Skip to content
AI Atlas
PaperActive

Clearing the Fog: Towards Installing and Refining Proactive Exploration Capabilities in LLM Agents

arxiv.org/abs/2608.14339

quality89

Updated 6 h ago · first seen 11 Sept 2026

paper_01M294FTFP0M4YHM3CBAK39TVK

Published
11 Sept 2026
T1 · 6 h ago
arXiv
2608.14339
T1 · 6 h ago
Category
cs.AI
T1 · 6 h ago

As of

Rewind the record: see this entity's attributes exactly as AI Atlas knew them on a given day.

Claim history · Abstract

1 claims · 1 propertiesShow all properties

Abstractabstract1

Claim history for Abstract
ValueValid from → toStatusSourceConfidenceExtractor
-cross Abstract: We study proactive exploration in LLM agents, i.e., the ability to explore an environment to acquire information that improves future decision-making. In this regard, we first identify two fundamental bottlenecks that hinder this capability and then propose \ours, a novel method designed to instill and refine proactive exploration. Specifically, \ours\ consists of two components: (1) Exploratory Data Construction, which synthesizes exploration-rich trajectories to mitigate the hindsight bias of standard demonstrations; and (2) RL Optimization with Contrastive Signal Guidance, which leverages contrastive trajectory pairs to distinguish productive exploration from redundant wandering. Extensive experiments demonstrate the effectiveness of \ours\ and provide insights into the characteristics of proactive exploration. Our code is available at: https://github.com/GuanZhizhao/SAFARI.currentcurrentarXiv (Atom API + RSS)T1highdeterministic

Claims are temporal and append-only: a new observation closes the previous claim (valid_to) instead of overwriting it. Conflicting claims from different sources are kept side by side and flagged — never averaged. Methodology →