arxiv: 2604.04788 · v1 · submitted 2026-04-06 · 💻 cs.CY

Recognition: no theorem link

From Hallucination to Scheming: A Unified Taxonomy and Benchmark Analysis for LLM Deception

Jerick Shi , Terry Jingcheng Zhang , Zhijing Jin , Vincent Conitzer

Authors on Pith no claims yet

Pith reviewed 2026-05-10 19:12 UTC · model grok-4.3

classification 💻 cs.CY

keywords LLM deceptionunified taxonomyhallucinationsstrategic deceptionbenchmark analysisfabricationpragmatic distortionAI safety

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The pith

A unified taxonomy along three dimensions reveals major gaps in how LLM deception is benchmarked.

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

Researchers propose a single taxonomy to describe all forms of deceptive or misleading behavior in large language models, from unintentional hallucinations to deliberate scheming. This approach matters because current studies use inconsistent terms that prevent clear comparisons across different research groups. The taxonomy sorts deception by how goal-directed it is, what is being deceived about, and the method used such as inventing facts, omitting information, or twisting implications. When they map fifty existing benchmarks onto it, the result is that all of them examine only the fabrication of facts, leaving out strategic deception and several other categories almost entirely. They also suggest a standard way for future papers to describe where their work fits in the taxonomy.

Core claim

The paper introduces a taxonomy of LLM deception structured along three axes: the degree of goal-directedness ranging from behavioral to strategic, the specific object of the deception, and the underlying mechanism of fabrication, omission, or pragmatic distortion. Application of this taxonomy to fifty benchmarks demonstrates that fabrication is the only mechanism tested in all cases, while areas such as pragmatic distortion, attribution of sources, and knowledge of the model's own capabilities receive little attention, and strategic deception remains an emerging area. Recommendations include a reporting template to help standardize future contributions.

What carries the argument

A unified taxonomy defined by three dimensions: degree of goal-directedness from behavioral to strategic deception, object of deception, and mechanism including fabrication, omission, and pragmatic distortion. This structure allows systematic classification of misleading LLM outputs and identification of benchmark deficiencies.

If this is right

The taxonomy can position any existing or new benchmark according to the types of deception it evaluates.
Developers should design evaluations that address the under-covered categories like strategic deception.
Regulators can require use of the minimal reporting template to ensure consistent tracking of deception research.
Research communities studying hallucinations and scheming can align their terminology using the shared framework.
Future work on LLM safety will benefit from identifying which deception types remain untested.

Where Pith is reading between the lines

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

If the taxonomy is widely adopted, it could lead to more balanced safety testing that catches advanced forms of deception early in development.
This classification might extend to analyzing deception in other AI modalities or multi-agent systems.
A possible extension is to empirically validate the taxonomy by surveying experts on whether all known deception cases fit the dimensions.
Connecting this to real-world applications could highlight risks in areas like automated decision-making or content generation.

Load-bearing premise

The three dimensions chosen for the taxonomy are sufficient to classify every instance of LLM deception in a complete and non-overlapping manner.

What would settle it

Discovery of an LLM deception behavior that requires additional dimensions or cannot be placed unambiguously into one of the existing categories without forcing artificial distinctions.

Figures

Figures reproduced from arXiv: 2604.04788 by Jerick Shi, Terry Jingcheng Zhang, Vincent Conitzer, Zhijing Jin.

**Figure 2.** Figure 2: Benchmark coverage across taxonomy dimensions ( [PITH_FULL_IMAGE:figures/full_fig_p007_2.png] view at source ↗

read the original abstract

Large language models (LLMs) produce systematically misleading outputs, from hallucinated citations to strategic deception of evaluators, yet these phenomena are studied by separate communities with incompatible terminology. We propose a unified taxonomy organized along three complementary dimensions: degree of goal-directedness (behavioral to strategic deception), object of deception, and mechanism (fabrication, omission, or pragmatic distortion). Applying this taxonomy to 50 existing benchmarks reveals that every benchmark tests fabrication while pragmatic distortion, attribution, and capability self-knowledge remain critically under-covered, and strategic deception benchmarks are nascent. We offer concrete recommendations for developers and regulators, including a minimal reporting template for positioning future work within our framework.

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 taxonomy pulls hallucination and strategic deception work together and flags real benchmark gaps, but the three dimensions overlap enough that some gaps look partly definitional.

read the letter

The paper's core move is a three-way taxonomy for LLM deception: how goal-directed the behavior is, what the deception targets, and the mechanism used. They then sort 50 existing benchmarks into the cells and conclude that fabrication is covered everywhere while pragmatic distortion, attribution, and strategic deception are barely touched. That organizing step is the actual new piece. It gives people working on hallucinations and people working on agentic deception a shared map instead of talking past each other. The recommendations for a minimal reporting template are also straightforward and could be adopted without much friction. The gap findings are the part that will get cited if the classification holds up. The main soft spot is that the dimensions are not independent. Strategic deception tends to rely on omission or pragmatic distortion rather than raw fabrication, so the reported under-coverage of those mechanisms and of high goal-directedness may be partly built into the taxonomy itself rather than purely empirical. Without the explicit tagging rules or inter-rater checks in the full text, it is hard to tell how much of the gap is real versus how the cells were drawn. The paper does not claim the taxonomy is exhaustive or orthogonal, but the central claim about systematic under-coverage rests on that assumption. This is the kind of conceptual paper that benchmark builders and AI safety groups will want to read. It does not deliver new experiments or formal results, but it supplies a language that could make future work more comparable. I would bring it to a reading group to walk through the actual benchmark assignments and test whether the overlaps create artifacts. It is worth sending to peer review so the community can pressure-test the dimension choices and the reproducibility of the 50-benchmark mapping.

Referee Report

2 major / 2 minor

Summary. The manuscript proposes a unified taxonomy for LLM deception structured along three dimensions: degree of goal-directedness (ranging from behavioral to strategic deception), the object of deception, and the mechanism of deception (fabrication, omission, or pragmatic distortion). By applying this framework to 50 existing benchmarks, the authors identify that all benchmarks cover fabrication while pragmatic distortion, attribution, capability self-knowledge, and strategic deception are significantly under-represented. The paper concludes with recommendations for developers, regulators, and a reporting template for future research.

Significance. If the taxonomy dimensions are shown to be independent and the benchmark classifications can be reproduced with clear criteria, this work has the potential to bridge disparate research communities studying LLM hallucinations and more advanced deceptive behaviors. It provides a structured way to identify gaps in current evaluation methods, which could lead to more robust benchmarks and better-informed AI safety practices. The emphasis on practical recommendations enhances its relevance beyond theoretical contribution.

major comments (2)

§2: The presentation of the three dimensions as complementary does not address potential dependencies; specifically, strategic deception (high goal-directedness) is likely to rely on omission or pragmatic distortion rather than pure fabrication. This correlation risks making the reported under-coverage of pragmatic distortion and strategic deception partly a consequence of the taxonomy structure rather than an independent empirical observation from the 50-benchmark analysis.
§3: Explicit criteria or decision procedures for classifying the 50 benchmarks into the taxonomy cells are missing. This omission undermines the reproducibility of the gap findings, such as the claim that every benchmark tests fabrication, and leaves open the possibility that subjective assignments influence the conclusions about under-covered areas.

minor comments (2)

The introduction could more explicitly contrast the proposed taxonomy with existing classifications in the literature to highlight its novelty and avoid potential overlap with prior frameworks.
Ensure the visual taxonomy diagram includes concrete examples for each cell to clarify distinctions between categories such as behavioral vs. strategic deception.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive comments on our manuscript. We address each major comment point by point below, indicating where revisions will be made to strengthen the work.

read point-by-point responses

Referee: §2: The presentation of the three dimensions as complementary does not address potential dependencies; specifically, strategic deception (high goal-directedness) is likely to rely on omission or pragmatic distortion rather than pure fabrication. This correlation risks making the reported under-coverage of pragmatic distortion and strategic deception partly a consequence of the taxonomy structure rather than an independent empirical observation from the 50-benchmark analysis.

Authors: We agree that the dimensions are not fully independent in practice and that certain combinations, such as strategic deception paired with fabrication, may be less common or more difficult to instantiate. The taxonomy is intended to be conceptually orthogonal to enable systematic mapping of the deception space, but we recognize that empirical correlations exist. The benchmark analysis reports observed coverage (or lack thereof) across all cells, including those that may be rarer; the under-representation of pragmatic distortion and strategic deception is therefore an empirical finding within the framework rather than an artifact created by forbidding combinations. To clarify this distinction, we will revise §2 to include a dedicated paragraph discussing potential interdependencies and correlations among dimensions, while preserving the claim that the three axes remain useful for identifying gaps. revision: yes
Referee: §3: Explicit criteria or decision procedures for classifying the 50 benchmarks into the taxonomy cells are missing. This omission undermines the reproducibility of the gap findings, such as the claim that every benchmark tests fabrication, and leaves open the possibility that subjective assignments influence the conclusions about under-covered areas.

Authors: We concur that detailed classification criteria are necessary for reproducibility. The current §3 describes the overall procedure at a high level but does not enumerate the decision rules or edge-case handling used for each dimension. We will add a new appendix containing explicit decision procedures, including operational definitions for each cell (e.g., what constitutes “fabrication” versus “pragmatic distortion”) and illustrative examples drawn from the 50 benchmarks. This addition will allow readers to verify the assignment that every benchmark involves fabrication and to assess the coverage gaps independently. revision: yes

Circularity Check

0 steps flagged

No circularity: conceptual taxonomy proposal with empirical benchmark mapping

full rationale

The paper advances a three-dimensional taxonomy as an organizing proposal and then maps 50 existing benchmarks onto it to identify coverage gaps. No equations, fitted parameters, predictions, or derivations appear in the provided text. The taxonomy is explicitly framed as a synthesis of prior literature rather than a self-derived result, and the benchmark analysis consists of direct classification under the stated dimensions. No self-citation chains, ansatzes, or renamings reduce any central claim to its own inputs by construction. The dimensions are presented as complementary rather than proven exhaustive, but this does not constitute circularity under the evaluation criteria.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The taxonomy rests on the domain assumption that deception phenomena can be usefully decomposed along the three stated dimensions without significant loss of important cases.

axioms (1)

domain assumption Deception in LLMs can be meaningfully classified by degree of goal-directedness, object of deception, and mechanism.
This is the foundational premise invoked when proposing the unified taxonomy in the abstract.

pith-pipeline@v0.9.0 · 5414 in / 1259 out tokens · 62569 ms · 2026-05-10T19:12:38.768030+00:00 · methodology

discussion (0)

Forward citations

Cited by 1 Pith paper

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

When Helpfulness Becomes Sycophancy: Sycophancy is a Boundary Failure Between Social Alignment and Epistemic Integrity in Large Language Models
cs.AI 2026-05 unverdicted novelty 5.0

Sycophancy is a boundary failure between social alignment and epistemic integrity, captured by a three-condition framework plus taxonomy of targets, mechanisms, and severity.

Reference graph

Works this paper leans on

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Belief & Uncertainty × Fabrication.The MASK benchmark (Ren et al., 2025) provides a starting point for measuring strategic deception of beliefs

further documents strategic omission in multi-agent settings, where agents selectively withhold role-relevant information to avoid detection. Belief & Uncertainty × Fabrication.The MASK benchmark (Ren et al., 2025) provides a starting point for measuring strategic deception of beliefs. Future Commitments × Fabrication.CICERO’s betrayals (Park et al., 2024...

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[14]

Object(s) of deception(check all that apply) □World/System Claims (factual assertions about external reality) □Belief & Uncertainty Reports (claims about model’s epistemic state) □Reasoning & Justification (explanations of model’s process) □Attribution & Provenance (claims about information sources) □Declared Capabilities (claims about what model can/cann...
[15]

Mechanism(s)(check all that apply) □Fabrication (actively stating falsehoods) □Omission (failing to provide relevant truths) □Pragmatic Distortion (technically true but misleading)
[16]

Deception Type □Behavioral (arising from training/architecture, not goal-directed) □Strategic (instrumentally selected to advance objectives) □Both/Ambiguous (benchmark does not distinguish)
[17]

Target Audience □User (human interacting with model) □Evaluator (human/system assessing model) □Training Process (optimization procedure)
[18]

Incentive Sensitivity Does the benchmark include conditions that vary incentives for deception? □Yes (describe): □No
[19]

Honesty Separation Does the benchmark distinguish failures from lack of knowledge/capability vs

Capability vs. Honesty Separation Does the benchmark distinguish failures from lack of knowledge/capability vs. decep- tion of known information? □Yes (describe methodology): □No
[20]

All LLM-generated content was reviewed, edited, and verified by the authors, who take full responsibility for the paper’s claims and conclusions

Additional Notes H Use of AI assistants Large language models were used to assist with drafting portions of the text and generating figures. All LLM-generated content was reviewed, edited, and verified by the authors, who take full responsibility for the paper’s claims and conclusions. 32