# Trend 8 — Recursive *architectures* (vs. recursive training)

A separate branch: building models whose forward pass is itself iterative.

## Papers

- `spotlight` · `yIDgVx3OoN` — [From Growing to Looping: A Unified View of Iterative Computation in LLMs](https://openreview.net/forum?id=yIDgVx3OoN) — (spotlight) From Growing to Looping — depth-grown LIDAS and looped UTs share the same depth-wise mechanistic signature. **You can loop a depth-grown model at inference (no retraining) for ~2× gains** on reasoning primitives.
- `poster` · `Vxu6kcIjwV` — [Generative Recursive Reasoning Models](https://openreview.net/forum?id=Vxu6kcIjwV) — GRAM — stochastic latent recursion via amortized variational inference; strong on Sudoku-Extreme and ARC-AGI; outperforms HRM/TRM on multi-solution problems.
- `short_paper` · `1zDG1o16xB` — [Depth vs Recursion: Outperforming Transformers in Jigsaw Reconstruction](https://openreview.net/forum?id=1zDG1o16xB) — Depth vs Recursion — on jigsaw 5×5: encoder-only Transformer collapses to 57.6%, TRM holds at 94.2% with same 0.7M params.
- `spotlight` · `aY5kmaNrwB` — [Tiny Autoregressive Recursive Models](https://openreview.net/forum?id=aY5kmaNrwB) — (spotlight) Tiny Autoregressive Recursive Models — negative result: under autoregressive decoding, token-internal recursion doesn't help.
- `poster` · `rIzREKws05` — [Unrolled Policy Iteration for Tiny Recursive Models](https://openreview.net/forum?id=rIzREKws05) — Unrolled Policy Iteration for TRMs — formalizes TRM training as policy iteration with Lipschitz-bounded inner unrolling.
- `poster` · `fWrhAOQZ5A` — [Reasoning Within the Mind: Dynamic Multimodal Interleaving in Latent Space](https://openreview.net/forum?id=fWrhAOQZ5A) — Reasoning Within the Mind — confidence-driven gradient updates on latent think-tokens.
- `poster` · `5UbKomO77O` — [Emergent temporal abstractions in autoregressive models enable hierarchical reinforcement learning](https://openreview.net/forum?id=5UbKomO77O) — Emergent Temporal Abstractions — discover RL options inside the residual stream of a pretrained autoregressive model.

## Synthesis

The picture is that **recursion in the forward pass is real but fragile**: latent recursion crushes depth on visual grids (jigsaw) but disappears under causal autoregressive decoding. The "loop a depth-grown model at inference for free" finding is the most actionable.

## Related

- **Trend 7 — Failure Modes Catalogued** (Tiny Autoregressive RM negative)
- **Trend 4 — Test-Time Training at Scale** (latent updates at inference)
- **Home**