AI Research / Evaluation / Verification Framework
An open-source research framework for structured LLM evaluation, claim verification, and source-grounded reflective reasoning. The repo describes modular components for retrieval, semantic scoring, skeptical claim checking, and benchmark-style epistemic assessment.
The page clearly presents an AI research project with a defined purpose: structured LLM evaluation and claim verification. The README gives enough substance for a public listing, including the framework’s modules, core capabilities, research motivation, and benchmark setup. It also shows concrete evidence of source retrieval, claim-level verification, and reproducible experimentation rather than a vague concept page.
The snapshot cuts off before the full benchmark and usage details, so a visitor cannot see setup instructions, runnable examples, or results quality in full. It also does not fully show how the included modules are wired together in practice or what inputs and outputs look like end to end.
Last evaluated 163 days ago. Current rank #1849. Down 73 spots in the rankings.
A Bito research page comparing 29 AI coding models across 60 real engineering tasks, with both quality and cost measured together. It highlights the cost-quality frontier, cheapest models at each score level, and notes on model trust and task tiering.
An autonomous research system that runs on a single workstation and aggressively checks its own claims with adversarial self-verification, replication, and calibration audits.
An MCP-based research agent that searches live web data through SQL and publishes reports with the full query and tool trajectory.
Uno is a research repository for speeding up LLM inference with discrete diffusion and lossless multi-token decoding. The repo includes inference, training, and evaluation code, plus installation steps, checkpoints, and example workflows.
