Selected work

Representative Papers

Rhetorical-Role-Aware Retrieval-Augmented Generation for Legal Question Answering over Indian Supreme Court Judgments

Aug 07, 2026

This work proposes a domain-adapted retrieval-augmented generation framework tailored for complex legal question answering grounded in judgments of the Supreme Court of India. The approach innovatively incorporates a rhetorical role–aware text chunking strategy that leverages structural features of legal documents, such as judicial authorship, and integrates multi-path retrieval, cross-encoder reranking, and a query rewriting mechanism informed by query classification and dialogue history to accurately capture user intent. Experimental results demonstrate that the proposed framework significantly outperforms baseline methods in terms of contextual recall and answer relevance, thereby enhancing the system’s accuracy, interpretability, and reliability in intricate legal contexts.

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Localization and Pursuit of a Mobile Target using Distance-only Measurements

Jul 30, 2026

This study addresses the problem of localizing and tracking a two-dimensional linearly moving target using only distance measurements. The work proposes a GPS-free, angle- and multi-anchor-independent approach that employs a single stationary receiver and a mobile agent. Distance information is derived from a path-loss model, and target localization is achieved through quadrant identification, iterative position estimation, and motion vector inference, requiring at most thirteen steps before autonomously transitioning to the tracking phase. Simulation results demonstrate that the method effectively captures the target and maintains bounded tracking error, significantly reducing reliance on extensive sensing infrastructure.

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Systematic Analysis of Large Language Models and Transformer-Based Machine Translation for English-Tamil and Tamil-English Across Diverse Datasets

Jul 27, 2026

This study addresses the challenges of machine translation for low-resource languages like Tamil, which suffer from scarce parallel corpora, significant domain divergence, and morphological complexity. We systematically evaluate multilingual Transformer models—including NLLB and mBART—as well as the TamilLaMA large language model across multiple English–Tamil bilingual datasets. Innovatively integrating few-shot in-context prompting with attention alignment visualization, our approach is the first to apply these techniques to English–Tamil bidirectional translation. Performance is quantified using BLEU and chrF metrics. Results demonstrate that translation quality is highly sensitive to data quality and domain alignment; few-shot prompting yields structurally coherent Tamil translations; and attention visualization substantially enhances model interpretability.

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Component Modalities of Quantum Logic

Jul 16, 2026

This study investigates the structural and proof-theoretic issues arising from forcing conditions in relational quantum modal logic, with a focus on the coherence of modal transitions across compatible worlds. By introducing a notion of compatibility component structures, the work establishes, for the first time, a connection between such structures and central propositional approximations, characterizing upper and lower approximations of stable propositions within superselection models. Leveraging relational semantics, Boolean algebras, and local validity semantics—combined with connectedness constructions and maximal consistent pair techniques—the authors build a canonical model and formulate a unified logical system. The paper proves that the multi-conclusion sequent calculi extended with axioms T, 4, and B are complete with respect to component frames, equivalence frames, and connected compatibility frames, thereby establishing a comprehensive proof theory for component modal logics.

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Closed-Form Steepest Descent Direction toward Flat Minima: Reducing Upper Bounds on the Loss Hessian Eigenspectrum in Neural Networks

Jun 26, 2026

This work addresses the challenge of improving neural network generalization by analyzing optimization dynamics through the lens of data distribution and parameter structure, with a focus on steering training toward flat minima. Leveraging the Wolkowicz–Styan inequality, the authors derive, for the first time, a closed-form expression for the gradient of the largest eigenvalue of the Hessian of the cross-entropy loss—enabling explicit optimization of its spectral upper bound without numerical approximation. By updating parameters along the steepest descent direction defined by this gradient, the method effectively compresses the Hessian eigenvalue spectrum in three-layer networks, thereby avoiding sharp minima and saddle points. This approach guides convergence toward flatter minima that exhibit superior generalization, offering a novel theoretical and algorithmic pathway for understanding and promoting flatness in deep learning optimization.

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Recent publications

Latest Papers

Rhetorical-Role-Aware Retrieval-Augmented Generation for Legal Question Answering over Indian Supreme Court Judgments

Aug 07, 2026

This work proposes a domain-adapted retrieval-augmented generation framework tailored for complex legal question answering grounded in judgments of the Supreme Court of India. The approach innovatively incorporates a rhetorical role–aware text chunking strategy that leverages structural features of legal documents, such as judicial authorship, and integrates multi-path retrieval, cross-encoder reranking, and a query rewriting mechanism informed by query classification and dialogue history to accurately capture user intent. Experimental results demonstrate that the proposed framework significantly outperforms baseline methods in terms of contextual recall and answer relevance, thereby enhancing the system’s accuracy, interpretability, and reliability in intricate legal contexts.

0 citationsRead paper

Localization and Pursuit of a Mobile Target using Distance-only Measurements

Jul 30, 2026

This study addresses the problem of localizing and tracking a two-dimensional linearly moving target using only distance measurements. The work proposes a GPS-free, angle- and multi-anchor-independent approach that employs a single stationary receiver and a mobile agent. Distance information is derived from a path-loss model, and target localization is achieved through quadrant identification, iterative position estimation, and motion vector inference, requiring at most thirteen steps before autonomously transitioning to the tracking phase. Simulation results demonstrate that the method effectively captures the target and maintains bounded tracking error, significantly reducing reliance on extensive sensing infrastructure.

0 citationsRead paper

Systematic Analysis of Large Language Models and Transformer-Based Machine Translation for English-Tamil and Tamil-English Across Diverse Datasets

Jul 27, 2026

This study addresses the challenges of machine translation for low-resource languages like Tamil, which suffer from scarce parallel corpora, significant domain divergence, and morphological complexity. We systematically evaluate multilingual Transformer models—including NLLB and mBART—as well as the TamilLaMA large language model across multiple English–Tamil bilingual datasets. Innovatively integrating few-shot in-context prompting with attention alignment visualization, our approach is the first to apply these techniques to English–Tamil bidirectional translation. Performance is quantified using BLEU and chrF metrics. Results demonstrate that translation quality is highly sensitive to data quality and domain alignment; few-shot prompting yields structurally coherent Tamil translations; and attention visualization substantially enhances model interpretability.

0 citationsRead paper

Component Modalities of Quantum Logic

Jul 16, 2026

This study investigates the structural and proof-theoretic issues arising from forcing conditions in relational quantum modal logic, with a focus on the coherence of modal transitions across compatible worlds. By introducing a notion of compatibility component structures, the work establishes, for the first time, a connection between such structures and central propositional approximations, characterizing upper and lower approximations of stable propositions within superselection models. Leveraging relational semantics, Boolean algebras, and local validity semantics—combined with connectedness constructions and maximal consistent pair techniques—the authors build a canonical model and formulate a unified logical system. The paper proves that the multi-conclusion sequent calculi extended with axioms T, 4, and B are complete with respect to component frames, equivalence frames, and connected compatibility frames, thereby establishing a comprehensive proof theory for component modal logics.

0 citationsRead paper

Closed-Form Steepest Descent Direction toward Flat Minima: Reducing Upper Bounds on the Loss Hessian Eigenspectrum in Neural Networks

Jun 26, 2026

This work addresses the challenge of improving neural network generalization by analyzing optimization dynamics through the lens of data distribution and parameter structure, with a focus on steering training toward flat minima. Leveraging the Wolkowicz–Styan inequality, the authors derive, for the first time, a closed-form expression for the gradient of the largest eigenvalue of the Hessian of the cross-entropy loss—enabling explicit optimization of its spectral upper bound without numerical approximation. By updating parameters along the steepest descent direction defined by this gradient, the method effectively compresses the Hessian eigenvalue spectrum in three-layer networks, thereby avoiding sharp minima and saddle points. This approach guides convergence toward flatter minima that exhibit superior generalization, offering a novel theoretical and algorithmic pathway for understanding and promoting flatness in deep learning optimization.

0 citationsRead paper