{ "context": { "cap_tokens": 317440, "max_model_len": 327680, "targets": [ { "attempted_context_tokens": 8192, "chars_per_token": 3.0, "clamped_tokens": 8192, "content_chunks": 102, "decode_tok_s": 74.34314691883012, "e2e_ms": 9694.684196030721, "effective_prefill_tok_s": 4115.069397253367, "estimated_prompt_tokens": 8192, "generation_ms": 8218.645905144513, "mean_inter_token_latency_ms": 13.45113896095665, "output_token_source": "usage", "output_tokens": 612, "prompt_tokens": 6074, "reasoning_chunks": 502, "status": "passed", "target_tokens": 8192, "time_to_first_output_ms": 1476.0382908862084, "ttft_ms": 8322.212195023894, "usage": { "completion_tokens": 612, "prompt_tokens": 6074, "total_tokens": 6686 }, "visible_generation_ms": 1372.4720010068268 }, { "attempted_context_tokens": 32768, "chars_per_token": 4.046, "clamped_tokens": 32768, "content_chunks": 57, "decode_tok_s": 74.31564671841245, "e2e_ms": 14597.199792973697, "effective_prefill_tok_s": 4668.066711179299, "estimated_prompt_tokens": 32767, "generation_ms": 7185.5662108864635, "mean_inter_token_latency_ms": 13.456116499787385, "output_token_source": "usage", "output_tokens": 535, "prompt_tokens": 34598, "reasoning_chunks": 468, "status": "passed", "target_tokens": 32768, "time_to_first_output_ms": 7411.633582087234, "ttft_ms": 13828.543795039877, "usage": { "completion_tokens": 535, "prompt_tokens": 34598, "total_tokens": 35133 }, "visible_generation_ms": 768.6559979338199 } ] }, "evaluation_protocol": { "budget_semantics": "visible_answer_tokens plus reasoning_headroom_tokens are sent as one max_tokens cap; the endpoint does not hard-partition the channels", "minimum_comparable_repetitions": 3, "minimum_pass_rate": 1.0, "reasoning_headroom_tokens": 8192, "records_finish_reason": true, "records_full_visible_answer": true, "records_reasoning_channel_metadata": true, "repetitions": 3, "version": 3, "visible_answer_tokens": 1024 }, "failures": [], "identity": { "base_url": "http://127.0.0.1:/v1", "candidate_id": "glm53-flash", "config_id": "r10-baseline", "engine": "vllm-glm53-exl3-v84", "gpu": "rtx-pro6000-maxq-tp2", "model": "glm53-flash-exl3-4bpw-v84-fp8-tp2-c4-327k-r10-vision8-apc-nospec", "started_at": "2026-09-22T01:54:57Z" }, "intelligence": { "checks": [ { "attempt_count": 3, "attempts": [ { "attempt": 1, "budget": { "legacy_max_tokens_as_visible": false, "max_completion_tokens": 9216, "reasoning_headroom_tokens": 8192, "visible_answer_tokens": 1024 }, "content": "```diff\n--- app.py\n+++ app.py\n@@ -1,2 +1,2 @@\n-timeout = 30\n+timeout = 45\n retries = 2\n```", "content_chars": 90, "content_excerpt": "```diff\n--- app.py\n+++ app.py\n@@ -1,2 +1,2 @@\n-timeout = 30\n+timeout = 45\n retries = 2\n```", "failure_class": null, "finish_reason": "stop", "latency_ms": 4993.786973878741, "reasoning_chars": 1204, "reasoning_excerpt": "The user wants me to edit app.py, changing `timeout = 30` to `timeout = 45` while leaving `retries = 2` unchanged. They want only a unified diff as output.\n\nThe original file:\n```\ntimeout = 30\nretries", "reasoning_field": "reasoning", "reasoning_tokens": null, "status": "passed", "text_checks": [ { "name": "diff_shape", "passed": true }, { "name": "removes_old_timeout", "passed": true }, { "name": "adds_new_timeout", "passed": true } ], "usage": { "completion_tokens": 366, "prompt_tokens": 48, "prompt_tokens_details": null, "total_tokens": 414 } }, { "attempt": 2, "budget": { "legacy_max_tokens_as_visible": false, "max_completion_tokens": 9216, "reasoning_headroom_tokens": 8192, "visible_answer_tokens": 1024 }, "content": "```diff\n--- app.py\n+++ app.py\n@@ -1,2 +1,2 @@\n-timeout = 30\n+timeout = 45\n retries = 2\n```", "content_chars": 90, "content_excerpt": "```diff\n--- app.py\n+++ app.py\n@@ -1,2 +1,2 @@\n-timeout = 30\n+timeout = 45\n retries = 2\n```", "failure_class": null, "finish_reason": "stop", "latency_ms": 5596.311673987657, "reasoning_chars": 1300, "reasoning_excerpt": "The user wants me to edit app.py, changing `timeout = 30` to `timeout = 45` while leaving `retries = 2` unchanged. They want only a unified diff as output.\n\nThe original file:\n```\ntimeout = 30\nretries", "reasoning_field": "reasoning", "reasoning_tokens": null, "status": "passed", "text_checks": [ { "name": "diff_shape", "passed": true }, { "name": "removes_old_timeout", "passed": true }, { "name": "adds_new_timeout", "passed": true } ], "usage": { "completion_tokens": 411, "prompt_tokens": 48, "prompt_tokens_details": null, "total_tokens": 459 } }, { "attempt": 3, "budget": { "legacy_max_tokens_as_visible": false, "max_completion_tokens": 9216, "reasoning_headroom_tokens": 8192, "visible_answer_tokens": 1024 }, "content": "```diff\n--- app.py\n+++ app.py\n@@ -1,2 +1,2 @@\n-timeout = 30\n+timeout = 45\n retries = 2\n```", "content_chars": 90, "content_excerpt": "```diff\n--- app.py\n+++ app.py\n@@ -1,2 +1,2 @@\n-timeout = 30\n+timeout = 45\n retries = 2\n```", "failure_class": null, "finish_reason": "stop", "latency_ms": 5434.922366868705, "reasoning_chars": 1266, "reasoning_excerpt": "The user wants me to edit app.py, changing `timeout = 30` to `timeout = 45` while leaving `retries = 2` unchanged. They want only a unified diff as output.\n\nThe original file:\n```\ntimeout = 30\nretries", "reasoning_field": "reasoning", "reasoning_tokens": null, "status": "passed", "text_checks": [ { "name": "diff_shape", "passed": true }, { "name": "removes_old_timeout", "passed": true }, { "name": "adds_new_timeout", "passed": true } ], "usage": { "completion_tokens": 399, "prompt_tokens": 48, "prompt_tokens_details": null, "total_tokens": 447 } } ], "id": "unified_diff_timeout_edit", "pass_count": 3, "pass_rate": 1.0, "required_pass_rate": 1.0, "status": "passed", "validator": "deterministic_text_checks" }, { "attempt_count": 3, "attempts": [ { "attempt": 1, "budget": { "legacy_max_tokens_as_visible": false, "max_completion_tokens": 9216, "reasoning_headroom_tokens": 8192, "visible_answer_tokens": 1024 }, "content": "The pipeline's sequential latency (550+1800+650 = 3000 ms) exceeds the 2500 ms budget, with the LLM as the dominant bottleneck, so a practical fix is to stream LLM tokens directly into TTS so synthesis overlaps generation instead of waiting for the full response.", "content_chars": 263, "content_excerpt": "The pipeline's sequential latency (550+1800+650 = 3000 ms) exceeds the 2500 ms budget, with the LLM as the dominant bottleneck, so a practical fix is to stream LLM tokens directly into TTS so synthesi", "failure_class": null, "finish_reason": "stop", "latency_ms": 4505.562733858824, "reasoning_chars": 1030, "reasoning_excerpt": "The user asks about a voice agent pipeline: STT (speech-to-text), LLM, TTS (text-to-speech). Total turn timeout is 2500 ms. Logs: STT=550 ms, LLM=1800 ms, TTS=650 ms. Sum = 550 + 1800 + 650 = 3000 ms,", "reasoning_field": "reasoning", "reasoning_tokens": null, "status": "passed", "text_checks": [ { "name": "identifies_budget_overrun", "passed": true }, { "name": "offers_latency_fix", "passed": true } ], "usage": { "completion_tokens": 329, "prompt_tokens": 71, "prompt_tokens_details": null, "total_tokens": 400 } }, { "attempt": 2, "budget": { "legacy_max_tokens_as_visible": false, "max_completion_tokens": 9216, "reasoning_headroom_tokens": 8192, "visible_answer_tokens": 1024 }, "content": "The serial pipeline latency (550 + 1800 + 650 = 3000 ms) exceeds the 2500 ms turn timeout, with the LLM alone consuming 72% of the budget — a practical fix is to stream LLM tokens directly into TTS so synthesis begins on the first sentence, overlapping the two stages and bringing end-to-end latency under the limit.", "content_chars": 316, "content_excerpt": "The serial pipeline latency (550 + 1800 + 650 = 3000 ms) exceeds the 2500 ms turn timeout, with the LLM alone consuming 72% of the budget — a practical fix is to stream LLM tokens directly into TTS so", "failure_class": null, "finish_reason": "stop", "latency_ms": 5442.6326979883015, "reasoning_chars": 1259, "reasoning_excerpt": "The user asks about a voice agent pipeline: STT (speech-to-text), LLM, TTS (text-to-speech). 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Sum = 550 + 1800 + 650 = 3000 ms,", "reasoning_field": "reasoning", "reasoning_tokens": null, "status": "passed", "text_checks": [ { "name": "identifies_budget_overrun", "passed": true }, { "name": "offers_latency_fix", "passed": true } ], "usage": { "completion_tokens": 400, "prompt_tokens": 71, "prompt_tokens_details": null, "total_tokens": 471 } }, { "attempt": 3, "budget": { "legacy_max_tokens_as_visible": false, "max_completion_tokens": 9216, "reasoning_headroom_tokens": 8192, "visible_answer_tokens": 1024 }, "content": "The serial pipeline latency (550 + 1800 + 650 = 3000 ms) exceeds the 2500 ms budget by 500 ms, with the LLM dominating at 60% of total time — so stream LLM tokens directly into TTS (or switch to a faster/smaller model) so synthesis overlaps generation and end-to-end latency drops under the timeout.", "content_chars": 299, "content_excerpt": "The serial pipeline latency (550 + 1800 + 650 = 3000 ms) exceeds the 2500 ms budget by 500 ms, with the LLM dominating at 60% of total time — so stream LLM tokens directly into TTS (or switch to a fas", "failure_class": null, "finish_reason": "stop", "latency_ms": 5464.32627690956, "reasoning_chars": 1252, "reasoning_excerpt": "The user asks about a voice agent pipeline: STT (speech-to-text), LLM, TTS (text-to-speech). 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