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- Research and Life in the Age of AI
How to Use AI Without Losing Your Thinking Power
I caught myself outsourcing my ability to reason — not just to code, but to think through everyday life. Here's the framework I built to take it back.
- Learning for Planning
Epilogue: Toward Uncertainty
What GABAR doesn't do, why that matters, and where the natural next chapter takes the same warehouse. Closer of the Learning for Planning series.
- Planning under Uncertainty
Epilogue: Open Questions
What HOO-POMDP and GammaZero don't yet do, where the next decade of partially-observable planning is heading, and how this all connects to the rest of AI. Closer of the Planning Under Uncertainty series.
- Learning for Planning My Research
You Don't Need to Rank All States. Just Rank the Actions in Front of You.
How a simple shift in learning objective — from global value functions to local action ranking — yields planning policies that generalize 8x beyond training size. A deep dive into GABAR. Part 4 of the Learning for Planning series.
- Planning under Uncertainty My Research
Your Belief About the World Is a Graph. Now Your Planner Can Use It.
How encoding uncertainty as graph structure enables POMDP planners to generalize far beyond their training size. A deep dive into GammaZero. Part 4 of the Planning Under Uncertainty series.
- Learning for Planning
Your Planning State Is a Graph
Why fixed-size vectors can't represent a planning problem, and what to do instead. Part 3 of the Learning for Planning series.
- Planning under Uncertainty My Research
Teaching Robots to Tidy Up: Planning Under Uncertainty in Multi-Room Environments
How hierarchical planning with object-oriented beliefs enables robots to rearrange objects when they can't see everything at once. Part 3 of the Planning Under Uncertainty series.
- Learning for Planning
What to Learn — A Survey of Learning Objectives
Three families of methods, each picking a different target for the model to predict. The choice of target ends up mattering more than the choice of architecture. Part 2 of the Learning for Planning series.
- Planning under Uncertainty
MCTS for POMDPs
Exact POMDPs are intractable. Online tree search makes them solvable in practice. The price: every modern POMDP solver depends on heuristics — and that's exactly where learning fits in. Part 2 of the Planning Under Uncertainty series.
- Learning for Planning
The Scaling Problem
Planning is sound, complete, and optimal. It is also exponentially hard. Learning is how we get past that wall — but to see why it works, we first need to feel the wall. Part 1 of the Learning for Planning series.
- Planning under Uncertainty
Planning When You Can't See the Whole World
Classical planning assumes you know everything. Real agents almost never do. Belief states give us a principled way to plan under uncertainty — and an extraordinary computational cost in the bargain. Part 1 of the Planning Under Uncertainty series.
- Learning for Planning
Learning for Planning — Series Overview
A four-part series (plus epilogue) on how graph neural networks let learned policies generalize from small, solvable training instances to problems 8x larger — without ever retraining.
- Planning under Uncertainty
Planning Under Uncertainty — Series Overview
A four-part series (plus epilogue) on planning when the agent can't see the full state — POMDPs, belief tracking, online tree search, and the two strategies (abstraction and learning) that make large partially-observable problems tractable.
- LLMs Automated Planning and Agents
Epilogue: Where the Field Goes Next
A forward-looking companion to the seven-post series. Limitations of HALO, the meta-learning frame, open challenges, and the research directions that will define the next few years.
- LLMs Automated Planning and Agents My Research
Training the Conductor
Stop prompting the orchestrator at every step. Let the verifier you already trust supervise a small local model — and watch the bill drop by 99%. Part 7 of the Planning in the Era of LLMs series.
- LLMs Automated Planning and Agents
From English to Plans: The NL-to-PDDL Frontier
NL2Plan, agentic PDDL generation, and the orchestrator bottleneck — when the conductor can't keep up with the orchestra. Part 6 of the Planning in the Era of LLMs series.
- LLMs Automated Planning and Agents
The Modern Playbook: LLMs That Help Planners
LLM-Modulo, heuristic generation, and the generate-verify loop that turned 12% into 82%. Part 5 of the Planning in the Era of LLMs series.
- LLMs Automated Planning and Agents
LLMs Try to Plan (It Goes Badly)
PlanBench, Mystery Blocksworld, and the sobering evidence that frontier models can't reliably sequence three actions. Part 4 of the Planning in the Era of LLMs series.
- LLMs Automated Planning and Agents
50 Years of Planning Algorithms (In 15 Minutes)
From STRIPS to Fast Downward — a tour of the solvers your LLM agent should be working with, not replacing. Part 3 of the Planning in the Era of LLMs series.
- LLMs Automated Planning and Agents
The Formal Planning Primer
States, actions, goals, and the language that makes them precise — everything you need to follow the rest of this series. Part 2 of the Planning in the Era of LLMs series.
- LLMs Automated Planning and Agents
Making LLM Agents Actually Plan: A Roadmap
Why your LLM agent fails at multi-step tasks, and a 7-part guide to fixing it. Part 1 of the Planning in the Era of LLMs series.