Large language model (LLM) agents for health can accumulate reusable textual skills from task feedback, such as calculation criteria, measurement time points, and formula units. Deciding which skills to retain is consequential: useful rules improve later answers, while faulty generalizations propagate errors. We propose CausalEvo, a method for causal skill self-evolution that estimates the relative effects of deliberately changed skill sets while holding the model weights, feedback cases, and rule count fixed. At each evolution step, a frozen LLM proposes candidate rules, which are pooled with the incumbent skills so that the current skill set remains a feasible choice. Paired skill replacements against shared background rules are scored on the same feedback cases; least squares converts the score differences into relative skill values, and the top-ranked rules are retained, favoring incumbents on ties. A decision-loss bound separates candidate support, approximation, and estimation errors. On six MedCalc-Bench-Verified tasks with 120 held-out cases, CausalEvo achieves 60.00% accuracy, compared with 55.83% for SkillOpt and 51.67% without skills. Across the recorded training configurations, which use different optimization budgets, it consumes 55.6% fewer training tokens than SkillOpt. Gains concentrate in particular calculations alongside regressions on others, and paired accuracy intervals include zero. CausalEvo turns task feedback into controlled decisions about skill retention and replacement. Project repository: https://github.com/txbrook/CausalEvo
Figures & tables
Figure 1: Overview of CausalEvo. Incumbent skills ( St=Mt ) and model-generated proposals form a candidate pool. On a fixed feedback case, two skill sets share k−1 background rules and exchange one rule. The frozen actor scores both skill sets; paired score differences fit relative skill values for top- k selection, with ties favoring incumbent rules.
Method
Accuracy (%)
Accuracy 95% CI
Valid outputs (%)
Accuracy on valid outputs (%)
No skill
51.67
[43.33, 60.00]
100.00
51.67
SkillOpt
55.83
[47.50, 64.17]
100.00
55.83
CausalEvo
60.00
[52.50, 67.50]
99.17
60.50
Table 1: Main results on 120 held-out cases with thinking-off inference. Accuracy gives equal weight to the six tasks; invalid outputs count as incorrect. Valid-only accuracy is a secondary diagnostic with method-specific denominators. Bold marks the highest observed overall accuracy.
Figure 2: Final task accuracy for the three frozen skill states. Each task has 20 held-out cases, so five percentage points equal one case. Invalid outputs count as incorrect. Exact values appear in Appendix C.4 .
Figure 3: Macro accuracy over learning steps on the fixed 120-case test panel. Step 0 uses an empty skill. Error bars are conditional 95% task-stratified bootstrap intervals. Dashed lines show no skill and the selected SkillOpt endpoint as fixed references.
Setting
Final accuracy (%)
Phase mean (%)
No skill
62.50
61.94
Shuffled credit
62.50
68.68
CausalEvo
70.83
73.33
Table 2: Credit-assignment ablation on a separate dataset with a 24-case test set. Phase mean averages the six phase-end seen-task accuracies. Bold marks the highest observed value in each column.
Figure 4: Training token use in the recorded configurations, whose optimization budgets differ; output includes reasoning. Exact counts are in Appendix C.2 .
Appendix figures & tables9 assets
Supplementary material from the paper’s appendix.
Appendix
Figure 5: Actor prompt. Selected rules follow the fixed instruction; the no-skill state leaves that field empty. Field headings indicate message structure.
Figure 6: Skill-proposal template, with m=6 at initialization and m=2 for a nonempty incumbent. The lower fields summarize the structured user payload. The program selects rules from the scored comparisons.
Setting
Value
Model identifier
deepseek/deepseek-v4-flash-0731
Temperature
Actor 0, proposer 0.3
Output caps
Actor 4,096; proposer 8,192 tokens
State constraint
Four rules, 5–80 words each
Comparison design
Random six-cycle; six pairs, 12 actor calls per valid evolution step
Appendix
Table 3: Settings for skill evolution.
Method
Requests
Input tokens
Output tokens including reasoning
Total tokens
SkillOpt
839
3,924,153
1,036,499
4,960,652
CausalEvo
336
1,186,029
1,016,551
2,202,580
Appendix
Table 4: Training usage for the main experiment.
Method
SIRS
HEART
PERC
CURB-65
Child-Pugh
Cockcroft– Gault
No skill
65
25
40
65
45
70
SkillOpt
85
40
55
70
40
45
CausalEvo
75
50
35
80
30
90
Appendix
Table 5: Final accuracy (%) on each task, with 20 held-out cases per task. Bold marks the highest observed accuracy in each column.
Method
Correct
Valid but incorrect
Invalid
Length truncations
No skill
62
58
0
0
SkillOpt
67
53
0
0
CausalEvo
72
47
1
0
Appendix
Table 6: Final-state output decomposition on the 120-case panel. All values are counts. Invalid outputs remain errors in the primary accuracy metric; truncation is one possible invalid-output cause.
Figure 7: Complete deployed skill from the main experiment at step 24. The actor receives these four rules after its fixed task instruction. The rule text is preserved from the saved skill artifact.
ID
Task
Teaching
Feedback
Development
51
SIRS
4
32
4
18
HEART
4
18
4
48
PERC
4
10
4
45
CURB-65
4
6
4
15
Child-Pugh
4
4
4
2
Cockcroft–Gault
4
2
4
Appendix
Table 7: Fixed allocation. Each task is introduced for four consecutive rounds. Feedback can come from tasks introduced previously; early tasks consequently appear more often.
Figure 8: Accuracy at each phase end on all tasks introduced so far. The evaluated task mixture expands with each phase.