# MonitorOperativeAgent evaluation on long-horizon benchmarks
# Machine: 4x H100-80GB (GPUs 0-3), engine: vLLM, model: Qwen3.5-122B-A10B-FP8
# Inference: Qwen3.5 non-thinking mode (temperature=0.6)
# Benchmarks: LifelongAgentBench only (LogHub done at 94%, DeepPlanning needs >32K ctx)
# Note: PaperArena excluded (HF repo Melmaphother/PaperArena-Data is empty)
#       AMA-Bench excluded (no public dataset yet, paper arxiv:2602.22769)

[meta]
name = "qwen3.5-122b-monitor-operative"
description = "Evaluate MonitorOperativeAgent on long-horizon monitoring benchmarks"

[defaults]
temperature = 0.6          # Qwen3.5 thinking mode recommended
max_tokens = 1024          # Keep short to prevent hangs; answers are brief

[judge]
model = "gpt-5-mini-2025-08-07"
temperature = 0.0
max_tokens = 4096

[run]
max_workers = 1            # Sequential for episode mode; 1 worker for agent state safety
output_dir = "results/qwen3.5-122b-monitor-operative/"
seed = 42
telemetry = true
gpu_metrics = true

[[models]]
name = "Qwen/Qwen3.5-122B-A10B-FP8"
engine = "vllm"
param_count_b = 122.0
active_params_b = 10.0
gpu_peak_tflops = 989.5        # H100 SXM FP16 peak TFLOPS
gpu_peak_bandwidth_gb_s = 3350.0  # H100 SXM memory bandwidth
num_gpus = 4

# --- Benchmarks ---

[[benchmarks]]
name = "lifelong-agent"
backend = "jarvis-direct"
max_samples = 50
