collaborators

13 papers

cs.CV2026

To Erase, or Not to Erase: Robust Training-Free Concept Erasure with Preservation aware Adaptive Ranked Subspace Expansion

Shaswati Saha, Rajasekhar Anguluri, Manas Gaur

Concept erasure techniques (CETs) edit text-to-image diffusion models to erase undesired targets such as NSFW content or copyrighted styles, while preserving model utility on benig…

cs.CL2026

From Concept-Aligned Tokens to Vulnerable Features: Mechanistic Localization of Jailbreaks

Nilanjana Das, Mathew Dawit, Aman Chadha +1

Jailbreak attacks expose a persistent failure mode in safety-aligned LLMs: models can be pushed into harmful behavior, but the internal representations enabling this shift remain p…

cs.CL2026

Ranking Free RAG: Replacing Re-ranking with Selection in RAG for Sensitive Domains

Yash Saxena, Ankur Padia, Mandar S Chaudhary +3

Retrieval-Augmented Generation (RAG) systems deployed in sensitive domains must provide interpretable evidence selection and robust safeguards against data poisoning, yet current a…

cs.CL2026

Structure-Aware Chunking for Tabular Data in Retrieval-Augmented Generation

Pooja Guttal, Varun Magotra, Vasudeva Mahavishnu +3

Tabular documents such as CSV and Excel files are widely used in enterprise data pipelines, yet existing chunking strategies for retrieval-augmented generation (RAG) are primarily…

cs.SE2026

Analyzing Chain of Thought (CoT) Approaches in Control Flow Code Deobfuscation Tasks

Seyedreza Mohseni, Sarvesh Baskar, Edward Raff +1

Code deobfuscation is the task of recovering a readable version of a program while preserving its original behavior. In practice, this often requires days or even months of manual…

cs.CL2026

Do LLMs Adhere to Label Definitions? Examining Their Receptivity to External Label Definitions

Seyedali Mohammadi, Bhaskara Hanuma Vedula, Hemank Lamba +4

Do LLMs genuinely incorporate external definitions, or do they primarily rely on their parametric knowledge? To address these questions, we conduct controlled experiments across mu…