AGENTIC-SYSTEMSApril 20, 20261 min read7 views

LACE: Lattice Attention for Cross-thread Exploration

LACE is a new framework that enables large language models to share insights across parallel reasoning paths through cross-thread attention. This collaborative approach significantly outperforms standard parallel search, boosting reasoning accuracy by over 7 points.

Computer Science > Artificial Intelligence

Title:LACE: Lattice Attention for Cross-thread Exploration

View PDF HTML (experimental)Abstract:Current large language models reason in isolation. Although it is common to sample multiple reasoning paths in parallel, these trajectories do not interact, and often fail in the same redundant ways. We introduce LACE, a framework that transforms reasoning from a collection of independent trials into a coordinated, parallel process. By repurposing the model architecture to enable cross-thread attention, LACE allows concurrent reasoning paths to share intermediate insights and correct one another during inference. A central challenge is the absence of natural training data that exhibits such collaborative behavior. We address this gap with a synthetic data pipeline that explicitly teaches models to communicate and error-correct across threads. Experiments show that this unified exploration substantially outperforms standard parallel search, improving reasoning accuracy by over 7 points. Our results suggest that large language models can be more effective when parallel reasoning paths are allowed to interact.

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Source: arXiv cs.AI Recent

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