Value Alignment

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Improving Evaluation Realism with Inference-Time Compute and Deployment Scaffolds
Sep 2, 2026Axel Ahlqvist, Richard Guan, Juan-Pablo Rivera +6Value Alignment
1Meridian Visiting Researcher Programme · 2Cambridge Boston Alignment Initiative · 3UK AI Security Institute +2
Contextual Value Alignment via Multilayer Combinatorial Fusion
Aug 7, 2026Yuanhong Wu, Djallel Bouneffouf, D. Frank HsuLLM Alignment Moral Reasoning in Language Models
Dept. of Computer and Information Science, Fordham University, New York, NY, USA · T. J. Watson Research Center, IBM Research, Yorktown Heights, NY, USA
D2VBench: Benchmarking Large Language Models with Value Dilemmas in Daily Scenarios
Jul 22, 2026Siyi Hao, Yidi Cao, Linhao Yu +2LLM Evaluation LLM Alignment
TJUNLP Lab, School of Computer Science and Technology, Tianjin University, China · The International Joint Institute of Tianjin University, Fuzhou, China
Pancasila-Dilemmas: Evaluating Large Language Models on Indonesian Human Value Dilemmas Grounded in Pancasila
Jul 20, 2026Supryadi, Irfan, Julianti +4LLM Evaluation LLM Alignment
TJUNLP Lab, School of Computer Science and Technology, Tianjin University, China · Universitas Universal, Indonesia · Beijing Language and Culture University, Beijing, China +2
Toward Contemplative LLM: A Modular Framework for Evaluating and Enhancing LLM Alignment in Mental Health
Jul 12, 2026Asher Sprigler, Yang-Yang Feng, Iftach Amir +5LLM Alignment Moral Reasoning in Language Models
Electrical and Computer Engineering Purdue University West Lafayette, IN, USA · Biomedical Engineering Washington University in St. Louis St. Louis, MO, USA · Psychological & Brain Sciences Washington University in St. Louis St. Louis, MO, USA +3
Towards Value-Constrained Credit Assignment in Fully Delegated AI Cooperatives
Jun 26, 2026Young Yoon, Jimin Kim, Soyeon ParkGradient-Based Attribution Value Alignment
Hongik University, Seoul, Republic of Korea
Reinforcement Learning Towards Broadly and Persistently Beneficial Models
Jun 22, 2026Akshay V. Jagadeesh, Rahul K. Arora, Khaled Saab +5LLM Alignment LLM Safety Alignment
OpenAI
Position: Align AI to Our Aspirations, Not Our Flaws
Jun 11, 2026Nikita Kazeev, Bui Nhat Huyen PhanAI Alignment Pluralistic Alignment
1National University of Singapore · 2Shopee Singapore.
Sycophancy Towards Researchers Drives Performative Misalignment
Jun 7, 2026David D. Baek, Xinnuo Li, Anay Gupta +4LLM Sycophancy LLM Alignment
Massachusetts Institute of Technology, Cambridge, MA, USA · University of Michigan, Ann Arbor, MI, USA · Georgia Institute of Technology, Atlanta, GA, USA +1
Building Comparative Motivation Profiles with Instrumental Interventions
Jun 6, 2026David Vella Zarb, Rustem Turtayev, Taywon Min +2LLM Safety Evaluation Causal Interventions in Language Models
MATS · University of Cambridge · KAIST +1
Fog of Love: Engineering Virtuous Agent Behavior with Affinity-based Reinforcement Learning in a Game Environment
Jun 3, 2026Ajay Vishwanath, Christian OmlinReinforcement Learning Cooperative MARL
IKT, University of Agder, Jon Lilletuns vei, Grimstad, 4879, Agder, Norway.
Behavioural Analysis of Alignment Faking
May 26, 2026Nathaniel Mitrani Hadida, Rhea Karty, David Williams-King +1LLM Alignment Value Alignment
University of Cambridge · ERA · Harvard University +1
Alignment Tampering: How Reinforcement Learning from Human Feedback Is Exploited to Optimize Misaligned Biases
May 26, 2026Dongyoon Hahm, Dylan Hadfield-Menell, Kimin LeeLLM Alignment Data Poisoning Attacks
1KAIST · 2MIT
DVMap: Fine-Grained Pluralistic Value Alignment via High-Consensus Demographic-Value Mapping
May 14, 2026Pengyun Zhu, Yuqi Ren, Zhen Wang +2LLM Alignment Pluralistic Alignment
TJUNLP Lab, School of Computer Science and Technology, Tianjin University, China
Toward Stable Value Alignment: Introducing Independent Modules for Consistent Value Guidance
May 12, 2026Wenhao Chen, Sirui Sun, Shengyuan Bai +1LLM Alignment LLM Safety Alignment
School of Electronics Engineering and Computer Science, Peking University · Yuanpei College, Peking University · State Key Laboratory of General Artificial Intelligence, School of Intelligence Science and Technology, Peking University
Positive Alignment: Artificial Intelligence for Human Flourishing
May 11, 2026Ruben Laukkonen, Seb Krier, Chloé Bakalar +13LLM Alignment AI Alignment
Department of Psychiatry, University of Oxford · 2Flourishing Intelligence Program, Centre for Eudaimonia and Human Flourishing,2026 Linacre College, University of Oxford · 4LIFE +10
Tatemae: Detecting Alignment Faking via Tool Selection in LLMs
Apr 29, 2026Matteo Leonesi, Francesco Belardinelli, Flavio Corradini +1Deception in Language Models LLM Safety Alignment
Department of Computing Imperial College London
Relative Principals, Pluralistic Alignment, and the Structural Value Alignment Problem
Apr 22, 2026Travis LaCroixAI Alignment AI Governance
Durham University, United Kingdom
Norms at a Price: Why RL-Based Alignment Can Promise Conditional Compliance at Best
Date pendingKevin Baum, Rūta Binkytė, Felix JahnAI Alignment LLM Safety Alignment
German Research Center for Artificial Intelligence (DFKI), Saarbrücken · Institute for Ethics in Technology, Hamburg University of Technology (TUHH) · Oxford Internet Institute, University of Oxford

Sep 2, 2026, cs.AIJ/K move · Enter open · S save

Improving Evaluation Realism with Inference-Time Compute and Deployment Scaffolds

Axel Ahlqvist, Richard Guan, Juan-Pablo Rivera, Adeline Kassler+5

1Meridian Visiting Researcher Programme · 2Cambridge Boston Alignment Initiative · 3UK AI Security Institute+2

Value Alignment

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A core obstacle to alignment evaluation is evaluation awareness: capable models can tell when they are being tested rather than deployed, weakening the conclusions a safety evaluation can support. We present two techniques that make simulated alignment evaluations harder to distinguish from real deployments. Our first technique, critique refinement, spends additional inference-time compute on each simulator action: the simulator generates multiple candidate actions, refines them using feedback from an instance of the target model on how to make them more realistic, and continues the evaluation with the most deployment-like candidate. Our second technique, DISH (Deployment-Imitating SWE-Agent Harness), wraps the target in an agent harness, reducing the gap between simulated and real deployment environments in coding settings. We test the techniques on multiple target models and find that they compose: applying both yields larger realism gains than either alone. Our results show that automated approaches can improve the realism of alignment evaluations, and that these improvements use additional compute more effectively than making the audits longer.

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