1 citations · 3 across the 22 of their papers we have counts for
23 papers
Metalens-transparent ultrasound transducer module for extended depth photoacoustic microscopy
Krishnendu Samanta, Myunghoo Lee, Shubham Mirg +5
Optical-resolution photoacoustic microscopy (ORPAM) provides label-free optical-absorption contrast at millimeter-scale tissue depths with micrometer-scale lateral resolution. Howe…
Legislating World-Model-Based Planning with Legal Reasoning
Dylan Waldner, Yiannis Kantaros, Guido Governatori +2
As robotic systems grow more general, legal norms are needed to integrate them into society. This paper extends the isomorphism problem of aligning legal source texts with their en…
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…
Semantic Uncertainty-Guided Orchestration in Hierarchical Multi-Agent Systems
John Knowlton, Aritra Guha, Risto Miikkulainen
As large language model (LLM)-based multi-agent systems become increasingly capable, coordinating agents under uncertainty becomes a fundamental challenge. Existing orchestration s…
Overcoming Forgetting in LLM Fine-Tuning with Evolution Strategies
Kajetan Schweighofer, Conor F. Hayes, Roberto Dailey +2
Evolution Strategies (ES) has recently emerged as a competitive alternative to reinforcement learning (RL) for large language model (LLM) fine-tuning, offering advantages through s…
Efficient Pre-Training of LLMs through Truncated SVD Representations
Kaivan Kamali, Kajetan Schweighofer, Hormoz Shahrzad +3
LLM pretraining is extremely costly; therefore, parameter-efficient LLM architectures have recently emerged as a compelling research direction. One such promising approach is to re…
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…
Optimize Wider, Not Deeper: Consensus Aggregation for Policy Optimization
Zelal Su, Mustafaoglu, Sungyoung Lee +3
Proximal policy optimization (PPO) approximates the trust region update using multiple epochs of clipped SGD. Each epoch may drift further from the natural gradient direction, crea…
TerraLingua: Emergence and Analysis of Open-endedness in LLM Ecologies
Giuseppe Paolo, Jamieson Warner, Hormoz Shahrzad +3
As autonomous agents increasingly operate in real-world digital ecosystems, understanding how they coordinate, form institutions, and accumulate shared culture becomes both a scien…
Optimizing Chlorination in Water Distribution Systems via Surrogate-assisted Neuroevolution
Rivaaj Monsia, Daniel Young, Olivier Francon +1
Ensuring the microbiological safety of large, heterogeneous water distribution systems (WDS) typically requires managing appropriate levels of disinfectant residuals including chlo…
Quantized Evolution Strategies: High-precision Fine-tuning of Quantized LLMs at Low-precision Cost
Yinggan Xu, Kajetan Schweighofer, Risto Miikkulainen +1
Post-Training Quantization (PTQ) is essential for deploying Large Language Models (LLMs) on memory-constrained devices, yet it renders models static and difficult to fine-tune. Sta…
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…
The Blessing of Dimensionality in LLM Fine-tuning: A Variance-Curvature Perspective
Qiyao Liang, Jinyeop Song, Yizhou Liu +4
Weight-perturbation evolution strategies (ES) can fine-tune billion-parameter language models with surprisingly small populations (e.g., ), contradicting classical…
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…
Leveraging Evolutionary Surrogate-Assisted Prescription in Multi-Objective Chlorination Control Systems
Rivaaj Monsia, Olivier Francon, Daniel Young +1
This short, written report introduces the idea of Evolutionary Surrogate-Assisted Prescription (ESP) and presents preliminary results on its potential use in training real-world ag…
Evolutionary Policy Optimization
Zelal Su "Lain" Mustafaoglu, Keshav Pingali, Risto Miikkulainen
A key challenge in reinforcement learning (RL) is managing the exploration-exploitation trade-off without sacrificing sample efficiency. Policy gradient (PG) methods excel in explo…
Transformer Based Time-Series Forecasting for Stock
Shuozhe Li, Zachery B Schulwol, Zachery B Schulwolf +1
To the naked eye, stock prices are considered chaotic, dynamic, and unpredictable. Indeed, it is one of the most difficult forecasting tasks that hundreds of millions of retail tra…
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…
Semantic Density: Uncertainty Quantification for Large Language Models through Confidence Measurement in Semantic Space
Xin Qiu, Risto Miikkulainen
With the widespread application of Large Language Models (LLMs) to various domains, concerns regarding the trustworthiness of LLMs in safety-critical scenarios have been raised, du…
Optimizing the Design of an Artificial Pancreas to Improve Diabetes Management
Ashok Khanna, Olivier Francon, Risto Miikkulainen
Diabetes, a chronic condition that impairs how the body turns food into energy, i.e. blood glucose, affects 38 million people in the US alone. The standard treatment is to suppleme…
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…
Effective Regularization Through Loss-Function Metalearning
Santiago Gonzalez, Xin Qiu, Risto Miikkulainen
Evolutionary computation can be used to optimize several different aspects of neural network architectures. For instance, the TaylorGLO method discovers novel, customized loss func…