ED2: An Environment Dynamics Decomposition Framework for World Model Construction. - Cong Wang; Tianpei Yang; Jianye Hao; Yan Zheng 002; Hongyao Tang; Fazl Barez; Jinyi Liu 002; Jiajie Peng; Haiyin Piao; Zhixiao Sun (CoRR, 2021)

The Larger they are, the Harder they Fail: Language Models do not Recognize Identifier Swaps in Python. - Antonio Valerio Miceli Barone; Fazl Barez; Shay B. Cohen; Ioannis Konstas (ACL (Findings), 2023)

Detecting Edit Failures In Large Language Models: An Improved Specificity Benchmark. - Jason Hoelscher-Obermaier; Julia Persson; Esben Kran; Ioannis Konstas; Fazl Barez (ACL (Findings), 2023)

Benchmarking Specialized Databases for High-frequency Data. - Fazl Barez; Paul Bilokon; Ruijie Xiong (CoRR, 2023)

Exploring the Advantages of Transformers for High-Frequency Trading. - Fazl Barez; Paul Bilokon; Arthur Gervais; Nikita Lisitsyn (CoRR, 2023)

Fairness in AI and Its Long-Term Implications on Society. - Ondrej Bohdal; Timothy M. Hospedales; Philip H. S. Torr; Fazl Barez (CoRR, 2023)

System III: Learning with Domain Knowledge for Safety Constraints. - Fazl Barez; Hosien Hasanbieg; Alesandro Abbate (CoRR, 2023)

N2G: A Scalable Approach for Quantifying Interpretable Neuron Representations in Large Language Models. - Alex Foote; Neel Nanda; Esben Kran; Ioannis Konstas; Fazl Barez (CoRR, 2023)

Neuron to Graph: Interpreting Language Model Neurons at Scale. - Alex Foote; Neel Nanda; Esben Kran; Ioannis Konstas; Shay B. Cohen; Fazl Barez (CoRR, 2023)

DeepDecipher: Accessing and Investigating Neuron Activation in Large Language Models. - Albert Garde; Esben Kran; Fazl Barez (CoRR, 2023)

AI Systems of Concern. - Kayla Matteucci; Shahar Avin; Fazl Barez; Seán Ó hÉigeartaigh (CoRR, 2023)

Interpreting Reward Models in RLHF-Tuned Language Models Using Sparse Autoencoders. - Luke Marks; Amir Abdullah; Luna Mendez; Rauno Arike; Philip H. S. Torr; Fazl Barez (CoRR, 2023)

Locating Cross-Task Sequence Continuation Circuits in Transformers. - Michael Lan; Fazl Barez (CoRR, 2023)

Measuring Value Alignment. - Fazl Barez; Philip H. S. Torr (CoRR, 2023)

Large Language Models Relearn Removed Concepts. - Michelle Lo; Fazl Barez; Shay B. Cohen (ACL (Findings), 2024)

Towards Interpretable Sequence Continuation: Analyzing Shared Circuits in Large Language Models. - Michael Lan; Philip Torr 001; Fazl Barez (EMNLP, 2024)

Interpreting Context Look-ups in Transformers: Investigating Attention-MLP Interactions. - Clement Neo; Shay B. Cohen; Fazl Barez (EMNLP, 2024)

Understanding Addition in Transformers. - Philip Quirke; Fazl Barez (ICLR, 2024)

Position: Near to Mid-term Risks and Opportunities of Open-Source Generative AI. - Francisco Eiras; Aleksandar Petrov; Bertie Vidgen; Christian Schröder de Witt; Fabio Pizzati; Katherine Elkins; Supratik Mukhopadhyay; Adel Bibi; Botos Csaba; Fabro Steibel; Fazl Barez; Genevieve Smith; et al. (ICML, 2024)

Value-Evolutionary-Based Reinforcement Learning. - Pengyi Li 001; Jianye Hao; Hongyao Tang; Yan Zheng 002; Fazl Barez (ICML, 2024)

Interpreting Learned Feedback Patterns in Large Language Models. - Luke Marks; Amir Abdullah; Clement Neo; Rauno Arike; David Krueger 001; Philip Torr 001; Fazl Barez (NeurIPS, 2024)

Sleeper Agents: Training Deceptive LLMs that Persist Through Safety Training. - Evan Hubinger; Carson Denison; Jesse Mu; Mike Lambert; Meg Tong; Monte MacDiarmid; Tamera Lanham; Daniel M. Ziegler; Tim Maxwell; Newton Cheng; Adam S. Jermyn; Amanda Askell; et al. (CoRR, 2024)

Increasing Trust in Language Models through the Reuse of Verified Circuits. - Philip Quirke; Clement Neo; Fazl Barez (CoRR, 2024)

Near to Mid-term Risks and Opportunities of Open Source Generative AI. - Francisco Eiras; Aleksandar Petrov; Bertie Vidgen; Christian Schröder de Witt; Fabio Pizzati; Katherine Elkins; Supratik Mukhopadhyay; Adel Bibi; Botos Csaba; Fabro Steibel; Fazl Barez; Genevieve Smith; et al. (CoRR, 2024)

Visualizing Neural Network Imagination. - Nevan Wichers; Victor Tao; Riccardo Volpato; Fazl Barez (CoRR, 2024)

Risks and Opportunities of Open-Source Generative AI. - Francisco Eiras; Aleksandar Petrov; Bertie Vidgen; Christian Schröder de Witt; Fabio Pizzati; Katherine Elkins; Supratik Mukhopadhyay; Adel Bibi; Aaron Purewal; Botos Csaba; Fabro Steibel; Fazel Keshtkar; et al. (CoRR, 2024)

Sycophancy to Subterfuge: Investigating Reward-Tampering in Large Language Models. - Carson Denison; Monte MacDiarmid; Fazl Barez; David Duvenaud; Shauna Kravec; Samuel Marks; Nicholas Schiefer; Ryan Soklaski; Alex Tamkin; Jared Kaplan; Buck Shlegeris; Samuel R. Bowman; et al. (CoRR, 2024)

Sparse Autoencoders Reveal Universal Feature Spaces Across Large Language Models. - Michael Lan; Philip Torr 001; Austin Meek; Ashkan Khakzar; David Krueger 001; Fazl Barez (CoRR, 2024)

PoisonBench: Assessing Large Language Model Vulnerability to Data Poisoning. - Tingchen Fu; Mrinank Sharma; Philip Torr 001; Shay B. Cohen; David Krueger 001; Fazl Barez (CoRR, 2024)

Enhancing Neural Network Interpretability with Feature-Aligned Sparse Autoencoders. - Luke Marks; Alasdair Paren; David Krueger 001; Fazl Barez (CoRR, 2024)

Jailbreak Defense in a Narrow Domain: Limitations of Existing Methods and a New Transcript-Classifier Approach. - Tony T. Wang 001; John Hughes; Henry Sleight; Rylan Schaeffer; Rajashree Agrawal; Fazl Barez; Mrinank Sharma; Jesse Mu; Nir Shavit; Ethan Perez (CoRR, 2024)

Best-of-N Jailbreaking. - John Hughes; Sara Price; Aengus Lynch; Rylan Schaeffer; Fazl Barez; Sanmi Koyejo; Henry Sleight; Erik Jones; Ethan Perez; Mrinank Sharma (CoRR, 2024)

In Which Areas of Technical AI Safety Could Geopolitical Rivals Cooperate? - Ben Bucknall; Saad Siddiqui; Lara Thurnherr; Conor McGurk; Ben Harack; Anka Reuel; Patricia Paskov; Casey Mahoney; Sören Mindermann; Scott Singer; Vinay Hiremath; Charbel-Raphaël Ségerie; et al. (FAccT, 2025)

Towards Interpreting Visual Information Processing in Vision-Language Models. - Clement Neo; Luke Ong; Philip Torr 001; Mor Geva; David Krueger 001; Fazl Barez (ICLR, 2025)

Open Problems in Machine Unlearning for AI Safety. - Fazl Barez; Tingchen Fu; Ameya Prabhu; Stephen Casper; Amartya Sanyal; Adel Bibi; Aidan O'Gara; Robert Kirk; Ben Bucknall; Timothy Fist; Luke Ong; Philip Torr 001; et al. (CoRR, 2025)

Rethinking AI Cultural Evaluation. - Michal Bravansky; Filip Trhlík; Fazl Barez (CoRR, 2025)

Trust Me, I'm Wrong: High-Certainty Hallucinations in LLMs. - Adi Simhi; Itay Itzhak; Fazl Barez; Gabriel Stanovsky; Yonatan Belinkov (CoRR, 2025)

Do Sparse Autoencoders Generalize? A Case Study of Answerability. - Lovis Heindrich; Philip Torr 001; Fazl Barez; Veronika Thost (CoRR, 2025)

Same Question, Different Words: A Latent Adversarial Framework for Prompt Robustness. - Tingchen Fu; Fazl Barez (CoRR, 2025)

AILuminate: Introducing v1.0 of the AI Risk and Reliability Benchmark from MLCommons. - Shaona Ghosh; Heather Frase; Adina Williams; Sarah Luger; Paul Röttger; Fazl Barez; Sean McGregor; Kenneth Fricklas; Mala Kumar; Quentin Feuillade-Montixi; Kurt Bollacker; Felix Friedrich; et al. (CoRR, 2025)

In Which Areas of Technical AI Safety Could Geopolitical Rivals Cooperate? - Ben Bucknall; Saad Siddiqui; Lara Thurnherr; Conor McGurk; Ben Harack; Anka Reuel; Patricia Paskov; Casey Mahoney; Sören Mindermann; Scott Singer; Vinay Hiremath; Charbel-Raphaël Ségerie; et al. (CoRR, 2025)

Scaling sparse feature circuit finding for in-context learning. - Dmitrii Kharlapenko; Stepan Shabalin; Fazl Barez; Arthur Conmy; Neel Nanda (CoRR, 2025)

SafetyNet: Detecting Harmful Outputs in LLMs by Modeling and Monitoring Deceptive Behaviors. - Maheep Chaudhary; Fazl Barez (CoRR, 2025)

Precise In-Parameter Concept Erasure in Large Language Models. - Yoav Gur-Arieh; Clara Suslik; Yihuai Hong; Fazl Barez; Mor Geva (CoRR, 2025)

Beyond Linear Steering: Unified Multi-Attribute Control for Language Models. - Narmeen Oozeer; Luke Marks; Fazl Barez; Amirali Abdullah (CoRR, 2025)

Beyond Monoliths: Expert Orchestration for More Capable, Democratic, and Safe Large Language Models. - Philip Quirke; Narmeen Oozeer; Chaithanya Bandi; Amir Abdullah; Jason Hoelscher-Obermaier; Jeff M. Phillips; Joshua Greaves; Clement Neo; Fazl Barez; Shriyash Upadhyay (CoRR, 2025)

The Singapore Consensus on Global AI Safety Research Priorities. - Yoshua Bengio; Tegan Maharaj; Luke Ong; Stuart Russell 001; Dawn Song; Max Tegmark; Lan Xue; Ya-Qin Zhang; Stephen Casper; Wan Sie Lee; Sören Mindermann; Vanessa Wilfred; et al. (CoRR, 2025)

Establishing Best Practices for Building Rigorous Agentic Benchmarks. - Yuxuan Zhu; Tengjun Jin; Yada Pruksachatkun; Andy Zhang; Shu Liu; Sasha Cui; Sayash Kapoor; Shayne Longpre; Kevin Meng; Rebecca Weiss; Fazl Barez; Rahul Gupta 001; et al. (CoRR, 2025)

Rethinking Safety in LLM Fine-tuning: An Optimization Perspective. - Minseon Kim; Jin Myung Kwak; Lama Alssum; Bernard Ghanem; Philip Torr 001; David Krueger 001; Fazl Barez; Adel Bibi (CoRR, 2025)