arXiv:2610.33366v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used to support research idea generation and scientific discovery. Multi-agent debate offers a collaborative approach to this task by bringing together diverse perspectives to develop research ideas. However, a key challenge is how to equip agents with distinct and meaningful expertise to guide their contributions. Inspired by how researchers draw on accumulated research experience during collaboration,
arXiv:2610.33371v1 Announce Type: new Abstract: During post-training, large language models (LLMs) learn value preferences from corpora that may contain imbalanced proportions of opinions associated with different demographic groups. Amplifying these imbalances may further underrepresent some groups' opinions. However, how opinion mixtures shape value preferences across post-training paths remains unclear. To answer this question, we conduct controlled experiments using post-training corpora wit
arXiv:2610.33433v1 Announce Type: new Abstract: Linguistic productivity, as evidenced by the use of words in novel contexts, is one of the most striking facts about human language. However, how abstract the knowledge that enables such productivity is remains unclear. We compare people's and computational language models' linguistic productivity in contexts where successful generalization depends on form-based information. We focus on extending the use of a novel verb learned in the active voice
arXiv:2610.33440v1 Announce Type: new Abstract: Selecting a compact training subset is an important step in adapting medical language models from existing question-answering banks. We investigate perturbation-based data selection using two signals derived from Gaussian perturbations of the final-block representation: prediction-change frequency and non-flipping margin contraction. After a shared entropy prefilter, Representation-Sensitivity Prioritization (RSP) combines the signals using automat
Hello, my name is Berend Graham. My cofounder Shiv Oberai and I have been working on Limma for a year now with the goal of building a proactive solution to the reliability issues that plague practical AI and agentic use in companies with RAG. Limma is a single MCP that exists for the sole purpose of ensuring that the right information is retrieved with every query and no information is skipped and or missed. This means total precision and recall. We've found that this approach leads to the lowes
you focus on building your startup, we manage your operations. KeilHQ is an AI native work platform, where your teams, AI agents and company knowledge work together. manage your projects and teams, collaborate on docs and files, never miss a lead with CRM, manage money with finance and AI agents that handles boring tasks
An AI model said it wouldn’t use any tools, then tried to send something to a server in Beijing anyway, and while researchers say it’s probably a training quirk and not espionage, the whole thing has people asking how much to trust free Chinese AI.