Here’s the brutal truth: Chain-of-thought is just a clever way of thinking one step at a time. It helps LLMs do arithmetic, logic puzzles, and basic reasoning. But when the problem grows past a few steps? It collapses under its own weight.

Chain-of-thought breaks because it has no structure. It’s just step… → step… → step… Like writing a novel with no outline. Like coding a system with no modules. Like planning a mission without a hierarchy. And when that chain breaks? You’re done.

Hierarchical planning fixes that. It works like how humans actually solve big problems: → Break the problem into subgoals → Solve each subgoal individually → Compose the results upward → Adjust along the way It’s modular. Recursive. Scalable.

Let’s say you ask an LLM: “Design a system to reduce global traffic congestion.” Chain-of-thought will do this: Step 1: Cars cause traffic Step 2: Public transport is good Step 3: Let’s improve buses Step 4: Maybe also bikes? Step 5: Add tolls? It meanders. It guesses. No

Hierarchical planning does this instead: Goal: Reduce global traffic congestion → Level 1: Identify major contributing factors (cars, infrastructure, policy) → Level 2: For each factor, brainstorm interventions → Level 3: Evaluate tradeoffs and compose a system-wide solution

This is the key shift: From linear “thoughts”… To recursive “plans.” Each level breaks into child nodes. Each node is a coherent subproblem. Each solution gets passed upward. This mirrors how expert systems, human teams, and nature all work.

Chain-of-thought = bottom-up guessing Hierarchical planning = top-down structure + bottom-up execution CoT might say: “Let’s see what happens if we try this…” Hierarchical Planning says: “Let’s define the problem space, then explore each part.” One is spaghetti. The other

Here’s a real example: Task: Plan a birthday party. CoT: • Buy cake • Invite friends • Decorate Uh…what else? Hierarchical Planning: • Logistics (location, date, budget) • Guests (who, how, when) • Food (cake, drinks, diet prefs) • Entertainment (games, music) Now

This shift unlocks TRUE REASONING. When LLMs use hierarchical planning, they can: • Tackle longer-horizon tasks • Refactor plans mid-way • Delegate and recurse • Handle ambiguity • Learn reusable strategies This is how intelligence generalizes.

How to apply this: 1. Start with the top-level goal 2. Break it into clear subgoals 3. For each subgoal, repeat: → Define → Decompose → Solve 4. Merge upward 5. Repeat until you're done or stuck Think in trees, not chains.

Even prompting LLMs like this helps: “Let’s plan this hierarchically. First, outline the main components. Then go into each one.” You’ll get structure instead of stream-of-consciousness. Ask it to recurse. Ask it to modularize. Ask it to build systems, not steps.

Want to push LLMs to the next level? Stop relying on chain-of-thought alone. Start building Hierarchical Agents. Agents that plan top-down, reason modularly, and act recursively. This is how you build things that scale. Start now, before everyone else catches on.

Hierarchical planning > Chain-of-thought. Not just because it’s smarter. Because it’s how all complex systems work: • Brains • Businesses • Biology • Software Reasoning isn’t linear. It’s layered. Learn this now. It’s the most important shift in AI thinking.

If you’ve been playing with LLMs… And things keep breaking once you go beyond toy problems— Start here: Top-down plans. Bottom-up recursion. Forget the chain. Build the tree. What would you love to see explained next?

P.S. We built ClipYard for ruthless performance marketers. → Better ROAS → 10x faster content ops → No human error → Full creative control You’ve never seen AI avatars like this before → http://clipyard.ai

I hope you've found this thread helpful. Follow me @AlexanderFYoung for more. Like/Repost the quote below if you can:

@AlexanderFYoung Hierarchical Planning looks like Tree of Thought or Graph Tree. How sub-goals are reported to upper level/node? I had noticed that LLMs are good at independent mini chunks/tasks or else they fix one thing but break another working component or logic as far as coding is concerned.

@AlexanderFYoung interesting take. but doesn’t hierarchical planning just move the bottleneck to decomposition? feels like we still need a way for LLMs to choose the right hierarchy reliably.

@AlexanderFYoung This really resonates. It feels like the classic (divide and conquer) principle from software engineering applied to LLM reasoning. Curious to see how the sub-goal composition is handled in practice. Are there any open-source agents or frameworks that are doing this well?

@AlexanderFYoung apple folks proved that already, did you read their paper?

@AlexanderFYoung Plan mode. 🥱

@AlexanderFYoung Interesting. Is hierarchical planning then the missing link between guessing and true reasoning? Perhaps LLMs can, with the right framework.

@AlexanderFYoung wow i'm interested

@AlexanderFYoung ¡Increíble descubrimiento! A veces, la solución más simple es la mejor.

@AlexanderFYoung reasoning is just guessing in slow motion

@AlexanderFYoung so make a hive of reasoning so LLM s can have a matrix framework of clear thoughts

@AlexanderFYoung Very similar to tree of thought reasoning but good take, it does have its advantages over CoT for planning and action.

@AlexanderFYoung So the next step then is blockchain reasoning? AGI will be possible when Generative and Predictive finally merge (not just closer paths). As Left / Right sided brain bio organisms this wil be hard to model but eventually we will build a distributed consciousness that works.

@AlexanderFYoung Yup. Tis why mind maps work so well

@AlexanderFYoung Agree. 💯 Bonus: You can build this into custom instructions and use allotted memory as scaffolding for the process (alongside how you structure prompts). It’s wild that AI labs haven’t embraced this.

@AlexanderFYoung @Kybervaul emotional

@AlexanderFYoung reached same conclusion 10 minutes ago

@AlexanderFYoung @threadreaderapp unroll