LaneRoPE: Positional Encoding for Collaborative Parallel Reasoning and Generation

Computer Science > Artificial Intelligence [Submitted on 26 May 2026] LaneRoPE: Positional Encoding for Collaborative Parallel Reasoning and Generation Gabriele Cesa, Thomas Hehn, Aleix Torres-Camps, Àlex Batlle Casellas, Jordi Ros-Giralt, Arash Behboodi, Tribhuvanesh Orekondy Parallel LLM test-time scaling techniques (e.g., best-of-N) require drawing N>1 sequences conditioned on the same input prompt. These methods boost accuracy while exploiting the computational efficiency of batching N generations. However, each sequence in the batch is traditionally generated independently and hence does not reuse intermediate generations, computations, or observations from other sequences. In this paper, we propose LaneRoPE to enable coordination and collaboration among N>1 sequences at generation time. LaneRoPE involves two key ideas: (a) an inter-sequence attention mask to make sampling of sequences dependent on one another; and (b) a RoPE extension that injects positional information that captures relative positions between tokens, both within and outside a particular sequence. We evaluate our approach on mathematical reasoning tasks and find promising results: LaneRoPE enables collaboration among sequences, yielding additional accuracy gains under limited generated sequence length. Importantly, since LaneRoPE enables coordination with minimal changes to the underlying LLM architecture and introduces a negligible overhead at inference time, it is appealing to rapidly incorporate parallel reasoning into existing LLM inference pipelines. Subjects: Artificial Intelligence (cs.AI) Cite as: arXiv:2605.27570 [cs.AI]   (or arXiv:2605.27570v1 [cs.AI] for this version)   https://doi.org/10.48550/arXiv.2605.27570 Focus to learn more Submission history From: Gabriele Cesa [view email] [v1] Tue, 26 May 2026 18:43:15 UTC (631 KB) Access Paper: HTML (experimental) view license Current browse context: cs.AI < prev   |   next > new | recent | 2026-05 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)