Can Language Models Falsify? Evaluating Algorithmic Reasoning with
  Counterexample Creation
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.
67c01587925b73feaf61ac41
Shiven Sinha
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2025-02-27T12:54:41.540Z
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Shiven Sinha
user
shivensinha4
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Shashwat Goel
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2025-02-27T22:09:13.498Z
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Shashwat Goel
user
shash42
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Ponnurangam Kumaraguru
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2025-03-11T16:09:29.886Z
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https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/yaMsT2jYsj6mvvYU1gvO_.jpeg
ponnurangam kumaraguru
user
pk-profgiri
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Jonas Geiping
claimed_verified
2025-06-27T08:58:31.527Z
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Jonas Geiping
user
JonasGeiping
67c01587925b73feaf61ac45
Matthias Bethge
67c01587925b73feaf61ac46
Ameya Prabhu
claimed_verified
2025-02-27T12:54:39.585Z
6464a0d41683d3c81f51924a
https://cdn-avatars.huggingface.co/v1/production/uploads/6464a0d41683d3c81f51924a/s7yYVwfUB4WOhVFJS6A6T.jpeg
Ameya Prabhu
user
AmeyaPrabhu
Can Language Models Falsify? Evaluating Algorithmic Reasoning with
  Counterexample Creation
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.
REFUTE is a benchmark for evaluating language models' ability to falsify incorrect solutions, improving their role in scientific discovery and self-improvement.
Language Models REFUTE reasoning agents code execution dynamically updating benchmark algorithmic problem solving counterexamples incorrect solutions self-improvement reflective reasoning