Papers by Dinil Mon Divakaran
Benchmarking LLMs and LLM-based Agents in Practical Vulnerability Detection for Code Repositories (2025.acl-long)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) have shown promise in software vulnerability detection, especially on function-level benchmarks like Devign and BigVul. |
| Approach: | They propose a JIT vulnerability detection benchmark linking each function to its vulnerability-introducing and fixing commits. |
| Outcome: | The proposed JIT vulnerability detection benchmark enables comprehensive evaluation of detection capabilities. |
CLaRE-ty Amid Chaos: Quantifying Representational Entanglement to Predict Ripple Effects in LLM Editing (2026.findings-acl)
Copied to clipboard
| Challenge: | Large language models (LLMs) are outdated or incorrect over time due to unintended ripple effects that propagate even to the hidden space. |
| Approach: | They propose a lightweight representation-level technique to identify where ripple effects may occur by detecting entanglement between facts using forward activations from a single intermediate layer. |
| Outcome: | The proposed method achieves 62.2% improvement in Spearman correlation with ripple effects while being 2.74 faster and using 2.85 less peak GPU memory. |