Reasoning Model is Stubborn: Diagnosing Instruction Overriding in
  Reasoning Models
Authors: name: Doohyuk Jang, name: Yoonjeon Kim, name: Chanjae Park, name: Hyun Ryu, name: Eunho Yang

Large language models have demonstrated remarkable proficiency in long and
complex reasoning tasks. However, they frequently exhibit a problematic
reliance on familiar reasoning patterns, a phenomenon we term reasoning
rigidity. Despite explicit instructions from users, these models often
override clearly stated conditions and default to habitual reasoning
trajectories, leading to incorrect conclusions. This behavior presents
significant challenges, particularly in domains such as mathematics and logic
puzzle, where precise adherence to specified constraints is critical. To
systematically investigate reasoning rigidity, a behavior largely unexplored in
prior work, we introduce a expert-curated diagnostic set, . Our
dataset includes specially modified variants of existing mathematical
benchmarks, namely AIME and MATH500, as well as well-known puzzles deliberately
redesigned to require deviation from familiar reasoning strategies. Using this
dataset, we identify recurring contamination patterns that occur when models
default to ingrained reasoning. Specifically, we categorize this contamination
into three distinctive modes: (i) Interpretation Overload, (ii) Input Distrust,
and (iii) Partial Instruction Attention, each causing models to ignore or
distort provided instructions. We publicly release our diagnostic set to
facilitate future research on mitigating reasoning rigidity in language models.