We take a problem first approach to ML research, especially the sub-field of "Agentic" AI. That is, we aspire to build systems which interact with the environment (human, software, nature etc) and get measurably better with the feedback to solve a given problem.
Our Research Contributions
Our team focuses on developing next-generation agentic AI systems that can reason, plan, and execute complex tasks autonomously. We work at the intersection of large language models, multi-agent systems, and behavioral evaluation frameworks.
Our research spans novel evaluation methodologies for compound AI systems, contextual routing mechanisms for optimized model selection, and frameworks that assess AI behaviors rather than static benchmarks.
Optimizing model selection through contextual bandit algorithms for efficient compound AI systems.
Click to read more →A comprehensive evaluation framework and sandbox environment for testing LLM agents in real enterprise scenarios.
Click to read more →A novel framework for behavioral evaluation of AI systems beyond traditional benchmark metrics.
Click to read more →Investigating the capability of multi-modal language models to detect and locate fine-grained details within images.
Click to read more →A comprehensive framework combining tabular and textual information through hybrid graph structures for improved QA performance.
Click to read more →A novel approach using hyper-relational knowledge graphs and query-aligned schemas for accurate multi-hop question answering with LLMs.
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