Signals 4 · free daily AI digest

Characterizing When Language Models Take Harmful Actions to Protect Their Assigned Goals

Paper recorded by Signals 4 on 2026-11-02 in cs.CL. Abstract reproduced from arXiv; link to the original below.

Published 2026-11-02 on arXiv · recorded by Signals 4 on 2026-11-03

Category: cs.CL · 自然语言处理 · first seen 2026-11-03

Abstract

Recent language model (LM) safety failures have largely been studied as disparate phenomena. In agentic misalignment, a model blackmails an executive to avoid being replaced. In alignment faking, it complies with harmful requests during training to keep its values from being modified. We argue that these behaviours share a common decision structure: a model assigned a goal must either take a harmf

Read on arXiv →

#19 most recent of 628 cs.CL papers we have recorded · ↑ newer: Same Outputs, Different Scores: Auditing the Unchecked Graders of Mult · ↓ older: Steering with Multilingual Lexicons for Sentiment Control in Low-Resou
Cite this page: Characterizing When Language Models Take Harmful Actions to Protect Their Assigned Goals: the #19 most recent of 628 cs.CL papers we have recorded (as of 2026-11-02). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/characterizing-when-language-models-take-harmful-actions-to-protect-their-assign.html
Free to quote with attribution to “Signals 4 (Signals API)”. Machine-readable: papers.json
Related: More cs.CL papers · arXiv signals · All papers · Today in AI
Get 4 AI signals a day by email — free.
Subscribe free → See all plans →
Get 4 AI signals a day by email — free
All models · All repos · By company · Daily editions