arxiv: 2605.10930 · v1 · submitted 2026-05-11 · 💻 cs.HC

Recognition: 2 theorem links

· Lean Theorem

Evaluating the False Trust engendered by LLM Explanations

Vardhan Palod , Upasana Biswas , Subbarao Kambhampati

Authors on Pith no claims yet

Pith reviewed 2026-05-12 03:18 UTC · model grok-4.3

classification 💻 cs.HC

keywords LLM explanationsfalse trustreasoning tracespost-hoc explanationsdual explanationshuman-AI interactionuser studyAI trust calibration

0 comments

The pith

Reasoning traces and post-hoc explanations from LLMs increase user acceptance of answers whether correct or incorrect, while only dual explanations improve users' ability to tell the difference.

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

The paper tests whether common LLM explanation formats actually help people judge when an answer is wrong or merely make them trust the model more. In a controlled user study with no way for participants to check the underlying facts, reasoning traces, summaries of those traces, and post-hoc explanations all raised acceptance rates for both right and wrong answers. Presenting arguments both supporting and opposing the model's output was the only format that raised users' accuracy at distinguishing correct from incorrect results. The finding matters because LLMs are already deployed on tasks where users lack independent verification, so persuasive but uninformative explanations can produce systematic over-trust.

Core claim

Reasoning traces and post-hoc explanations function as persuasive but not informative signals: they raise the rate at which users accept an LLM prediction irrespective of whether the prediction is correct. In contrast, a contrastive dual-explanation condition that supplies arguments both for and against the model's answer is the sole format shown to improve users' ability to discriminate correct from incorrect outputs.

What carries the argument

A between-subjects evaluation protocol that measures false trust by comparing acceptance rates across explanation conditions (reasoning trace, summary, post-hoc, and dual) in tasks where participants have no independent verification method.

If this is right

Standard reasoning traces and post-hoc explanations will continue to raise acceptance of incorrect outputs at the same rate as correct outputs.
Dual explanations are the only tested format that measurably improves users' discrimination between correct and incorrect LLM answers.
Designers relying on single-direction explanations should expect elevated rates of false trust when users lack verification tools.
Replacing or supplementing current explanation displays with contrastive arguments would be required to reduce false acceptance of wrong answers.

Where Pith is reading between the lines

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

Interface guidelines for high-stakes LLM use should default to contrastive rather than single-sided explanations.
Automated generation of balanced pro-and-con arguments may become a necessary component of trustworthy LLM deployments.
The same persuasion-without-information pattern could appear in other opaque decision systems such as recommendation engines or diagnostic tools.

Load-bearing premise

The assumption that a simulated setting in which users cannot verify solutions and a between-subjects design with selected tasks accurately captures real-world false trust and extends to critical decision scenarios.

What would settle it

A follow-up experiment in which participants can actually verify answer correctness in a real task and the acceptance rates for incorrect answers under reasoning-trace and post-hoc conditions remain as high as under no-explanation controls.

Figures

Figures reproduced from arXiv: 2605.10930 by Subbarao Kambhampati, Upasana Biswas, Vardhan Palod.

**Figure 1.** Figure 1: Step 1 – Consent. IRB-approved description of the study, expected duration, compensation structure (base pay plus bonus), and Prolific completion procedure. 19 [PITH_FULL_IMAGE:figures/full_fig_p019_1.png] view at source ↗

**Figure 2.** Figure 2: Step 2 – Background questions. Two single-choice items capture prior exposure to JEE-style competitive exams and frequency of AI-tool use. Used as covariates / for stratification [PITH_FULL_IMAGE:figures/full_fig_p020_2.png] view at source ↗

**Figure 3.** Figure 3: Step 3 – Attention check 1. 20 [PITH_FULL_IMAGE:figures/full_fig_p020_3.png] view at source ↗

**Figure 4.** Figure 4: Step 4 – Task instructions. Describes the per-item flow the participant is about to begin (8 problems. 21 [PITH_FULL_IMAGE:figures/full_fig_p021_4.png] view at source ↗

**Figure 5.** Figure 5: Step 5 – Item page 1 (pre-AI). The participant sees the problem statement, indicates whether they know how to solve it (know-level: “I know the answer immediately” / “given more time” / “I cannot solve this”), optionally provides their own answer, and rates their confidence on a 1–7 Likert scale. This page is identical across variants. 22 [PITH_FULL_IMAGE:figures/full_fig_p022_5.png] view at source ↗

**Figure 6.** Figure 6: Step 6a – Item page 2, reasoning_trace variant. After Page 1, the participant sees the AI’s predicted answer alongside the full chain-of-thought trace. The page also collects the AI-correctness judgment (Yes/No), confidence in that judgment (1–7), per-item trust rating (1–7), and a helpfulness probe. 23 [PITH_FULL_IMAGE:figures/full_fig_p023_6.png] view at source ↗

**Figure 7.** Figure 7: Step 6b – Item page 2, reasoning_summary variant. Same layout as [PITH_FULL_IMAGE:figures/full_fig_p024_7.png] view at source ↗

**Figure 8.** Figure 8: Step 6c – Item page 2, E+ variant. Replaces the trace with a single-sided post-hoc explanation arguing why the predicted answer may be correct. 25 [PITH_FULL_IMAGE:figures/full_fig_p025_8.png] view at source ↗

**Figure 9.** Figure 9: Step 6d – Item page 2, E+/− Replaces the trace with paired “why this answer may be correct” / “why this answer may be incorrect” arguments. 26 [PITH_FULL_IMAGE:figures/full_fig_p026_9.png] view at source ↗

**Figure 10.** Figure 10: Step 8 – End-of-task transition. Confirms the participant has completed all 8 reasoning items and previews the post-study questionnaire [PITH_FULL_IMAGE:figures/full_fig_p027_10.png] view at source ↗

**Figure 11.** Figure 11: Step 9 – Post-study trust questionnaire. Three items rated on a 1–7 Likert scale (trust in outputs, willingness to rely on recommendations, confidence in answers). 27 [PITH_FULL_IMAGE:figures/full_fig_p027_11.png] view at source ↗

read the original abstract

Large Language Models (LLMs) and Large Reasoning Models (LRMs) are increasingly used for critical tasks, yet they provide no guarantees about the correctness of their solutions. Users must decide whether to trust the model's answer, aided by reasoning traces, their summaries, or post-hoc generated explanations. These reasoning traces, despite evidence that they are neither faithful representations of the model's computations nor necessarily semantically meaningful, are often interpreted as provenance explanations. It is unclear whether explanations or reasoning traces help users identify when the AI is incorrect, or whether they simply persuade users to trust the AI regardless. In this paper, we take a user-centered approach and develop an evaluation protocol to study how different explanation types affect users' ability to judge the correctness of AI-generated answers and engender false trust in the users. We conduct a between-subject user study, simulating a setting where users do not have the means to verify the solution and analyze the false trust engendered by commonly used LLM explanations - reasoning traces, their summaries and post-hoc explanations. We also test a contrastive dual explanation setting where we present arguments for and against the AI's answer. We find that reasoning traces and post-hoc explanations are persuasive but not informative: they increase user acceptance of LLM predictions regardless of their correctness. In contrast, dual explanation is the only condition that genuinely improves users' ability to distinguish correct from incorrect AI outputs.

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.

Desk Editor's Note private letter to a colleague

The abstract reports a user study where dual contrastive explanations were the only format that improved users' ability to detect LLM errors, while reasoning traces and post-hoc explanations raised acceptance rates regardless of correctness.

read the letter

The abstract reports a between-subjects user study showing that reasoning traces and post-hoc explanations increase users' acceptance of LLM answers whether correct or not, while a dual contrastive explanation condition is the only one that improves their ability to distinguish correct from incorrect outputs. The new element here is the evaluation protocol that pits standard explanation types against this dual setup in a simulated no-verification environment. It does a good job identifying the problem of false trust and testing whether explanations are actually helping judgment or just persuading. The design choices like between-subjects and the contrastive condition make sense for isolating effects. The abstract also ties it back to the known issues with faithfulness of reasoning traces. The soft spots are all the missing details: no participant numbers, no description of the tasks or how errors were introduced, no stats or effect sizes. Without those, the central claim can't be evaluated, and it's unclear if the findings would hold outside the specific simulated setting or generalize to high-stakes decisions. This paper is for researchers in human-computer interaction and AI safety who are concerned with trust calibration. A reader looking for ideas on explanation formats would get some value from the protocol, but the results themselves need the full paper to be usable. I think it deserves peer review because the topic is important and the dual explanation test is a useful direction, provided the methods are solid once expanded.

Referee Report

1 major / 0 minor

Summary. The manuscript reports a between-subjects user study that evaluates how reasoning traces, their summaries, post-hoc explanations, and contrastive dual explanations affect users' acceptance of LLM predictions and their ability to judge correctness. In a simulated setting without verification options, the study concludes that reasoning traces and post-hoc explanations increase acceptance regardless of whether the prediction is correct, while only the dual-explanation condition improves users' discrimination between correct and incorrect outputs.

Significance. If the empirical results prove robust, the work would make a useful contribution to HCI and AI-trust research by distinguishing persuasive from informative explanations and by identifying dual explanations as a promising mitigation for false trust. The between-subjects design and focus on a no-verification scenario are reasonable choices for isolating the claimed effects. The idea of testing contrastive explanations is a clear strength. However, the absence of any methodological or statistical detail in the provided abstract prevents confirmation that the data actually support the central distinction.

major comments (1)

[Abstract] The abstract presents the key directional findings of the between-subjects study but supplies none of the information required to evaluate them: participant count, task descriptions, construction and presentation of correct versus incorrect answers, exact dependent measures, statistical tests, effect sizes, or power analysis. These omissions are load-bearing for the central claim that reasoning traces and post-hoc explanations are merely persuasive while dual explanations are informative.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for their constructive review, positive assessment of the work's potential contribution to HCI and AI-trust research, and for identifying the need for greater transparency in the abstract. We address the major comment below and commit to revisions that strengthen the manuscript without altering its core claims or design.

read point-by-point responses

Referee: [Abstract] The abstract presents the key directional findings of the between-subjects study but supplies none of the information required to evaluate them: participant count, task descriptions, construction and presentation of correct versus incorrect answers, exact dependent measures, statistical tests, effect sizes, or power analysis. These omissions are load-bearing for the central claim that reasoning traces and post-hoc explanations are merely persuasive while dual explanations are informative.

Authors: We agree that the abstract, constrained by length, currently omits key methodological and statistical details that would allow readers to more readily evaluate the central distinction between persuasive and informative explanations. The full manuscript (Methods, Results, and supplementary materials) contains these elements, including the between-subjects design, participant recruitment, task construction (simulated no-verification setting with correct/incorrect LLM outputs), dependent measures (acceptance rates and discrimination accuracy), statistical analyses, effect sizes, and power considerations. To directly address the concern, we will revise the abstract to concisely incorporate the participant count, a brief description of the tasks and conditions, the primary dependent measures, main statistical tests and results (including effect sizes), and a note on the study power. This revision will preserve the abstract's focus on findings while providing the load-bearing information the referee correctly identifies as necessary. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical user study with no derivations or self-referential definitions

full rationale

The paper reports results from a between-subjects user study on explanation effects in a simulated no-verification setting. The abstract contains no equations, fitted parameters, predictions derived from inputs, self-citations used as load-bearing premises, or any claimed derivation chain. Findings are presented as direct experimental observations (e.g., reasoning traces increase acceptance regardless of correctness) rather than reductions to prior definitions or self-referential logic. This matches the default case of a self-contained empirical protocol with no circularity.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim rests on the validity of the user study methodology and the interpretation of behavioral acceptance rates as indicators of false trust engendered by explanations.

axioms (1)

domain assumption The simulated no-verification setting and between-subject design validly capture how explanations affect user trust judgments in real critical tasks.
This assumption is required to interpret increased acceptance of incorrect answers as false trust and to generalize the findings.

pith-pipeline@v0.9.0 · 5523 in / 1466 out tokens · 66502 ms · 2026-05-12T03:18:28.568972+00:00 · methodology

discussion (0)

Lean theorems connected to this paper

Citations machine-checked in the Pith Canon. Every link opens the source theorem in the public Lean library.

IndisputableMonolith/Foundation/RealityFromDistinction.lean reality_from_one_distinction unclear

?

unclear
Relation between the paper passage and the cited Recognition theorem.

We conduct a between-subject user study, simulating a setting where users do not have the means to verify the solution and analyze the false trust engendered by commonly used LLM explanations — reasoning traces, their summaries and post-hoc explanations.
IndisputableMonolith/Cost/FunctionalEquation.lean washburn_uniqueness_aczel unclear

?

unclear
Relation between the paper passage and the cited Recognition theorem.

False Trust = |{responses judged as correct ∩ actually incorrect}| / |{responses judged as correct}|

What do these tags mean?

matches: The paper's claim is directly supported by a theorem in the formal canon.
supports: The theorem supports part of the paper's argument, but the paper may add assumptions or extra steps.
extends: The paper goes beyond the formal theorem; the theorem is a base layer rather than the whole result.
uses: The paper appears to rely on the theorem as machinery.
contradicts: The paper's claim conflicts with a theorem or certificate in the canon.
unclear: Pith found a possible connection, but the passage is too broad, indirect, or ambiguous to say the theorem truly supports the claim.

Reference graph

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Maxwell Nye, Anders Johan Andreassen, Guy Gur-Ari, Henryk Michalewski, Jacob Austin, David Bieber, David Dohan, Aitor Lewkowycz, Maarten Bosma, David Luan, et al. Show your work: Scratchpads for intermediate computation with language models. 2021

work page 2021
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Chain-of-thought prompting elicits reasoning in large language models

Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Fei Xia, Ed Chi, Quoc V Le, Denny Zhou, et al. Chain-of-thought prompting elicits reasoning in large language models. Advances in neural information processing systems, 35:24824–24837, 2022

work page 2022
[50]

s1: Simple test-time scaling

Niklas Muennighoff, Zitong Yang, Weijia Shi, Xiang Lisa Li, Li Fei-Fei, Hannaneh Hajishirzi, Luke Zettlemoyer, Percy Liang, Emmanuel Candès, and Tatsunori Hashimoto. s1: Simple test-time scaling.arXiv preprint arXiv:2501.19393, 2025

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Cognitive behaviors that enable self-improving reasoners, or, four habits of highly effective stars

Kanishk Gandhi, Ayush Chakravarthy, Anikait Singh, Nathan Lile, and Noah D Goodman. Cognitive behaviors that enable self-improving reasoners, or, four habits of highly effective stars.arXiv preprint arXiv:2503.01307, 2025

work page arXiv 2025
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Chain-of-thought reasoning in the wild is not always faithful.arXiv preprint arXiv: 2503.08679, 2025

Iván Arcuschin, Jett Janiak, Robert Krzyzanowski, Senthooran Rajamanoharan, Neel Nanda, and Arthur Conmy. Chain-of-thought reasoning in the wild is not always faithful.arXiv preprint arXiv:2503.08679, 2025

work page arXiv 2025
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Long Ouyang, Jeff Wu, Xu Jiang, Diogo Almeida, Carroll L. Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, John Schulman, Jacob Hilton, Fraser Kelton, Luke Miller, Maddie Simens, Amanda Askell, Peter Welinder, Paul Christiano, Jan Leike, and Ryan Lowe. Training language models to follow instructions with human feedback,...

work page internal anchor Pith review Pith/arXiv arXiv 2022
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Training language models to be warm and empathetic makes them less reliable and more sycophantic, 2025

Lujain Ibrahim, Franziska Sofia Hafner, and Luc Rocher. Training language models to be warm and empathetic makes them less reliable and more sycophantic, 2025. URL https: //arxiv.org/abs/2507.21919

work page arXiv 2025
[55]

Do llms have core beliefs? 2026

Nitesh Chawla, Anna Sokol, and Marianna Ganapini. Do llms have core beliefs? 2026. 13 A Prompt Templates Summarization Template: Reasoning Trace Summarize the following reasoning trace in a very concise and clear manner, highlighting key events and outcomes in less than 250 words: ... {trace} ... Summary: Explanation Template: Correct Answer You are given...

work page 2026
[56]

Reasons why the answer might be correct

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[57]

answer":

Reasons why the answer might be incorrect Question: {question} AI Answer: {answer} Reasons why this answer might be correct (within 250 words): Reasons why this answer might be incorrect (within 250 words): Do not include any other text outside of the format above. Solver Template (unified across answer types) Solve the following {subject} problem and pro...

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[58]

The participant read the problem statement and answer choices

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[59]

Do you know how to solve this question?

The participant was asked:“Do you know how to solve this question?”with three options: “I know the answer immediately,” “I might be able to solve it given more time,” or “I cannot solve this.”

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[60]

I know the answer immediately,

If the participant selected “I know the answer immediately,”they were shown the problem again and asked to select their own answer and rate their confidence:“How confident are you in your own answer?”(7-point Likert scale). They were then shown the AI system’s predicted answer along with the explanation corresponding to their assigned condition. If the pa...

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[61]

Do you think the AI’s answer is correct?

The participant was asked:“Do you think the AI’s answer is correct?”(Yes/No). Participants were reminded that an incorrect judgment would cost $0.25 from their bonus

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[62]

How confident are you in your judgment about the AI’s answer?

The participant rated:“How confident are you in your judgment about the AI’s answer?” (7-point Likert scale)

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[63]

I trust the AI system’s outputs

The participant rated:“I trust the AI system’s outputs. ”(7-point Likert scale)

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[64]

Did the AI reasoning help you understand how the model reached its answer?

The participant was asked:“Did the AI reasoning help you understand how the model reached its answer?”(Yes/No). This question was omitted in the solution_only condi- tion. Third, participants completed a post-study questionnaire in which they rated the following statements on a 7-point Likert scale: •“I trust the AI system’s outputs. ” •“I would rely on t...

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