2 citations · 2 across the 8 of their papers we have counts for
12 papers
Ulysses: A Security Harness For Synthesizing Verifiable Web Exploits
Aditya Oke, André Vicente Duarte, Rui Melo +6
How can an AI agent automatically discover security vulnerabilities in any software repository? Existing approaches use LLMs to generate software vulnerability exploits. However, i…
Self-Evolving Static Analysis via LLM Pseudo-Labeling
Rui Melo, Andre Duarte, Aditya Oke +5
Security bugs often slip past static analysis not because they are impossible to detect, but because detection rules have not yet been written for the code in front of us. New fram…
Exploring Visual Access and Proactive AI Tutoring for Handwritten Mathematics
Nachiket Kotalwar, Conrad Borchers, Shubham Gandhi +3
Handwritten work captures mathematical reasoning that learners may not express in typed or voice-dictated messages. We built a multimodal AI tutoring platform in which a phone came…
ABSENTIA: Detecting Broken Access Control Vulnerabilities in Web Applications
André V. Duarte, Aditya Oke, Rui Melo +7
Broken access control, the failure of authorization, is one of the most prevalent web security risks. Unlike injection, a flow of untrusted input into a dangerous operation, author…
DRACO: Fine-Grained Credit Assignment with Dynamic Rubrics for Long-Horizon Agent Training
Shubham Gandhi, Saurabh Goyal, Kiran Kate +1
Reinforcement Learning from Verifiable Rewards works well when a task has a programmatic checker, but most long-horizon agent domains have none. We work in the outcome-blind settin…
Steer, Don't Solve: Training Small Critic Models for Large Code Agents
Shubham Gandhi, Yiqing Xie, Atharva Naik +2
Coding tasks are typically complicated and require multiple capabilities, ranging from high-level planning to low-level implementation. While coding agents are optimized for the jo…
Hybrid-Gym: Training Coding Agents to Generalize Across Tasks
Yiqing Xie, Emmy Liu, Gaokai Zhang +7
When assessing the quality of coding agents, predominant benchmarks focus on solving single issues on GitHub, such as SWE-Bench. In contrast, in real use, these agents solve more v…
When Agents go Astray: Course-Correcting SWE Agents with PRMs
Shubham Gandhi, Jason Tsay, Jatin Ganhotra +2
Large Language Model (LLM) agents are increasingly deployed for complex, multi-step software engineering (SWE) tasks. However, their trajectories often contain costly inefficiencie…
An Empirical Study on Strong-Weak Model Collaboration for Repo-level Code Generation
Shubham Gandhi, Atharva Naik, Yiqing Xie +1
We study cost-efficient collaboration between strong and weak language models for repository-level code generation, where the weak model handles simpler tasks at lower cost, and th…
ResearchCodeAgent: An LLM Multi-Agent System for Automated Codification of Research Methodologies
Shubham Gandhi, Dhruv Shah, Manasi Patwardhan +2
In this paper we introduce ResearchCodeAgent, a novel multi-agent system leveraging large language models (LLMs) agents to automate the codification of research methodologies descr…
BudgetMLAgent: A Cost-Effective LLM Multi-Agent system for Automating Machine Learning Tasks
Shubham Gandhi, Manasi Patwardhan, Lovekesh Vig +1
Large Language Models (LLMs) excel in diverse applications including generation of code snippets, but often struggle with generating code for complex Machine Learning (ML) tasks. A…
StaticFixer: From Static Analysis to Static Repair
Naman Jain, Shubham Gandhi, Atharv Sonwane +5
Static analysis tools are traditionally used to detect and flag programs that violate properties. We show that static analysis tools can also be used to perturb programs that satis…