Can Large Reasoning Models do Analogical Reasoning under Perceptual
  Uncertainty?
Authors: name: Giacomo Camposampiero, name: Michael Hersche, name: Roger Wattenhofer, name: Abu Sebastian, name: Abbas Rahimi

This work presents a first evaluation of two state-of-the-art Large Reasoning
Models (LRMs), OpenAI's o3-mini and DeepSeek R1, on analogical reasoning,
focusing on well-established nonverbal human IQ tests based on Raven's
progressive matrices. We benchmark with the I-RAVEN dataset and its more
difficult extension, I-RAVEN-X, which tests the ability to generalize to longer
reasoning rules and ranges of the attribute values. To assess the influence of
visual uncertainties on these nonverbal analogical reasoning tests, we extend
the I-RAVEN-X dataset, which otherwise assumes an oracle perception. We adopt a
two-fold strategy to simulate this imperfect visual perception: 1) we introduce
confounding attributes which, being sampled at random, do not contribute to the
prediction of the correct answer of the puzzles and 2) smoothen the
distributions of the input attributes' values. We observe a sharp decline in
OpenAI's o3-mini task accuracy, dropping from 86.6% on the original I-RAVEN to
just 17.0% -- approaching random chance -- on the more challenging I-RAVEN-X,
which increases input length and range and emulates perceptual uncertainty.
This drop occurred despite spending 3.4x more reasoning tokens. A similar trend
is also observed for DeepSeek R1: from 80.6% to 23.2%. On the other hand, a
neuro-symbolic probabilistic abductive model, ARLC, that achieves
state-of-the-art performances on I-RAVEN, can robustly reason under all these
out-of-distribution tests, maintaining strong accuracy with only a modest
reduction from 98.6% to 88.0%. Our code is available at
https://github.com/IBM/raven-large-language-models.