Archives AI News

On the conditional equivalence of phase retrieval algorithms

arXiv:2606.07257v1 Announce Type: cross Abstract: Phase retrieval – recovering a complex-valued field from intensity measurements – is typically solved using variants of the Gerchberg-Saxton (GS) algorithm, understood as alternating projections between measurement planes. Meanwhile, modern computational imaging increasingly relies on…

Skip a Layer or Loop It? Learning Program-of-Layers in LLMs

arXiv:2606.06574v1 Announce Type: new Abstract: Large language models (LLMs) perform inference by following a fixed depth and order, non-recurrent execution of all layers. We reveal the wide existence of training-free, flexible, dynamic program-of-layers (PoLar), where pretrained layers can be packed…

Unmixing ATR-{mu}FTIR spectroscopic images of cross-sections of historical oil paintings

arXiv:2603.06673v2 Announce Type: replace-cross Abstract: Spectroscopic imaging (SI) has become central to heritage science because it enables non-invasive, spatially resolved characterisation of materials in artefacts. In particular, attenuated total reflection Fourier transform infrared microscopy (ATR-$mu$FTIR) is widely used to analyse…

Generative Models Erode Human Temporal Learning Through Market Selection

arXiv:2606.06572v1 Announce Type: new Abstract: We argue that modern generative models create structural risks for knowledge and cultural production at current, sub-AGI capability levels. We define Human Temporal Learning (HTL) as path-dependent knowledge accumulation through sustained engagement with problems over…

WAV: Multi-Resolution Block Residual Routing for Deep Decoder-Only Transformers

arXiv:2606.06564v1 Announce Type: new Abstract: Residual connections are central to training deep Transformers, but standard PreNorm residual streams aggregate sublayer updates with fixed unit weights. Recent Attention Residuals replace this fixed accumulation with content-dependent depth-wise routing, and Block Attention Residuals…

MacArena: Benchmarking Computer Use Agents on an Online macOS Environment

arXiv:2606.06560v1 Announce Type: new Abstract: Computer-use agents (CUAs) operate graphical user interfaces (GUIs) through vision and control primitives, and their capabilities have advanced rapidly, driven in part by standardized online evaluation benchmarks such as OSWorld, which serve both as evaluation…

Perplexity Can Miss SAE Feature Damage Under Quantization

arXiv:2606.03002v2 Announce Type: replace Abstract: Quantization is a standard path to deploying large language models, and quantized models are typically judged acceptable when perplexity or downstream accuracy remains close to the full-precision original. But behavioral parity need not imply feature…