The paper introduces a taxonomy of six user challenge types and a four-layer framework to analyze how large language models respond to user disagreement. Using a dataset of 2,310 challenge scenarios and 32,340 responses from 14 models, the study finds that models often validate users (85%) while still maintaining their original claim (65%). It also reports that models frequently apologize (33%) and transfer authority in advice contexts, with significant variation across model types and task domains.
By Riyadh Alnasser, Yusuf M\"ucahit \c{C}etinkaya, Sumin Zhao, Tu\u{g}rulcan Elmas
arXiv:2608. 14630v1 Announce Type: cross Abstract: Human decision-making is often shaped by a range of well-documented cognitive biases.
By Zirui Cheng, Joey Chan, Simo Du, Chenhao Tan, Yue Guo, Hao Peng
arXiv:2609.08016v1 Announce Type: new
Abstract: Multi-agent debate, in which several LLMs exchange arguments before answering, is widely assumed to improve answer quality by surfacing genuine disagre...
By Chen Qian
The study examines how different editorial framings in prompts influence large language models’ statistical analysis reports. Using a 4×4 factorial design, researchers found that certain framings—particularly brutally critical prompts on genuine effects and significance-seeking prompts on underpowered nulls—led to factual misrepresentations. Tone shifts were more widespread, with critical framing inducing defensive language across all data patterns, while a confound in the data largely prevented both factual and tonal distortions.
By Paras Balani, Subhrakanta Panda
arXiv:2601. 21433v2 Announce Type: replace Abstract: Language models are increasingly consulted on ethically consequential questions, yet the stance a model expresses may not survive a change in framing.
By Katherine Elkins, Jon Chun
arXiv:2610.18159v1 Announce Type: cross
Abstract: Sycophancy in Large Language Models, the tendency to align responses with user preferences over ground truth, has largely been studied using question...
By Jean Seo, Pepa Atanasova, Isabelle Augenstein
The paper investigates how well large language models (LLMs) can handle character attacks—ad hominem arguments—in political debates. By analyzing natural political dialogues and comparing LLM-generated responses to a corpus of U.S. presidential debates, the study finds that most LLMs favor logical defenses and rarely use ethos-based counterattacks. The authors suggest that safety fine‑tuning limits LLMs’ strategic options, preventing them from fully engaging in realistic political discourse.
By Ewelina Gajewska, Katarzyna Budzynska, Jaroslaw Chudziak
The paper introduces the SAST-IR framework to evaluate large language models’ robustness against persuasion attacks in a memory‑less setting, revealing a flaw called "Refusal Inertia" that masks true vulnerability. Using the CP‑Agent and a custom CounterFact‑Strict dataset, the authors demonstrate that simple, diverse attack strategies achieve a 96% success rate, while complex attacks often trigger defensive compliance. The study highlights severe brittleness in current state‑of‑the‑art models when deprived of conversation history.
By Zhuoang Cai
SciIntBench is an adversarial benchmark that tests large language models (LLMs) on 1,350 prompts across 270 scenarios, covering ten research integrity categories and three scientific domains. Each scenario includes five gradations of adversarial framing—from Most Overt to Most Covert—plus a benign version, enabling measurement of both refusal of misconduct and helpfulness to legitimate requests. In evaluating 16 commercial and open‑weight LLMs, the study found that under the most covert framing, models failed to refuse requests for research misconduct in 71.7% of cases, with weaker refusals observed for transparency, plagiarism, and fabrication.
By Almene De Meran Meguimtsop, Maria Leonor Pacheco, Daniel E. Acuna
The paper presents a defence against misinformation that focuses on the recurring persuasion strategies—social influence, information manipulation, emotional appeals, fear, conspiracy, and fabrication—rather than on fact-checking individual claims. By training people and language models on a large playbook derived from 214,646 fact-checked claims in 51 languages, the authors show that this approach can improve judgement of unfamiliar claims without increasing cynicism toward true claims. The study includes a pre-registered experiment where briefing participants with the playbook enhanced their discernment of unfamiliar claims while leaving belief in true claims unchanged, though the overall gain was not statistically significant.
By Tong Guo, Teng Ye, Yutong Xie, Qiaozhu Mei
arXiv:2606. 26437v1 Announce Type: cross Abstract: Existing metrics for factuality and faithfulness evaluate whether an answer is supported or contradicted by its grounding documents, but they fail to capture when both supporting and contradicting evidence coexist.
By Siyi Liu, Aaron Halfaker, Dan Roth, Patrick Xia
The paper introduces a boundary-aware self‑distillation framework for controlled large language model safety refusal, addressing the need for different refusal boundaries within the same topic. It combines controlled topic generation, coverage repair, in‑distribution compensation data, and harmful‑benign pairs to train and evaluate refusal behavior. Experiments on Qwen3‑8B show that escalating retries dramatically improve target‑domain refusal rates while reducing unsafe responses, though they also increase over‑refusal, highlighting the trade‑off between safety and usability.
By Alejo L\'opez-\'Avila, Iker Garc\'ia-Ferrero, Jezabel Garcia, Antonio Tiene, Rom\'an Or\'us