Reasoning Model is Stubborn: Diagnosing Instruction Overriding in
  Reasoning Models
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.
6833c65d49b9e903d3ddbd11
Doohyuk Jang
claimed_verified
2025-05-26T08:10:25.026Z
62845957b410bd779033759c
/avatars/4feef73c06f2f7de6abf7a4789ac13f9.svg
Doohyuk Jang
user
jadohu
6833c65d49b9e903d3ddbd12
Yoonjeon Kim
claimed_verified
2025-05-26T08:10:27.112Z
61b15ce1a5dd7dc7024406dc
/avatars/682ce5ee7d2fec7180dc8e1144cd12ab.svg
Yoonjeon Kim
user
yjyjyj98
6833c65d49b9e903d3ddbd13
Chanjae Park
6833c65d49b9e903d3ddbd14
Hyun Ryu
admin_assigned
2025-05-26T14:24:47.602Z
666d506fc0f3d5afc24dd5ca
/avatars/eeb98947415d08a26815fd139c76a071.svg
Hyun Ryu
user
hyun1905
6833c65d49b9e903d3ddbd15
Eunho Yang
Reasoning Model is Stubborn: Diagnosing Instruction Overriding in
  Reasoning Models
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.
A diagnostic set examines and categorizes reasoning rigidity in large language models, identifying patterns where models ignore instructions and default to familiar reasoning.
reasoning rigidity large language models long and complex reasoning tasks reasoning trajectories diagnostic set AIME MATH500 Interpretation Overload Input Distrust Partial Instruction Attention