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Akarsh Kumar

[Twitter] [Google Scholar] [GitHub]

Contact akarshkumar0101(at)gmail(dot)com

Bio
I'm a Ph.D. student at MIT CSAIL working with Phillip Isola.
I'm also a research intern at Sakana AI working with Yujin Tang and David Ha. I also collaborate with Ken Stanley.
My research is supported by the NSF GRFP!
Previously, I graduated from UT Austin where I worked with Peter Stone, Risto Miikkulainen, and Alexander Huth.

Research
I want to understand intelligence and all the different processes that emerge it.
More specifically, I'm interested in:

  • Applying principles from natural evolution and artificial life to create better AI systems.
  • Open-ended processes which keep creating "interesting" artifacts indefinitely.
  • Evolving intelligence from scratch, without the internet, like how natural evolution created us.
In practice, I'm espcially interested in meta-learning, RL, automatic environment generation, and multi-agent self-play.

Research

Questioning Representational Optimism in Deep Learning: The Fractured Entangled Representation Hypothesis

Akarsh Kumar, Jeff Clune, Joel Lehman, Kenneth O. Stanley

arXiv 2025

TLDR: We hypothesize that the standard paradigm of training networks today is producing spaghetti-like representations of it's (good) output behavior, leading to ineffective OOD generalization, creativity, and continual learning.

[Arxiv] [Code] [Tweet]

Automating the Search for Artificial Life with Foundation Models

Akarsh Kumar, Chris Lu, Louis Kirsch, Yujin Tang, Kenneth O. Stanley, Phillip Isola, David Ha

ALife Journal 2024

TLDR: ASAL is a new ALife paradigm using CLIP to search for (1) target, (2) open-ended, and (3) diverse simulations. ASAL helps us understand "life as it could be" in arbitrary artificial worlds.

[Web Paper] [Arxiv] [Blog] [Code] [Tweet]

Learning In-Context Decision Making with Synthetic MDPs

Akarsh Kumar, Chris Lu, Louis Kirsch, Phillip Isola

AutoRL @ ICML 2024

TLDR: Generalist in-context RL agents trained on only synthetic MDPs generalize to real MDPs.

[Paper]

Effective Mutation Rate Adaptation through Group Elite Selection

Akarsh Kumar, Bo Liu, Risto Miikkulainen, Peter Stone

GECCO 2022

TLDR: Explains why genetic algorithms fail to self-adapt their mutation rate and proposes group selection to fix it.

[Paper] [Code]

Physically Plausible Pose Refinement using Fully Differentiable Forces

Akarsh Kumar, Aditya R. Vaidya, Alexander G. Huth

EPIC @ CVPR 2021

TLDR: Accurately refining pose estimations by differentiably modeling the physics of the scene.

[Paper] [Presentation]

Talks

Speaker at Detection and Emergence of Complexity Conference
May 2025
[Website] [Video (coming soon)]

MIT Embodied Intelligence Seminar
March 2025
[Video]

Kaiming He's Lab
March 2025

Michael Levin's Lab
April 2025
[Video]

Jeff Gore's Lab
March 2025

AutoRL @ ICML 2024
May 2024
[Video]