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fix: patch the errorneous skip judge logic
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core/framework/graph/event_loop_node.py

Lines changed: 21 additions & 21 deletions
Original file line numberDiff line numberDiff line change
@@ -885,27 +885,27 @@ async def execute(self, ctx: NodeContext) -> NodeResult:
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# next iteration. If we let the judge fire now it sees
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# "missing outputs" and injects RETRY feedback *before* the
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# user's answer, confusing the LLM.
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_continue_count += 1
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if ctx.runtime_logger:
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iter_latency_ms = int((time.time() - iter_start) * 1000)
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verdict_fb = (
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"Auto-blocked for user input (pre-interaction)"
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if _cf_auto
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else "Blocked for ask_user input (skip judge)"
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)
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ctx.runtime_logger.log_step(
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node_id=node_id,
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node_type="event_loop",
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step_index=iteration,
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verdict="CONTINUE",
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verdict_feedback=verdict_fb,
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tool_calls=logged_tool_calls,
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llm_text=assistant_text,
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input_tokens=turn_tokens.get("input", 0),
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output_tokens=turn_tokens.get("output", 0),
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latency_ms=iter_latency_ms,
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)
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continue
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# _continue_count += 1
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# if ctx.runtime_logger:
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# iter_latency_ms = int((time.time() - iter_start) * 1000)
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# verdict_fb = (
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# "Auto-blocked for user input (pre-interaction)"
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# if _cf_auto
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# else "Blocked for ask_user input (skip judge)"
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# )
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# ctx.runtime_logger.log_step(
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# node_id=node_id,
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# node_type="event_loop",
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# step_index=iteration,
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# verdict="CONTINUE",
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# verdict_feedback=verdict_fb,
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# tool_calls=logged_tool_calls,
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# llm_text=assistant_text,
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# input_tokens=turn_tokens.get("input", 0),
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# output_tokens=turn_tokens.get("output", 0),
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# latency_ms=iter_latency_ms,
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# )
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# continue
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# 6i. Judge evaluation
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should_judge = (

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