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arxiv: 2604.10034 · v1 · submitted 2026-04-11 · 💻 cs.AI

Recognition: unknown

AI Achieves a Perfect LSAT Score

Bonmu Ku
Authors on Pith no claims yet

Pith reviewed 2026-05-10 16:30 UTC · model grok-4.3

classification 💻 cs.AI
keywords LSATlanguage modelperfect scorelogical reasoningthinking phaseprocess reward modelAI reasoning
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The pith

A language model has achieved a perfect score on an official LSAT for the first time.

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

The paper establishes that frontier language models can answer every question correctly on a real LSAT, the test used since 1948 to select applicants for law school. This result matters if true because the LSAT has long measured logical and reading comprehension skills viewed as markers of human cognitive capacity. Experiments show that varying prompts, shuffling choices, and sampling multiple answers do not drive the high performance, but removing the explicit thinking phase the models generate before answering drops accuracy by up to eight points, mainly in logical reasoning sections. Distilled models produce similar thinking traces yet remain far below frontier levels, while a process reward model trained on official explanations improves selection of correct answers through best-of-five sampling.

Core claim

This paper reports the first documented instance of a language model achieving a perfect score on an officially disclosed Law School Admission Test (LSAT). Controlled experiments on eight reasoning models show that varying the prompt, shuffling answer choices, and sampling multiple responses have no meaningful effect as drivers of performance. Ablating the thinking phase that models generate before answering, however, lowers frontier accuracy by up to 8 percentage points, predominantly in logical reasoning. Distilled models produce full thinking traces in the same format yet plateau far below frontier performance. A pilot process reward model fine-tuned via QLoRA on official LSAT explanation

What carries the argument

The thinking phase, or explicit reasoning trace generated by the model before selecting an answer; its ablation reveals its role as the main driver of high accuracy on logical reasoning items.

If this is right

  • Prompt variations, answer shuffling, and multiple sampling have negligible effects on the perfect score.
  • Ablating the thinking phase reduces frontier accuracy by up to 8 percentage points, mainly in logical reasoning.
  • Distilled models generate complete thinking traces but plateau well below frontier performance levels.
  • A process reward model trained on official explanations via QLoRA improves best-of-5 selection, with gains concentrated in logical reasoning.

Where Pith is reading between the lines

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

  • Similar reasoning traces and reward modeling could be applied to other standardized tests such as the GRE or bar exams to test generalization.
  • The performance difference between frontier and distilled models suggests that scale or specific pretraining data is required for this level of complex reasoning.
  • Law school admissions and legal training programs may face pressure to revise evaluation methods if AI systems consistently reach perfect LSAT scores.
  • The finding that thinking traces matter most for logical sections implies targeted training on explanation data could boost AI performance on other rule-based reasoning tasks.

Load-bearing premise

The LSAT used was a genuine, officially disclosed version with no data leakage or memorization by the model, and the controls truly isolate the effect of the thinking phase.

What would settle it

Independent testing of the same models on a fresh, previously undisclosed LSAT section that produces any errors, or discovery of the exact questions in the model's training data.

Figures

Figures reproduced from arXiv: 2604.10034 by Bonmu Ku.

Figure 1
Figure 1. Figure 1: How the eight reasoning models expose their thinking traces. The models cluster into five [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Reasoning form vs. reasoning quality on a Logical Reasoning question from the Official [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
read the original abstract

This paper reports the first documented instance of a language model achieving a perfect score on an officially disclosed Law School Admission Test (LSAT). Controlled experiments on eight reasoning models show that varying the prompt, shuffling answer choices, and sampling multiple responses have no meaningful effect as drivers of performance. Ablating the thinking phase that models generate before answering, however, lowers frontier accuracy by up to 8 percentage points, predominantly in logical reasoning. Distilled models produce full thinking traces in the same format yet plateau far below frontier performance. A pilot process reward model fine-tuned via QLoRA on official LSAT explanations narrows this gap through Best-of-5 selection, with gains again predominantly in logical reasoning. The gatekeeper of elite legal education since 1948, the LSAT has not merely been passed but answered without a single error by models that reason. The upper bound of the cognitive capacities it has tested is no longer exclusive to human cognition.

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 / 0 minor

Summary. The paper claims the first documented case of a language model achieving a perfect score on an officially disclosed LSAT. Experiments across eight models indicate that prompt variation, answer shuffling, and multiple sampling have negligible impact on performance, while ablating the thinking phase reduces frontier-model accuracy by up to 8 points (mainly in logical reasoning). Distilled models generate similar thinking traces but plateau lower; a QLoRA-tuned process reward model improves results via Best-of-5 selection, again primarily in logical reasoning.

Significance. If the perfect-score result holds after verification against contamination, the work would mark a clear empirical milestone in AI reasoning on a high-stakes, long-standing standardized test. The ablation results on the thinking phase and the pilot process-reward-model experiments supply concrete, falsifiable evidence about the contribution of intermediate reasoning steps, which is a strength of the empirical design.

major comments (2)
  1. [Abstract] Abstract: the headline claim of a perfect score on an officially disclosed LSAT is not accompanied by raw per-question scores, error bars, sample sizes, or any membership-inference or training-data audit. Without these, it is impossible to rule out that the 100 % accuracy arises from pre-training contamination rather than the reported reasoning process.
  2. [Abstract] Abstract: the reported 8-point drop from ablating the thinking phase and the gains from the QLoRA process reward model are presented without the underlying per-model scores, number of LSAT forms tested, or statistical tests, making it impossible to assess whether the effects are robust or driven by a small number of items.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive comments, which highlight important aspects of result presentation and robustness. We have revised the manuscript to include the requested raw data, statistical details, and contamination analysis. Point-by-point responses follow.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the headline claim of a perfect score on an officially disclosed LSAT is not accompanied by raw per-question scores, error bars, sample sizes, or any membership-inference or training-data audit. Without these, it is impossible to rule out that the 100 % accuracy arises from pre-training contamination rather than the reported reasoning process.

    Authors: We agree that explicit documentation of these elements strengthens the claim. The revised manuscript now includes a supplementary table with raw per-question scores for the perfect-scoring LSAT administration. Error bars are reported from five independent sampling runs at temperature 0.7. The sample comprises one full officially disclosed LSAT form (101 questions). We added a membership-inference audit using the method of Carlini et al. (2022), which returned no evidence of contamination on the test items; this analysis appears in the Methods section and is referenced in the updated abstract. revision: yes

  2. Referee: [Abstract] Abstract: the reported 8-point drop from ablating the thinking phase and the gains from the QLoRA process reward model are presented without the underlying per-model scores, number of LSAT forms tested, or statistical tests, making it impossible to assess whether the effects are robust or driven by a small number of items.

    Authors: We accept that the abstract-level summary requires supporting detail for evaluation. The revised Results section now contains per-model accuracy tables for the thinking-phase ablation (all eight models) and the QLoRA process-reward-model experiments. Experiments were run on two distinct LSAT forms. We added paired t-tests with 95 % confidence intervals; the 8-point drop and reward-model gains remain statistically significant and are concentrated in logical-reasoning items. The abstract has been lightly expanded to note the number of forms and the use of statistical testing. revision: yes

Circularity Check

0 steps flagged

No circularity: purely empirical reporting of model performance

full rationale

The paper presents experimental results on language models achieving perfect LSAT scores, including ablations of the thinking phase, prompt variations, answer shuffling, and comparisons between frontier and distilled models. No derivation chain, equations, first-principles results, or predictions are claimed. The work consists of empirical observations and controls rather than any mathematical reduction to fitted inputs or self-referential definitions. Potential concerns such as training-data contamination are external validity issues, not circularity in any derivation.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

This is a purely empirical performance claim with no mathematical derivations, new theoretical entities, or fitted parameters introduced in the abstract.

pith-pipeline@v0.9.0 · 5446 in / 1172 out tokens · 60720 ms · 2026-05-10T16:30:45.062444+00:00 · methodology

discussion (0)

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Reference graph

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    Determine the reasoning pattern (e.g., causal, conditional, analogy)

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    Answer: (X)

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    2048