New paper stress tests AI driven research pipelines and shows current LLM agents break in predictable ways:
- Authors evaluated six specialized LLM agents - built on models such as Gemini 2.5 Pro and Claude - across the full research pipeline, from hypothesis generation to paper writing.
- Three attempts failed due to issues including outdated tooling, loss of context, and the tendency to overstate weak findings.
- Only the fourth attempt, focused on detecting jailbreaks, succeeded and was accepted to Agents4Science 2025.
- From these experiments, the authors identified six major failure modes, such as bias toward familiar data and flawed experimental design, and proposed mitigations including stricter verification and comprehensive logging.
- Authors argue that effective AI-scientist systems require incremental task decomposition, strict verification at every stage, explicit recovery mechanisms, and exhaustive logging.
- Companion papers and discussions reinforce the conclusion that LLMs lack robust scientific reasoning in isolation and must be guided by human oversight rather than treated as fully autonomous researchers.
The real debate out there is whether agents/LLMs are just very large pattern-matching systems-mapping inputs to outputs at massive cost-or something closer to real intelligence. General intelligence is more than pattern recognition, and piling on complexity doesn't magically produce consciousness. Consciousness isn't an engineering byproduct.
A good chart summarizing LLMs limitations is shown below:
Happy to hear the thoughts of the community.
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Carlos Salas
Portfolio Manager & Freelance Investment Research Consultant
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