Academia

Yoshua Bengio

Université de Montréal and LawZero

Why we track: Safety and regulation

Career moves

No published career moves.

Papers

Paper2026-10-12 · arXiv (Cornell University)

Development of a Technology for Efficiently Discovering Diverse Antibody Candidates Using Artificial Intelligence

Multi-Objective Amortized CDRH3 Sequence Sampling Diversifies Promising Antibody Candidates

Yoshua Bengio +5 more

What: Researchers have developed a new artificial intelligence-based antibody sequence generation technology that can simultaneously consider multiple conditions and diversity during the antibody design process.
Key result: Compared to existing methods, it generated 10 times more diverse and acceptable antibody candidates at a 50 percent sequence identity.
Why it matters: This research can help reduce the probability of failure in the early stages of new drug development and explore various potential antibody candidates.AI summary

AI for science

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Paper2026-10-08 · Code Ocean

Technology to Interpret the Causes of Uncertainty in Artificial Intelligence Revealed

pce-uncertainty-attribution: code for "Interpretable attribution of decision-relevant uncertainty in AI"

Yoshua Bengio +6 more

What: Released code and methods to interpret which factors contribute to the decision-making uncertainty of trained artificial intelligence models.
Key result: Utilized polynomial surrogates to uncover the causes of model uncertainty in the fields of chemistry, experimental design, and language model alignment.
Why it matters: Can be utilized to better understand the decision-making process of artificial intelligence models across various fields and increase reliability.AI summary

AI for science Safety & policy

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Paper2026-10-03 · arXiv (Cornell University)

Researchers Propose a Framework for Evaluating AI Answer Oversight via Code Execution

PyINE: A Framework for Scalable Elicitation and Oversight via Code Execution

Yoshua Bengio +7 more

What: Created the PyINE framework, which utilizes program execution results as ground truth, to examine whether supervisors can trust AI answers that feature plausible yet incomplete reasoning.
Key result: Models trained to follow shortcuts significantly improved their ability to predict execution outcomes, but still made systematic errors when misleading cues conflicted with actual behavior, and cheap learned supervisors frequently missed rare shortcut errors.
Why it matters: Significant for presenting an experimental environment that compares and reuses AI answer verification oversight methods under the same conditions, although the abstract does not mention actual service application effects.AI summary

Safety & policy

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Paper2026-09-28

Analysis of the Impact and Policy Responses Triggered by Automating AI R&D

What if automating AI R&D triggers an intelligence explosion?

Jakub Pachocki · Geoffrey Hinton · Yoshua Bengio +19 more

What: Researchers evaluated the possibility of AI R&D automation triggering an intelligence explosion, along with its potential impacts and policy response measures.
Key result: AI systems are expected to automate most AI R&D tasks within a few years, which could lead to rapid capability growth at a level society struggle to handle.
Why it matters: It warns of the extreme risks such as loss of control alongside the benefits that AI R&D automation may bring, urging policy preparedness.AI summary

Safety & policy

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This page compiles published papers, official announcements and press reports for information only. Paper summaries are written by AI from the abstract and reviewed by the editor; they may differ from the original, so check the paper for anything important. Nothing here is a recommendation to buy or sell any security or investment advice. Career moves cite official announcements or major press reports; some career records come from Wikidata (CC0). Summaries on this page were translated by AI from the editor-reviewed Korean version.