LoRA, PEFT, instruction tuning and domain adaptation — adapting a pretrained model without paying to train one.
arXiv:2610.29090v1 Announce Type: new Abstract: Abdominal organ segmentation is essential in modern medical applications. While existing deep learning approaches demonstrate promising performance, th...
The paper introduces Latent Space Configuration (LSC) for fine‑tuning neural networks, enabling the creation of structured latent spaces that accelerate top‑K retrieval with O(1) candidate search. Using ArcFace‑pretrained IResNet50 models, the authors demonstrate up to three‑fold GPU speed‑ups and 40% CPU speed‑ups over exhaustive search, while also showing that reducing latent space dimensionality further speeds up queries and can improve rank‑N metrics.
arXiv:2610.29507v1 Announce Type: new Abstract: Temporal reasoning is widely assumed to decompose into two modes: temporal arithmetic (date and time calculations), and temporal semantics (understandi...
arXiv:2610.29996v1 Announce Type: cross Abstract: Natural-language software requirements often omit assumptions about performance, operating conditions, user behaviour, and domain constraints, affect...
BatchBench is a new leaderboard designed to assess AI robustness to batch effects and domain shift in biomedical images. It evaluates performance on unseen experimental batches and data sources using the large JUMP-CP dataset, which is generated via the Cell Painting assay and exhibits pronounced batch effects. The benchmark aims to highlight remaining performance gaps despite recent domain adaptation advances.
arXiv:2606.10279v2 Announce Type: replace-cross Abstract: Supervised fine-tuning with synthetic rationales is widely assumed to improve clinical prediction by teaching models not only what to predict...
arXiv:2610.24899v2 Announce Type: replace-cross Abstract: While LLM agents are increasingly capable of gathering and synthesizing evidence from a single homogeneous source, such as structured tables...
The paper proposes a machine‑learning–based wireless image transmission scheme that merges compression and channel coding via a multi‑view autoencoder, enabling multiple transmitters to share the same channel resources simultaneously in a non‑orthogonal multiple‑access (NOMA) setting. A progressive fine‑tuning algorithm doubles the number of users at each iteration while preserving orthogonalized performance, allowing the system to support up to 64 users with only a 2.4 % increase in trainable parameters. The method improves recovered image quality and outperforms existing NOMA‑based approaches across various datasets, metrics, and channel conditions.
arXiv:2603.22364v4 Announce Type: replace Abstract: Diffusion models achieve strong generative performance but often rely on classifier-free guidance (CFG), an inference-time intervention that modifi...
arXiv:2604.18936v2 Announce Type: replace Abstract: Despite the growing application of large language models (LLMs) to theoretical physics, there has been little academic exploration of how domain-sp...
arXiv:2610.29896v1 Announce Type: new Abstract: Existing social bias benchmarks evaluate large language models mostly on fluent, well-formed English, reflecting native speaker usage and leaving non-n...
Weight-only quantization keeps LLM activations in floating point and stores weights as low-bit integers, turning the dominant workload into floating-point-integer (FP-INT) general matrix multiplicatio...
Recent advances in frontier AI models have demonstrated remarkable capabilities in visual understanding, reasoning, planning, and tool use, raising the possibility of using general-purpose AI models f...
Reliable validation of autonomous driving cyber-physical systems requires testing not only nominal driving behavior but also failures arising from faults in sensing and embedded computation. Physical...
Existing social bias benchmarks evaluate large language models mostly on fluent, well-formed English, reflecting native speaker usage and leaving non-native writers unrepresented. To address this gap,...
Text-to-Image models reproduce societal stereotypes, and existing mitigation applies one demographic correction to the whole scene. In multi-person scene generation this creates a trade-off: global st...
Indoor localization systems are critical for Industry-4.0 applications. Visible Light Communication (VLC)-based systems offer advantages such as immunity to electromagnetic interference, high accuracy...
Diffusion language models generate by iteratively denoising a masked sequence under bidirectional attention, without the strict left-to-right commitment of autoregressive decoding. Whether this differ...
The rapid expansion of the Internet of Things enables seamless collaboration among connected devices, making indoor localization a critical component of industrial automation. However, conventional lo...
Long-context continual pre-training (LCCP) is widely used to improve long-context capability, but how its benefits scale with the number of tokens used for LCCP remains unclear. We investigate this qu...