37 citations · 158 across the 41 of their papers we have counts for
42 papers
Information-Gain Rewards over Diversity-Pruned Tests: GT-Anchored Verifier Co-Training for Reliable Code Generation
Ana Nunez, Peyman Najafirad
Self-play methods that co-train a single language model as both coder and test author promise to move code-generation RL beyond fixed test suites, but they suffer from two coupled…
GRCD: Grounded Region Change Detection for Multi-Finding Chest X-Ray Pairs
OFM Riaz Rahman Aranya, Peyman Najafirad, Kevin Desai
Radiologists routinely compare current and prior chest X-rays to track disease progression, producing follow-up reports that describe multiple findings, each localised to an anatom…
Memory-Guided Tree Search with Cross-Branch Knowledge Transfer for LLM Solver Synthesis
Fatemeh Haji, Javier Delarosa Quiros, Peyman Najafirad
Combinatorial optimization (CO) underlies decision-making from logistics to chip design, where infeasible solutions are operationally unusable and small quality gains translate int…
Residual Paving: Diagnosing the Routing Bottleneck in Selective Refusal Editing
Bryce Hinkley, Peyman Najafirad
We study selective refusal editing as a three-way control problem: induce non-refusal on designated edit prompts while preserving benign behavior and harmful refusals outside the e…
Agentic Adversarial Rewriting Exposes Architectural Vulnerabilities in Black-Box NLP Pipelines
Mazal Bethany, Kim-Kwang Raymond Choo, Nishant Vishwamitra +1
Multi-component natural language processing (NLP) pipelines are increasingly deployed for high-stakes decisions, yet no existing adversarial method can test their robustness under…
Reflective Agreement: Combining Self-Mixture of Agents with a Sequence Tagger for Robust Event Extraction
Fatemeh Haji, Mazal Bethany, Cho-Yu Jason Chiang +2
Event Extraction (EE) involves automatically identifying and extracting structured information about events from unstructured text, including triggers, event types, and arguments.…
A Comprehensive Evaluation of the Sensitivity of Density-Ratio Estimation Based Fairness Measurement in Regression
Abdalwahab Almajed, Maryam Tabar, Peyman Najafirad
The prevalence of algorithmic bias in Machine Learning (ML)-driven approaches has inspired growing research on measuring and mitigating bias in the ML domain. Accordingly, prior re…
Machine Learning Fairness in House Price Prediction: A Case Study of America's Expanding Metropolises
Abdalwahab Almajed, Maryam Tabar, Peyman Najafirad
As a basic human need, housing plays a key role in enhancing health, well-being, and educational outcome in society, and the housing market is a major factor for promoting quality…
CAMOUFLAGE: Exploiting Misinformation Detection Systems Through LLM-driven Adversarial Claim Transformation
Mazal Bethany, Nishant Vishwamitra, Cho-Yu Jason Chiang +1
Automated evidence-based misinformation detection systems, which evaluate the veracity of short claims against evidence, lack comprehensive analysis of their adversarial vulnerabil…
FLAVARS: A Multimodal Foundational Language and Vision Alignment Model for Remote Sensing
Isaac Corley, Simone Fobi Nsutezo, Anthony Ortiz +4
Remote sensing imagery is dense with objects and contextual visual information. There is a recent trend to combine paired satellite images and text captions for pretraining perform…
Enhancing Reverse Engineering: Investigating and Benchmarking Large Language Models for Vulnerability Analysis in Decompiled Binaries
Dylan Manuel, Nafis Tanveer Islam, Joseph Khoury +3
Security experts reverse engineer (decompile) binary code to identify critical security vulnerabilities. The limited access to source code in vital systems - such as firmware, driv…
Improving LLM Reasoning with Multi-Agent Tree-of-Thought Validator Agent
Fatemeh Haji, Mazal Bethany, Maryam Tabar +3
Multi-agent strategies have emerged as a promising approach to enhance the reasoning abilities of Large Language Models (LLMs) by assigning specialized roles in the problem-solving…
Jailbreaking Large Language Models with Symbolic Mathematics
Emet Bethany, Mazal Bethany, Juan Arturo Nolazco Flores +2
Recent advancements in AI safety have led to increased efforts in training and red-teaming large language models (LLMs) to mitigate unsafe content generation. However, these safety…
AutoSafeCoder: A Multi-Agent Framework for Securing LLM Code Generation through Static Analysis and Fuzz Testing
Ana Nunez, Nafis Tanveer Islam, Sumit Kumar Jha +1
Recent advancements in automatic code generation using large language models (LLMs) have brought us closer to fully automated secure software development. However, existing approac…
Enhancing Source Code Security with LLMs: Demystifying The Challenges and Generating Reliable Repairs
Nafis Tanveer Islam, Joseph Khoury, Andrew Seong +2
With the recent unprecedented advancements in Artificial Intelligence (AI) computing, progress in Large Language Models (LLMs) is accelerating rapidly, presenting challenges in est…
Enhancing Event Reasoning in Large Language Models through Instruction Fine-Tuning with Semantic Causal Graphs
Mazal Bethany, Emet Bethany, Brandon Wherry +4
Event detection and text reasoning have become critical applications across various domains. While LLMs have recently demonstrated impressive progress in reasoning abilities, they…
Unintentional Security Flaws in Code: Automated Defense via Root Cause Analysis
Nafis Tanveer Islam, Mazal Bethany, Dylan Manuel +2
Software security remains a critical concern, particularly as junior developers, often lacking comprehensive knowledge of security practices, contribute to codebases. While there a…
Can Reinforcement Learning Unlock the Hidden Dangers in Aligned Large Language Models?
Mohammad Bahrami Karkevandi, Nishant Vishwamitra, Peyman Najafirad
Large Language Models (LLMs) have demonstrated impressive capabilities in natural language tasks, but their safety and morality remain contentious due to their training on internet…
Estimating Earthquake Magnitude in Sentinel-1 Imagery via Ranking
Daniele Rege Cambrin, Isaac Corley, Paolo Garza +1
Earthquakes are commonly estimated using physical seismic stations, however, due to the installation requirements and costs of these stations, global coverage quickly becomes impra…
On the Consistency of Fairness Measurement Methods for Regression Tasks
Abdalwahab Almajed, Maryam Tabar, Peyman Najafirad
With growing applications of Machine Learning (ML) techniques in the real world, it is highly important to ensure that these models work in an equitable manner. One main step in en…
RASTeR: Robust, Agentic, and Structured Temporal Reasoning
Dan Schumacher, Fatemeh Haji, Tara Grey +7
Temporal question answering (TQA) remains a challenge for large language models (LLMs), particularly when retrieved content may be irrelevant, outdated, or temporally inconsistent.…
LLM-Powered Code Vulnerability Repair with Reinforcement Learning and Semantic Reward
Nafis Tanveer Islam, Joseph Khoury, Andrew Seong +4
In software development, the predominant emphasis on functionality often supersedes security concerns, a trend gaining momentum with AI-driven automation tools like GitHub Copilot.…
Code Security Vulnerability Repair Using Reinforcement Learning with Large Language Models
Nafis Tanveer Islam, Mohammad Bahrami Karkevandi, Peyman Najafirad
With the recent advancement of Large Language Models (LLMs), generating functionally correct code has become less complicated for a wide array of developers. While using LLMs has s…
Image Safeguarding: Reasoning with Conditional Vision Language Model and Obfuscating Unsafe Content Counterfactually
Mazal Bethany, Brandon Wherry, Nishant Vishwamitra +1
Social media platforms are being increasingly used by malicious actors to share unsafe content, such as images depicting sexual activity, cyberbullying, and self-harm. Consequently…