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cs.CL2026
PreUnlearn: Auditing Collateral Knowledge Damage Before Large Language Model Unlearning
Bo Su, Ankit Shah, Thai Le
Machine unlearning for large language models (LLMs) aims to remove specified knowledge while preserving the rest of the model's capabilities. However, the boundary between knowledg…
cs.CL2026
Inference Time Optimization with Confidence Dynamics
Yu Wang, Minghao Liu, Jiayun Wang +3
Inference time optimization techniques, such as repeated sampling, have significantly advanced the reasoning capabilities of Large Language Models (LLMs). However, the critical rol…
cs.CL2026
Training-Free Agentic AI: Probabilistic Control and Coordination in Multi-Agent LLM Systems
Mohammad Parsa Hosseini, Ankit Shah, Saiyra Qureshi +3
Multi-agent large language model (LLM) systems enable complex, long-horizon reasoning by composing specialized agents, but practical deployment remains hindered by inefficient rout…