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Ramesh Karri

7 accepted papers

2026

BOP-ASK: Object-Interaction Reasoning for Vision-Language Models

CVPR 2026

Vision-Language Models (VLMs) have achieved impressive performance on spatial reasoning benchmarks, yet these evaluations mask critical weaknesses in understanding object interactions. Current benchmarks test high-level relationships ("left of," "behind", etc.) but ignore fine-grained spatial unders

Cited by 0SourcecodeScholar
2026

Towards Effective Offensive Security LLM Agents: Hyperparameter Tuning, LLM as a Judge, and a Lightweight CTF Benchmark

AAAI 2026technical

Recent advances in LLM agentic systems have improved the automation of offensive security tasks, particularly for Capture the Flag (CTF) challenges. We systematically investigate the key factors that drive agent success and provide a detailed recipe for building effective LLM-based offensive securit

Cited by 0SourcePDFScholar
2025

EnIGMA: Interactive Tools Substantially Assist LM Agents in Finding Security Vulnerabilities

ICML 2025poster

Although language model (LM) agents have demonstrated increased performance in multiple domains, including coding and web-browsing, their success in cybersecurity has been limited. We present *EnIGMA*, an LM agent for autonomously solving Capture The Flag (CTF) challenges. We introduce new tools an…

Cited by 0SourcePDFScholar
2025

VeriLoC: Line-of-Code Level Prediction of Hardware Design Quality from Verilog Code

NeurIPS 2025poster

Modern chip design is complex, and there is a crucial need for early-stage prediction of key design-quality metrics like timing and routing congestion directly from Verilog code (a commonly used programming language for hardware design). It is especially important yet complex to predict individual…

Cited by 0SourceScholar
2025

VeriThoughts: Enabling Automated Verilog Code Generation using Reasoning and Formal Verification

NeurIPS 2025poster

This paper introduces VeriThoughts, a novel dataset designed for reasoning-based Verilog code generation. We establish a new benchmark framework grounded in formal verification methods to evaluate the quality and correctness of generated hardware descriptions. Additionally, we present a suite of spe…

Cited by 0SourcecodeScholar
2024

NYU CTF Bench: A Scalable Open-Source Benchmark Dataset for Evaluating LLMs in Offensive Security

NeurIPS 2024poster

Large Language Models (LLMs) are being deployed across various domains today. However, their capacity to solve Capture the Flag (CTF) challenges in cybersecurity has not been thoroughly evaluated. To address this, we develop a novel method to assess LLMs in solving CTF challenges by creating a scala…

2024

Retrieval-Guided Reinforcement Learning for Boolean Circuit Minimization

ICLR 2024poster

Logic synthesis, a pivotal stage in chip design, entails optimizing chip specifications encoded in hardware description languages like Verilog into highly efficient implementations using Boolean logic gates. The process involves a sequential application of logic minimization heuristics (``synthesis…

Cited by 5SourcePDFScholar