As training corpora grow, screening licenses for AI training data at scale is likely to fall to large language models, but a screening system must distinguish when a document settles a proposed use from when the answer depends on facts outside it. We formalize this distinction under Korean copyright law. Supreme Court of Korea authority supports five types of extrinsic fact that may be required to decide whether a grant reaches a use or binds the rightsholder: consideration, usage of trade, the rightsholder’s conduct, market substitution, and upstream title. A practicing Korean intellectual property lawyer applies this framework to Korean licenses, terms of service, and standard-form contracts, yielding a benchmark of whether each document settles five AI-related uses and, if not, which facts are missing. We evaluate frontier proprietary, open-weight, and Korean-specialized models under increasingly detailed legal procedures and broader retrieved context. Neither the lawyer’s screening procedure, additional context, nor even the governing clause reliably improves models’ ability to distinguish determinate from indeterminate cases; the governing clause instead increases abstention on many determinate items. These interventions mainly shift decision thresholds rather than discriminability, a difference obscured by aggregate accuracy. Automated license screening has two limits: some questions are indeterminate from the instrument because resolving them requires facts outside it, and models over-abstain on questions the document does settle.

Large Language Models (LLMs) often memorize and regenerate sensitive or protected content that requires inference-time suppression. While machine unlearning has emerged as a highly active area of research, existing unlearning methods often degrade performance on retain dataset or require costly retraining. In this paper, we introduce Exponential-Tilted Unlearning (ETU), an inference-time method that formulates unlearning as the KL I-projection of the base next-token distribution under a user-specified upper bound 𝜀 on the forget-token mass, with a closed-form exponential tilt whose scalar multiplier 𝜆 is set analytically by 𝜀. Under a single shared protocol, (i) ETU satisfies the 𝜀 constraint across nine instruction-tuned dense and Mixture-of-Experts backbones on TOFU, RWKU, and WPU while preserving retain utility; (ii) the same calibrated family covers the full 𝜀 spectrum with the strict zero-mass case as a limiting operating point; and (iii) representative weight-update methods expose no such 𝜀-calibrated residual-mass control.

Recent advances in Large Language Models (LLMs) have enabled multi-agent systems that simulate real-world interactions with near-human reasoning. While previous studies have extensively examined biases related to protected attributes such as race, the emergence and propagation of biases on socially contentious issues in multi-agent LLM interactions remain underexplored. This study explores how LLM agents shape public opinion through debates on five contentious topics. By simulating over 2,500 debates, we analyze how initially neutral agents, assigned a centrist disposition, adopt specific stances over time. Statistical analyses reveal significant group conformity mirroring human behavior; LLM agents tend to align with numerically dominant groups or more intelligent agents, exerting a greater influence. These findings underscore the crucial role of agent intelligence in shaping discourse and highlight the risks of bias amplification in online interactions. Our results emphasize the need for policy measures that promote diversity and transparency in LLM-generated discussions to mitigate the risks of bias propagation within anonymous online environments.