mLLMCelltype vs tf

mLLMCelltype annotates scRNA-seq clusters by polling several LLMs and reconciling their answers into a consensus label, shipping as paired R and Python packages. The 2.0 line has settled into a rhythm: broaden the provider roster, then harden the parsing and retry paths that decide whether a given provider's answer survives into the consensus. Version 2.0.8 is pure reliability work, disabling DeepSeek V4's thinking mode because it exhausted the response budget before labels were returned, and raising non-streaming timeouts to 120 seconds.

The centre of gravity has moved from adding models to defending against them. Recent notes read as a catalogue of ways an LLM response can be malformed: numbered lists, preamble headers, annotation-internal colons, a mid-list Unknown, thinking blocks that precede the answer, rate limits returned as HTTP 200 with an error buried in the body. Each of those could previously shift or drop a cluster's annotation, which for a consensus tool is the failure that matters most. Provider additions now land as routine catalogue growth rather than a change in what the package can do.

Expect the next release to continue the reliability arc with more provider-specific timeout and parsing guards, and a CRAN publication of 2.0.8 to close the gap the notes themselves flag. Whether return_reasoning grows from an option into the default per-cluster evidence record is the open question these entries do not yet answer.