Title: Shoot First, Ask Questions Later? Building Rational Agents that Explore and Act Like People
Primary area: applications to neuroscience & cognitive science
Keywords: Bayesian experimental design, information-seeking, question asking, Collaborative Battleship, expected information gain (EIG), explore-exploit tradeoffs, resource rationality, probabilistic inference, Monte Carlo sampling, symbolic grounding, code generation, reasoning, decision-oriented dialogue, cognitive modeling, human behavior, language model agents, scientific discovery

Abstract: Many high-stakes applications of AI require forming data-driven hypotheses and making targeted guesses; e.g., in scientific and diagnostic settings. Given limited resources, to what extent do agents based on language models (LMs) act rationally? We develop methods to benchmark and enhance agentic information-seeking, drawing on insights from human behavior. First, we introduce a strategic decision-oriented dialogue task called *Collaborative Battleship*, in which a partially-informed *Captain* must balance exploration (asking questions) and action (taking shots), while a fully-informed *Spotter* must provide accurate answers under an information bottleneck. Compared to human players (N=42), we find that LM agents struggle to ground answers in context, generate informative questions, and select high-value actions. Next, to address these gaps, we develop novel Monte Carlo inference strategies for LMs based on principles from Bayesian Experimental Design (BED). For Spotter agents, our approach boosts accuracy by up to 14.7% absolute over LM-only baselines; for Captain agents, it raises expected information gain (EIG) by up to 0.227 bits (94.2% of the achievable noise ceiling). Combined, these components yield sharper targeting (+0.303–0.374 F1), and enable weaker LMs, such as Llama-4-Scout, to outperform both humans (8% → 82% win rate) and frontier models (0% → 67% win rate vs. GPT-5) at ≈1% of GPT-5's cost. We replicate these findings on *Guess Who?* where our methods significantly boost accuracy (+28.3–42.4 p.p.), demonstrating their general applicability for building rational information-seeking agents.

TL;DR: We introduce a collaborative Battleship task to evaluate information-seeking in humans and agents; insights from Bayesian Experimental Design (BED) yield inference-time strategies for building resource-rational agents in discovery settings.