Showing 94 publications by Fazl Barez
Pretraining Curricula Enable Selective Fine-tuning
Bruijns SA, Rubruck J, Whitefield MH, Sandbrink KJ, Barez F et al. (2026)
BibTeX
@misc{pretrainingcurr-2026/7,
title={Pretraining Curricula Enable Selective Fine-tuning},
author={Bruijns SA, Rubruck J, Whitefield MH, Sandbrink KJ, Barez F et al.},
year = "2026"
}
AIMO Interpretability Challenge
Štefánik M, Mondorf P, Waldis A, Liu Q, Yang C et al. (2026)
BibTeX
@misc{aimointerpretab-2026/7,
title={AIMO Interpretability Challenge},
author={Štefánik M, Mondorf P, Waldis A, Liu Q, Yang C et al.},
year = "2026"
}
Position: Don't Just "Fix it in Post": A Science of AI Must Study Training Dynamics
Biderman S, Khan MA, Mireshghallah N, Arnett C, Barez F et al. (2026)
BibTeX
@misc{positiondontjus-2026/6,
title={Position: Don't Just "Fix it in Post": A Science of AI Must Study Training Dynamics},
author={Biderman S, Khan MA, Mireshghallah N, Arnett C, Barez F et al.},
year = "2026"
}
Running the Gauntlet: Re-evaluating the Capabilities of Agents Beyond Familiar Environments
Vysotskyi M, Lin R, Biziel G, Zakrzewski M, Montagna S et al. (2026)
The Capability Frontier: Benchmarks Miss 82% of Model Performance
Fowler B, Smith R, Graviet DT, Myers W, Greaves J et al. (2026)
Beyond Linear Probes: Dynamic Safety Monitoring for Language Models
Oldfield J, Torr P, Patras I, Bibi A & Barez F (2026)
Beyond Linear Steering: Unified Multi-Attribute Control for Language Models
Oozeer N, Marks L, Jain S, Barez F & Abdullah A (2026)
Beyond alignment: Why robotic foundation models need context-aware safety.
Robey A, Ravichandran Z, Jones EK, Perlo J, Barez F et al. (2026), Science Robotics, 11(113), eaef2191
Make Mechanistic Interpretability Auditable: A Call to Develop Guidelines via Continuous Collaborative Reviewing
Lan M, Oozeer NF, Bandi C, Quirke P, Meek A et al. (2026)
BibTeX
@misc{makemechanistic-2026/4,
title={Make Mechanistic Interpretability Auditable: A Call to Develop Guidelines via Continuous Collaborative Reviewing},
author={Lan M, Oozeer NF, Bandi C, Quirke P, Meek A et al.},
year = "2026"
}
Curveball Steering: The Right Direction To Steer Isn't Always Linear
Raval S, Song HJ, Wu L, Harrasse A, Phillips JM et al. (2026)
AutoControl Arena: Synthesizing Executable Test Environments for Frontier AI Risk Evaluation
Li C, Lu P, Pan X, Barez F & Yang M (2026)
Towards Understanding Subliminal Learning: When and How Hidden Biases Transfer
Schrodi S, Kempf E, Barez F & Brox T (2026)
Old Habits Die Hard: How Conversational History Geometrically Traps LLMs
Simhi A, Barez F, Tutek M, Belinkov Y & Cohen SB (2026)
Same Answer, Different Representations: Hidden instability in VLMs
Wani FA, Suglia A, Saxena R, Gema AP, Kwan W-C et al. (2026)
Make Mechanistic Interpretability Auditable: A Call to Develop Guidelines via Continuous Collaborative Reviewing
Lan M, Oozeer NF, Bandi C, Quirke P, Meek A et al. (2026), 3516-3540
BibTeX
@inproceedings{makemechanistic-2026/1,
title={Make Mechanistic Interpretability Auditable: A Call to Develop Guidelines via Continuous Collaborative Reviewing},
author={Lan M, Oozeer NF, Bandi C, Quirke P, Meek A et al.},
booktitle={Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
pages={3516-3540},
year = "2026"
}
Rigorous Interpretation Is a Form of Evaluation
Lee I, Liu E, Jiao C, Joshi B, Yogatama D et al. (2026), 1-11
Full-Stack Alignment: Co-Aligning AI and Institutions with Thick Models of Value
Edelman J, Zhi-Xuan T, Lowe R, Klingefjord O, Wang-Mascianica V et al. (2025)
BibTeX
@misc{fullstackalignm-2025/12,
title={Full-Stack Alignment: Co-Aligning AI and Institutions with Thick Models of Value},
author={Edelman J, Zhi-Xuan T, Lowe R, Klingefjord O, Wang-Mascianica V et al.},
year = "2025"
}
Beyond Monoliths: Expert Orchestration for More Capable, Democratic, and Safe Language Models
Quirke P, Oozeer N, Bandi C, Abdullah A, Hoelscher-Obermaier J et al. (2025)
Interpreting Learned Feedback Patterns in Large Language Models
Marks L, Abdullah A, Neo C, Arike R, Krueger D et al. (2025)
Embodied AI: Emerging Risks and Opportunities for Policy Action
Perlo J, Robey A, Barez F, Floridi L & Mökander J (2025)
Trust Me, I'm Wrong: LLMs Hallucinate with Certainty Despite Knowing the Answer
Simhi A, Itzhak I, Barez F, Stanovsky G & Belinkov Y (2025)
Rethinking Safety in LLM Fine-tuning: An Optimization Perspective
Kim M, Kwak JM, Alssum L, Ghanem B, Torr P et al. (2025)
Establishing Best Practices for Building Rigorous Agentic Benchmarks
Zhu Y, Jin T, Pruksachatkun Y, Zhang A, Liu S et al. (2025)
The Singapore Consensus on Global AI Safety Research Priorities
Bengio Y, Maharaj T, Ong L, Russell S, Song D et al. (2025)
PoisonBench: Assessing Large Language Model Vulnerability to Data Poisoning
Fu T, Sharma M, Torr P, Cohen SB, Krueger D et al. (2025)
In which areas of technical AI safety could geopolitical rivals cooperate?
Bucknall B, Siddiqui S, Thurnherr L, McGurk C, Harack B et al. (2025), FAccT '25: Proceedings of the 2025 ACM Conference on Fairness, Accountability, and Transparency, 3148-3161
BibTeX
@inproceedings{inwhichareasoft-2025/6,
title={In which areas of technical AI safety could geopolitical rivals cooperate?},
author={Bucknall B, Siddiqui S, Thurnherr L, McGurk C, Harack B et al.},
booktitle={8th Annual ACM Conference on Fairness, Accountability, and Transparency (FAccT 2025)},
pages={3148-3161},
year = "2025"
}
Quantifying Feature Space Universality Across Large Language Models via Sparse Autoencoders
Lan M, Torr P, Meek A, Khakzar A, Krueger D et al. (2025)
SafetyNet: Detecting Harmful Outputs in LLMs by Modeling and Monitoring Deceptive Behaviors
Chaudhary M & Barez F (2025)
In Which Areas of Technical AI Safety Could Geopolitical Rivals Cooperate?
Bucknall B, Siddiqui S, Thurnherr L, McGurk C, Harack B et al. (2025)
BibTeX
@misc{inwhichareasoft-2025/4,
title={In Which Areas of Technical AI Safety Could Geopolitical Rivals Cooperate?},
author={Bucknall B, Siddiqui S, Thurnherr L, McGurk C, Harack B et al.},
year = "2025"
}
Towards Interpreting Visual Information Processing in Vision-Language Models
Neo C, Ong L, Torr P, Geva M, Krueger D et al. (2025)
Scaling sparse feature circuit finding for in-context learning
Kharlapenko D, Shabalin S, Barez F, Conmy A & Nanda N (2025)
AILuminate: Introducing v1.0 of the AI Risk and Reliability Benchmark from MLCommons
Ghosh S, Frase H, Williams A, Luger S, Röttger P et al. (2025)
Benchmarking Specialized Databases for High-frequency Data
Barez F, Bilokon P & Xiong R (2025), The Journal of FinTech, 05(01), 2550002
Towards interpreting visual information processing in vision-language models
Neo C, Ong L, Torr P, Geva M, Krueger D et al. (2025), Proceedings of the 13th International Conference on Learning Representations (ICLR 2025)
BibTeX
@inproceedings{towardsinterpre-2025/1,
title={Towards interpreting visual information processing in vision-language models},
author={Neo C, Ong L, Torr P, Geva M, Krueger D et al.},
booktitle={13th International Conference on Learning Representations (ICLR 2025)},
year = "2025"
}
Trust Me, I’m Wrong: LLMs Hallucinate with Certainty Despite Knowing the Answer
Simhi A, Itzhak I, Barez F, Stanovsky G & Belinkov Y (2025), 14665-14688
BibTeX
@inproceedings{trustmeimwrongl-2025/1,
title={Trust Me, I’m Wrong: LLMs Hallucinate with Certainty Despite Knowing the Answer},
author={Simhi A, Itzhak I, Barez F, Stanovsky G & Belinkov Y},
booktitle={Findings of the Association for Computational Linguistics: EMNLP 2025},
pages={14665-14688},
year = "2025"
}
POISONBENCH: Assessing Language Model Vulnerability to Poisoned Preference Data
Fu T, Sharma M, Torr P, Cohen S, Krueger D et al. (2025), Proceedings of Machine Learning Research, 267, 17806-17831
BibTeX
@inproceedings{poisonbenchasse-2025/1,
title={POISONBENCH: Assessing Language Model Vulnerability to Poisoned Preference Data},
author={Fu T, Sharma M, Torr P, Cohen S, Krueger D et al.},
pages={17806-17831},
year = "2025"
}