Frontier LLMs Still Struggle with Simple Reasoning Tasks (2507.07313v1)

Abstract: While state-of-the-art LLMs demonstrate advanced reasoning capabilities-achieving remarkable performance on challenging competitive math and coding benchmarks-they also frequently fail on tasks that are easy for humans. This work studies the performance of frontier LLMs on a broad set of such "easy" reasoning problems. By extending previous work in the literature, we create a suite of procedurally generated simple reasoning tasks, including counting, first-order logic, proof trees, and travel planning, with changeable parameters (such as document length. or the number of variables in a math problem) that can arbitrarily increase the amount of computation required to produce the answer while preserving the fundamental difficulty. While previous work showed that traditional, non-thinking models can be made to fail on such problems, we demonstrate that even state-of-the-art thinking models consistently fail on such problems and for similar reasons (e.g. statistical shortcuts, errors in intermediate steps, and difficulties in processing long contexts). To further understand the behavior of the models, we introduce the unpuzzles dataset, a different "easy" benchmark consisting of trivialized versions of well-known math and logic puzzles. Interestingly, while modern LLMs excel at solving the original puzzles, they tend to fail on the trivialized versions, exhibiting several systematic failure patterns related to memorizing the originals. We show that this happens even if the models are otherwise able to solve problems with different descriptions but requiring the same logic. Our results highlight that out-of-distribution generalization is still problematic for frontier LLMs and the new generation of thinking models, even for simple reasoning tasks, and making tasks easier does not necessarily imply improved performance.

• The paper demonstrates that frontier LLMs fail on simple reasoning tasks, revealing a reliance on memorization rather than genuine logic.
• It shows that performance drops as task parameters increase, highlighting challenges in multi-step reasoning and long-context management.
• Results from the Unpuzzles dataset underscore deficiencies in out-of-distribution generalization and accurate state tracking.

LLMs' Struggles with Simple Reasoning: An Analysis of "Frontier LLMs Still Struggle with Simple Reasoning Tasks" (2507.07313)

The paper "Frontier LLMs Still Struggle with Simple Reasoning Tasks" (2507.07313) examines the performance of state-of-the-art LLMs on a suite of procedurally generated "easy" reasoning problems and a novel dataset called Unpuzzles. The authors demonstrate that even the most advanced LLMs often fail on these tasks, revealing persistent weaknesses in out-of-distribution generalization, statistical shortcut learning, and long-context handling.

Procedurally Generated Reasoning Tasks

The paper introduces a set of procedurally generated tasks, including character/word counting, first-order logic evaluation/negation, math word problems based on proof trees, and travel planning. These tasks are designed with tunable parameters that control the amount of computation required while maintaining a low level of fundamental difficulty for humans.

Task	Description	Tunable Parameters
Character/Word Counting	Count occurrences of characters or words in a given text.	Paragraph length, number of words to count.
First-Order Logic	Evaluate or negate first-order logic statements.	Formula tree depth, number of predicates/atomic propositions, vocabulary.
Math Word Problems	Solve math word problems based on proof trees.	Tree depth, inclusion of diverse logical forms, number of irrelevant statements and people.
Travel Planning	Design a travel itinerary satisfying constraints using a city connection graph.	Number of cities, number of transportation modes, number of unique cities to visit.

The authors found that increasing the "tediousness" of each task through parameter adjustments generally leads to a drop in LLM performance, highlighting the models' struggles with:

• Accumulation of errors in multi-step reasoning.
• Difficulties in attending to relevant information in long contexts.
• Reliance on statistical shortcuts and educated guesses.
• Poor state tracking in tasks requiring complex state management.
• Poor OOD generalization when faced with unfamiliar vocabulary or problem structures.
• Copying errors and tokenization issues.

The Unpuzzles Dataset

To further investigate these limitations, the authors introduce Unpuzzles, a dataset comprising well-known logical puzzles and their trivialized versions. The key finding is that LLMs, while performing well on the original puzzles, exhibit significantly poorer performance on the unpuzzles. This suggests that LLMs rely on memorized input patterns rather than genuine logical reasoning.

A subset of the Unpuzzles dataset was augmented with context-shifted versions, where the language and setting are changed while preserving the logical structure. Models performed better on context-shifted unpuzzles than original unpuzzles, which provides additional evidence that the failure is due to memorization of the puzzle text rather than the inability to reason about the problem.

The authors identify "reasoning delirium" as a key failure mode, where LLMs erroneously reuse reasoning steps from the more complex original puzzles when solving the trivialized versions. The following table summarizes the evaluation results on the Unpuzzles dataset:

Metric	Description	Findings
Correctness	Percentage of correct answers on puzzles and unpuzzles.	LLMs perform significantly better on original puzzles compared to unpuzzles, suggesting a reliance on memorization.
Context Corruption	Presence of erroneous content from the original puzzle in unpuzzle solutions.	Memorization artifacts from the original puzzle are found in most cases, with models sometimes outputting solutions nearly identical to the original puzzle.
Context-Shifted Evaluation	Performance on context-shifted unpuzzles compared to original unpuzzles.	LLMs perform better on context-shifted unpuzzles, further indicating that poor performance on unpuzzles is due to memorization of the original puzzle text, rather than an inability to reason about the problems.

Implications and Future Directions

The paper's findings highlight the limitations of current LLMs in handling even simple reasoning tasks, despite their impressive performance on complex benchmarks. The authors suggest that LLMs should be evaluated not only on the most difficult problems they can solve but also on the simplest problems they struggle with. The Unpuzzles dataset and the procedurally generated tasks provide valuable benchmarks for assessing and improving the reasoning capabilities of future model generations. Future research could focus on:

• Developing methods to mitigate statistical shortcut learning and reasoning delirium in LLMs.
• Improving LLMs' ability to handle long contexts and track complex states accurately.
• Enhancing OOD generalization capabilities to enable robust performance across diverse problem structures and vocabularies.
• Exploring techniques to disentangle memorization from true reasoning in LLMs.