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A focused feed for RLHF, preference data, rater protocols, agent evaluation, and LLM-as-judge research. Every paper includes structured metadata for quick triage.

Total papers: 40 Search mode: keyword RSS

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Obscure but Effective: Classical Chinese Jailbreak Prompt Optimization via Bio-Inspired Search

Xun Huang, Simeng Qin, Xiaoshuang Jia, Ranjie Duan, Huanqian Yan, Zhitao Zeng · Feb 26, 2026

Citations: 0
Red Team Automatic Metrics Multilingual
  • Owing to its conciseness and obscurity, classical Chinese can partially bypass existing safety constraints, exposing notable vulnerabilities in LLMs.
  • To enhance readability and evaluation accuracy, we further design a classical Chinese to English translation module.
MEDSYN: Benchmarking Multi-EviDence SYNthesis in Complex Clinical Cases for Multimodal Large Language Models

Boqi Chen, Xudong Liu, Jiachuan Peng, Marianne Frey-Marti, Bang Zheng, Kyle Lam · Feb 25, 2026

Citations: 0
Expert Verification Automatic Metrics MedicineCoding
  • Multimodal large language models (MLLMs) have shown great potential in medical applications, yet existing benchmarks inadequately capture real-world clinical complexity.
  • We introduce MEDSYN, a multilingual, multimodal benchmark of highly complex clinical cases with up to 7 distinct visual clinical evidence (CE) types per case.
Alignment-Weighted DPO: A principled reasoning approach to improve safety alignment

Mengxuan Hu, Vivek V. Datla, Anoop Kumar, Zihan Guan, Sheng Li, Alfy Samuel · Feb 24, 2026

Citations: 0
Pairwise PreferenceRed Team General
  • Recent advances in alignment techniques such as Supervised Fine-Tuning (SFT), Reinforcement Learning from Human Feedback (RLHF), and Direct Preference Optimization (DPO) have improved the safety of large language models (LLMs).
  • Furthermore, inspired by failure patterns in CoT fine-tuning, we introduce Alignment-Weighted DPO, which targets the most problematic parts of an output by assigning different preference weights to the reasoning and final-answer segments.
Assessing Risks of Large Language Models in Mental Health Support: A Framework for Automated Clinical AI Red Teaming

Ian Steenstra, Paola Pedrelli, Weiyan Shi, Stacy Marsella, Timothy W. Bickmore · Feb 23, 2026

Citations: 0
Red Team Simulation Env Medicine
  • Large Language Models (LLMs) are increasingly utilized for mental health support; however, current safety benchmarks often fail to detect the complex, longitudinal risks inherent in therapeutic dialogue.
  • We introduce an evaluation framework that pairs AI psychotherapists with simulated patient agents equipped with dynamic cognitive-affective models and assesses therapy session simulations against a comprehensive quality of care and risk…
SAMAS: A Spectrum-Guided Multi-Agent System for Achieving Style Fidelity in Literary Translation

Jingzhuo Wu, Jiajun Zhang, Keyan Jin, Dehua Ma, Junbo Wang · Feb 23, 2026

Citations: 0
Automatic Metrics Multi Agent Multilingual
  • To address this, we introduce the Style-Adaptive Multi-Agent System (SAMAS), a novel framework that treats style preservation as a signal processing task.
  • Extensive experiments on translation benchmarks show that SAMAS achieves competitive semantic accuracy against strong baselines, primarily by leveraging its statistically significant advantage in style fidelity.
FENCE: A Financial and Multimodal Jailbreak Detection Dataset

Mirae Kim, Seonghun Jeong, Youngjun Kwak · Feb 20, 2026

Citations: 0
Red Team Automatic Metrics General
  • A baseline detector trained on FENCE achieves 99 percent in-distribution accuracy and maintains strong performance on external benchmarks, underscoring the dataset's robustness for training reliable detection models.
IndicJR: A Judge-Free Benchmark of Jailbreak Robustness in South Asian Languages

Priyaranjan Pattnayak, Sanchari Chowdhuri · Feb 18, 2026

Citations: 0
Red Team CodingMultilingual
  • Safety alignment of large language models (LLMs) is mostly evaluated in English and contract-bound, leaving multilingual vulnerabilities understudied.
  • We introduce Indic Jailbreak Robustness (IJR), a judge-free benchmark for adversarial safety across 12 Indic and South Asian languages (2.1 Billion speakers), covering 45216 prompts in JSON (contract-bound) and Free (naturalistic) tracks.
Helpful to a Fault: Measuring Illicit Assistance in Multi-Turn, Multilingual LLM Agents

Nivya Talokar, Ayush K Tarun, Murari Mandal, Maksym Andriushchenko, Antoine Bosselut · Feb 18, 2026

Citations: 0
Red Team LawMultilingual
  • LLM-based agents execute real-world workflows via tools and memory.
  • We introduce STING (Sequential Testing of Illicit N-step Goal execution), an automated red-teaming framework that constructs a step-by-step illicit plan grounded in a benign persona and iteratively probes a target agent with adaptive…
Intent Laundering: AI Safety Datasets Are Not What They Seem

Shahriar Golchin, Marc Wetter · Feb 17, 2026

Citations: 0
Red Team General
  • We systematically evaluate the quality of widely used AI safety datasets from two perspectives: in isolation and in practice.
  • In practice, we evaluate whether these datasets genuinely measure safety risks or merely provoke refusals through triggering cues.
Unlocking Reasoning Capability on Machine Translation in Large Language Models

Sara Rajaee, Sebastian Vincent, Alexandre Berard, Marzieh Fadaee, Kelly Marchisio, Tom Kocmi · Feb 16, 2026

Citations: 0
Critique Edit Long Horizon MathCoding
  • We systematically evaluate several open- and closed-weights RLMs on the WMT24++ benchmark and find that enabling explicit reasoning consistently degrades translation quality across languages and models.
Citations: 0
Pairwise Preference Automatic Metrics CodingMultilingual
  • Yet safety pipelines, benchmarks, and alignment still largely target English and a handful of high-resource languages, implicitly assuming safety and factuality ''transfer'' across languages.
  • We synthesize recent findings indicating that (i) safety guardrails weaken sharply on low-resource and code-mixed inputs, (ii) culturally harmful behavior can persist even when standard toxicity scores look acceptable, and (iii)…
Citations: 0
Pairwise Preference Long Horizon Multilingual
  • The methodological trajectory moves from classical supervised adaptation for task-specific demands to decoding-time alignment for safety, finally leveraging human feedback and preference modeling to achieve sociolinguistic acuity.

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