Representation and Motivation Are Superadditive: Unlocking Exploration for LLM Agents in Structured Search

LLM agents are increasingly expected to discover solutions beyond what is already known, yet prior work shows that they search locally, optimizing among known options rather than pursuing unexplored alternatives. We identify an upstream representational bottleneck in structured search: when alternatives must be recovered from relational history, unexplored regions must first be represented as candidate destinations before motivation can redirect choice. In a task adapted from Wu et al. (2018), baseline agents across DeepSeek V4.1 Flash, Qwen3.8-flash, and GPT-4.1-mini select new positions with mean uncertainty percentiles of only 1.6–3.6, compared with 22.8 for matched humans; the deficit persists when provider-native reasoning is enabled and in a GPT-5.6 terra diagnostic. Factorially combining a coverage representation with topology-free motivation produces superadditive gains in search extent: the joint effect exceeds the sum of the individual effects by 4.2–12.6 uncertainty-percentile points. The combination raises the uncertainty percentile to 21.9–23.2, closing the roughly order-of-magnitude gap to the human benchmark. Same-information controls isolate coverage status from generic history summarization, while rendering controls show that computing the same coverage fields does not guarantee their use in choice. In a restless bandit with named arms and 857 human participants, the same motivation increases switching without a positive interaction. Together, the task contrast identifies a structural boundary: motivation acts directly among named alternatives but gains leverage from representation when destinations must be recovered from history.