Probabilistic Tiny Recursive Model
The article discusses the introduction of a new framework called Probabilistic Tiny Recursive Model (PTRM) designed to enhance the performance of Tiny Recursive Models (TRM) in solving complex reasoning tasks. PTRM incorporates stochastic exploration through Gaussian noise, allowing for improved accuracy without the need for retraining. The framework demonstrates significant accuracy gains across various benchmarks while maintaining a low parameter count.
- ▪Probabilistic Tiny Recursive Model (PTRM) addresses limitations of traditional Tiny Recursive Models (TRM) by introducing stochastic exploration.
- ▪PTRM achieves substantial accuracy improvements on benchmarks like Sudoku-Extreme and Pencil Puzzle Bench.
- ▪The model operates with only 7 million parameters, achieving nearly double the accuracy of leading large language models at a fraction of the cost.
arXiv cs.AI files mainly under ai research. We currently carry 1,128 of its stories.
Story provenance
Source · retrieval · rights · ranking — open for full record
inspect →
Story provenance
Attribution is not the same as permission. This drawer separates discovery metadata, excerpts, WeSearch-generated summaries, reuse status, and whether the publisher receives the visit. Nothing here claims a legal grant the publisher has not made.
Record
| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.19943 |
| Publication time | Wed, 20 May 2026 00:00:00 -0400 |
| Retrieval time | 2026-05-20T04:04:59.484Z |
| Last seen | 2026-05-20T04:04:59.484Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | Z4-e_Dqc_guO |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
Rights status (four layers)
WeSearch handling by dimension
| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
Computer Science > Artificial Intelligence arXiv:2605.19943 (cs) [Submitted on 19 May 2026] Title:Probabilistic Tiny Recursive Model Authors:Amin Sghaier, Ali Parviz, Alexia Jolicoeur-Martineau View a PDF of the paper titled Probabilistic Tiny Recursive Model, by Amin Sghaier and 2 other authors View PDF HTML (experimental) Abstract:Tiny Recursive Models (TRM) solve complex reasoning tasks with a fraction of the parameters of modern large language models (LLMs) by iteratively refining a latent state and final answer. While powerful, their deterministic recursion can lead to convergence at suboptimal solutions, without escape mechanism. A common workaround relies on task-specific input perturbations at test time combined with answer aggregation via voting.
…
Excerpt limited to ~120 words for fair-use compliance. The full article is at arXiv cs.AI.
Discussion
0 commentsMore from arXiv cs.AI
