{
 "model": "glm-5.3-flash-recal",
 "ctx": 260000,
 "gen": 2200,
 "rounds": [
  {
   "round": 1,
   "prompt_tokens": 225032,
   "completion_tokens": 2712,
   "wall_s": 64.1,
   "finish_reason": "length",
   "four_gram_max": 2,
   "four_gram_top": "at least 2,000 words",
   "eight_gram_max": 1,
   "eight_gram_top": "The user wants a detailed essay of at",
   "max_consec_line_repeat": 1,
   "degenerate_4gram_ge12": false,
   "tail": "hmarks but degenerate during extended generation. The most informative evaluations combine these approaches, testing models under conditions that approximate real-world usage while maintaining the controlled variables needed for scientific comparison. As context windows continue to expand and models are deployed in increasingly demanding applications, the sophistication of evaluation methodologies"
  },
  {
   "round": 2,
   "prompt_tokens": 224997,
   "completion_tokens": 2712,
   "wall_s": 58.4,
   "finish_reason": "length",
   "four_gram_max": 3,
   "four_gram_top": "- Why long context",
   "eight_gram_max": 1,
   "eight_gram_top": "The user has provided a long filler document",
   "max_consec_line_repeat": 1,
   "degenerate_4gram_ge12": false,
   "tail": "asymmetry; per-channel/per-token grouping; outliers\n- Eviction/compression alternatives (sinks, heavy hitters) as complementary\n- Evaluation protocol: sweep precision, measure NIAH/perplexity deltas, latency, memory\n- Sensitivity of retrieval to key perturbations; softmax sensitivity\n- Practical guidance: keep K higher precision or per-channel scales; quantize V aggressively; validate at multiple\n"
  }
 ],
 "verdict": "PASS"
}