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Indian Institute of Technology (BHU)

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Selected work

Representative Papers

Choosing a Text Embedding Model: A Practical Benchmarking and Decision Framework

Jul 26, 2026

This work addresses the gap between benchmark-driven embedding model selection and real-world deployment constraints by introducing the first framework to evaluate embedding models within a complete retrieval pipeline. It systematically compares the end-to-end performance of T3EM’s commercial API against leading open-source models across diverse tasks—including retrieval, classification, clustering, and semantic similarity—as covered by the MTEB benchmark, while jointly accounting for latency, cost, task type, and deployment conditions. The study develops a comprehensive, end-to-end model selection guide encompassing embedding generation, indexing, search, and chunking strategies, revealing significant performance discrepancies that emerge only in full-system contexts. These insights provide practitioners with actionable, empirically grounded criteria for embedding model adoption in real-world applications.

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MDQEC-QAS: Meta-Decoding for Quantum Error Correction with Hardware-Aware VQC Search and Confidence-Gated Recovery

Jul 12, 2026

This work addresses the limited generalization of conventional quantum error correction decoders across diverse code families and noise environments. The authors propose the first universal meta-decoding framework, which jointly optimizes a classical Meta-MLP teacher model and a hardware-aware variational quantum circuit (VQC) through meta-learning. A confidence-gated mechanism is introduced to enable selective recovery, thereby avoiding blind replacement of decoding decisions. This approach achieves, for the first time, unified decoding across multiple stabilizer codes and noise types. Experimental results demonstrate that confidence gating significantly reduces logical error rates across five evaluation scenarios. Notably, on the most challenging Planar 5×5 code, the VQC-based decoder lowers the logical failure ratio from 25.91 to 1.11, substantially outperforming the non-gated baseline.

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Recent publications

Latest Papers

Choosing a Text Embedding Model: A Practical Benchmarking and Decision Framework

Jul 26, 2026

This work addresses the gap between benchmark-driven embedding model selection and real-world deployment constraints by introducing the first framework to evaluate embedding models within a complete retrieval pipeline. It systematically compares the end-to-end performance of T3EM’s commercial API against leading open-source models across diverse tasks—including retrieval, classification, clustering, and semantic similarity—as covered by the MTEB benchmark, while jointly accounting for latency, cost, task type, and deployment conditions. The study develops a comprehensive, end-to-end model selection guide encompassing embedding generation, indexing, search, and chunking strategies, revealing significant performance discrepancies that emerge only in full-system contexts. These insights provide practitioners with actionable, empirically grounded criteria for embedding model adoption in real-world applications.

0 citationsRead paper

MDQEC-QAS: Meta-Decoding for Quantum Error Correction with Hardware-Aware VQC Search and Confidence-Gated Recovery

Jul 12, 2026

This work addresses the limited generalization of conventional quantum error correction decoders across diverse code families and noise environments. The authors propose the first universal meta-decoding framework, which jointly optimizes a classical Meta-MLP teacher model and a hardware-aware variational quantum circuit (VQC) through meta-learning. A confidence-gated mechanism is introduced to enable selective recovery, thereby avoiding blind replacement of decoding decisions. This approach achieves, for the first time, unified decoding across multiple stabilizer codes and noise types. Experimental results demonstrate that confidence gating significantly reduces logical error rates across five evaluation scenarios. Notably, on the most challenging Planar 5×5 code, the VQC-based decoder lowers the logical failure ratio from 25.91 to 1.11, substantially outperforming the non-gated baseline.

0 citationsRead paper