Tiny Recursive Message Passing applies recursive transformers and deep supervision to message passing neural networks, achieving high efficiency. The approach yields about 99% test accuracy on Sudoku-Extreme with only 16k parameters, training quickly on modern GPUs or CPUs. It also demonstrates zero‑shot transfer to larger problem instances, outperforming popular neural baselines on N‑queens and Boolean satisfiability with fewer parameters.
arXiv:2609.39967v1 Announce Type: cross Abstract: Recursive reasoning models apply a small shared Transformer block many times to refine a latent state. This gives them large effective depth with few...
Recursive reasoning models apply a small shared Transformer block many times to refine a latent state. This gives them large effective depth with few parameters and makes them strong on algorithmic ta...
arXiv:2606. 29150v1 Announce Type: new Abstract: Discrete flow models have recently shown promising performance on few-step text generation; however, when naively applied to structured reasoning tasks such as Sudoku and Zebra puzzles, they converge confidently to incorrect answers (solving only $\sim$36% of Sudoku puzzles).
arXiv:2605.08605v2 Announce Type: replace-cross Abstract: We introduce the Lattice Deduction Transformer (LDT), a recurrent transformer that approximates logically sound deduction by projecting its l...
arXiv:2605. 18079v2 Announce Type: replace Abstract: Existing expressivity results for transformers typically rely on hardmax attention, high precision, and other architectural modifications that disconnect them from the models used in practice.
arXiv:2609.37066v1 Announce Type: cross Abstract: Post-training is central to mathematical reasoning in modern large language models (LLMs), but endpoint pass@1 alone underidentifies what has changed...
arXiv:2602. 01997v3 Announce Type: replace-cross Abstract: Recent work has shown that layer pruning can effectively compress large language models (LLMs) while retaining strong performance on classification benchmarks, often with little or no finetuning.
arXiv:2606. 15686v1 Announce Type: new Abstract: Large language models often appear strong on symbolic and algorithmic tasks, yet this apparent strength can hide brittle behaviour when problems become longer, harder, or slightly out of distribution.
Flow Reasoning Models (FRMs) are a new framework that turns continuous flow models into efficient recurrent reasoners for structured tasks. By self‑conditioning a flow model on its own past outputs, FRMs iteratively refine solutions, allowing parallel decision making and revision. The authors introduce Fixed‑Point Forcing (FPF) to mitigate exposure bias at deeper recursion, and report near‑perfect solve rates on Sudoku‑Extreme, Zebra, and Maze‑Unique, outperforming existing masked‑diffusion and specialized baselines while using far fewer inference FLOPs.
arXiv:2606. 26488v1 Announce Type: new Abstract: Recursive reasoning models can solve complex structured tasks with only a few million parameters by repeatedly updating a latent state.
The paper introduces InfiLoop, a loop-native residual connection designed for looped Transformers to prevent performance degradation as iterations increase. InfiLoop learns which past computations to retain and how much to accept from each new update, combining content-based weighting with learned temporal decay to maintain a running summary of recurrent states. Across extensive reasoning tasks, a 7M-parameter InfiLoop model outperforms existing recursive architectures, achieving 97.9% exact accuracy on Sudoku-Extreme and 13.6% pass@2 on ARC-AGI-2, and continues to improve with test-time looping beyond 20,000 effective steps.
arXiv:2607. 16237v1 Announce Type: cross Abstract: Recursive reasoning models solve hard puzzles by applying compact, weight-tied blocks over many refinement steps.