Intent Alignment Under Ambiguity
Evaluating Muse Glimmer 30B and Qwen3.8 27B on a new benchmark for inferring user intent under ambiguous or incomplete instructions with local inference.
Evaluating Muse Glimmer 30B and Qwen3.8 27B on a new benchmark for inferring user intent under ambiguous or incomplete instructions with local inference.
A practical model-comparison tool for finding lower-cost alternatives with similar measured capability.
A 143-prompt adversarial benchmark of hallucination and epistemic control. Meta’s Muse Glimmer sets a new high-water mark, but then scores lower when it thinks harder.
Store factual knowledge in a temporal, provenance-backed claim graph rather than unstructured markdown text files.
What eight open models reveal about current-news research, summarization fidelity, and agent design.
Challenge the assumption that improvements in context size, retrieval quality, model scale, and memory systems will eventually produce something equivalent to long-term memory, understanding, and reliable reasoning. …
Evaluating local models on repository investigation and planning tasks and comparing Qwen3.6, Gemma4, GLM-4.7-Flash, and GPT-OSS-20B, across multiple dimensions of repository-agent quality. Our results show that …
This article describes a local research stack that combines SearXNG and Crawl4AI to provide current web search and page retrieval for local language models. It explains how the components interact, how they are …
We benchmark open-weight vision-language models for local document OCR and semantic page reconstruction. The goal is to identify a practical production configuration that balances extraction quality and operational …
We explore the use of “no-think” mode for LLM judges in a local evaluation harness. No-think mode suppresses the reasoning phase, which can improve latency and compliance for strict-output tasks. Our …