modal-labs/autoinference-agentic-mix-v2

Autoinference Agentic Mix v2

300 real SWE-agent trajectories from TIGER-Lab/SWE-Next-SFT-Trajectories, expanded into one request per assistant turn. Each row carries the conversation up to that turn and the model generates the turn. Replaying a trajectory in turn_index order re-sends a growing prefix, which is how an agent loop actually hits a prefix cache.

What changed from v1

v1 kept only requests with at least 34k prefix tokens. That cut trajectories down to about 5 requests each, so a closed-loop replay restarted every few requests and the measured cache hit rate was mostly restart accounting. In v2 every trajectory keeps at least 30 turns and a session amortizes its cold prefill the way a real session does. The structural hit rate ceiling of this corpus is about 95.5%.

requests	10,307
trajectories	300
turns per trajectory	30 to 44, median 34
input tokens per request (Qwen2.5 tokenizer)	median 18,246, p90 29,193, max 49,652
output tokens per request	p50 48, p90 244

max_tokens on each row is the length of the recorded assistant turn, so decode work tracks the trajectory. The 34k build (451 requests, 82 trajectories) is still at autoinference-agentic-mix-v1.

Format

One JSON object per line with messages, max_tokens, trajectory_id, turn_index, input_tokens_qwen25, output_tokens_qwen25, and source. tool turns are remapped to user and adjacent same-role turns are merged, so any chat template applies. All trajectories share one short system prompt (about 56 tokens). That is the intended shared prefix: sessions reuse it, the way real traffic reuses a common system prompt. Some trajectories are rollouts of the same task and also share their opening user message, so replay tooling should place any per-session salt at the first non-system message, after the system prompt and before the task.