5 citations · 10 across the 18 of their papers we have counts for
6 papers · 1 filter
Go-UT-Bench: A Fine-Tuning Dataset for LLM-Based Unit Test Generation in Go
Yashshi Pipalani, Hritik Raj, Rajat Ghosh +2
Training data imbalance poses a major challenge for code LLMs. Most available data heavily over represents raw opensource code while underrepresenting broader software engineering…
A Multi-Agent Framework for Stateful Inference-Time Search
Arshika Lalan, Rajat Ghosh, Aditya Kolsur +1
Recent work explores agentic inference-time techniques to perform structured, multi-step reasoning. However, stateless inference often struggles on multi-step tasks due to the abse…
RANGER -- Repository-Level Agent for Graph-Enhanced Retrieval
Pratik Shah, Rajat Ghosh, Aryan Singhal +1
General-purpose automated software engineering (ASE) includes tasks such as code completion, retrieval, repair, QA, and summarization. These tasks require a code retrieval system t…
BAR Conjecture: the Feasibility of Inference Budget-Constrained LLM Services with Authenticity and Reasoning
Jinan Zhou, Rajat Ghosh, Vaishnavi Bhargava +2
When designing LLM services, practitioners care about three key properties: inference-time budget, factual authenticity, and reasoning capacity. However, our analysis shows that no…
Predictive Scaling Laws for Efficient GRPO Training of Large Reasoning Models
Datta Nimmaturi, Vaishnavi Bhargava, Rajat Ghosh +2
Fine-tuning large language models (LLMs) for reasoning tasks using reinforcement learning methods like Group Relative Policy Optimization (GRPO) is computationally expensive. To ad…
AILuminate: Introducing v1.0 of the AI Risk and Reliability Benchmark from MLCommons
Shaona Ghosh, Heather Frase, Adina Williams +99
The rapid advancement and deployment of AI systems have created an urgent need for standard safety-evaluation frameworks. This paper introduces AILuminate v1.0, the first comprehen…