HAMON uses passive optical components to perform long-horizon time-series forecasting, outperforming top digital models on ETTm2 across all horizons and on ETTh2 at all but the longest horizon. It achieves up to 14% lower MSE and relies on physical optical propagation without trainable digital layers, demonstrating that passive optical mixing can produce competitive forecasts.
HAMON: Passive Optical Forecasting for Long-Horizon Time-Series
HAMON: Passive Optical Forecasting Core
HAMON uses passive optical diffraction to generate forecasts, outperforming digital baselines on ETTm2 at all horizons and ETTh2 at all but the longest horizon. It achieves up to 14% lower MSE and operates without trainable digital mixing, relying instead on physical optical propagation.
Symphony orchestrates sparse and dense tensors with hierarchical heterogeneous processing
The article proposes Symphony, a hybrid programmable and specialized architecture designed to address memory system inefficiencies in current high-performance architectures like GPUs. It focuses on orchestrating data throughout the memory hierarchy to reduce unnecessary data movement and distance.
GPT-5 and GPT-5 Nano match experts in microbial oncogenesis research appraisal
A study demonstrates that GPT-5 and GPT-5 Nano achieve expert-level performance in extracting evidence and critically appraising research publications on microbial oncogenesis. Researchers benchmarked these models alongside Gemini 2.5 Pro and Gemini 2.5 Flash against domain experts using a dataset of 24 papers focused on MMTV-LV and breast cancer.
GPT-5 and GPT-5 Nano match experts in microbial oncogenesis evidence extraction
A study benchmarking large language models on systematic evidence synthesis for microbial oncogenesis found that GPT-5 and GPT-5 Nano perform indistinguishably from domain experts. Researchers evaluated Gemini 2.5 Pro, Gemini 2.5 Flash, GPT-5, and GPT-5 Nano on 24 research papers using a structured template of 77 items across multiple question types.
M$^3$R-Bench introduces evidence-grounded benchmark for multimodal metaphor understanding
Researchers introduce M$^3$R-Bench, a unified benchmark containing 1,000 image-text instances with human-verified annotations designed to evaluate evidence-grounded multimodal metaphor understanding. The benchmark provides joint annotations for metaphor occurrence, Target-Source mapping, sentiment, and stage-wise explanations based on Conceptual Metaphor Theory.