title: Can Language Models Falsify? Evaluating Algorithmic Reasoning with
    Counterexample Creation
summary: There is growing excitement about the potential of Language Models (LMs) to
  accelerate scientific discovery. Falsifying hypotheses is key to scientific
  progress, as it allows claims to be iteratively refined over time. This process
  requires significant researcher effort, reasoning, and ingenuity. Yet current
  benchmarks for LMs predominantly assess their ability to generate solutions
  rather than challenge them. We advocate for developing benchmarks that evaluate
  this inverse capability - creating counterexamples for subtly incorrect
  solutions. To demonstrate this approach, we start with the domain of
  algorithmic problem solving, where counterexamples can be evaluated
  automatically using code execution. Specifically, we introduce REFUTE, a
  dynamically updating benchmark that includes recent problems and incorrect
  submissions from programming competitions, where human experts successfully
  identified counterexamples. Our analysis finds that the best reasoning agents,
  even OpenAI o3-mini (high) with code execution feedback, can create
  counterexamples for only <9% of incorrect solutions in REFUTE, even though
  ratings indicate its ability to solve up to 48% of these problems from scratch.
  We hope our work spurs progress in evaluating and enhancing LMs' ability to
  falsify incorrect solutions - a capability that is crucial for both
  accelerating research and making models self-improve through reliable
  reflective reasoning.
paper_authors._id: 67c01587925b73feaf61ac41
paper_authors.name: Shiven Sinha
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-02-27T12:54:41.540Z
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paper_authors.fullname: Shiven Sinha
paper_authors.type: user
paper_authors.user: shivensinha4
paper_authors._id: 67c01587925b73feaf61ac42
paper_authors.name: Shashwat Goel
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-02-27T22:09:13.498Z
paper_authors._id: 6506832221ac448013f94995
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/6506832221ac448013f94995/sVUI1JV4Dxan5l-MqNze4.jpeg
paper_authors.fullname: Shashwat Goel
paper_authors.type: user
paper_authors.user: shash42
paper_authors._id: 67c01587925b73feaf61ac43
paper_authors.name: Ponnurangam Kumaraguru
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-03-11T16:09:29.886Z
paper_authors._id: 67a6a4b7f379cef464950268
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/yaMsT2jYsj6mvvYU1gvO_.jpeg
paper_authors.fullname: ponnurangam kumaraguru
paper_authors.type: user
paper_authors.user: pk-profgiri
paper_authors._id: 67c01587925b73feaf61ac44
paper_authors.name: Jonas Geiping
paper_authors._id: 67c01587925b73feaf61ac45
paper_authors.name: Matthias Bethge
paper_authors._id: 67c01587925b73feaf61ac46
paper_authors.name: Ameya Prabhu
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-02-27T12:54:39.585Z
paper_authors._id: 6464a0d41683d3c81f51924a
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/6464a0d41683d3c81f51924a/s7yYVwfUB4WOhVFJS6A6T.jpeg
paper_authors.fullname: Ameya Prabhu
paper_authors.type: user
paper_authors.user: AmeyaPrabhu
paper_title: Can Language Models Falsify? Evaluating Algorithmic Reasoning with
    Counterexample Creation
paper_summary: There is growing excitement about the potential of Language Models (LMs) to
  accelerate scientific discovery. Falsifying hypotheses is key to scientific
  progress, as it allows claims to be iteratively refined over time. This process
  requires significant researcher effort, reasoning, and ingenuity. Yet current
  benchmarks for LMs predominantly assess their ability to generate solutions
  rather than challenge them. We advocate for developing benchmarks that evaluate
  this inverse capability - creating counterexamples for subtly incorrect
  solutions. To demonstrate this approach, we start with the domain of
  algorithmic problem solving, where counterexamples can be evaluated
  automatically using code execution. Specifically, we introduce REFUTE, a
  dynamically updating benchmark that includes recent problems and incorrect
  submissions from programming competitions, where human experts successfully
  identified counterexamples. Our analysis finds that the best reasoning agents,
  even OpenAI o3-mini (high) with code execution feedback, can create
  counterexamples for only <9% of incorrect solutions in REFUTE, even though
  ratings indicate its ability to solve up to 48% of these problems from scratch.
  We hope our work spurs progress in evaluating and enhancing LMs' ability to
  falsify incorrect solutions - a capability that is crucial for both
  accelerating research and making models self-improve through reliable
  reflective reasoning.
paper_ai_keywords: Language Models (LMs) algorithmic problems solving REFUTE code execution counterexamples dynamically updating benchmark OpenAI o3-mini (high) data-efficient reflective reasoning