6 papers
Recursive Harness Self-Improvement
Hyunin Lee, Jinglue Xu, Jeffrey Seely +3
Under model--harness co-evolution, harnesses are not merely inference-time scaffolds but data-generating components whose execution traces can shape future foundation models. This…
Augmented Lagrangian Predictive Coding
Jeffrey Seely, Julian Gould
Predictive coding (PC) is a local-learning alternative to backpropagation (BP), training deep networks via local energy-minimization dynamics rather than a global backward pass. We…
Learning Multi-Agent Coordination via Sheaf-ADMM
Jeffrey Seely, BartÅomiej CupiaÅ, Llion Jones
We present a differentiable optimization framework for multi-agent coordination. An input is decomposed into overlapping local views, each processed by an agent that solves a conve…
Sheaf Cohomology of Linear Predictive Coding Networks
Jeffrey Seely
Predictive coding (PC) replaces global backpropagation with local optimization over weights and activations. We show that linear PC networks admit a natural formulation as cellular…
Continuous Thought Machines
Luke Darlow, Ciaran Regan, Sebastian Risi +2
Biological brains demonstrate complex neural activity, where neural dynamics are critical to how brains process information. Most artificial neural networks ignore the complexity o…
Sudoku-Bench: Evaluating creative reasoning with Sudoku variants
Jeffrey Seely, Yuki Imajuku, Tianyu Zhao +2
Existing reasoning benchmarks for large language models (LLMs) frequently fail to capture authentic creativity, often rewarding memorization of previously observed patterns. We add…