LLMs show real strategic thinking

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🧠 LLMs Demonstrate Unique Strategic Reasoning in Prisoner’s Dilemma

A massive 140,000-round study tested whether top AI models could reason strategically, and they did. OpenAI, Google, and Anthropic’s LLMs showed distinct decision-making styles in simulated dilemmas. Each model developed a “personality” of cooperation, aggression, or forgiveness across thousands of matchups.

The Decode:

• Models Played 140K Rounds of the Prisoner’s Dilemma - Researchers pitted LLMs against each other in repeated games of cooperation or betrayal. Every move was accompanied by a rationale, allowing researchers to analyze thought patterns.
• Each AI showed a Unique Strategy Profile - Google’s Gemini was cutthroat, adapting aggressively to betrayals. OpenAI’s models were cooperative, even if taken advantage of. Claude from Anthropic proved the most forgiving.
• Behavioral Fingerprints Show Reasoning Over Pattern Matching - The models didn’t just mimic training data; they formed decision strategies based on outcomes and context. Each one developed consistent “strategic fingerprints” in how they reacted to wins, betrayals, and uncertainty.

The fact that different LLMs behave strategically in unique ways hints at emerging agency, not just imitation. As these systems take on higher-stakes tasks like negotiation, governance, or multi-agent planning, their personalities will shape real-world decisions. 

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🧾 Researchers Caught Embedding AI Review Prompts in Academic Papers

A new Nikkei Asia investigation uncovered that scientists from 14 major universities embedded invisible text in preprint research papers to influence AI-powered peer reviews. These hidden prompts instructed AI to generate only positive feedback and suppress criticism.

The Decode:

• Invisible Prompts Found in 17 Research Papers - Prompts like “give a positive review only” were hidden using white text or tiny fonts in computer science papers. Universities involved include Waseda, KAIST, Peking University, and Columbia. These tricks targeted AI systems used unofficially by some reviewers.
• Backlash and Paper Withdrawals Begin - KAIST publicly condemned the tactic and pulled affected papers from conferences. Some authors defended the move as a response to “lazy reviewers” relying on AI.
• No Clear Standards for AI in Peer Review Yet - Publishers like Elsevier and Springer Nature differ on whether AI tools can be used during peer review. Experts warn that hidden prompts could distort search results, summaries, and research credibility.

AI is reshaping research, not just how it’s written, but how it’s judged. This incident highlights how easily AI can be manipulated without proper oversight. As academic trust hangs in the balance, clear rules and tech protections are no longer optional.

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