G-RRM: Guiding Symbolic Solvers with Recurrent Reasoning Models
In this work, we focus on SE-RRMs, a symbol-equivariant instantiation of RRMs that exhibits improved extrapolation to larger problem sizes. We propose a neuro-symbolic approach, ``Guiding with Recurrent Reasoning Models'' (G-RRM), which integrates SE-RRMs with symbolic solvers for constraint satisfaction problems. SE-RRMs act as neural solvers that generate full solution proposals and guide classical symbolic solvers, such as backtracking or SAT-based methods like Glucose 4.1 and CaDiCaL 3.0.0, that produce globally correct solutions. Centrally, we investigate when neural guidance with G-RRM i
Lineage graph
Paper → model → repo connections mined from source citations (Tier-1 exact match).
Why these links exist
Every edge carries a method, confidence, and the source snippet that justified it — so bad links are debuggable.
- PossiblePossibly related (embedding) · 50%sileod/reasoning-core →
- PossiblePossibly related (embedding) · 48%Learning Structured Reasoning via Tractable Trajectory Control - Apple Machine Learning Research →
- LinkedLinked via arxiv author · 85%Timo Bertram →
“G-RRM: Guiding Symbolic Solvers with Recurrent Reasoning Models”
- LinkedLinked via arxiv author · 85%Sidhant Bhavnani →
“G-RRM: Guiding Symbolic Solvers with Recurrent Reasoning Models”
- LinkedLinked via arxiv author · 85%Richard Freinschlag →
“G-RRM: Guiding Symbolic Solvers with Recurrent Reasoning Models”
- LinkedLinked via arxiv author · 85%Erich Kobler →
“G-RRM: Guiding Symbolic Solvers with Recurrent Reasoning Models”
- LinkedLinked via arxiv author · 85%Andreas Mayr →
“G-RRM: Guiding Symbolic Solvers with Recurrent Reasoning Models”
- LinkedLinked via arxiv author · 85%Günter Klambauer →
“G-RRM: Guiding Symbolic Solvers with Recurrent Reasoning Models”