63 citations · 164 across the 23 of their papers we have counts for
23 papers
Beyond the Best Guess: Improving LLM Solution Coverage with Evolution Strategies
Conor F. Hayes, Elliot Meyerson, Kajetan Schweighofer +4
Large Language Models (LLMs) are increasingly deployed in discovery domains such as math and science. The usual approach is to present the problem to the model and use its answer a…
Caesar: Deep Agentic Web Exploration for Creative Answer Synthesis
Jason Liang, Elliot Meyerson, Risto Miikkulainen
To advance from passive retrieval to creative discovery of new ideas, autonomous agents must be capable of deep, associative synthesis. However, current agentic frameworks prioriti…
Fine-Tuning Language Models to Know What They Know
Sangjun Park, Elliot Meyerson, Xin Qiu +1
Evaluating true metacognition in Large Language Models (LLMs) is difficult due to biases and heuristics. This paper presents a framework to measure and enhance LLM metacognition wh…
Solving a Million-Step LLM Task with Zero Errors
Elliot Meyerson, Giuseppe Paolo, Roberto Dailey +6
LLMs have achieved remarkable breakthroughs in reasoning, insights, and tool use, but chaining these abilities into extended processes at the scale of those routinely executed by h…
Evolution Strategies at Scale: LLM Fine-Tuning Beyond Reinforcement Learning
Xin Qiu, Yulu Gan, Conor F. Hayes +6
Fine-tuning large language models (LLMs) for downstream tasks is an essential stage of modern AI deployment. Reinforcement learning (RL) has emerged as the dominant fine-tuning par…
Position: Scaling LLM Agents Requires Asymptotic Analysis with LLM Primitives
Elliot Meyerson, Xin Qiu
Decomposing hard problems into subproblems often makes them easier and more efficient to solve. With large language models (LLMs) crossing critical reliability thresholds for a gro…
Unlocking the Potential of Global Human Expertise
Elliot Meyerson, Olivier Francon, Darren Sargent +2
Solving societal problems on a global scale requires the collection and processing of ideas and methods from diverse sets of international experts. As the number and diversity of h…
Discovering Effective Policies for Land-Use Planning with Neuroevolution
Daniel Young, Olivier Francon, Elliot Meyerson +5
How areas of land are allocated for different uses, such as forests, urban areas, and agriculture, has a large effect on the terrestrial carbon balance, and therefore climate chang…
Language Model Crossover: Variation through Few-Shot Prompting
Elliot Meyerson, Mark J. Nelson, Herbie Bradley +4
This paper pursues the insight that language models naturally enable an intelligent variation operator similar in spirit to evolutionary crossover. In particular, language models o…
Simple Genetic Operators are Universal Approximators of Probability Distributions (and other Advantages of Expressive Encodings)
Elliot Meyerson, Xin Qiu, Risto Miikkulainen
This paper characterizes the inherent power of evolutionary algorithms. This power depends on the computational properties of the genetic encoding. With some encodings, two parents…
The Traveling Observer Model: Multi-task Learning Through Spatial Variable Embeddings
Elliot Meyerson, Risto Miikkulainen
This paper frames a general prediction system as an observer traveling around a continuous space, measuring values at some locations, and predicting them at others. The observer is…
From Prediction to Prescription: Evolutionary Optimization of Non-Pharmaceutical Interventions in the COVID-19 Pandemic
Risto Miikkulainen, Olivier Francon, Elliot Meyerson +3
Several models have been developed to predict how the COVID-19 pandemic spreads, and how it could be contained with non-pharmaceutical interventions (NPIs) such as social distancin…
Effective Reinforcement Learning through Evolutionary Surrogate-Assisted Prescription
Olivier Francon, Santiago Gonzalez, Babak Hodjat +4
There is now significant historical data available on decision making in organizations, consisting of the decision problem, what decisions were made, and how desirable the outcomes…
Quantifying Point-Prediction Uncertainty in Neural Networks via Residual Estimation with an I/O Kernel
Xin Qiu, Elliot Meyerson, Risto Miikkulainen
Neural Networks (NNs) have been extensively used for a wide spectrum of real-world regression tasks, where the goal is to predict a numerical outcome such as revenue, effectiveness…
Modular Universal Reparameterization: Deep Multi-task Learning Across Diverse Domains
Elliot Meyerson, Risto Miikkulainen
As deep learning applications continue to become more diverse, an interesting question arises: Can general problem solving arise from jointly learning several such diverse tasks? T…
Evolutionary Neural AutoML for Deep Learning
Jason Liang, Elliot Meyerson, Babak Hodjat +3
Deep neural networks (DNNs) have produced state-of-the-art results in many benchmarks and problem domains. However, the success of DNNs depends on the proper configuration of its a…
Evolutionary Architecture Search For Deep Multitask Networks
Jason Liang, Elliot Meyerson, Risto Miikkulainen
Multitask learning, i.e. learning several tasks at once with the same neural network, can improve performance in each of the tasks. Designing deep neural network architectures for…
Pseudo-task Augmentation: From Deep Multitask Learning to Intratask Sharing---and Back
Elliot Meyerson, Risto Miikkulainen
Deep multitask learning boosts performance by sharing learned structure across related tasks. This paper adapts ideas from deep multitask learning to the setting where only a singl…
Beyond Shared Hierarchies: Deep Multitask Learning through Soft Layer Ordering
Elliot Meyerson, Risto Miikkulainen
Existing deep multitask learning (MTL) approaches align layers shared between tasks in a parallel ordering. Such an organization significantly constricts the types of shared struct…
Discovering Evolutionary Stepping Stones through Behavior Domination
Elliot Meyerson, Risto Miikkulainen
Behavior domination is proposed as a tool for understanding and harnessing the power of evolutionary systems to discover and exploit useful stepping stones. Novelty search has show…
Evolving Deep Neural Networks
Risto Miikkulainen, Jason Liang, Elliot Meyerson +8
The success of deep learning depends on finding an architecture to fit the task. As deep learning has scaled up to more challenging tasks, the architectures have become difficult t…
Reuse of Neural Modules for General Video Game Playing
Alexander Braylan, Mark Hollenbeck, Elliot Meyerson +1
A general approach to knowledge transfer is introduced in which an agent controlled by a neural network adapts how it reuses existing networks as it learns in a new domain. Network…
DMVP: Foremost Waypoint Coverage of Time-Varying Graphs
Eric Aaron, Danny Krizanc, Elliot Meyerson
We consider the Dynamic Map Visitation Problem (DMVP), in which a team of agents must visit a collection of critical locations as quickly as possible, in an environment that may ch…