We introduce QueryGym, an interactive environment for building, testing, and evaluating LLM-based query planning agents. Existing frameworks often tie agents to specific query language dialects or obscure their reasoning; QueryGym instead requires agents to construct explicit sequences of relational algebra operations, ensuring engine-agnostic evaluation and transparent step-by-step planning. The environment is implemented as a Gymnasium interface that supplies observations---including schema details, intermediate results, and execution feedback---and receives actions that represent database exploration (e.g., previewing tables, sampling column values, retrieving unique values) as well as relational algebra operations (e.g., filter, project, join).We detail the motivation and the design of the environment. In the demo, we showcase the utility of the environment by contrasting it with contemporary LLMs that query databases. QueryGym serves as a practical testbed for research in error remediation, transparency, and reinforcement learning for query generation.

Generating behaviors that align with human expectations is a key requirement for human-robot collaboration. Potential behavior misalignment could lead to the robot performing actions with unanticipated, potentially dangerous side effects even while pursuing human goals. In this paper, we introduce a novel metric called Goal State Divergence (GSD) which quantifies the difference between the state a robot achieved in response to a human-specified goal and what the human expected. In cases where GSD cannot be directly calculated, we show how it can be approximated using maximal and minimal bounds. We then leverage GSD in our novel human-robot goal alignment design (HRGAD) problem, which identifies a minimal set of environment modifications that can reduce such mismatches. We show the effectiveness of our method in reducing the goal state divergence by empirically evaluating our approach on several planning benchmarks.