ΑΙhub.org
monthly digest
 

AIhub monthly digest: August 2025 – causality and generative modelling, responsible multimodal AI, and IJCAI in Montréal and Guangzhou


by
29 August 2025



share this:
Panda and tiger reading

Welcome to our monthly digest, where you can catch up with any AIhub stories you may have missed, peruse the latest news, recap recent events, and more. This month, we dive into the world of agents, learn about responsible multimodal AI, apply generative AI to computer networks, and dig into the RoboCup@Work League.

Agentic AI

The AIhub coffee corner captures the musings of AI experts over a short conversation. This month, Sanmay Das, Tom Dietterich, Sabine Hauert, Sarit Kraus, and Michael Littman tackled the topic of agentic AI, discussing recent developments, and lessons learned from the decades of research in the autonomous agents and multiagent systems community.

International Joint Conference on Artificial Intelligence

The 34th International Joint Conference on Artificial Intelligence (IJCAI2025) took place in Montréal from 16-22 August, with a satellite event currently being held (from 29-31 August) in Guangzhou, China. You can find out more about the programmes of both venues here, and get a flavour of what attendees got up to in our social media round-ups: Part one | Part two.

We’ve already reported on the prestigious IJCAI awards that were announced ahead of the conference. During the event itself, the distinguished paper awards were presented at the opening ceremony. You can also hear from the next generation of AI researchers based in Canada in this series of 90-second pitches.

Inside the RoboCup@Work League

This year’s annual RoboCup event, where teams gathered from across the globe to take part in competitions across a number of leagues, took place in Salvador, Brazil from 15-21 July. Ahead of the event, we spoke to Christoph Steup to find out more about the @Work League, the tasks that teams need to complete, and future plans for the League.

The value of prediction in identifying the worst-off

At this year’s International Conference on Machine Learning (ICML2025), Unai Fischer-Abaigar, Christoph Kern and Juan Carlos Perdomo won an outstanding paper award for their work We hear from Unai about the main contributions of the paper, why prediction systems are an interesting area for study, and further work they are planning in this space.

Responsible multimodal AI

Our series featuring the AAAI / ACM SIGAI Doctoral Consortium participants continued this month with no fewer than five interviews. Firstly, we heard from Flávia Carvalhido, a PhD student at the University of Porto, and found out about her work on responsible multimodal AI, what inspired her to study AI, and how she found her first conference experience.

Applying generative AI to computer networks

Shaghayegh (Shirley) Shajarian is applying generative AI to computer networks. Shaghayegh told us about her research developing AI-driven agents that assist with some network operations, such as log analysis, troubleshooting, and documentation. Her goal is to reduce the manual work that network teams deal with every day and move toward more autonomous, self-running networks.

Causality and generative modeling

Aneesh Komanduri, a final-year PhD student at the University of Arkansas, gave us the low-down on his research at the intersection of causal inference, representation learning, and generative modeling. His dissertation specifically explores two core areas: causal representation learning and counterfactual generative modeling.

Game-theoretic integration of safety, interaction and learning for human-centered autonomy

Haimin Hu filled us in on his research covering the algorithmic foundations of human-centered autonomy. Through his work, Haimin aims to ensure autonomous systems are performant, verifiable, and trustworthy when deployed in human-populated space.

Computing education and generative AI

In this interview, Benyamin Tabarsi told us about his work at the intersection of generative AI and computing education. We found out more about what he’s investigated so far during his PhD, what is particularly interesting about this research area, and what inspired him to undertake a PhD in the field.


Our resources page
Our events page
Seminars in 2025
AAAI/ACM SIGAI Doctoral Consortium interview series
AAAI Fellows interview series
AfriClimate AI series
AI around the world focus series



tags: , , , ,


Lucy Smith is Senior Managing Editor for AIhub.
Lucy Smith is Senior Managing Editor for AIhub.

            AIhub is supported by:



Subscribe to AIhub newsletter on substack



Related posts :

Causal models for decision systems: an interview with Matteo Ceriscioli

  21 Apr 2026
How can we integrate causal knowledge into agents or decision systems to make them more reliable?

A model for defect identification in materials

  20 Apr 2026
A new model measures defects that can be leveraged to improve materials’ mechanical strength, heat transfer, and energy-conversion efficiency.

‘Probably’ doesn’t mean the same thing to your AI as it does to you

  17 Apr 2026
Are you sure you and the AI chatbot you’re using are on the same page about probabilities?

Interview with Xinwei Song: strategic interactions in networked multi-agent systems

  16 Apr 2026
Xinwei Song tells us about her research using algorithmic game theory and multi-agent reinforcement learning.

2026 AI Index Report released

  15 Apr 2026
Find out what the ninth edition of the report, which was published on 13 April, says about trends in AI.

Formal verification for safety evaluation of autonomous vehicles: an interview with Abdelrahman Sayed Sayed

  14 Apr 2026
Find out more about work at the intersection of continuous AI models, formal methods, and autonomous systems.

Water flow in prairie watersheds is increasingly unpredictable — but AI could help

  13 Apr 2026
In recent years, the Prairies have seen bigger swings in climate conditions — very wet years followed by very dry ones.

Identifying interactions at scale for LLMs

  10 Apr 2026
Model behavior is rarely the result of isolated components; rather, it emerges from complex dependencies and patterns.



AIhub is supported by:







Subscribe to AIhub newsletter on substack




 















©2026.02 - Association for the Understanding of Artificial Intelligence