3 papers
cs.AI2026
Explaining and Breaking the Safety-Helpfulness Ceiling via Preference Dimensional Expansion
ShiYing Huang, Liang Lin, Yuer Li +6
In the realm of multi-objective alignment for large language models, balancing disparate human preferences often manifests as a zero-sum conflict. Specifically, the intrinsic tensi…
cs.DB2026
Pruning Minimal Reasoning Graphs for Efficient Retrieval-Augmented Generation
Ning Wang, Kuanyan Zhu, Daniel Yuehwoon Yee +4
Retrieval-augmented generation (RAG) is now standard for knowledge-intensive LLM tasks, but most systems still treat every query as fresh, repeatedly re-retrieving long passages an…
cs.AI2026
Falcon-H1R: Pushing the Reasoning Frontiers with a Hybrid Model for Efficient Test-Time Scaling
Falcon LLM Team, Iheb Chaabane, Puneesh Khanna +8
This work introduces Falcon-H1R, a 7B-parameter reasoning-optimized model that establishes the feasibility of achieving competitive reasoning performance with small language models…