The QIAS 2026 shared task, co-located with LREC 2026, evaluated large language models on Islamic inheritance reasoning using the MAWARITH benchmark of 12,500 Arabic cases. Sixteen teams submitted systems employing prompt…
Researchers at CohereLabs argue that large language models suffer from a 'cultural data funnel,' where cultural signals decline sharply during post-training while geographically concentrated data dominates. They release …
Researchers introduced DLawBench, a benchmark for evaluating large language models on multi-turn legal consultation tasks. The benchmark includes 461 cases from Chinese and U.S. law and tests models on four client types;…
A new study finds that post-training techniques, including supervised fine-tuning and reinforcement learning, can transform large language models into effective medical coders for ICD coding, challenging prior assumption…
A new study finds that large language models' preferences and values shift significantly depending on the deployment context, such as whether the model is writing a Reddit post or a news article. Across five LLMs and ove…
Researchers introduced MedLatentDx, a latent multi-agent communication framework that enables cross-hospital rare-disease diagnosis by exchanging compact latent KV blocks instead of identifiable clinical text, improving …
Researchers applied likelihood ratio-based forensic text comparison to Japanese digital texts for the first time, fusing stylometric and embedding systems to estimate authorship likelihood ratios. The fused system improv…
Researchers introduced Compliance Asymmetry (A = BCR/HCR), a bidirectional diagnostic for LLM compliance, and found that models exhibit direction-blind moral compliance—following helpful and harmful nudges at nearly iden…
Researchers propose Spiking Fourier Graph Operators (SpikF-GO), a spiking neural network model for multivariate time series forecasting that uses a hypervariate graph to model inter-variable dependencies. SpikF-GO achiev…
Researchers introduced llada.cpp, the first NPU-aware inference framework for accelerating diffusion large language models on smartphones, achieving 17x-42x latency reduction over CPU baselines while preserving generatio…
Researchers introduced a fully GPU-based workflow for building physics emulators of hypersonic flows, integrating accelerated data generation with neural emulator training augmented by uncertainty quantification and phys…
Researchers at arXiv have causally demonstrated that the weight norm sets the grokking timescale in neural networks, settling a dispute over whether weight norm causes the delayed generalization. By intervening on the no…
Researchers introduced D2H-AD, a hybrid anomaly detection framework using Hyperdimensional Computing that outperforms five baselines including One-Class SVM and Autoencoders across benchmark datasets. The lightweight mod…
Researchers identified the double-drift phenomenon causing instability in diffusion policy-gradient methods and proposed DiPOD, a framework that interleaves self-distillation with policy-improving gradient updates to mai…
Researchers introduced neural slack variables, a primal-side approach that converts constraint enforcement into a regression problem by coupling a primary network with an auxiliary network. The method achieved zero measu…
A new thesis investigates how Bayesian principles can improve understanding of modern deep learning systems, introducing the Deep Variational Implicit Process (DVIP) and post-hoc methods VaLLA and FMGP for uncertainty es…
Researchers propose SuperThoughts, a method that compresses pairs of consecutive Chain-of-Thought tokens into single latent representations and decodes two tokens per step, doubling inference throughput while reducing Co…
Researchers developed the LANTERN framework, a neural network that models health-state transition probabilities for long-term care using irregular longitudinal data. The model outperformed logistic regression and gradien…
Researchers propose Gefen, a memory-efficient optimizer that reduces AdamW's memory footprint by ~8x while maintaining performance, enabling larger microbatches and improved throughput in deep learning training. Gefen au…
A developer has released faida-mcp, an AI agent tool that provides access to Kenya's capital markets, including the Nairobi Securities Exchange, Treasury bonds, unit trusts, and diaspora investment options. The tool is a…