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Overview

The Overview page is where Mint Recursive lands. At a glance it tells you whether the Mint Recursive backend is healthy, what is running right now, and how your model versions compare on the SciCode benchmark.

Chinese-only UI

Mint Recursive's web UI is localised in Chinese only. The tile and column names below are given in English; the labels you see in the product will be Chinese. For a labelled screenshot see the Chinese overview page.

Resource monitoring

The Resource monitoring card uses a fixed 5-minute window (perf_recent) and reports:

  • Service — the Mint Recursive service status (from get_server_capabilities).
  • Last 5-min requests — request count, split into training / inference.
  • Last 5-min training/inference p50 latency — training and inference p50 latency, with TTFT (time to first token) and save sampler sub-metrics.
  • Session status — session counts by state: active, idle, training, inference (from resource_status).
  • Recent active training throughput — training throughput (fb mean, step mean).
  • Recent active inference throughput — inference throughput (input, output).

Request counts and latency come from the groups surface. The rate card shows only active_compute_rate — the active compute rate. Performance-metric protocol mismatches do not change service health status; missing fields show -. Click Refresh to re-poll.

Tasks

The Tasks panel lists background jobs across four categories, each with a counter:

  • Official evaluations
  • Question-bank evaluations
  • Training
  • Data generation

Select a category to see its jobs with status, duration, and Details / Log links. The sidebar task pill (bottom-left) summarises the most recent completion.

SciCode evaluation comparison

SciCode evaluation comparison shows external reference models' SciCode scores for context. It only displays external reference models — not your local models or training versions. Local-model results live in the evaluation records and training curves. Scoring is SciCode single-generation sub-step pass rate.

Loop timeline

Loop timeline visualises the recursive self-improvement loop over time — when each solve → score → train cycle ran and which model version it produced.