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Human Feedback and Eval Paper Explorer

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: 35 Search mode: keyword RSS

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The Trinity of Consistency as a Defining Principle for General World Models

Jingxuan Wei, Siyuan Li, Yuhang Xu, Zheng Sun, Junjie Jiang, Hexuan Jin · Feb 26, 2026

Citations: 0
Simulation Env Long Horizon Law
  • To complement this conceptual framework, we introduce CoW-Bench, a benchmark centered on multi-frame reasoning and generation scenarios.
  • CoW-Bench evaluates both video generation models and UMMs under a unified evaluation protocol.
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.
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…
Citations: 0
Automatic Metrics Multi Agent Law
  • We introduce Whisper: Courtside Edition, a novel multi-agent large language model (LLM) pipeline that enhances Whisper transcriptions without retraining.
  • The pipeline intercepts Whisper's initial transcript, applies specialized LLM agents for domain context identification, named entity recognition, and jargon detection, and generates compact prompts that guide Whisper's decoder.
Citations: 0
Human EvalAutomatic Metrics Law
  • Vichara surpasses existing judgment prediction benchmarks on both datasets, with GPT-4o mini achieving the highest performance (F1: 81.5 on PredEx, 80.3 on ILDC_expert), followed by Llama-3.1-8B.
  • Human evaluation of the generated explanations across Clarity, Linking, and Usefulness metrics highlights GPT-4o mini's superior interpretability.
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.
HLE-Verified: A Systematic Verification and Structured Revision of Humanity's Last Exam

Weiqi Zhai, Zhihai Wang, Jinghang Wang, Boyu Yang, Xiaogang Li, Xander Xu · Feb 15, 2026

Citations: 0
Expert VerificationCritique Edit Automatic Metrics Law
  • Humanity's Last Exam (HLE) has become a widely used benchmark for evaluating frontier large language models on challenging, multi-domain questions.
  • However, community-led analyses have raised concerns that HLE contains a non-trivial number of noisy items, which can bias evaluation results and distort cross-model comparisons.
The Subjectivity of Respect in Police Traffic Stops: Modeling Community Perspectives in Body-Worn Camera Footage

Preni Golazizian, Elnaz Rahmati, Jackson Trager, Zhivar Sourati, Nona Ghazizadeh, Georgios Chochlakis · Feb 10, 2026

Citations: 0
Pairwise PreferenceRubric Rating Law
  • By sampling annotators from police-affiliated, justice-system-impacted, and non-affiliated Los Angeles residents, we enable the systematic study of perceptual differences across diverse communities.
  • To this end, we (i) develop a domain-specific evaluation rubric grounded in procedural justice theory, LAPD training materials, and extensive fieldwork; (ii) introduce a rubric-driven preference data construction framework for…
APEX-Agents

Bertie Vidgen, Austin Mann, Abby Fennelly, John Wright Stanly, Lucas Rothman, Marco Burstein · Jan 20, 2026

Citations: 0
Rubric RatingExpert Verification Automatic Metrics Long Horizon Law
  • We introduce the AI Productivity Index for Agents (APEX-Agents), a benchmark for assessing whether AI agents can execute long-horizon, cross-application tasks created by investment banking analysts, management consultants, and corporate…
  • We test eight agents for the leaderboard using Pass@1.
Multimodal Multi-Agent Empowered Legal Judgment Prediction

Zhaolu Kang, Junhao Gong, Qingxi Chen, Hao Zhang, Jiaxin Liu, Rong Fu · Jan 19, 2026

Citations: 0
Simulation Env Multi Agent Law
  • Furthermore, we build JurisMM, a large dataset with over 100,000 recent Chinese judicial records, including both text and multimodal video-text data, enabling comprehensive evaluation.
  • Experiments on JurisMM and the benchmark LawBench validate our framework's effectiveness.

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