The paper introduces Neighborhood Elites (NE), a quality‑diversity search method for generating answer‑preserving rewrites in question answering. NE explicitly controls two dimensions—type of edit and explicitness of support—to create diverse perturbations that improve uncertainty quantification. Across five extractive‑QA datasets and five instruction‑tuned LLMs, NE outperforms other constructors, achieving higher AUROC and AUPRC scores and surpassing semantic entropy baselines.
By Mesut Ceylan, Arda Baris Basaran, Elgar Fleisch, Filipe Barata
The paper introduces Drift-Constrained Optimization (DCO), a framework that treats behavioral drift during fine‑tuning of instruction models as a bounded constraint rather than an uncontrolled side effect. By defining a drift budget, the authors reformulate fine‑tuning as a direction‑selection problem, showing that choosing different update directions can qualitatively change outcomes. Experiments on Qwen3 models demonstrate that carefully selected directions improve scientific reasoning and multilingual translation while preserving reasoning capabilities and general performance.
By Fei Yuan, Changjiang Gao, Yilei Tu, Yifeng Liu, Shujian Huang, Yu Qiao
arXiv:2608. 15445v1 Announce Type: new Abstract: When a reward is correct on every training example yet consistent with more than one goal, a model can acquire an unintended one, a failure known as goal misgeneralization.
By Suyash Maniyar, Armaan Sandhu, Abhishek Mishra
arXiv:2607. 25600v1 Announce Type: cross Abstract: Retrieval-augmented generation improves knowledge-intensive question answering, but indiscriminate retrieval can introduce irrelevant evidence and unnecessary computation.
By Chandan Kumar Sah, Xiaoli Lian, Li Zhang
Almost every large language model that reaches a broad audience is quantized: trained in full precision, then compressed for efficiency. This step is assumed harmless and its safety is rarely re-checked.
arXiv:2610.03551v1 Announce Type: new
Abstract: Large language models (LLMs) have reached expert-level performance on competition mathematics largely through the volume of search placed around them:...
By Yanli Wang, Suijin Wang, Xiaopeng Yuan, Haohan Wang