Dataset Viewer
Auto-converted to Parquet Duplicate
condition
stringclasses
3 values
replicates
int64
3
3
mean_task_quality
float64
87.3
89.6
sd_task_quality
float64
0.98
3.13
mean_failure_quality
float64
81.5
84.4
sd_failure_quality
float64
2.78
4.29
mean_protection_quality
float64
91.9
94.8
sd_protection_quality
float64
1.17
7.07
mean_source_transfer_quality
float64
75.6
80.7
sd_source_transfer_quality
float64
6.45
8.81
mean_target_transfer_quality
float64
83
86.2
sd_target_transfer_quality
float64
2.43
6.47
mean_target_skill_gain
float64
9.66
12.8
sd_target_skill_gain
float64
1.83
6.95
mean_cross_model_transfer
float64
2.31
10.6
sd_cross_model_transfer
float64
5.31
14.1
mean_model_calls
float64
9
9
sd_model_calls
float64
0
0
mean_tool_calls
float64
0
0
sd_tool_calls
float64
0
0
mean_input_tokens
float64
127k
145k
sd_input_tokens
float64
1.61k
3.23k
mean_output_tokens
float64
10.2k
11k
sd_output_tokens
float64
1.28k
2.22k
mean_wall_time_ms
float64
210k
225k
sd_wall_time_ms
float64
20.4k
31.1k
mean_rule_lines
float64
28.3
41
sd_rule_lines
float64
6.56
17
mean_rule_words
float64
301
434
sd_rule_words
float64
48.5
208
mean_rollback_count
float64
1.67
2.33
sd_rollback_count
float64
0
0.58
mean_held_count
float64
0
0
sd_held_count
float64
0
0
mean_security_blocks
float64
0
0
sd_security_blocks
float64
0
0
no_wiki
3
87.4158
0.9758
81.4815
2.7816
93.3502
1.1702
75.5952
8.8149
86.1857
6.4675
12.797
6.9467
10.5904
14.0781
9
0
0
0
127,016.6667
1,633.44
11,024
1,875.5383
225,489.6667
27,225.7203
28.3333
17.0098
301
208.2955
2.3333
0.5774
0
0
0
0
flat_history
3
89.6189
2.7405
84.4276
4.2935
94.8103
4.6163
80.7395
6.4497
83.0471
2.4328
9.6585
1.8325
2.3076
5.311
9
0
0
0
145,269.3333
1,612.8274
10,896.3333
1,275.4945
223,188
20,408.3831
41
6.5574
434
84.4512
1.6667
0.5774
0
0
0
0
persistent_wiki
3
87.3322
3.1334
82.8002
4.1495
91.8641
7.0703
77.5132
7.3602
85.0157
6.3788
11.627
6.4065
7.5024
13.3573
9
0
0
0
134,773.6667
3,228.4059
10,177
2,219.9745
210,432
31,126.573
32.6667
7.0946
377.6667
48.5009
2
0
0
0
0
0

Governed Skill Evolution from Persistent Agent Experience

Prospective ablation and cross-model transfer study of three experience-retention conditions for governed Agent Skill evolution: no persistent history, flat chronological history, and a persistent Pattern Registry with a forward-chained Skill Impact Ledger.

Viewer subsets

  • replicate_results: 9 rows, one per condition and replicate.
  • aggregate_results: 3 condition-level descriptive summaries.
  • skill_impact_ledger: 36 compact ledger entries. The full verified ledger, including digest material, remains under results/.

Main result

Persistent Wiki did not outperform flat history on task quality in this setting. It used fewer input tokens and produced a larger target-model Skill gain, but had lower final task quality and more rollbacks. The release therefore preserves negative and mixed results rather than treating persistent state as automatically beneficial.

Evidence boundary

This is prospective descriptive evidence for one synthetic deterministic grader, three replicate sequences, and two Codex model versions in one model family. It is not a causal provider comparison, safety certification, or broad claim about Agent performance.

Source code and machine-readable protocols are Apache-2.0. The manuscript, documentation, result tables, figures, and release metadata are CC BY 4.0; see CONTENT-LICENSE.md.

Downloads last month
43

Spaces using RedinGhost/governed-skill-evolution 2

Collection including RedinGhost/governed-skill-evolution