CAMI: Practical Cost-Aware Agent-Guided Multi-Indexing for Semantic Retrieval
Efficient offline exploration of multi-index retrieval methods.
Hi! I'm Adnan. I currently work at IBM Research as a Research Engineer in the Data for AI team. My work is centered on retrieval optimization and streamlining how models ingest and utilize large amounts of knowledge across many structured and unstructured sources.
Beyond my core role, I'm interested in building long-horizon agents, with a particular focus on curating the right training data and creating meaningful metrics to measure their progress. I also enjoy probing frontier models to understand their limits and identifying the specific gaps where their reasoning or reliability starts to drop off.
In the past, I worked on multimodal understanding in collaboration with Prof. Vivek Gupta (ASU), Prof. Dan Roth (UPenn), and Adobe Research. I also spent time researching LLM jailbreaking as part of the Stanford AI Researcher project.
I'm always open to discussing research, new opportunities, or just exchanging ideas. Feel free to reach out!
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Efficient offline exploration of multi-index retrieval methods.
Consistency and robustness in chart understanding.
Structured insights from reviews and seller descriptions.
Reasoning across related charts.
LLMs plus static rules for code-data profiling.