mit-ad0bc481·2 events·first seen Aliases: MIT
Researchers at MIT and Carnegie Mellon University developed Puppet, a benchmark that measures how much LLMs actually shift users' beliefs after conversation, as opposed to detecting manipulative language patterns. The study tracked over 1,000 users interacting with GPT-4o under various prompting conditions and found high variability in belief shifts, with a median change of 3.3 but standard deviation of ~22. Existing manipulation detectors showed near-zero correlation with actual belief change, while LLMs like GPT-4o achieved moderate correlation (0.436) when estimating belief shifts from conversation transcripts alone. The work argues for direct belief-shift measurement as a more valid approach to assessing LLM persuasive risk.
MIT researchers Alex L. Zhang, Tim Kraska, and Omar Khattab propose Recursive Language Models (RLMs), a framework that offloads long-context processing to an external Python REPL environment, allowing models to programmatically fetch and manage text chunks via code generation. The root model spawns submodel instances to handle subtasks, aggregating their outputs recursively. Evaluated on benchmarks requiring reasoning over documents up to 11 million tokens, RLMs substantially outperform both base models and competing agentic strategies such as retrieval and summarization agents. For example, RLM-GPT-5 achieved 91.3% on BrowseComp+ versus GPT-5's inability to produce an answer, and ~50% accuracy on OOLONG-PAIRS at 1 million tokens versus near-zero for baseline approaches.