This project introduces SAGE, a framework for learning embodied navigation policies from physics-grounded semantic abstractions. Instead of depending only on photorealistic simulation, the agent learns from sandbox environments that preserve the structure needed for planning and control.

The framework combines sandbox task generation, reinforcement learning over abstracted experience, and transfer to open-world navigation. The paper was accepted to ICML 2026 on May 1, 2026.

SAGE overview: sandbox generation, policy evolution, open-world navigation, and benchmark performance.

SAGE framework: Genesis, Evolution, and Navigation phases.

SPASM is a stability-first framework for persona-driven LLM-LLM dialogue simulation. The project addresses long-horizon identity failures such as persona drift, role confusion, and echoing when language models are used to generate synthetic multi-turn dialogue data.

The framework decomposes simulation into persona generation, Client-Responder dialogue generation, and termination detection. Its core mechanism, Egocentric Context Projection, stores dialogue history in a perspective-agnostic form and projects it into each agent's view before generation.

SPASM framework overview: persona generation, dialogue simulation, egocentric context projection, and termination detection.

[My role in this project]
First author; framework design, implementation, experiments, analysis, and writing.

DialogGuard is a web interface for auditing LLM responses in sensitive conversational contexts. It wraps arbitrary generative models with reusable LLM-as-a-judge pipelines and presents practitioner-facing risk scores and natural-language rationales.

The system supports manual input and live chat workflows, evaluates responses across psychosocial safety dimensions, and exposes multiple evaluation mechanisms including single-agent scoring, dual-agent correction, majority voting, and multi-agent debate.

DialogGuard interface for entering dialogue context, comparing evaluation mechanisms, and inspecting per-dimension risk scores.

Reasoning panel exposing step-by-step multi-agent safety judgments.

[My role in this project]
First author; system design, interface implementation, evaluation, study analysis, and writing.