Alexia Jolicoeur-Martineau
Summary: Research Scientist at Samsung SAIL Montréal. Lead author of TRM (Tiny Recursive Model) and co-author of HRM (Hierarchical Reasoning Model). PhD from Mila/Université de Montréal. Research focus: recursive reasoning, deep supervision, ACT, small-model reasoning for ARC-AGI. Emerging star in efficient reasoning architectures.
Profile
| Field | Details |
|---|---|
| Current Role | Research Scientist, Samsung SAIL Montréal |
| PhD | Mila / Université de Montréal (2022-2024) |
| Thesis | "Recurrent Neural Networks for Reasoning" (or similar) |
| Key Papers | TRM (2025, lead), HRM (2025, co-author) |
| Research Focus | Recursive reasoning, deep supervision, ACT, ARC-AGI, small models |
| Google Scholar | Profile |
| Twitter/X | @ajolicoeurm |
| Website | alexiajm.github.io |
Significance for You
Perfect research match for your interests:
| Your Interest | Her Work |
|---|---|
| Recursive / latent space reasoning | HRM/TRM: recursive latent updates |
| Adaptive computation / ACT | Q-learning halting in HRM/TRM |
| Deep supervision | Primary innovation in TRM (19%→39%) |
| Small efficient models | 7M params beating 7B LLMs on ARC |
| World models / planning | Recursive latent = implicit planning |
PhD advisor potential:
- Industry lab (Samsung) — may co-advise with academic partner (Mila/UdeM)
- Early career → likely taking students
- Publishes at top venues (ICLR, NeurIPS)
- Montréal = can visit Mila ecosystem easily
Target tier: Tier 1 — exact research match, emerging leader, accessible lab.
Key Publications
| Paper | Year | Role | Innovation | Your Wiki |
|---|---|---|---|---|
| TRM: Tiny Recursive Model | 2025 | Lead | 7M params, flat recursion, deep supervision primary driver, 45% ARC-AGI-1 | paper-trm |
| HRM: Hierarchical Reasoning Model | 2025 | Co-author | H/L hierarchy, deep supervision, ACT, 40.3% ARC-AGI-1 | paper-hrm |
| PhD work | 2022-24 | Lead | Recurrent reasoning foundations | — |
Research Themes (from papers)
- Deep supervision > hierarchy — TRM ablation: deep supervision gives +20% absolute; hierarchy only +3.3%
- ACT without replay buffers — Q-learning stability via Post-Norm + AdamW (bounded params)
- Inference-time compute scaling — Train M_max=8, test M_max=16 zero-shot gains
- Flat recursion beats hierarchy — TRM (single net) > HRM (two nets) with 4× fewer params
- Small models for reasoning — 7M params competitive with LLMs on ARC with 1000× less compute
Contact Strategy
When: Summer 2025 (pre-PhD applications) How: Email + attach your best paper/work (DSA-ViT project page, any preprints) Angle:
"Your TRM paper showed deep supervision is the key driver for recursive reasoning. I've been adapting DSA (DeepSeek Sparse Attention) to Vision Transformers for efficient long-range reasoning. I'd love to explore: (1) TRM-style deep supervision for visual reasoning / video understanding, (2) ACT for adaptive visual computation, (3) recursive latent reasoning for world models."
Follow-up: If responsive, propose virtual meeting; mention Montréal visit possibility.
Related Wiki Pages
- samsung-sail-montreal — Her lab
- paper-hrm — HRM breakdown
- paper-trm — TRM breakdown
- hrm-reasoning — Architecture concept
- trm-recursive-reasoning — Architecture concept
- deep-supervision — Core mechanism
- act-adaptive-computation — Halting mechanism
- target-labs — Your PhD target list
Sources
- arxiv-2510.04871: TRM paper (lead author)
- arxiv-2506.21734: HRM paper (co-author)