6 papers
Rubric-Grounded RL: Structured Judge Rewards for Generalizable Reasoning
Manish Bhattarai, Ismael Boureima, Nishath Rajiv Ranasinghe +2
We argue that decomposing reward into weighted, verifiable criteria and using an LLM judge to score them provides a partial-credit optimization signal: instead of a binary outcome…
Benchmarking Large Language Models with Integer Sequence Generation Tasks
Daniel O'Malley, Manish Bhattarai, Nishath Rajiv Ranasinghe +2
We present a novel benchmark designed to rigorously evaluate the capabilities of large language models (LLMs) in mathematical reasoning and algorithmic code synthesis tasks. The be…
ARCS: Agentic Retrieval-Augmented Code Synthesis with Iterative Refinement
Manish Bhattarai, Miguel Cordova, Minh Vu +3
We present Agentic Retrieval-Augmented Code Synthesis (ARCS), a system that improves LLM-based code generation without fine-tuning. ARCS operates through a budgeted synthesize-exec…
Lost in OCR Translation? Vision-Based Approaches to Robust Document Retrieval
Alexander Most, Joseph Winjum, Ayan Biswas +4
Retrieval-Augmented Generation (RAG) has become a popular technique for enhancing the reliability and utility of Large Language Models (LLMs) by grounding responses in external doc…
Enhancing Cross-Language Code Translation via Task-Specific Embedding Alignment in Retrieval-Augmented Generation
Manish Bhattarai, Minh Vu, Javier E. Santos +2
We introduce a novel method to enhance cross-language code translation from Fortran to C++ by integrating task-specific embedding alignment into a Retrieval-Augmented Generation (R…
HEAL: Hierarchical Embedding Alignment Loss for Improved Retrieval and Representation Learning
Manish Bhattarai, Ryan Barron, Maksim Eren +8
Retrieval-Augmented Generation (RAG) enhances Large Language Models (LLMs) by integrating external document retrieval to provide domain-specific or up-to-date knowledge. The effect…