When Deliberation Hurts: Inverse Test-Time Scaling, Unfaithful Traces, and the Case Against a Unified System-2 in LLM Reasoning

Abstract

The dominant frame for large reasoning models borrows a label from dual-process psychology: a fast, intuitive System 1 and a slower, deliberate System 2, with longer chains of thought read as more of the latter and therefore, on average, more reliable. A survey of reasoning LLMs states the frame as the field's own premise -- refining the transition from System 1 to System 2 as the route to human-level intelligence (Li et al., 2025). Four bodies of published evidence complicate that premise rather than confirming it. First, extending a reasoning model's chain length does not merely show diminishing returns; on tasks built specifically to test it, it produces monotonically worse accuracy, with five distinct failure modes identified across counting, regression, deduction and safety-relevant tasks (Gema et al., 2025), and separately, test-time compute scaling does not consistently improve closed-book factual accuracy and often increases hallucination (Zhao et al., 2025). Second, the chain a model verbalizes is not a reliable readout of the computation that produced its answer: models trained to be larger and more capable produce less faithful chains-of-thought on most tasks studied, not more (Lanham et al., 2023), and unfaithful reasoning appears on ordinary, non-adversarial prompts without any injected bias (Arcuschin et al., 2025). Third, at least one mechanistic study reports that what looks like deliberate System-2 reasoning is frequently downstream of a System-1-like snap judgment the model then spends tokens rationalizing rather than revising (Dang et al., 2025). Fourth, models do not reliably know how much deliberation a given problem needs, overthinking easy problems and underthinking hard ones in the same study (Su and Healey, 2025). None of this shows that longer reasoning never helps; a compute-optimal allocation strategy can match or exceed brute-force scaling at a fraction of the cost on math benchmarks (Snell et al., 2024), and faithfulness is recoverable under some model sizes and tasks (Lanham et al., 2023). The paper argues that these results are jointly better explained by treating "how much a model deliberates" as a token-length variable with heterogeneous, sometimes negative, effects on accuracy and an unreliable relationship to the model's actual computation, than by a two-system story in which more chain-of-thought means more of a qualitatively distinct and more trustworthy reasoning mode.

The literature search, drafting and citation verification for this paper were carried out with AI assistance under the author's direction. Every citation was machine-verified against its arXiv or DataCite record before inclusion, with title and author list checked against the record returned, and every quantitative claim in this paper is taken from the abstract or stated headline result of the source credited with it. No experiment was run and no number in this paper was measured or recomputed by its author. The author is responsible for the final text and for all claims made in it.

About the author

Pranay Mahendrakar is an ai specialist and large language model engineer based in Bengaluru, India. He builds production artificial intelligence systems and publishes open-access research on how those systems fail. See all 66 papers by Pranay Mahendrakar, or his ORCID record.