Author name cluster

Kelsey Sikes

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

3 papers

1 author row

AAAI Conference 2026 System Paper

QueryGym: Step-by-Step Interaction with Relational Databases

Haritha Ananthakrishnan
Harsha Kokel
Kelsey Sikes
Debarun Bhattacharjya
Michael Katz
Shirin Sohrabi
Kavitha Srinivas

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.

PDF Details DOI

AAAI Conference 2026 Conference Paper

Reducing Goal State Divergence with Environment Design

Kelsey Sikes
Sarah Keren
Sarath Sreedharan

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.

PDF Details DOI

HAXP Workshop 2024 Workshop Paper

Reducing Human-Robot Goal State Divergence with Environment Design

Kelsey Sikes
Sarah Keren
Sarath Sreedharan

One of the most difficult challenges in creating successful human-AI collaborations is aligning a robot’s behavior with a human user’s expectations. When this fails to occur, a robot may misinterpret their specified goals, prompting it to perform actions with unanticipated, potentially dangerous side effects. To avoid this, we propose a new metric we call Goal State Divergence (GSD), which represents the difference between a robot’s final goal state and the one a human user expected. In cases where GSD cannot be directly calculated, we show how it can be approximated using maximal and minimal bounds. We then input the GSD value into our novel human-robot goal alignment (HRGA) design problem, which identifies a minimal set of environment modifications that can prevent mismatches like this. To show the effectiveness of GSD for reducing differences between human-robot goal states, we empirically evaluate our approach on several standard benchmarks.

PDF