arXiv AI
How Language Models Choose Sides: Internal Representations of Instruction Hierarchy
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Rewired or Gated? How Instruction Tuning Shapes Knowledge-Conflict Circuits in LLMs
arXiv:2609.25602v1 Announce Type: new Abstract: In language models, the choice between believing the prompt and believing the weights is made by a handful of identifiable attention heads. Instruction...
arXiv AI
Sep 2
Do as I Say, Not as I Do: Instruction-Induction Conflict in LLMs
The paper investigates how large language models balance instruction-following with pattern completion when the two objectives conflict. By creating dialogues where a user instruction to act in a target way T is opposed by assistant turns that demonstrate a competing pattern P, the authors measure instruction-following rates across 13 models and 16 instructions over up to 50 turns. Results show wide variability (1%–99%) in instruction adherence, with robustness influenced by instruction content, output format, and chain-of-thought reasoning, but overall instruction-following remains brittle under induction pressure.
arXiv AI
Jun 16
Constitutional Value Potentials: reading and steering internal priority margins in language models
arXiv:2606. 15420v1 Announce Type: cross Abstract: A constitution tells a language model what to value, but little tells us whether it does.
arXiv AI
Jun 9
Where Instruction Hierarchy Breaks: Diagnosing and Repairing Failures in Reasoning Language Models
arXiv:2606. 07808v1 Announce Type: new Abstract: Reasoning language models deployed in agentic workflows must follow an instruction hierarchy: when instructions from different sources conflict, the model should obey the highest-privilege applicable instruction.
arXiv AI
Aug 17
When Personal Memory Has No Single Answer: Evaluating LLM Agents under Irreducible Conflict
arXiv:2608. 13921v1 Announce Type: new Abstract: LLM agents increasingly maintain personal memory across sessions, but it can conflict.
arXiv AI
Jul 29
Measuring and Improving Behavioral Consistency in Large Language Models through Fact-Heuristic-Emotion State Enforcement
arXiv:2607. 24765v1 Announce Type: cross Abstract: Large language models (LLMs) can give different answers to the same decision problem across runs, and reverse a decision when their own prior answer returns as context.