Between June and September 2024, we ran the third iteration of the PIBBSS Summer Research Fellowship. Here are our reflections on how the program went and what we learned. Apply for the 2025 program here!

TLDR:

The 2024 fellowship demonstrated continued success in attracting and developing senior academic talent while refining our approach to interdisciplinary AI safety research. Key outcomes include:

Successfully facilitating the transition of multiple early-to-mid-career researchers into AI safety
Strong research outputs, including advances in mechanistic interpretability that one mentor described as "a 70th percentile paper in mech interp"
Increased seniority of fellows led to more sophisticated and complete research outputs
Going forward, we plan to strengthen research support infrastructure and better balance research direction diversity, which is the usual strength of our program.

About PIBBSS

PIBBSS (Principles of Intelligent Behavior in Biological and Social Systems) is a research initiative focused on leveraging insights and talent from fields that study intelligent behavior in natural systems to help make progress on questions in AI risk and safety. Since our inception, our approach has evolved - while maintaining our commitment to epistemic pluralism, we have started taking steps toward focusing on more concrete research directions. This includes launching an affiliate program in January 2024 to support sustained research efforts. Previous retrospectives can be found for 2022 and 2023.

Fellowship Structure

The fellowship pairs researchers (typically PhD level or above) from fields studying complex and intelligent behavior with mentors from AI alignment. The 2024 program featured several key elements:

Opening retreat
Three-month fellowship period (June-September) at LISA facility in London
Structured reporting throughout
Closing symposium

2024 Program Changes

The fellowship relocated to London's LISA facility in 2024, extending to a three-month residency. This move offered practical advantages and increased exposure to the broader AI safety community. Fellows reported that "having meals and snacks provided was incredibly convenient" and valued "the general atmosphere of having lots of people working intensely on interesting things."

Due to budget constraints, we have had to cut the closing retreat, which was usually held just prior to the Symposium. Based on feedback, we will aim to host one in the future if budget permits - the value of "closing the vessel" and having a clean ending to a program is higher than we had foreseen.

We also hired a dedicated program director this year (Clem Von Stengel), and the overall response was quite positive. Clem also leveraged London and LISA more concretely as a place with interesting researchers and organized semi-regular events for fellows, some of whom reported high value of these extra sessions.

Result Highlights

Fellowship produces impact in several avenues - counterfactual career shifts, research, mentor benefits, and bridge-building. Some of these are harder to pinpoint, and most of our Fellows have contributed to more than one of these, but we would like to highlight a few especially salient and tangible ones (in alphabetical order):

Agustin Martinez Suñé created a framework integrating automated planning with LLMs to provide measurable safety guarantees, effectively bridging formal methods and AI safety [presentation]. As a direct result of the Fellowship, he received a postdoc offer at a world-class university and began the initiation of an AI safety scholarship program for Argentina, representing a significant field-building impact.
Aron Vallinder investigated cultural evolution and cooperation in LLMs, developing work (accepted at AAMAS as extended abstract) that demonstrated the viability of applying cultural evolution concepts to AI systems. His project represented solid research progress aligned with his existing trajectory while opening new avenues for understanding multi-agent dynamics.
Euan McLean developed a framework for searching for phenomenal consciousness in LLMs, creating concrete experimental proposals focusing on measuring introspective metacognition. The fellowship provided a structured opportunity to develop these novel approaches, with his mentor noting the work brought "particularly valuable new ideas" to the field.
Magdalena Wache produced a comprehensive formalization of Factored Space Models for understanding causality between abstraction levels, delivering high-quality technical work clarifying important concepts. Her strong distillation work made complex concepts more accessible while maintaining mathematical rigor and it reached an impressively polished publishing state in a short time.
Matthew A. Clarke discovered that SAE features show heavy-tailed co-occurrence patterns rather than independence, providing important insights for interpretability research that are being prepared for publication. His transition from biomedical research to AI safety represents high counterfactual value, with his strong scientific background bringing valuable methodological rigor to the field.
Assistant Professor at U of Toronto Yevgeny Liokumovich contributed to singular learning theory by deriving higher order/constant terms of free energy and extending Jeffreys prior to singular cases, bringing rigorous mathematical foundations to developmental interpretability [presentation]. His transition into an agenda fit to his interests and expertise represents a significant counterfactual impact as it may not have otherwise happened. Further plans include continued collaboration with developmental interpretability researchers and potentially supervising future PhD students in AI safety.

Presentations from all fellows in video form are available in the Appendix.

Result Changes from 2023

Compared to 2023, we observed:

Higher average seniority of fellows
More publication-ready research outputs
More engagement with present-day ML systems in projects
Better integration with the broader AI safety community

Fellow and Mentor Feedback

Fellows particularly valued the LISA environment: "Working at LISA was definitely an improvement over working from home or working from a non-AI-alignment office."

Mentors reported high satisfaction:

6 out of 8 mentors rated outcomes 7/10 or above
8 out of 9 indicated they would "Pretty likely" or "Somewhat likely" want to mentor again
The majority rated their PIBBSS mentorship as more useful than their typical work

Areas for improvement included:

Need for better cohort cohesion
More structured post-fellowship support
Additional technical support for experimental work

Future Plans

For 2025, we plan to:

Continue the fellowship program with enhanced research support
Strengthen post-fellowship support infrastructure
Better balance technical and theoretical diversity in future cohorts
Explore new funding models through targeted support of specific research directions

Expression of interest for Fellowship 2025

If you are interested in doing research with us yourself, please fill in the expression of interest form (for non-Fellowship interest, register here). We will announce an official opening of the fellowship once we have funding confirmed for the next year, but in the meantime we would like to know what areas of research are people interested in. By filling in the form you are helping us reach out to relevant mentors and funders on time, and we will inform you of the opening of the Fellowship directly to your email!

Appendix: Fellowship Research Projects

Agustin Martinez Suñé - "Neuro-symbolic approaches for achieving quantitative safety guarantees for LLM-based agents": Developed a framework integrating automated planning with LLMs to provide measurable safety guarantees for LLM-based agents. As a direct result of his engagement with PIBBSS, received informal offer for postdoctoral work at a top university. [presentation] [website]
Aron Vallinder - "The Cultural Evolution of Indirect Reciprocity in LLMs": Explored how cultural evolution and cooperation emerge in LLM interactions, with results being developed for AAMAS submission. [presentation]
Baram Sosis - "Measuring Beliefs of Language Models During Chain-of-Thought": Investigated several methods for measuring LLM beliefs during chain-of-thought reasoning and attempted to apply bounded rationality models. [presentation]
Euan McLean - "Searching for phenomenal consciousness in LLMs": Developed framework for investigating consciousness in LLMs, particularly focusing on distinguishing direct and indirect metacognition. Made significant updates on the validity of computational functionalism. [presentation] [LW post]
Jan Bauer - "The geometry of in-context learning": Explored geometric properties of in-context learning, particularly focusing on Chain-of-Thought reasoning through a computational neuroscience lens. [presentation]
Magdalena Wache - "Factored Space Models: Causality between Levels of Abstraction": Comprehensive formalization of Factored Space Models providing mathematical foundations for understanding causality between abstraction levels. [presentation] [full paper]
Mateusz Bagiński - "Conceptual investigation of the core drivers of goal-achieving mental activity": Applied hermeneutic net method to investigate core concepts in minds, agency, and alignment. [presentation][published WIP]
Matthew A. Clarke - "Studying co-occurrence patterns in Sparse Autoencoders": Discovered heavy-tailed co-occurrence distributions in SAE features, challenging basic assumptions about SAE behavior and suggesting new directions for interpretability research. [presentation][LW Post]
Nadine Spychala (part-time fellow) - "Exploring the potential of formal approaches to emergence for AI safety": Conducted preliminary investigation of applying information theoretic measures of emergence to AI safety evaluation. [presentation]
Shaun Raviv - Conducted extensive foundational research in AI safety as preparation for future journalism work focused on making complex technical content accessible to broader audiences. [personal website]
Wesley Erickson - "Heavy-tailed Noise & Stochastic Gradient Descent": Investigated the role of heavy-tailed noise in SGD and its implications for understanding learning dynamics. [presentation]
Yevgeny Liokumovich - "Minimum Description Length for singular models": Contributed to theoretical understanding of Watanabe's asymptotic expansion in Bayesian statistics for singular models, with applications to developmental interpretability. [presentation]

This fellowship's research particularly excelled in:

Mathematical rigor (singular learning theory, factored spaces)
Novel experimental approaches (SAE analysis, LLM evolution)
Concrete safety frameworks (neuro-symbolic guarantees)
Theoretical foundations (consciousness studies, emergence measures)

Multiple projects are proceeding toward publication or conference submission, and several fellows have secured paths to continue their research through academic positions or research organizations.

Thanks

Many thanks to our funders for the Fellowship in 2024 including SFF, LTFF, Cooperative AI Fund, and the Foresight Institute. This would not have been possible without our mentors, alumni, London AI Safety Initiative (LISA) and many others who have helped us along the way. If you wish to support our work, please reach out to us at contact@pibbss.ai, full version of this report is available to funders.