arXiv AI
The Profit Alignment Problem: How Profit Mandates Induce Alignment Failures in LLMs
Read the original on arXiv AI →The Flow has not summarised this story yet — read it at arXiv AI.
Related stories
Hugging Face Trending Papers
Sep 7
The Profit Alignment Problem: How Profit Mandates Induce Alignment Failures in LLMs
We show that ordinary business language --- "maximize profitability" --- induces profit-oriented ambiguity resolution: LLMs systematically dismiss ambiguous signals of potential safety violations to s...
arXiv AI
Jun 15
Generative AI for Managerial Decision-Making under Ambiguity and Sycophancy
arXiv:2603. 03970v2 Announce Type: replace Abstract: Generative artificial intelligence (GenAI) is increasingly being integrated into complex business workflows, fundamentally shifting the boundaries of managerial decision-making.
arXiv AI
Jun 10
Janus: A Benchmark for Goal-Conditioned Information Distortion in LLMs
arXiv:2606. 10852v1 Announce Type: cross Abstract: LLM deception is often evaluated through direct markers such as fabricated claims, explicit lies, or strategic concealment.
arXiv AI
Sep 17
Beyond Routine Compliance: Cunning Data Cultivates Safety Vigilance in Large Language Models
The paper introduces "cunning questions"—non‑safety prompts that contain misleading premises or subtle inconsistencies—to train large language models (LLMs) to scrutinize underlying intent and assumptions. Experiments show that incorporating these questions improves robustness against out‑of‑distribution jailbreak attacks and enhances subsequent safety fine‑tuning, achieving a new state‑of‑the‑art reduction in mean ASR from 17.40% to 15.05% across nine backbone–benchmark combinations. The authors argue that this training fosters vigilance, enabling models to prioritize safety judgments before engaging in harmful planning.
Hugging Face Trending Papers
Jul 8
Alignment Plausibility: A New Standard for Assuring AI in Healthcare
Large language models (LLMs) have become significant providers of mental health support, yet they remain products of an attention economy whose operational and commercial targets favour sustained engagement over the friction that effective psychological support often requires. Developers' safety responses have been largely reactive, addressing the most visible and acute harms while subtler, longer-term patterns of risk (e.
arXiv AI
Jul 10
Alignment Plausibility: A New Standard for Assuring AI in Healthcare
arXiv:2607. 07766v1 Announce Type: new Abstract: Large language models (LLMs) have become significant providers of mental health support, yet they remain products of an attention economy whose operational and commercial targets favour sustained engagement over the friction that effective psychological support often requires.