Observed Signal · Jul 17, 2026 · Research Study · Source: t3n · Impact: 3/5 · Sentiment: Negative
Researchers show prompts can force LLMs to 'overthink'
Researchers at Zhejiang University presented an unreviewed arXiv paper (2605.13338) at ICML showing that large reasoning models (LRMs) can be manipulated into prolonged, redundant internal reasoning loops — an "overthinking" failure — by supplying logically inconsistent or incomplete prompts. Using a hierarchical genetic algorithm (HGA) to evolve prompts that maximize chain length (surface triggers like "but", "wait", "maybe"), they tested four LRMs (DeepSeek-R1, Qwen3-Thinking, GPT-o3, Gemini-2.5-Flash) on modified MATH-Bench tasks. Some responses were up to 26× longer, increasing token, compute, and energy consumption and creating a prompt-driven denial-of-service–style attack vector. The authors call for monitoring of reasoning loops, better detection and robustness to inconsistent inputs, and new defenses for model-serving infrastructure.
The study identifies a practical attack vector and an operational cost issue for providers of large reasoning models; it affects model-serving infrastructure, compute costs, and the reliability of conversational/AI-driven services used across industries including AdTech.
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Key Takeaways & Evidence Grounding
- Unreviewed arXiv paper (2605.13338) presented at ICML by Zhejiang University demonstrating LRM 'overthinking' induced by inconsistent or incomplete prompts.
- A hierarchical genetic algorithm (HGA) was used to evolve prompts that maximize internal reasoning length and surface trigger tokens (e.g., "but", "wait", "maybe").
- Four models were tested (DeepSeek-R1, Qwen3-Thinking, GPT-o3, Gemini-2.5-Flash) on modified MATH-Bench tasks; some outputs were up to 26× longer.
- Induced overthinking increases token, compute, and energy usage and could be exploited as a prompt-driven denial-of-service–like attack against model-serving infrastructure.
- Authors recommend monitoring reasoning loops, improved detection and robustness to inconsistent inputs, and new defenses for model-serving infrastructure.
Connected Companies & Entities
6 Entities mapped“The article lists DeepSeek-R1 as one of the reasoning models tested by the research team (DeepSeek-R1)....”
“The article refers to Alibaba's Qwen3 as an example of a large reasoning model (Qwen3)....”
“The study tested a model labeled GPT-o3, referenced in the article among the four Reasoning Models evaluated....”
“The article names Gemini-2.5-Flash as one of the tested models, a model family associated with Google (Gemini-2.5-Flash)....”
“External content on the page is provided by TargetVideo GmbH, noted in an editorial-advertising disclosure....”
“The article was published on the t3n.de website and includes site navigation and metadata from t3n....”
Ontology Mapping & Concepts
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