13 papers
No Unique Minimizer, No Problem: On the Consistency of Robust Neural Classifiers
Subhabrata Majumdar, Anand Deo, Partha Pratim Saha +1
Neural network classifiers trained by cross-entropy minimization are highly sensitive to label noise and adversarial contamination. While robust alternatives offer bounded influenc…
OTAP: Structure-Aware Optimal Transport for Evaluating Planning and Execution in Agent Trajectories
Babak Barazandeh, Subhabrata Majumdar, George Michailidis
Large language model agents solve tasks by generating trajectories that interleave planning, tool calls, and intermediate results. Current evaluation metrics reduce such a trajecto…
Distribution-Specific Curvature Control with Finite-Sample Guarantees for Open-Weight Safety
Domenic Rosati, Ali Dadsetan, Hong Huang +5
A short fine-tuning run can undo the safety guards of an open-weight model---retraining a refusal-trained assistant to aid weapons development or produce hate speech. Preventing su…
Localized LoRA-MoE: Block-wise Low-Rank Experts With Adaptive Routing
Babak Barazandeh, Subhabrata Majumdar, Vinay Prithyani +1
Large Language Models (LLMs) and high-dimensional perception networks increasingly rely on parameter-efficient fine-tuning (PEFT) to adapt to diverse operational contexts. However,…
Limits of Reliability and Scaling in Language Models
Subhabrata Majumdar
Large language models (LLMs) are trained and evaluated as though perfect reliability is achievable for any task given sufficient scale. We show that this assumption is information-…
PsychoPass: Geometric Profiling of Multi-Turn Adversarial LLM Conversations
Muberra Ozmen, Subhabrata Majumdar
Multi-turn jailbreak attacks on large language models (LLMs) reveal a mismatch in current guardrails: they operate on individual turns, while attacks unfold as trajectories across…