From the 1 of 5 linked papers with an AI index.
5 papers
One Frozen Simulator Is Not Enough: Simulator Collapse in Multi-Agent RL
Simon Yu, Nicholas Tomlin, Marwa Abdulhai +7
Multi-agent reinforcement learning for human-AI interaction typically relies on a single large language model to simulate user behavior. We show that this approach systematically f…
Verbalized Sampling: How to Mitigate Mode Collapse and Unlock LLM Diversity
Jiayi Zhang, Simon Yu, Derek Chong +4
The paper identifies typicality bias in preference data as a key cause of mode collapse in aligned large language models and introduces Verbalized Sampling, a training‑free prompti…
Shepherd: Enabling Programmable Meta-Agents via Reversible Agentic Execution Traces
Simon Yu, Derek Chong, Ananjan Nandi +4
As LLM agent systems take on more complex tasks, they increasingly rely on meta-agents: higher-order agents that create, operate on and manage other agents. Meta-agent operations s…
SecureForge: Finding and Preventing Vulnerabilities in LLM-Generated Code via Prompt Optimization
Houjun Liu, Lisa Einstein, John Yang +5
LLM coding agents now generate code at an unprecedented scale, yet LLM-generated code introduces cybersecurity vulnerabilities into codebases without human involvement. Even when f…
Humans and transformer LMs: Abstraction drives language learning
Jasper Jian, Christopher D. Manning
Categorization is a core component of human linguistic competence. We investigate how a transformer-based language model (LM) learns linguistic categories by comparing its behaviou…