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Birla Institute of Technology and Science

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Research library76linked papers
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Selected work

Representative Papers

Grounding Large Language Models in DSGE Simulators for Policy Generation and Forecasting

Oct 01, 2026

This study addresses the lack of dynamic consistency verification and long-horizon credit assignment mechanisms when large language models (LLMs) formulate economic policies. To overcome these limitations, this work proposes a closed-loop interactive framework that embeds instruction-tuned LLMs within a Dynamic Stochastic General Equilibrium (DSGE) simulator. Policy actions are optimized using the Proximal Policy Optimization (PPO) reinforcement learning algorithm, thereby establishing an evaluation paradigm grounded in economic consequences rather than textual plausibility. This approach effectively resolves the challenge of delayed reward propagation, enabling dynamic simulation and rigorous assessment of policy effects under historical shocks. Ultimately, this research provides a quantifiable and verifiable pathway for AI-driven macroeconomic decision-making.

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Tree Navigation Without LLM Summaries: A Matched-Cost Study of Hierarchical Retrieval for Long-Document QA

Sep 30, 2026

This study addresses the limitations of flat retrieval methods, which often overlook complementary evidence in long documents, and summary-tree approaches that rely on costly LLM-based indexing. We propose NavTree, which constructs a deterministic, balanced segmentation tree without any LLM calls to serve as a navigational scaffold. By integrating hybrid lexical-dense representations with frontier traversal algorithms, NavTree effectively localizes critical leaf nodes, revealing that the advantages of hierarchical retrieval stem primarily from structural navigation rather than summary content. Experimental evaluations demonstrate that NavTree significantly outperforms BM25 under matching-cost assessments and consistently surpasses RAPTOR variants across multi-hop question answering tasks. Ultimately, this work achieves high-performance retrieval with zero indexing cost.

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CORTEX: A Verified Experience Layer for Generalist Agents

Sep 27, 2026

This study addresses the limitations of AI agents in validating historical solutions and adapting to environmental changes by proposing a formalized system contract coupled with an empirical verification layer. Methodologically, the approach records task conditions and proof trajectories, enabling a meta-controller to precisely replay, adapt, or synthesize prior experiences. A challenge-driven development loop is further constructed through context orchestration, validator chains, and typed storage. The core contribution lies in demonstrating that general intelligence can advance through reusable programs rather than weight updates. Evaluations across clinical and policy domains confirm perfect invariance in strategy execution, providing a preliminary yet viable pathway toward artificial general intelligence.

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CLAIRE: A Schema-Grounded Hybrid Workflow for Healthcare Administrative Form Completion

Sep 26, 2026

This study addresses the inefficiency and error-proneness of information extraction in medical form filling by proposing CLAIRE, a hybrid workflow. The method adopts a "schema-grounded, verification-first" architecture that automates form completion through field state discovery, source-to-field mapping, deterministic validation, and audit trails. Large language models from the Qwen series are restricted to assisting with mapping tasks without authorization for critical operations, while a bounded error-correction mechanism ensures data rigor. Benchmark evaluations demonstrate that CLAIRE achieves both a success rate and an accuracy of 1.000, substantially reducing the burden of manual review and enhancing processing throughput.

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Recent publications

Latest Papers

Grounding Large Language Models in DSGE Simulators for Policy Generation and Forecasting

Oct 01, 2026

This study addresses the lack of dynamic consistency verification and long-horizon credit assignment mechanisms when large language models (LLMs) formulate economic policies. To overcome these limitations, this work proposes a closed-loop interactive framework that embeds instruction-tuned LLMs within a Dynamic Stochastic General Equilibrium (DSGE) simulator. Policy actions are optimized using the Proximal Policy Optimization (PPO) reinforcement learning algorithm, thereby establishing an evaluation paradigm grounded in economic consequences rather than textual plausibility. This approach effectively resolves the challenge of delayed reward propagation, enabling dynamic simulation and rigorous assessment of policy effects under historical shocks. Ultimately, this research provides a quantifiable and verifiable pathway for AI-driven macroeconomic decision-making.

0 citationsRead paper

Tree Navigation Without LLM Summaries: A Matched-Cost Study of Hierarchical Retrieval for Long-Document QA

Sep 30, 2026

This study addresses the limitations of flat retrieval methods, which often overlook complementary evidence in long documents, and summary-tree approaches that rely on costly LLM-based indexing. We propose NavTree, which constructs a deterministic, balanced segmentation tree without any LLM calls to serve as a navigational scaffold. By integrating hybrid lexical-dense representations with frontier traversal algorithms, NavTree effectively localizes critical leaf nodes, revealing that the advantages of hierarchical retrieval stem primarily from structural navigation rather than summary content. Experimental evaluations demonstrate that NavTree significantly outperforms BM25 under matching-cost assessments and consistently surpasses RAPTOR variants across multi-hop question answering tasks. Ultimately, this work achieves high-performance retrieval with zero indexing cost.

0 citationsRead paper

CORTEX: A Verified Experience Layer for Generalist Agents

Sep 27, 2026

This study addresses the limitations of AI agents in validating historical solutions and adapting to environmental changes by proposing a formalized system contract coupled with an empirical verification layer. Methodologically, the approach records task conditions and proof trajectories, enabling a meta-controller to precisely replay, adapt, or synthesize prior experiences. A challenge-driven development loop is further constructed through context orchestration, validator chains, and typed storage. The core contribution lies in demonstrating that general intelligence can advance through reusable programs rather than weight updates. Evaluations across clinical and policy domains confirm perfect invariance in strategy execution, providing a preliminary yet viable pathway toward artificial general intelligence.

0 citationsRead paper

CLAIRE: A Schema-Grounded Hybrid Workflow for Healthcare Administrative Form Completion

Sep 26, 2026

This study addresses the inefficiency and error-proneness of information extraction in medical form filling by proposing CLAIRE, a hybrid workflow. The method adopts a "schema-grounded, verification-first" architecture that automates form completion through field state discovery, source-to-field mapping, deterministic validation, and audit trails. Large language models from the Qwen series are restricted to assisting with mapping tasks without authorization for critical operations, while a bounded error-correction mechanism ensures data rigor. Benchmark evaluations demonstrate that CLAIRE achieves both a success rate and an accuracy of 1.000, substantially reducing the burden of manual review and enhancing processing throughput.

0 citationsRead paper