Anand Eswaran

Adnan Qidwai, Anand Eswaran, Sonam Mishra et al.

RAG ingestion pipelines frequently augment search corpus index with semantic enrichment indices (e.g., synthetic queries or summaries generated from corpus chunks) that are subsequently queried alongside the base index to improve retrieval via better alignment between document representations and user intent. While these supplementary representations substantially improve retrieval quality, they introduce a computational bottleneck: the configuration space of enrichment types and generator models is combinatorial, and the cost of exhaustive index-time evaluation scales linearly with corpus size. We introduce CAMI (Cost-Aware Multi-Indexing), a framework that formalizes multi-index construction as a budgeted, multi-objective portfolio selection problem. CAMI targets the upstream decision of which enrichment views to generate and materialize before the retrieval backend is applied. CAMI incorporates three primary mechanisms: (i) an agentic discovery phase that proposes corpus-specific representation templates; (ii) an atomic-unit search procedure that evaluates individual enrichment-model pairs and recombines them via fidelity-local closure to identify synergistic portfolios; and (iii) a confidence-aware promotion schedule that prunes unpromising configurations early, decoupling optimization spend from total corpus size. We evaluate CAMI across diverse retrieval corpora. Our findings reveal that the framework systematically isolates high-recall portfolios under strict budget constraints, outperforming standard content-only baselines in challenging settings by up to 9.4% recall@10. Further, CAMI is able to systematically identify these high-recall portfolios using up to 5x less budget compared to random search baselines, making our approach practical in real production scenarios.

Pankaj Thorat, Adnan Qidwai, Adrija Dhar et al.

Data profiling is critical in machine learning for generating descriptive statistics, supporting both deeper understanding and downstream tasks like data valuation and curation. This work addresses profiling specifically in the context of code datasets for Large Language Models (code-LLMs), where data quality directly influences tasks such as code generation and summarization. Characterizing code datasets in terms of programming language concepts enables better insights and targeted data curation. Our proposed methodology decomposes code data profiling into two phases: (1) an offline phase where LLMs are leveraged to derive and learn rules for extracting syntactic and semantic concepts across various programming languages, including previously unseen or low-resource languages, and (2) an online deterministic phase applying these derived rules for efficient real-time analysis. This hybrid approach is customizable, extensible to new syntactic and semantic constructs, and scalable to multiple languages. Experimentally, our LLM-aided method achieves a mean accuracy of 90.33% for syntactic extraction rules and semantic classification accuracies averaging 80% and 77% across languages and semantic concepts, respectively.