Cognitive observability for LLM agents. Cognometric instruments + self-healing reflex (F10) + MCP server. Pure-Python, MIT, no LLM required. 9-for-9 on K=1 phase transition. Every Mind Leaves Vitals (DOI 10.5281/zenodo.19777921).

Code for NAACL paper When Quantization Affects Confidence of Large Language Models?

Enterprise-grade LLM evaluation framework | Multi-model benchmarking, honest dashboards, system profiling | Academic metrics: MMLU, TruthfulQA, HellaSwag | Zero fake data | PyPI: llm-benchmark-toolkit | Blog: https://dev.to/nahuelgiudizi/building-an-honest-llm-evaluation-framework-from-fake-metrics-to-real-benchmarks-2b90

A hallucination detection pipeline for Large Language Models (LLMs).

Official code for "From Fact to Judgment: Investigating the Impact of Task Framing on LLM Conviction in Dialogue Systems" (IWSDS 2026)

Evaluation of Llama-3.1-8B Base vs Instruct on TruthfulQA using few-shot prompting and automatic judge models

PT-GAT Transformer Diagnostics: task-relative hallucination diagnosis with adequacy triggers, evidence conditioning, and anti-collapse baselines.

Multilingual hallucination evaluation framework for Large Language Models across Indian languages using TruthfulQA, NLLB-200, and mechanistic interpretability.

CAP6640-Spring2026: Benchmarks GPT-3.5, GPT-4, Claude Haiku, and Gemini on GSM8k and TruthfulQA, measuring accuracy, self-consistency, and confidence calibration.

Multi-agent framework for hallucination detection and correction in LLM outputs using retrieval-grounded verification. MSc AI/ML dissertation (LJMU).

Does instruction tuning make language models more sycophantic? A paired causal study across Qwen, Llama, and Gemma on TruthfulQA, showing the effect is family-dependent in both magnitude and direction. 7,200 evaluations, 12 ATE estimates with paired t-tests and bootstrap CIs.

A tool to evaluate and compare local LLMs running on Ollama or LM Studio under identical conditions using deepeval's public benchmarks (MMLU, TruthfulQA, GSM8K).