19 citations · 29 across the 10 of their papers we have counts for
10 papers
Chow-Liu Ordering for Long-Context Reasoning in Chain-of-Agents
Naman Gupta, Vaibhav Singh, Arun Iyer +8
Sequential multi-agent reasoning frameworks such as Chain-of-Agents (CoA) handle long-context queries by decomposing inputs into chunks and processing them sequentially using LLM-b…
COSMIR: Chain Orchestrated Structured Memory for Iterative Reasoning over Long Context
Naman Gupta, Shreeyash Gowaikar, Arun Iyer +6
Reasoning over very long inputs remains difficult for large language models (LLMs). Common workarounds either shrink the input via retrieval (risking missed evidence), enlarge the…
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…
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…
Class-Level Code Generation from Natural Language Using Iterative, Tool-Enhanced Reasoning over Repository
Ajinkya Deshpande, Anmol Agarwal, Shashank Shet +4
LLMs have demonstrated significant potential in code generation tasks, achieving promising results at the function or statement level across various benchmarks. However, the comple…
FiGURe: Simple and Efficient Unsupervised Node Representations with Filter Augmentations
Chanakya Ekbote, Ajinkya Pankaj Deshpande, Arun Iyer +2
Unsupervised node representations learnt using contrastive learning-based methods have shown good performance on downstream tasks. However, these methods rely on augmentations that…
CodePlan: Repository-level Coding using LLMs and Planning
Ramakrishna Bairi, Atharv Sonwane, Aditya Kanade +6
Software engineering activities such as package migration, fixing errors reports from static analysis or testing, and adding type annotations or other specifications to a codebase,…
Using Large-scale Heterogeneous Graph Representation Learning for Code Review Recommendations at Microsoft
Jiyang Zhang, Chandra Maddila, Ram Bairi +6
Code review is an integral part of any mature software development process, and identifying the best reviewer for a code change is a well-accepted problem within the software engin…
User Embedding based Neighborhood Aggregation Method for Inductive Recommendation
Rahul Ragesh, Sundararajan Sellamanickam, Vijay Lingam +2
We consider the problem of learning latent features (aka embedding) for users and items in a recommendation setting. Given only a user-item interaction graph, the goal is to recomm…
HeteGCN: Heterogeneous Graph Convolutional Networks for Text Classification
Rahul Ragesh, Sundararajan Sellamanickam, Arun Iyer +2
We consider the problem of learning efficient and inductive graph convolutional networks for text classification with a large number of examples and features. Existing state-of-the…