arXiv AI July 29, 2026 By Gi-Hun Lee, Joong Yull Park

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.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

arXiv AI

Sep 1

How Language Models Choose Sides: Internal Representations of Instruction Hierarchy

arXiv:2608.28648v1 Announce Type: new Abstract: We study how instruction-tuned LLMs arbitrate direct conflicts between system and user instructions. We introduce a benchmark of 41 paired constraints...

By Enrique Balp-Straffon, Chih-Hao Hsu, Rushiraj Gadhvi, Sunishchal Dev, Callum Stuart McDougall, Anusha Mujumdar

llms benchmarks

arXiv Machine Learning

Sep 23

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...

By Shubham Santosh Pandere, Gautam Ranka, Ritika Varshney, Navya Deshmukh, Roushni Sareen, Roshan Kumar Singh

llms fine-tuning benchmarks safety

arXiv AI

Sep 12

Off-Target Effects of Response-Style Alignment in a Korean 27B Language Model

arXiv:2609. 11291v1 Announce Type: new Abstract: We post-train Qwen3.

By Hyojung Han

llms benchmarks safety

arXiv AI

Aug 25

Evaluation Awareness in Language Models: Representation, Verbalization, and Control

arXiv:2608.21766v1 Announce Type: cross Abstract: Both capability and safety benchmarks rest upon the assumption that the behavior of language models undergoing a test is informative about their beha...

By Farzaneh Heidari, Amin Memarian, Guillaume Rabusseau

llms fine-tuning benchmarks safety

arXiv Machine Learning

Aug 31

The Instability of Safety: How Random Seeds and Temperature Expose Inconsistent LLM Refusal Behavior

The paper challenges the assumption that large language models (LLMs) produce deterministic safety responses by examining how random seeds and temperature settings affect refusal decisions. Across four instruction‑tuned models and 876 harmful prompts, 18‑28% of prompts flipped between refusal and compliance depending on sampling configuration, with higher temperatures reducing decision stability. The authors introduce a Safety Stability Index (SSI) and recommend multi‑sample evaluation protocols that account for stochastic variation rather than relying on single‑shot tests.

By Erik Larsen

llms safety

arXiv AI

Aug 11

Reproducing and Stress-Testing Two Approaches to LLM Reasoning Reliability: Test-Time Probability Aggregation and Logic-Representation Editing

arXiv:2608. 08514v1 Announce Type: new Abstract: We independently reproduce two recent methods for making large language model (LLM) reasoning more reliable, and stress-test them across domains and models (RPC across four new task domains with Qwen3-8B, LCF across four 7-8B models).

By Minhan Cho, Jimin Kweon

llms data-engineering

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Related stories

How Language Models Choose Sides: Internal Representations of Instruction Hierarchy

Rewired or Gated? How Instruction Tuning Shapes Knowledge-Conflict Circuits in LLMs

Off-Target Effects of Response-Style Alignment in a Korean 27B Language Model

Evaluation Awareness in Language Models: Representation, Verbalization, and Control

The Instability of Safety: How Random Seeds and Temperature Expose Inconsistent LLM Refusal Behavior

Reproducing and Stress-Testing Two Approaches to LLM Reasoning Reliability: Test-Time Probability Aggregation and Logic-Representation Editing