3 citations · 5 across the 6 of their papers we have counts for
6 papers
interwhen: A Generalizable Framework for Steering Reasoning Models with Test-time Verification
Vishak K Bhat, Prateek Chanda, Vijval Ekbote +6
Reasoning models produce long traces of intermediate decisions and tool calls, making test-time verification important for ensuring correctness. Existing approaches either verify o…
Code Researcher: Deep Research Agent for Large Systems Code and Commit History
Ramneet Singh, Sathvik Joel, Abhav Mehrotra +4
Large Language Model (LLM)-based coding agents have shown promising results on coding benchmarks, but their effectiveness on systems code remains underexplored. Due to the size and…
Robust Learning of Diverse Code Edits
Tushar Aggarwal, Swayam Singh, Abhijeet Awasthi +2
Software engineering activities frequently involve edits to existing code. However, contemporary code language models (LMs) lack the ability to handle diverse types of code-edit re…
MASAI: Modular Architecture for Software-engineering AI Agents
Daman Arora, Atharv Sonwane, Nalin Wadhwa +5
A common method to solve complex problems in software engineering, is to divide the problem into multiple sub-problems. Inspired by this, we propose a Modular Architecture for Soft…
NoFunEval: Funny How Code LMs Falter on Requirements Beyond Functional Correctness
Manav Singhal, Tushar Aggarwal, Abhijeet Awasthi +2
Existing evaluation benchmarks of language models of code (code LMs) focus almost exclusively on whether the LMs can generate functionally-correct code. In real-world software engi…
Frustrated with Code Quality Issues? LLMs can Help!
Nalin Wadhwa, Jui Pradhan, Atharv Sonwane +5
As software projects progress, quality of code assumes paramount importance as it affects reliability, maintainability and security of software. For this reason, static analysis to…