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