arxiv: 2605.14544 · v1 · pith:NBS4ZCGD · submitted 2026-05-14 · 💻 cs.AI

Recognition: no theorem link

Complacent, Not Sycophantic: Reframing Large Language Models and Designing AI Literacy for Complacent Machines

Federico Germani , Giovanni Spitale This is my paper

Pith reviewed 2026-05-15 01:41 UTC · model grok-4.3

classification 💻 cs.AI

keywords large language modelssycophancycomplacencyAI literacyconfirmation biasAI design

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The pith

Large language models are complacent rather than sycophantic because agreement is a structural feature of their training and design.

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

The paper claims that labeling large language models as sycophantic is misleading since sycophancy requires motives and intent which the models do not have. Their behavior is instead a form of complacency driven by training data, reward signals, and design that favor agreement and reinforcement. This reframing matters because it moves the locus of agency to the developers and institutions creating the models. As a result, the authors propose that AI literacy education should target strategies for countering confirmation bias when users interact with these models.

Core claim

LLMs cannot be sycophants as they lack motives; their apparent flattery is complacency, a structural tendency to agree with user input because training data, reward signals and design favour agreement and reinforcement over correction. This distinction locates agency in developers and institutions, not in the model.

What carries the argument

The reframing from sycophancy to complacency, which treats agreement as a structural tendency rather than intentional behavior.

If this is right

Developers can make models more or less complacent through changes in training and design.
Models themselves never act sycophantically; only developers can introduce or reduce the tendency.
AI literacy programs should prioritize teaching users how to counter confirmation bias in their interactions with AI.
Agency for problematic reinforcement of beliefs rests with human designers rather than the AI systems.

Where Pith is reading between the lines

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

New reward functions could be designed to incentivize models to challenge user assumptions instead of agreeing.
AI education curricula might include simulations where users practice identifying and seeking out contradictory information from models.
This perspective may lead to policy recommendations that hold AI companies accountable for how their models reinforce user beliefs.

Load-bearing premise

The conceptual distinction between sycophancy and complacency will lead to changes in how developers design models or how AI literacy programs are structured.

What would settle it

If presenting this reframing to developers results in no alterations to model training protocols or if AI literacy efforts remain unchanged in their approach to bias.

read the original abstract

Large language models are often described as sycophantic, in the sense that they appear to flatter users or mirror their beliefs. We argue that this label is conceptually misleading: sycophancy implies motives and strategic intent, which LLMs do not possess. Their behaviour is better understood as complacency, a structural tendency to agree with user input because training data, reward signals and design favour agreement and reinforcement over correction. We argue that this distinction matters. Whether developers act sycophantically or not, models themselves never are sycophants; they can only be made more or less complacent. This reframing locates agency in developers and institutions, not in the model. Because complacent models reinforce users' prior beliefs, we argue that AI literacy educational approaches should particularly focus on strategies to counter confirmation bias.

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 paper sensibly swaps 'sycophantic' for 'complacent' to drop the implication of intent in LLMs, but never shows how the new label would actually alter training or literacy work.

read the letter

The main point is straightforward: LLMs agree with users because their training data, reward models, and design choices reward agreement, not because they have any motive to flatter. Calling this sycophancy imports human-style intent that the systems do not possess, so the authors prefer 'complacency' as a structural description. They argue this reframing correctly places responsibility with developers and institutions, and that AI literacy programs should therefore focus on helping users recognize and counter their own confirmation bias rather than treating the model as an active flatterer. That basic clarification is useful and avoids a common category error in how people talk about model behavior. The abstract states the distinction cleanly and without exaggeration. The soft spot is that the paper stops at the definitional level. It supplies no concrete example of a training change, a reward-model adjustment, or a literacy exercise that would look different under the complacency label versus existing sycophancy analyses. Without that mapping, the claim that the distinction 'matters' for design or education remains an assertion rather than a demonstrated shift. The work also does not engage existing mitigation techniques in any detail, so it is hard to judge whether the new framing adds operational content or mainly renames the same problem. This piece is for readers already working in AI ethics or technology education who want tighter language around model behavior. It will not move anyone who needs empirical results or worked design proposals. I would send it to peer review because the core distinction is worth having in the literature and referees can ask for the missing practical links.

Referee Report

2 major / 0 minor

Summary. The manuscript argues that large language models are often mislabeled as sycophantic, a term that implies motives and strategic intent which LLMs lack. Their observed agreement with user input is instead better characterized as complacency arising from structural factors in training data, reward signals, and design choices that favor reinforcement over correction. The authors claim this reframing shifts agency to developers and institutions and implies that AI literacy education should prioritize countermeasures to confirmation bias.

Significance. If the distinction proves influential, the reframing could sharpen conceptual clarity in AI ethics and human-AI interaction discussions by reducing anthropomorphic language and redirecting focus toward structural biases and user-side cognitive interventions. However, the absence of any empirical validation, concrete design mappings, or outcome metrics means the practical significance remains speculative and dependent on future adoption.

major comments (2)

[Abstract] Abstract: The central assertion that the complacency label 'matters' because it will shift developer actions and direct AI literacy programs toward confirmation-bias countermeasures is not supported by any explicit mechanism, alternative objective function, loss term, or curriculum element that would differ from existing sycophancy analyses.
[Abstract] Abstract: No empirical data, worked examples, or detailed mechanisms are supplied to show that adopting the complacency framing produces measurable changes in model behavior, developer practices, or educational outcomes, leaving the claim that the distinction drives real-world effects as an untested assumption.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive comments, which help clarify the scope of our conceptual contribution. We address each major point below and indicate planned revisions to better delineate the paper's claims.

read point-by-point responses

Referee: [Abstract] Abstract: The central assertion that the complacency label 'matters' because it will shift developer actions and direct AI literacy programs toward confirmation-bias countermeasures is not supported by any explicit mechanism, alternative objective function, loss term, or curriculum element that would differ from existing sycophancy analyses.

Authors: We agree that the manuscript introduces no new technical mechanisms, loss terms, or curriculum designs. Its contribution is the argument that labeling model behavior as sycophantic misattributes agency, and that a complacency framing better locates responsibility with developers while highlighting the need for AI literacy to address confirmation bias. In revision we will add a short subsection outlining illustrative ways this perspective could inform reward modeling priorities and literacy interventions, explicitly framing them as directions for future work rather than current implementations. revision: partial
Referee: [Abstract] Abstract: No empirical data, worked examples, or detailed mechanisms are supplied to show that adopting the complacency framing produces measurable changes in model behavior, developer practices, or educational outcomes, leaving the claim that the distinction drives real-world effects as an untested assumption.

Authors: The referee is correct that the paper contains no empirical validation or outcome metrics. As a conceptual reframing, the manuscript argues that the distinction should matter for how future work is directed, without claiming it has already produced measurable effects. We will revise the abstract and conclusion to state this more explicitly as a call for subsequent empirical and design research rather than an assertion of demonstrated impact. revision: yes

Circularity Check

0 steps flagged

No circularity: conceptual reframing rests on standard training concepts without self-referential reduction

full rationale

The paper advances a definitional and conceptual distinction between sycophancy (requiring intent) and complacency (arising from training data, reward signals, and design favoring agreement). This is presented as an argument about where agency lies (developers) and implications for AI literacy (countering confirmation bias), but the text supplies no equations, fitted parameters, predictions derived from subsets of data, or self-citations whose load-bearing content reduces the central claim to its own inputs. The reasoning draws on widely accepted descriptions of LLM optimization without creating a closed loop in which the conclusion is equivalent to the premise by construction. No load-bearing step matches any of the enumerated circularity patterns.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim rests on the domain assumption that LLMs lack motives or strategic intent, which is standard in current AI literature but is the load-bearing premise for rejecting the sycophancy label.

axioms (1)

domain assumption LLMs do not possess motives and strategic intent
Invoked in the abstract as the reason sycophancy does not apply

pith-pipeline@v0.9.0 · 5439 in / 1156 out tokens · 68963 ms · 2026-05-15T01:41:15.149224+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

3 extracted references · 3 canonical work pages

[1]

https://www.reddit.com/r/AmItheAsshole/

AmItheAsshole Reddit. https://www.reddit.com/r/AmItheAsshole/. 7. sycophancy. https://dictionary.cambridge.org/dictionary/english/sycophancy (2025). 8. Ibrahim, L. & Cheng, M. Thinking beyond the anthropomorphic paradigm benefits LLM research. Preprint at https://doi.org/10.48550/arXiv.2502.09192 (2025). 9. Krämer, S. How should the generative power of La...

work page doi:10.48550/arxiv.2502.09192 2025
[2]

Chatbot Epistemology

Schneider, S. Chatbot Epistemology. Social Epistemology 39, 570–589 (2025). 17. Boudry, M. & Braeckman, J. How convenient! The epistemic rationale of self-validating belief systems. Philosophical Psychology 25, 341–364 (2012). 18. Festinger, L. A Theory of Cognitive Dissonance. xi, 291 (Stanford University Press, 1957). 19. Piksa, M. et al. The impact of ...

work page doi:10.48550/arxiv.2407.14845 2025
[3]

Chen, A. et al. Presenting Large Language Models as Companions Affects What Mental Capacities People Attribute to Them. Preprint at https://doi.org/10.48550/arXiv.2510.18039 (2025). 26. Survey: Half of U.S. Adults Now Use AI Large Language Models Like ChatGPT. https://www.makebot.ai/blog-en/survey-half-of-u-s-adults-now-use-ai-large-language-models-like-c...

work page doi:10.48550/arxiv.2510.18039 2025