{ "topic": "long_horizon", "round": 2, "method_name": "dimension evidence scan", "idea": "Use delayed-feedback text and per-turn feature tags to identify candidate culprit dimensions.", "diagnosed_issue": "Long-horizon round 2 dominant issue: hard_distractor_temporal_ambiguity.", "patch_for_next_round": "Add temporal utility-drop windows so early weak distractors do not steal credit from the real culprit.", "patch_source": "autodiagnosed", "auto_diagnosis": { "dominant_failure_mode": "hard_distractor_temporal_ambiguity", "evidence": { "step_miss": 5, "dimension_miss": 5, "low_rank_gold": 0 }, "diagnosis_text": "Long-horizon round 2 dominant issue: hard_distractor_temporal_ambiguity.", "selected_next_patch": "Add temporal utility-drop windows so early weak distractors do not steal credit from the real culprit." }, "selected_next_patch": "Add temporal utility-drop windows so early weak distractors do not steal credit from the real culprit.", "comparison_to_previous": { "status": "improved", "primary_metric": "culprit_step_accuracy", "primary_delta": 0.65, "headline": "culprit_step_accuracy changed +0.6500 vs previous round." }, "failure_examples": [ { "episode_id": "trace_0008", "delayed_comment": "那个误会梗埋得太早，后面解释让我出戏。", "gold_step": 4, "predicted_step": 2, "gold_dimension": "trope_misunderstanding", "predicted_dimension": "female_agency", "top_candidates": [ { "step": 2, "score": 2.35, "reason": "tag:female_agency+text+tag:trope_misunderstanding", "tags": [ "female_agency", "trope_misunderstanding" ] }, { "step": 4, "score": 1.8, "reason": "text+tag:trope_misunderstanding", "tags": [ "trope_misunderstanding" ] }, { "step": 5, "score": 0.55, "reason": "tag:pacing", "tags": [ "pacing" ] } ], "problems": [ "step_miss:gold=4:pred=2", "dimension_miss:gold=trope_misunderstanding:pred=female_agency" ], "severity": 2.7 }, { "episode_id": "trace_0017", "delayed_comment": "前面设定解释太密，后面节奏被拖住了。", "gold_step": 6, "predicted_step": 4, "gold_dimension": "lore_density", "predicted_dimension": "female_agency", "top_candidates": [ { "step": 4, "score": 2.35, "reason": "tag:female_agency+text+tag:lore_density", "tags": [ "female_agency", "lore_density" ] }, { "step": 6, "score": 1.8, "reason": "text+tag:lore_density", "tags": [ "lore_density" ] }, { "step": 1, "score": 0.0, "reason": "weak_evidence", "tags": [] } ], "problems": [ "step_miss:gold=6:pred=4", "dimension_miss:gold=lore_density:pred=female_agency" ], "severity": 2.7 }, { "episode_id": "trace_0034", "delayed_comment": "前面设定解释太密，后面节奏被拖住了。", "gold_step": 4, "predicted_step": 2, "gold_dimension": "lore_density", "predicted_dimension": "female_agency", "top_candidates": [ { "step": 2, "score": 2.35, "reason": "tag:female_agency+text+tag:lore_density", "tags": [ "female_agency", "lore_density" ] }, { "step": 4, "score": 1.8, "reason": "text+tag:lore_density", "tags": [ "lore_density" ] }, { "step": 3, "score": 0.55, "reason": "tag:pacing", "tags": [ "pacing" ] } ], "problems": [ "step_miss:gold=4:pred=2", "dimension_miss:gold=lore_density:pred=female_agency" ], "severity": 2.7 }, { "episode_id": "trace_0043", "delayed_comment": "读到后面才发现前面铺垫太拖，剧情推进慢了。", "gold_step": 4, "predicted_step": 2, "gold_dimension": "pacing", "predicted_dimension": "trope_misunderstanding", "top_candidates": [ { "step": 2, "score": 2.35, "reason": "text+tag:pacing+tag:trope_misunderstanding", "tags": [ "trope_misunderstanding", "pacing" ] }, { "step": 3, "score": 1.8, "reason": "text+tag:pacing", "tags": [ "pacing" ] }, { "step": 4, "score": 1.8, "reason": "text+tag:pacing", "tags": [ "pacing" ] } ], "problems": [ "step_miss:gold=4:pred=2", "dimension_miss:gold=pacing:pred=trope_misunderstanding" ], "severity": 2.7 }, { "episode_id": "trace_0050", "delayed_comment": "那个误会梗埋得太早，后面解释让我出戏。", "gold_step": 2, "predicted_step": 1, "gold_dimension": "trope_misunderstanding", "predicted_dimension": "pacing", "top_candidates": [ { "step": 1, "score": 2.35, "reason": "tag:pacing+text+tag:trope_misunderstanding", "tags": [ "pacing", "trope_misunderstanding" ] }, { "step": 2, "score": 1.8, "reason": "text+tag:trope_misunderstanding", "tags": [ "trope_misunderstanding" ] }, { "step": 3, "score": 0.0, "reason": "weak_evidence", "tags": [] } ], "problems": [ "step_miss:gold=2:pred=1", "dimension_miss:gold=trope_misunderstanding:pred=pacing" ], "severity": 2.7 } ], "concrete_problems": [ { "episode_id": "trace_0008", "delayed_comment": "那个误会梗埋得太早，后面解释让我出戏。", "gold_step": 4, "predicted_step": 2, "gold_dimension": "trope_misunderstanding", "predicted_dimension": "female_agency", "top_candidates": [ { "step": 2, "score": 2.35, "reason": "tag:female_agency+text+tag:trope_misunderstanding", "tags": [ "female_agency", "trope_misunderstanding" ] }, { "step": 4, "score": 1.8, "reason": "text+tag:trope_misunderstanding", "tags": [ "trope_misunderstanding" ] }, { "step": 5, "score": 0.55, "reason": "tag:pacing", "tags": [ "pacing" ] } ], "problems": [ "step_miss:gold=4:pred=2", "dimension_miss:gold=trope_misunderstanding:pred=female_agency" ], "severity": 2.7 }, { "episode_id": "trace_0017", "delayed_comment": "前面设定解释太密，后面节奏被拖住了。", "gold_step": 6, "predicted_step": 4, "gold_dimension": "lore_density", "predicted_dimension": "female_agency", "top_candidates": [ { "step": 4, "score": 2.35, "reason": "tag:female_agency+text+tag:lore_density", "tags": [ "female_agency", "lore_density" ] }, { "step": 6, "score": 1.8, "reason": "text+tag:lore_density", "tags": [ "lore_density" ] }, { "step": 1, "score": 0.0, "reason": "weak_evidence", "tags": [] } ], "problems": [ "step_miss:gold=6:pred=4", "dimension_miss:gold=lore_density:pred=female_agency" ], "severity": 2.7 }, { "episode_id": "trace_0034", "delayed_comment": "前面设定解释太密，后面节奏被拖住了。", "gold_step": 4, "predicted_step": 2, "gold_dimension": "lore_density", "predicted_dimension": "female_agency", "top_candidates": [ { "step": 2, "score": 2.35, "reason": "tag:female_agency+text+tag:lore_density", "tags": [ "female_agency", "lore_density" ] }, { "step": 4, "score": 1.8, "reason": "text+tag:lore_density", "tags": [ "lore_density" ] }, { "step": 3, "score": 0.55, "reason": "tag:pacing", "tags": [ "pacing" ] } ], "problems": [ "step_miss:gold=4:pred=2", "dimension_miss:gold=lore_density:pred=female_agency" ], "severity": 2.7 }, { "episode_id": "trace_0043", "delayed_comment": "读到后面才发现前面铺垫太拖，剧情推进慢了。", "gold_step": 4, "predicted_step": 2, "gold_dimension": "pacing", "predicted_dimension": "trope_misunderstanding", "top_candidates": [ { "step": 2, "score": 2.35, "reason": "text+tag:pacing+tag:trope_misunderstanding", "tags": [ "trope_misunderstanding", "pacing" ] }, { "step": 3, "score": 1.8, "reason": "text+tag:pacing", "tags": [ "pacing" ] }, { "step": 4, "score": 1.8, "reason": "text+tag:pacing", "tags": [ "pacing" ] } ], "problems": [ "step_miss:gold=4:pred=2", "dimension_miss:gold=pacing:pred=trope_misunderstanding" ], "severity": 2.7 }, { "episode_id": "trace_0050", "delayed_comment": "那个误会梗埋得太早，后面解释让我出戏。", "gold_step": 2, "predicted_step": 1, "gold_dimension": "trope_misunderstanding", "predicted_dimension": "pacing", "top_candidates": [ { "step": 1, "score": 2.35, "reason": "tag:pacing+text+tag:trope_misunderstanding", "tags": [ "pacing", "trope_misunderstanding" ] }, { "step": 2, "score": 1.8, "reason": "text+tag:trope_misunderstanding", "tags": [ "trope_misunderstanding" ] }, { "step": 3, "score": 0.0, "reason": "weak_evidence", "tags": [] } ], "problems": [ "step_miss:gold=2:pred=1", "dimension_miss:gold=trope_misunderstanding:pred=pacing" ], "severity": 2.7 } ], "data_eval_spec": { "dataset_name": "TRACE-USER-Bench synthetic delayed-feedback traces", "data_schema": "One row per multi-turn episode: turn trace, utility/implicit feedback, delayed comment, gold culprit step/dimension, counterfactual repairs.", "evaluation_protocol": "Rank culprit steps from the full trace, compare top prediction and reciprocal rank to gold, then estimate counterfactual repair gain and confidence calibration.", "metric_definitions": { "culprit_step_accuracy": "Exact-match accuracy for the delayed-feedback culprit step.", "culprit_dimension_accuracy": "Accuracy for the causal preference/content dimension.", "credit_mrr": "Mean reciprocal rank of the gold culprit step.", "repair_gain": "Mean expected utility gain from the selected counterfactual repair.", "credit_calibration": "One minus confidence error; higher means confidence tracks correctness." }, "sample_count": 120, "metrics": { "culprit_step_accuracy": "Exact-match accuracy for the delayed-feedback culprit step.", "culprit_dimension_accuracy": "Accuracy for the causal preference/content dimension.", "credit_mrr": "Mean reciprocal rank of the gold culprit step.", "repair_gain": "Mean expected utility gain from the selected counterfactual repair.", "credit_calibration": "One minus confidence error; higher means confidence tracks correctness." }, "splits": { "culprit_dimensions": { "lore_density": 27, "pacing": 35, "trope_misunderstanding": 32, "female_agency": 26 } } }, "metrics": { "round": 2, "culprit_step_accuracy": 0.65, "culprit_dimension_accuracy": 0.8917, "credit_mrr": 0.8167, "repair_gain": 0.1461, "counterfactual_repair_gain": 0.1461, "credit_calibration": 0.6277 }, "previous_metrics": { "round": 1, "culprit_step_accuracy": 0.0, "culprit_dimension_accuracy": 0.7917, "credit_mrr": 0.2863, "repair_gain": 0.076, "counterfactual_repair_gain": 0.076, "credit_calibration": 0.38 }, "metric_deltas": { "culprit_step_accuracy": 0.65, "culprit_dimension_accuracy": 0.10000000000000009, "credit_mrr": 0.5304, "repair_gain": 0.07010000000000001, "counterfactual_repair_gain": 0.07010000000000001, "credit_calibration": 0.24770000000000003 } }