LoRA, PEFT, instruction tuning and domain adaptation — adapting a pretrained model without paying to train one.
arXiv:2610.34875v1 Announce Type: new Abstract: Rubric-based evaluation asks which level of a rubric a response meets. It is usually done by prompting a large language model (LLM) as a judge, which n...
arXiv:2610.35114v1 Announce Type: new Abstract: Multilingual transformer models are often assumed to generalise across languages, but their effectiveness for coreference resolution in low-resource se...
arXiv:2610.35423v1 Announce Type: new Abstract: Operator manuals of agricultural implements are delivered in many languages, and the standards require that competent persons review every translation,...
arXiv:2610.34993v1 Announce Type: cross Abstract: Road anomalies such as collisions, violations, and sudden hazards demand more from video understanding than detection: a practical system must verify...
arXiv:2510.11620v3 Announce Type: replace Abstract: Test-time scaling seeks to improve reasoning through additional inference-time computation, commonly using solution sampling with majority voting t...
arXiv:2610.25086v2 Announce Type: replace Abstract: Looped language models increase computational depth by repeatedly applying shared layers, offering a way to improve model quality without adding pa...
arXiv:2610.34037v1 Announce Type: new Abstract: LVLM benchmarks tell us whether a model answers correctly, but not which visual conventions it has retained, nor where those conventions live. Sketches...
arXiv:2609.34826v2 Announce Type: replace Abstract: Humans often solve spatial problems by mentally simulating visual transformations. In contrast, conventional vision-language models (VLMs) reason p...
The paper discusses how instruction hierarchy (IH) can be improved by training large language models on simulated instruction conflicts, particularly a newly identified type called goal‑goal conflicts. By fine‑tuning Qwen3.5‑9B and Gemma4‑12B‑it on examples of these conflicts, the authors demonstrate significant gains in IH performance across a variety of real‑world tasks, while maintaining negligible impact on non‑conflict tasks. The study suggests that incorporating goal‑goal conflicts into IH training sets can enhance model adherence to higher‑priority instructions.
Large Language Models: A Plain-Language Overview for Engineers explains that LLMs are neural networks trained on vast text collections to predict subsequent words, and outlines key concepts such as Transformers, pre‑training, fine‑tuning, reinforcement learning from human feedback, retrieval‑augmented generation, chain‑of‑thought prompting, and tool‑using agents. It also discusses efficiency techniques like FlashAttention and QLoRA, provides a life‑cycle figure and comparative tables, and offers a checklist for evaluating LLM claims, including checks for data contamination, model‑based grading, long‑context handling, and hallucination. The article aims to give engineers a clear picture of how training changes and prompts affect generative AI tools like ChatGPT.
arXiv:2610.35817v1 Announce Type: cross Abstract: A robot policy needs to understand what a task is asking for in a scene, and how to move the robot to accomplish that task. To achieve these two skil...
arXiv:2610. 33882v1 Announce Type: cross Abstract: Hybrid post-training often mixes supervised fine-tuning (SFT) and reinforcement learning (RL), using training-side signals to set each sample's balance.
The paper introduces Counterfactual Test-Time Augmentation (CTTA) to mitigate shortcut exploitation in large language models. CTTA generates counterfactual edits of an input using an auxiliary model, then averages the LLM’s predictions across these edits, with theoretically optimal weights derived from the training distribution. The authors propose Select-CTTA, which selects among CTTA variants based on tests for assumption violations, and demonstrate that it outperforms other test-time methods and rivals models fine‑tuned on shortcut‑balanced data.
arXiv:2610.33964v1 Announce Type: cross Abstract: AI foundation models play a pivotal role in advancing heart disease diagnosis from electrocardiogram (ECG) data, particularly for rare diseases where...
arXiv:2610.35457v1 Announce Type: cross Abstract: Bayesian variants of Low-Rank Adaptation (LoRA) provide principled uncertainty estimation when fine-tuning large foundation models, but require speci...
arXiv:2610.33410v1 Announce Type: new Abstract: Time-series tasks require both accurate predictions and fast decisions. Advances in large models have introduced decision-oriented models that directly...
arXiv:2610.33788v1 Announce Type: new Abstract: Photoplethysmography (PPG) plays a central role in wearable health monitoring and clinical decision support. However, existing approaches to universal...
arXiv:2610.33841v1 Announce Type: cross Abstract: When an image and its caption disagree, can a user reliably control which one a vision-language model (VLM) follows? Accuracy alone cannot answer thi...
arXiv:2610.33909v1 Announce Type: cross Abstract: Out-of-distribution (OOD) detection identifies inputs outside the training distribution. Rank-based analyses such as RankFeat and RankWeight suggest...
arXiv:2610.34126v1 Announce Type: cross Abstract: Humanoid robots have recently achieved remarkable success in motion tracking. However, during real-world deployment, robots inevitably encounter dyna...