CodeSOTA, an open data terminal for reinforcement learning environments and state-of-the-art models, has launched a registry tracking 9,102 results, 163 models, 371 datasets, and 9 capability areas, w…
A new method called Forward-Pass-Only MLP training (FPO) adapts large language models without a backward pass, achieving 2.7–3.2x the throughput of standard fine-tuning at ~40% less peak training memo…
Trunchbull, a browser-based tool for running LLM benchmarks, lets users author tests and evaluate models in real time, with native support for the harbor authoring system and the Vercel AI SDK. It inc…
A new study from arXiv reveals that post-training quantization can silently alter how large language models reason even when task accuracy is preserved. Analyzing 30,000 chain-of-thought outputs from …
Researchers introduced Causal Attribution Pruning (CAP), a training-free method that identifies critical attention heads in large language models by measuring their causal impact on reasoning tasks. C…
Researchers at the Allen Institute for AI used training-data attribution to map which regions of the pretraining corpus support social-reasoning versus STEM-reasoning in the OLMo3-7B language model. T…
Linear probes applied to Qwen3-14B hidden states achieve 100% accuracy in distinguishing deductive, inductive, and abductive reasoning tasks, but this separation is entirely driven by format confounds…
Researchers at OpenPangu identified two failure modes in HiF8 W8A8 quantization-aware training for the OpenPangu-Embedded-1B model: amax saturation, which corrupts knowledge-sensitive representations …