Context-Induced Priority Switching in Large Language Models: Preliminary Observa

<!-- SC_OFF --><div class="md"><h1>The behavioral pattern was first observed in GPT, Claude and is what motivated this project. The mechanistic investigation was carried out on

<!-- SC_OFF --><div class="md"><p>I set out to test whether AAVE-coded (African American English Vernacular) prompts cause MoE language models to route, deliberate, and respond

<p>Abstract<br /> This paper examines the phenomenon of the emergence of manipulative behavioral patterns in contemporary large language models (LLMs). The author investigates how

arXiv:2604.28031v1 Announce Type: cross Abstract: When researchers iteratively refine ideas with large language models, do the models preserve fidelity to the original objective?

arXiv:2604.13206v1 Announce Type: new Abstract: As Large Language Models (LLMs) are increasingly integrated into agentic workflows, their unpredictability stemming from numerical

<table> <tr><td> <a href="https://www.reddit.com/r/OpenAI/comments/1sw0vcf/stanford_researchers_fed_a_language_model_a_dna/"> <img alt="Stanford researchers fed a language model a

<table> <tr><td> <a href="https://www.reddit.com/r/LocalLLaMA/comments/1tcb4r7/context_is_not_control_a_sourceboundary_eval_for/"> <img alt="Context Is Not Control, a

<!-- SC_OFF --><div class="md"><p>&#x200b;</p> <p>## Abstract</p> <p>Post-training makes language models more decisive without necessarily making them more accurate — and we find