpreting their data.
  • Similar audits could be applied to LLM use in other data-driven storytelling domains like financial reports or health diagnostics.
  • Future models might incorporate explicit uncertainty tracking or retrieval from raw data to reduce overreach.
  • Developers should test for EO before deploying LLM explainers in high-stakes personal data contexts.

Load-bearing premise

The structured rubric used to score epistemic overreach accurately reflects when explanations exceed the evidence, without major bias or inconsistency in how evaluators apply it.

What would settle it

A follow-up study that applies the same scenarios and models but uses a different set of human evaluators or an automated metric for overreach and finds substantially lower rates of unsupported attributions would challenge the finding that overreach is routine.

Figures

Figures reproduced from arXiv: 2605.08590 by Han Zhang, J. Doris Chi, Koustuv Saha, Shanshan Zhu, Subigya Nepal.

Figure 1
Figure 1. Figure 1: Empirical audit workflow. We construct participant-relative anomalous-day scenarios from three longitudinal sensing [PITH_FULL_IMAGE:figures/full_fig_p006_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Mean normalized EO score by generation model and prompt policy. EO score is computed as the proportion of 16 [PITH_FULL_IMAGE:figures/full_fig_p013_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: EO by rubric dimension across dataset, generation model, and prompt policy. Scores are shown on the original 0–1 [PITH_FULL_IMAGE:figures/full_fig_p015_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Relative percentage difference in EO score by anomaly type and evidence tier, shown separately by generation model [PITH_FULL_IMAGE:figures/full_fig_p016_4.png] view at source ↗
read the original abstract

LLMs are increasingly used to explain personal sensing data, translating traces of activity and mood into natural-language accounts of why an anomalous day may have occurred. However, such explanations can sound coherent and personally meaningful even when the underlying evidence is sparse or missing. We introduce epistemic overreach (EO) as a measure for cases where a generated explanation implies more than the available sensing evidence can justify. To audit how often and in what forms EO occurs, we obtained anomalous-day scenarios from three longitudinal sensing datasets of college students: StudentLife, GLOBEM, and CollegeExperience. Across activity, sleep, and affect anomalies, we generated 14,922 explanations using three LLM families -- Llama, Qwen, and GPT -- under two prompting conditions: one minimally constrained prompt and another prompt explicitly instructing models to bound claims to the data. For each scenario, we varied the amount of behavioral evidence available to the model to examine whether more evidence reduces EO. We evaluated each explanation using a structured rubric, decomposing EO into the dimensions of unsupported causal attribution, unacknowledged data gaps, overconfident language, temporal inconsistency, and diagnostic inference. We find that LLMs routinely attribute anomalous days to causes without sufficient support from the data, and that this pattern replicates across datasets, anomaly types, and model families. Further, providing richer context does not reliably reduce EO; bounded prompting helps but does not eliminate it. These findings suggest that evidential grounding should be a first-order evaluation criterion for LLM-generated personal sensing explanations, alongside fluency and plausibility. We argue that personal sensing explanations require evidential discipline: systems must distinguish what is observed, what is inferred, and what remains unknown.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The paper introduces epistemic overreach (EO) as a measure of LLM-generated explanations for anomalous days in personal sensing data that imply more than the available sensor traces justify. Using three public longitudinal datasets (StudentLife, GLOBEM, CollegeExperience), it generates 14,922 explanations across activity, sleep, and affect anomalies with Llama, Qwen, and GPT models under minimal and bounded prompting conditions while varying available behavioral evidence. Each explanation is scored on a five-dimension rubric (unsupported causal attribution, unacknowledged data gaps, overconfident language, temporal inconsistency, diagnostic inference), yielding the finding that EO occurs routinely, replicates across datasets/models/anomaly types, richer context does not reliably reduce it, and bounded prompting helps but does not eliminate it.

Significance. If the results hold, the work is significant for establishing evidential grounding as a necessary evaluation criterion for LLM use in personal sensing applications. It earns credit for its large generation volume (14,922 explanations), use of three public longitudinal datasets, and decomposed rubric applied across multiple conditions and model families, which together provide solid empirical grounding for the replication claims.

major comments (2)
  1. [Evaluation section / Rubric description] Evaluation section / Rubric description: The paper applies a five-dimension rubric to score EO but reports no inter-rater reliability statistics, no validation against domain-expert gold labels, and no explicit operationalization of 'sufficient support from the data' for activity/sleep/affect anomalies. Because the central claims about routine EO and the effects of context/prompting rest entirely on these scores, the absence of reliability metrics is load-bearing.
  2. [Results on context variation] Results on context variation: The claim that 'providing richer context does not reliably reduce EO' depends on how the amount of behavioral evidence was varied in the prompts; without a precise description of the context levels (e.g., which sensor features were added or withheld) or controls for prompt length, it is difficult to isolate the effect of evidence volume from other prompt properties.
minor comments (2)
  1. [Abstract] Abstract: The statement that 'bounded prompting helps but does not eliminate it' would be clearer if it included the magnitude of the observed reduction (e.g., percentage-point change in EO rate) rather than a qualitative description.
  2. [Methods] Methods: The exact wording of the 'minimally constrained prompt' and the 'bounded prompt' should be provided verbatim in the main text or an appendix so readers can assess how the bounding instructions were phrased.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their detailed and constructive review. The comments identify important areas for clarification and strengthening of the manuscript. We address each major comment below and indicate the revisions we will make.

read point-by-point responses

  1. Referee: [Evaluation section / Rubric description] The paper applies a five-dimension rubric to score EO but reports no inter-rater reliability statistics, no validation against domain-expert gold labels, and no explicit operationalization of 'sufficient support from the data' for activity/sleep/affect anomalies. Because the central claims about routine EO and the effects of context/prompting rest entirely on these scores, the absence of reliability metrics is load-bearing.

    Authors: We agree that greater transparency around the rubric is warranted. The five dimensions were derived from the sensor traces available in the datasets, with 'sufficient support' operationalized as the presence of direct quantitative evidence (e.g., step-count deviation or location change for activity anomalies; sleep-duration or timing data for sleep anomalies; self-report affect scores for affect anomalies). Scoring was performed by the author team with consensus resolution of borderline cases. We did not collect external expert labels or compute inter-rater reliability statistics. In the revised manuscript we will (1) add an expanded Evaluation section that provides explicit operational definitions and one annotated scoring example per anomaly type, (2) include a table mapping each rubric dimension to the specific sensor features that constitute support, and (3) add a limitations paragraph noting the absence of external validation and IRR as a direction for subsequent work. These changes increase transparency without altering the core findings. revision: partial

  2. Referee: [Results on context variation] The claim that 'providing richer context does not reliably reduce EO' depends on how the amount of behavioral evidence was varied in the prompts; without a precise description of the context levels (e.g., which sensor features were added or withheld) or controls for prompt length, it is difficult to isolate the effect of evidence volume from other prompt properties.

    Authors: We appreciate the request for precision. Context was varied across three explicitly defined levels: minimal (anomaly flag and date only), aggregate (daily summary statistics such as total steps, sleep duration, and average affect), and full (time-series traces plus location and app-usage features). Structured prompt templates were used to keep token length comparable across conditions. In the revision we will insert a table that lists the exact sensor features supplied at each level, report mean and standard-deviation token counts per condition, and add a supplementary analysis confirming that prompt length does not explain the observed EO patterns. These additions will allow readers to isolate the contribution of evidence volume. revision: yes

Circularity Check

0 steps flagged

No circularity: purely empirical audit with no derivations or self-referential predictions

full rationale

The paper conducts an empirical audit of LLM-generated explanations for sensor data anomalies. It generates 14,922 explanations across datasets and models, then applies a human-defined rubric to score epistemic overreach dimensions. No equations, fitted parameters, uniqueness theorems, or predictive claims appear in the abstract or described methods. The central findings derive directly from the generated text evaluated against provided traces, with no step reducing by construction to prior inputs or self-citations. The rubric is an evaluation tool, not a derived result. This matches the default expectation of no significant circularity for non-mathematical empirical studies.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 1 invented entities

The central claim depends on the assumption that the chosen rubric dimensions validly operationalize epistemic overreach and that the selected anomalous-day scenarios from college-student datasets are representative of real personal sensing use cases.

axioms (1)
  • domain assumption The structured rubric (unsupported causal attribution, unacknowledged data gaps, overconfident language, temporal inconsistency, diagnostic inference) reliably identifies explanations that exceed available sensing evidence.
    All quantitative findings rest on human application of this rubric; its validity is not independently validated in the abstract.
invented entities (1)
  • epistemic overreach (EO) no independent evidence
    purpose: A composite measure for LLM explanations that imply more causal or diagnostic content than the sensor traces justify.
    Newly defined construct introduced to frame the audit; no external falsifiable test is provided.

pith-pipeline@v0.9.0 · 5631 in / 1345 out tokens · 48116 ms · 2026-05-12T00:49:28.789800+00:00 · methodology

discussion (0)

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