Caught in the Story: Narrative Captivity in Multi-turn LLMs Conversation

arXiv:2605.15208v1 Announce Type: cross Abstract: Large Language Models are routinely compressed via post-training quantization to reduce inference costs and memory footprint for

arXiv:2604.26233v1 Announce Type: new Abstract: As Large Language Models (LLMs) are proposed as legal decision assistants, and even first-instance decision-makers, across a range

arXiv:2605.12922v1 Announce Type: new Abstract: Large language models can follow complex instructions in a single turn, yet over long multi-turn interactions they often lose the

arXiv:2604.09852v1 Announce Type: new Abstract: Reasoning models think in long, unstructured streams with no mechanism for compressing or organizing their own intermediate state.

arXiv:2604.22748v1 Announce Type: new Abstract: As AI systems move from generating text to accomplishing goals through sustained interaction, the ability to model environment

arXiv:2603.18085v1 Announce Type: new Abstract: Recent incidents have highlighted alarming cases where human-AI interactions led to negative psychological outcomes, including