Francois Chaubard
Visiting Partner, Y Combinator. On 1 show, 2 appearances. The Shows tab opens the full record on each.
founderinvestoracademicauthorhostscientist@FrancoisChauba1 ↗focal.systems ↗
Francois Chaubard is the founder and former CEO of Focal Systems, which automates retail operations using computer vision. He conducts AI research at Stanford and serves as a Visiting Partner at Y Combinator, where he hosts the Paper Club and Decoded series.
1shows
2appearances
19statements
7resolved
4supported
1contradicted
57%fully supported
1said about them ↓ Everything Francois Chaubard said on any show that made the record, most notable first. Each card names its show and opens the statement there.
Chaubard: HRM scored 70% on ARC Prize 1 without pre-training
“There is no pre-training at all. This starts from, like, literally Tagula-Rasa weights, and it can outperform at that time, if we go back, you know, we had O-three, if you remember back, way back when. And it, O-three gets zero. Literally zero, and this got, l…”
Chaubard: Recursive models discover problem-solving strategies without human teacher forcing
“That's the most important part, is that if we had Sudoku, and we know how to solve Sudoku, because like we were just, you know, dumb homo sapiens that didn't know how to solve Sudoku, like it would just have solved it. And that's why it's cool, because it actu…”
Chaubard predicts running recursive reasoning models inside giant LLM latent spaces
“What you can imagine is we found mapping from token space or from vision, from pixels, Some really cool latent space where, like, things are just nicely semantically separated, and we can, you know, makes it really easy for downstream tasks to do, but now in t…”
Chaubard: AI models are recursively distilling into one another, Claude into Kimi
“What I think is kind of happening right now, but like clawed kind of mother birds into, Kimmy too, and then now Kimmy too is post-training thinking machines, and so it just keeps going and going.”
Chaubard: Hierarchical Reasoning Models Offer Little Novelty Over Standard RNNs
“The, this is directly in the lineage of RNNs. There's not that much novel from, like, the RNN standpoint at least in my opinion.”
Chaubard: Chain of thought trained on bubble sort won't discover merge sort
“If you chain of thought it on all the bubble sort input and output, it will only do bubble sort. In fact, it won't even do bubble That well.”
Chaubard: 7M parameter TRM scored 87% on ARC Prize 1
“And so it's a twenty-eight million parameter model for HRM. Now she brings it down to a seven million parameter model. It actually gets from 70% to 87% on on ArcPrize one. And does actually quite well on ArcPrize two as well.”
Chaubard: Massive chip specialization will split training and inference data center specs
“We just see that at the chip level, there's going to be massive specialization. The specs that you would need for a training data center are going to be very different than the specs for a inference data center.”
Chaubard: CPU-based environment simulation bottlenecks on-policy reinforcement learning rollouts
“The, it's amazing how much of simulators, when you call environment.step, is still run on the CPU, and so that's usually the bottleneck for a lot of your on-policy rollouts”
Chaubard: Chip specialization and ASICs will proliferate across AI workloads
“From my vantage point, we're gonna see this proliferation, and where we have sufficient demand now, because there's so much demand for tokens, Where it makes sense to specialize at the chip level that will pay out and allow you to go through the full new produ…”
Chaubard: Inference stacks split between Nvidia for pre-fill and Cerebras for decode
“We already do see it a little bit on where a lot of the stack peep for inference. People will go to Nvidia for pre-fill and then they'll go to cerebris for decode engine.”
Chaubard: Standard LLMs sacrifice latent time compression unlike RNNs
“What you actually paid for that you have to give up is this latent reasoning thing and this compression in the time direction. There is no compression in LMs. Every single decode that I do, I still have to retain the entire, you know, Shakespeare novel just to…”
Chaubard: Transformers cannot sort lists longer than layer count in one pass
“In a one-shot basis. It's like literally that we know a theoretical lower bound that for comparison sort, you can't do better than n log n steps. And if I have a list that's 31 characters or elements long, and my transformer is 30, I run out of steps to do com…”
Chaubard: Sudoku, mazes, and rolling sums are incompressible reasoning problems for LLMs
“In HRM and TRM, they use Sudoku as an incompressible problem. Similarly, and so are mazes. Those are incompressible problems. Rolling sum, incompressible problem.”
Chauvard: Chain of thought makes LLMs Turing complete at test time
“And so it's completely true that at test time, they are turn complete. And you can simulate all turn computable functions at test time.”
Chaubard: Chain of thought fails on unsolved problems lacking human traces
“Unless you're training it on human labeled traces for which there's a lot of problems like the millennial prize problem. We don't have the trace for it.”
Chaubard: Recursive models tested on one step retain nearly full performance
“If you actually train on 16, and you test on only one, you get, like, seven eighths of the performance, or, like, almost all the performance. So it's actually quite interesting that this is just overdone, too much compute, and it doesn't actually help you all …”
Chaubard: MLP outperformed attention on Sudoku but scored zero on mazes
“Yeah, on Sudoku, MLP actually outperformed the attention, It was it scored zero on the maze, ah, the MLP scored zero on the maze, and so there's, it's not clear, it's not obvious that, ah, the transformer is always better.”
Chaubard: Prior to 2016, Researchers Believed RNNs Were Necessary for AGI
“An RNN is just a model that you recursively call again and again and again on itself, and we, We're very much in the belief that this was required to get to AGI peak RNN use was probably until 2016 with Alex Graves NeurIPS keynote, which is just fantastic, and…”
The other half of the tape: Francois Chaubard's own voice is left out of every number here. Other people bring the name up 1 time in 1 episode across the shows. every mention, with the transcript →
Every mention by year
tap a year for its mentions
the Y Combinator Startup Podcast 1
2026 1 mention in 1 episode
One line per show, most statements first. The link opens Francois's full record on that show: the calibration, argument clarity, speaking style and every statement made there.
| Show | Role there | Eps | Statements | Record | |
|---|---|---|---|---|---|
| Y COMBINATORLEDGER | Visiting Partner, Y Combinator | 2 | 19 | 57% 4/7 | full record on the Y Combinator Startup Podcast → |