Code editing requires a model to decide where to make changes, generate the new content, and preserve everything else. We study how masked diffusion language models divide these responsibilities across four editing interfaces: whole-file rewriting, search-and-replace, locate-then-infill, and token-level editing. Experiments on CanItEdit reveal a composition gap: diffusion models can generate coordinated changes when the correct edit locations are supplied, but much of this capability is lost when those locations must be predicted. Access to the intact original code helps the model fill multiple edit regions, yet does not resolve the difficulty of selecting those regions. By varying the editable regions while holding the generation model and decoding procedure fixed, we identify two distinct requirements for successful editing: covering every required change and placing precise boundaries around it. Missing a required region prevents the corresponding change, while widening regions to ensure coverage can sharply reduce success by requiring unchanged code to be regenerated. A sentence-level Wiki editing probe shows the same qualitative gap between supplied and predicted locations beyond code. These findings show why strong infilling capability alone does not ensure reliable editing: the interface must expose all required changes while limiting regeneration of unchanged code.
Figures & tables
Figure 1: Editing interfaces and the multi-span composition gap. (A) Four interfaces assign different structural decisions to the model and preservation to the runtime. (B) On 142 multi-span CanItEdit instances, gold-region old-visible infilling reaches 25.4% pass@1, compared with 11.3% for direct str_replace and 10.6% when predicted regions are composed with the infiller (Hybrid-multi). Matched oracle repairs of a separate predicted-hole baseline (P) expose complementary coverage and boundary errors. These repairs are diagnostics, not autonomous editors; routing and repair details appear in Appendix L .
Interface / structure
Overall ↑
Single ↑
Multi ↑
SynErr ↓
Exc. ↓
Autonomous DiffuCoder-family editors
Search-and-replace ( str_replace )
23.3
48.5
11.3
5.7
68.6
Locate-then-infill
12.9
27.9
5.6
15.2
70.0
Rewrite
11.4
27.9
3.5
23.8
63.3
Token-level editing
5.2
—
—
50.0
—
Structure and conditioning diagnostics
Table 1: CanItEdit results (%). Overall, Single, and Multi report pass@1 ( ↑ ); SynErr and Exc. are the percentages of evaluations whose harness status is SyntaxError and Exception ( ↓ ). Exception includes assertion failures and other runtime errors, and excludes SyntaxError and Timeout, so the columns do not sum to 100. Autonomous DiffuCoder-family recipes differ, so their scores are operating points rather than a causal interface ranking. Oracle rows supply edit positions; matched empty and old-visible fillers differ in source conditioning. Dashes indicate unavailable split or exception results.
Figure 3: Known-site generation controls (pass@1, %). Left: original-source context helps the matched filler on multi-span CanItEdit. Right: after matched SFT, middle placement helps on harder, single-gap HumanEval-Infilling tasks with oracle gap length.
Figure 4: Multi-span pass@1 of the fixed old-visible filler under controlled scaffold corruptions. Complete coverage alone is insufficient: expanding boundaries by one line performs worse than direct str_replace (dashed line), whereas a single tight extra hole retains much of the gold-scaffold advantage.
Scaffold
Recall (%)
pass@1 (%)
Clean gold
100
25.4
Drop one required span
66.0
6.3
Expand each range ±1
100
7.7
Add one false-positive hole
100
20.4
Add two false-positive holes
100
8.5
Table 2: Matched scaffold interventions on the old-visible filler. Recall is mean gold-span coverage on 142 multi-span CanItEdit examples; pass@1 is multi-span.
Interface / locations
Exact
SARI
ROUGE-L
BLEU
Edit F1
Coverage
Preserved
str_replace
0.0
0.411
0.882
0.795
0.061
6.8
97.5
Predicted locations + infill
3.1
0.437
0.807
0.728
0.376
45.3
82.3
Gold locations + infill
9.4
0.597
0.808
0.755
0.927
89.3
95.0
Table 3: FineEdit Wiki targeted editing on 64 grouped-split test examples. SARI, ROUGE-L, BLEU, and edit F1 use the [0,1] scale; exact match, coverage, and preservation are percentages. Predicted and gold locations use the same old-visible filler and decoder. Coverage measures gold edits realized in the output; preservation measures source retained outside gold edit regions.
Interface
Text generated
Structure predicted
Rewrite
U+V
Implicit in full output
Search-and-replace
A+V
Exact source anchors
Locate-then-infill
H+V
Regions and per-hole length
Token-level editing
Changed content
Operations across source tokens
Table 4: Generation responsibilities for a fixed edit. U is unchanged output text, V is replacement/insertion text, A is the copied old-anchor text, and H is the location-plan text, measured in tokens. Entries describe output content, excluding serialization/control overhead and repeated denoising passes; they are not runtime measurements.
Appendix figures & tables50 assets
Supplementary material from the paper’s appendix.
Appendix
Statistic
Mean
Median
Max
Source tokens
174.6
174
546
Target tokens
204.1
204
730
Instruction tokens
28.0
27
118
Edit distance (tokens)
49.5
26
722
Edit ratio ( source lenedit dist )
0.23
0.16
1.00
Appendix
Table 5: EditPackFT Python training set ( N=20,602 ). Token counts use the model tokenizer.
Table 7: Character and line count distributions for source and target code.
Operation
Mean
Std
Min
Med
P75
P95
Max
keep tokens
161.5
81.7
1
160
227
293
379
insert tokens
36.4
57.9
0
15
44
148
689
delete tokens
6.9
22.6
0
0
4
35
525
edit tokens
6.3
14.6
0
2
6
28
279
Edit distance
49.5
63.1
1
26
61
175
722
Edit ratio
0.23
0.21
0.003
0.16
0.33
0.70
1.00
Appendix
Table 8: Token-level edit operation distributions per example.
Change Type
Count
%
Mixed (insert+delete+edit)
14,362
69.7
Insert only
4,984
24.2
Delete only
993
4.8
Edit only (substitution)
263
1.3
Has insertions
17,426
84.6
Has deletions
7,603
36.9
Appendix
Table 9: Distribution of examples by change type.
Statistic
Mean
Std
Min
Med
P75
P95
Max
Groups per example
2.3
2.5
0
2
3
7
31
Tokens per group
15.6
30.2
1
6
15
65
611
Appendix
Table 10: Insert group statistics. An insert group is a contiguous run of insert operations.
#
Family
Supervised state
1
Fill pair
Fully expanded [MASK] slots; parallel fill; keep/edit/delete on source.
2
Expand trigger
Pre-mask source; <|expand|> on the last source token before each insert group.
3
Intermediate expand
Random partition of each group; every slot expand vs. fill; O(logN) doubling.
4
Over-expand recovery
N+1 slots; extra slot labelled EOS .
5
Partial-fill
Some insert slots already committed; remaining masks filled.
6
Spurious-expand
Bogus masks on non-insert positions, labelled EOS .
Appendix
Table 11: Token-level Levenshtein training families (compressed). Families 1–4 were the original four-type bank; 5–9 add partial-fill, recovery, residual, and no-op coverage.
Interface
Length unit
Other
Stage-1 plans
block-decision, size 32
confidence 0.1 ; numbered old code in the best runs
Stage-2 fill
block-decision, size 16
confidence 0.3 ; joint denoising of all holes
Rewrite SFT
fixed 256 or 512 steps
full-file generation; no Stage-1 plan
str_replace (headline)
block-append, step 1685
serialized (si→ti) ; runtime apply-in-order
str_replace (budget)
256-slot, step 1515
same interface, smaller generation budget
str_replace (pad)
random-pad, step 1685
same recipe as headline except padding
Appendix
Table 12: Decode settings for headline CanItEdit cells.
Matching gold trajectory states more closely improved length statistics, not functional editing (Table 15 ).
Appendix
Table 14: Failed finer-grained interventions. None became the method of the paper.
Stage-2 training
pass@1
SynErr
1-span
Multi
Exact len
Under-gen
Aligned old-visible
27.1
27.1
30.9
25.4
16.4
71.1
Round-consistent
23.8
29.0
33.8
19.0
21.5
65.9
Appendix
Table 15: Round-consistent / block-state-balanced Stage-2 training versus the aligned old-visible baseline (CanItEdit, gold locations, original-source context, unlocked block-decision). Exact-length and under-generation rates are among aligned completions. Rates are percentages. Diagnostic, not autonomous performance.
Setting
pass@1
SynErr
1-hole
multi
2
3
4+
Unlocked
27.1
27.1
30.9 / 35.3
25.4 / 23.2
44.4 / 11.1
7.1 / 28.6
16.7 / 31.7
δ=0 forced occupancy
28.1
42.4
47.1 / 32.4
19.0 / 47.2
33.3 / 35.2
21.4 / 60.7
5.0 / 51.7
δ=+1
27.6
46.2
42.6 / 29.4
20.4 / 54.2
40.7 / 37.0
17.9 / 60.7
3.3 / 66.7
δ=+2
24.8
50.5
41.2 / 38.2
16.9 / 56.3
35.2 / 38.9
14.3 / 60.7
1.7 / 70.0
δ=+4
14.8
56.7
26.5 / 44.1
9.2 / 62.7
20.4 / 48.1
7.1 / 60.7
0.0 / 76.7
δ=−1
4.3
71.0
1.5 / 69.1
5.6 / 71.8
11.1 / 70.4
3.6 / 67.9
1.7 / 75.0
Appendix
Table 16: Same-checkpoint forced-reference occupancy on the aligned old-visible filler (CanItEdit). Hole buckets are gold insert-span counts ( n=68/54/28/60 ). 1-hole, multi, and per-count columns are pass@1 / SyntaxError. Rates are percentages. Diagnostic, not autonomous performance.
unlocked ↓ / δ=0→
Pass
SynErr
Other
Pass
38
15
4
SynErr
12
30
15
Other
9
44
43
Appendix
Table 17: Paired unlocked →δ=0 outcome churn on the same evaluation instances. Pass is harness OK; SynErr is harness SyntaxError (IndentationError remains Other). 99/210 examples change outcome.
Holes
n
changed
Pass → SynErr
SynErr → Pass
1
68
28
0
9
2
54
21
9
3
3
28
17
0
0
4+
60
33
6
0
Appendix
Table 18: Unlocked →δ=0 churn by gold hole count. The 1-hole gain is a SyntaxError rescue; 2-hole and 4+ examples donate successful unlocked trajectories into SyntaxError.
Slice
Max gold hole
n
pass@1
SynErr
1-hole
1–16
14
57.1
14.3
1-hole
17–32
22
54.5
18.2
1-hole
33–64
12
8.3
33.3
1-hole
65+
20
0.0
70.0
All examples
65+
74
6.8
47.3
Total gold tokens 129+
—
60
8.3
45.0
Appendix
Table 19: Unlocked old-visible pass@1 and SyntaxError by gold hole length. The 65+ 1-hole collapse ( 0% / 70% ) is unchanged under δ=0 . Bucket boundaries are empirical for this checkpoint/protocol, not a universal threshold.
Split
n
cont. acc.
stop acc.
all holes
mean P(cont.)
Overall
210
57.3
73.3
50
0.281
1-hole
68
32.4
76.5
25
0.247
2-hole
54
69.9
71.3
16
0.308
3-hole
28
51.5
70.2
5
0.312
4+
60
65.0
74.0
4
0.270
Multi SynErr
67
74.0
63.3
5
0.375
Appendix
Table 20: Teacher-forced control-slot agreement on the δ=0 trajectory (observer-only: gold stop/continue still forced). Continue accuracy is agreement on non-final reference blocks; stop accuracy is agreement on the gold final block. These probabilities are not unconditional length-prediction accuracy.
Placement
Layout
exact-match
pass@1
Middle
prefix [MASK] suffix
79.2
93.6
End-prompted
chat(PREFIX, SUFFIX) then [MASK]
31.9
17.7
Appendix
Table 21: HumanEval-Infilling single_line placement probe (DiffuCoder-Instruct, gold-length masks, n=1033 ). The end-prompted row is the off-the-shelf protocol; it is not the matched-SFT end arm in Table 22 . pass@1 is the functional metric. Rates are percentages.
Evaluation
Middle
End
Δ
Off-the-shelf single_line pass@1
93.6
17.7
+75.9
Matched-SFT single_line pass@1
94.6
94.3
+0.3
exact match
84.2
82.5
+1.7
Matched-SFT multi_line pass@1
61.9
55.2
+6.7
exact match
37.3
33.1
+4.2
Appendix
Table 22: HumanEval-Infilling with oracle span length. Off-the-shelf end uses the end-prompted chat protocol; the matched-SFT arms share one serialization. Rates are percentages.
Evaluation
Metric
Middle
End
EditPackFT
exact match
19.2
19.8
syntax-ok
97.0
96.8
mean token distance
12.2
12.2
CanItEdit single-span
pass@1
42.4
47.0
string exact match
24.2
27.3
SyntaxError
28.8
25.8
Appendix
Table 23: Matched-SFT middle versus end on held-out EditPackFT ( n=400 ) and gold-location CanItEdit canonical single-span fills ( n=66 ), both with oracle span length.
Task
Output tokens
M2T
Replace
Delete
Insert
Structural share
Mathematics
121
137
4.4
1.6
1.2
5.0%
Prose
107
124
9.0
2.8
1.6
9.7%
Code writing
128
136
1.4
0.2
0.2
1.3%
Code editing
104
117
3.4
1.2
0.4
4.1%
Appendix
Table 24: LLaDA2.2 operation counts per greedy trace ( n=5 per task). Structural share is replacement, deletion, and insertion divided by all writes.
Interface
Explicit
Exact
Edit
Length
Multi-span
Source copied
location
boundary
count
allocation
serialization
by runtime
Whole-file rewrite
—
—
—
—
—
—
Search-and-replace
✓
✓
✓
—
✓
✓
Locate-then-infill
✓
✓
✓
✓
✓
✓
Token-level in-place
✓
✓
✓
✓
✓
✓
Appendix
Table 25: Structural commitments consumed explicitly by each runtime. A dash means that the decision remains latent in the generated output.
Locator
Parse
Exact
Pred./gold
pass@1
Multi
DiffuCoder
91.0
2.4
1.89 / 2.78
13.3
12.0
iLLaDA-8B
94.3
4.3
1.93 / 2.78
13.8
11.3
LLaDA-8B
86.7
3.3
1.81 / 2.78
11.9
10.6
Appendix
Table 26: Matched-layout Stage 1 locator outputs and CanItEdit pass@1 with the fixed DiffuCoder old-visible Stage 2 filler ( N=210 ). Parse and exact line-operation agreement are percentages; predicted/gold edits are means.
Knob
Rewrite
str_replace
Locate
Init
Instruct
Instruct
Stage-2 1610
LR
2×10−5
1×10−5
1×10−5
Batch / epochs
128 / 4
64 / 5
64 / 3
Decode temp
1.0
0.0
two-stage
Appendix
Table 27: Recipe confounds in Class A. Locate Stage 2 curriculum is part of the two-stage interface. Because these knobs are unmatched, Class A cells are within-family operating points rather than a causal ranking.
Interface
Setup
pass@1
SynErr
1-span
multi
Rewrite
SFT, 256 steps
9.0
36.2
20.6
3.5
Rewrite
SFT, 768 steps
10.0
13.8
20.6
4.9
str_replace
256-slot, step 1515
9.0
5.7
23.5
2.1
str_replace
random-pad, step 1685
4.3
11.9
7.4
2.8
Unified diff
DiffuCoder FT
4.3
1.0
13.2
0.0
Appendix
Table 28: Class A length-harness and serialization controls omitted from Table 1 (CanItEdit). Rates are percentages.
Bucket
n
str_replace
Locate
Rewrite
Gold loc + Qwen
1–5 changed lines
54
46.3
29.6
29.6
77.8
6–20 changed lines
112
20.5
9.8
7.1
39.3
>20 changed lines
44
2.3
0.0
0.0
9.1
Changed-line ratio <10%
28
21.4
21.4
10.7
89.3
Length similar
108
34.3
20.4
21.3
62.0
Length grow
100
12.0
5.0
1.0
22.0
Appendix
Table 29: Local vs. large-edit slices of CanItEdit. Class A cells except the last column (gold-location + Qwen, Class C). Length similar is ∣Δ∣≤5 source lines; length grow is Δ≥6 ; the remaining 2/210 shrink ( Δ≤−6 ). Changed-line ratio <10% is not the same slice as 1–5 changed lines: a long file with a small ratio can still be a large absolute edit.
Interface
Checkpoints
pass@1 (%)
Locate same-ckpt
645 / 1000 / 1290 / 1935
6.7 / 11.4 / 13.3 / 12.9
Rewrite 640 decode
256 / 512 / 768 steps
9.0 / 11.4 / 10.0
Rewrite sibling 0522
428 / 856 (512 decode)
10.5 / 11.4
str_replace 256-slot
303 / 606 / 909 / 1212 / 1515
11.9 / 8.1 / 8.1 / 7.6 / 9.0
Appendix
Table 30: Existing-checkpoint train and decode curves (CanItEdit). Rewrite 428/856 are a sibling batch-96 run; completions were chatty and fenced, then stripped before scoring. Block-append str_replace has only step 1685. Do not plot 428–640–856 as one curve.
Model
pass@1
SynErr
1-span
multi
GPT-4o
72.4
0
79.4
69.0
Qwen3-8B
51.4
0
67.6
43.7
Qwen2.5-Coder-7B-Instruct
48.6
0
58.8
43.7
Appendix
Table 31: Autoregressive rewrite references on CanItEdit, 105 descriptive instructions only . These cells are a different protocol from the full-evaluation tables and are not headline parity with dLLM results. Rates are percentages.
Interface
OK
SynErr
Assert.
Other
Timeout
Stable-DiffCoder rewrite
127
0
56
26
1
str_replace block-append
49
12
71
73
5
Locate+infill same ckpt
27
32
66
81
4
Rewrite SFT, 512 steps
24
50
37
96
3
Oracle loc + empty plan
52
57
45
56
0
Oracle loc + Qwen CoT
90
45
15
59
1
Appendix
Table 32: Coarse failure mix on CanItEdit (counts). AssertionError is among scored programs, not a proof of “right location, wrong body.” str_replace parse/apply failures are not SyntaxError.
Figure 5: Primary error class by interface (first-match cascade, percentages). wrong_localization is defined only for predicted-plan locate-then-infill. IndentationError is a harness Exception and is folded into syntax.
Interface
Fmt
Apply
No-op
Syn
Loc
Sem
Pass
Rewrite SFT, 512 steps
0
0
11
59
0
116
24
str_replace block-append
23
29
6
14
0
89
49
Locate+infill same ckpt
9
0
9
42
112
11
27
Oracle loc + empty plan
0
0
12
64
0
82
52
Oracle loc + Qwen CoT
0
0
1
58
0
61
90
Appendix
Table 33: Primary error cascade on CanItEdit (counts). First match; not a manual diagnosis. Locate wrong loc. is undefined on gold-scaffold oracles.
str_replace <old> spans, 142 multi-span cases
Mean gold-span recall
42.7%
All gold spans covered
21.8% (31/142)
Predicted K=1
45/142
Enter hybrid Stage 2 branch
53/142
Appendix
Table 34: Localization audits for autonomous structure predictors.
Metric
Value
Parseable plans
95.7%
Zero predicted edits
4.3%
Exact line-op match
13.3%
Nearest-anchor exact
57.9%
Mean predicted / gold edits
1.55 / 2.78
Appendix
Table 35: Location quality of predicted Stage 1 plans on CanItEdit.
Figure 6: Gold-scaffold Stage-2 pass@1 by perturbation family (shared multitask filler, gold-aware Qwen plans). Local one-boundary errors are often survivable; expanding each range by kept lines hurts more; translating the whole scaffold is catastrophic. Dashed/dotted lines: exact-gold privileged fill 42.9% and autonomous same-checkpoint 12.9% (references, not a ranking). Diagnostic, not autonomous performance.
Scaffold
Dist.
pass@1
SynErr
1-span
Multi
Exact gold ranges
0.00
42.9%
21.4%
57.4%
35.9%
Local typed ±1
Shift insert-gap −1
0.69
38.6%
21.0%
50.0%
33.1%
Shift insert-gap +1
0.71
37.1%
22.9%
50.0%
31.0%
Shift start +1 (where valid)
0.56
36.7%
21.4%
54.4%
28.2%
Shift end +1
1.66
33.3%
31.4%
55.9%
22.5%
Appendix
Table 36: Gold-scaffold Stage-2 fill under boundary perturbations (multitask step 1935 filler, gold-aware Qwen plan). Diagnostic, not autonomous performance.
Plan / conditioning
pass@1
1-span
Multi
SynErr
Gold loc. + Qwen CoT
42.9%
57.4%
35.9%
21.4%
Gold loc. + location-free gold-aware Qwen
42.9%
57.4%
35.9%
22.4%
Gold loc. + Qwen CoT (ep5 filler)
39.5%
48.5%
35.2%
27.1%
Gold loc. + empty plan (multitask 1935)
24.8%
42.6%
16.2%
27.1%
Gold loc. + input-only Qwen3-8B
24.8%
41.2%
16.9%
21.4%
Gold loc. + Qwen headers only
22.4%
42.6%
12.7%
16.7%
Appendix
Table 37: Gold-location Stage-2 fill under different plan texts. Rows above the last midrule use the multitask step 1935 filler unless noted (ep5). The last two rows are separately trained aligned Stage-2 diagnostics from DiffuCoder-Instruct (block 16, five epochs): empty_plan and original_source at global_step_1610 , not the plan-conditioned ep5 specialist. Only that pair is a matched test of old-source visibility; 20.5/27.1 vs. 24.8 is not. The integrated-diff pre-glue 20.5% cell in Table 43 is a third, different 20.5% . All rows are diagnostics, not autonomous performance.
Interface
1 hole
2
3
4+
n
68
54
28
60
Oracle loc. + Qwen CoT (C)
57.4%
50.0%
50.0%
16.7%
Oracle loc. + empty (C)
42.6%
22.2%
21.4%
8.3%
str_replace block-append (A)
48.5%
16.7%
17.9%
3.3%
Locate-and-infill same ckpt (A)
27.9%
9.3%
7.1%
1.7%
Rewrite SFT 512 (A)
27.9%
7.4%
3.6%
0.0%
Appendix
Table 38: Pass@1 by gold insert-span count (CanItEdit). Gold-location rows give Stage 2 every gold hole (empty-plan row: multitask 1935 filler). Class A rows are autonomous. 4+ pools 4–10 holes ( n=60 ).
Interface
1 hole
2
3
4+
n
68
54
28
60
Oracle loc. + Qwen CoT (1935)
57.4 / 20.6
50.0 / 22.2
50.0 / 21.4
16.7 / 21.7
Oracle loc. + empty (1935)
42.6 / 16.2
22.2 / 25.9
21.4 / 28.6
8.3 / 40.0
Aligned empty 1610
33.8 / 27.9
27.8 / 20.4
3.6 / 17.9
6.7 / 35.0
Aligned old-visible 1610
30.9 / 35.3
44.4 / 11.1
7.1 / 28.6
16.7 / 31.7
Appendix
Table 39: Pass@1 / SyntaxError by gold insert-span count (CanItEdit). SyntaxError is not monotonic in hole count. The old-visible 1-hole cell has the highest SyntaxError among 1–2 holes; privileged Qwen CoT SyntaxError is nearly flat through 4+ . Diagnostic, not autonomous performance.
Intervention
pass@1
1-span
Multi
Exact gold spans
42.9%
57.4%
35.9%
Extra false-positive span
39.5%
48.5%
35.2%
Shift first span +1 line
19.5%
29.4%
14.8%
Drop last gold span
5.7%
0.0%
8.5%
Appendix
Table 40: One-span interventions on gold scaffolds with gold-aware Qwen plans (filler 1935). Single-span drop is 0% by construction. Diagnostic, not autonomous performance.
Planner
Parse
Exact op
Mean pred
pass@1
Unconstrained Stage 1
95.7%
13.3%
1.55
12.9%
Gold- K prompt hint
89.0%
14.3%
1.80
11.9%
Appendix
Table 41: Gold edit-count prompt hint on Stage 1 (multitask 1935, confidence 0.1) filled by the same checkpoint. Raises exact-count agreement, not pass@1. Diagnostic, not a production decoder.
Arm
Full
Unaffected
Affected
D pre-glue
20.5
34.7
0.0
D ′ glue-fix
26.2
34.7
14.0
D ′′ canonicalize
28.1
34.7
18.6
Glue-fix + oracle length
27.1
30.6
22.1
Appendix
Table 42: EOF/no-newline glue split on the integrated-diff step-1610 filler (CanItEdit). Affected: 86 evaluations / 43 sources. Unaffected: 124 evaluations / 62 sources. Oracle-length locks gold occupancy on the glue-fixed scaffold (train–test mismatch). Rates are percentages. Diagnostic, not autonomous performance.
Arm
pass@1
1-span
Multi
SynErr
D pre-glue integrated
20.5
30.9
15.5
20.5
D ′ glue-fixed integrated
26.2
33.8
22.5
26.7
D ′′ canonicalized integrated
28.1
33.8
25.4
24.8
Local ±2 prefix control
28.1
38.2
23.2
21.9
Appendix
Table 43: Gold-scaffold construction with oracle locations. D, D ′ , and D ′′ share one integrated-diff step-1610 filler; only scaffold construction changes.
Scaffold
Passed
pass@1 (%)
Predicted baseline P
15/142
10.6
P+C (coverage)
19/142
13.4
P+B (boundaries)
20/142
14.1
P+C+B (both)
31/142
21.8
Gold
36/142
25.4
Appendix
Table 44: Paired oracle repairs of the separate predicted-hole baseline P on 142 multi-span CanItEdit examples. The gold row is the existing clean-scaffold result. Repairs change scaffold geometry, not the filler or decoder; none is an autonomous editor.
Interface
Setup
pass@1
SynErr
1-span
Multi
Direct str_replace
block-append 1685
23.3
5.7
48.5
11.3
Hybrid-multi
predicted <old> → old-visible
22.9
9.0
48.5
10.6
Hybrid-all
all unique <old> → old-visible
13.3
23.8
22.1
9.2
Gold loc. + old-visible
aligned 1610
27.1
27.1
30.9
25.4
Gold loc. + aligned empty
aligned 1610
20.5
26.7
33.8
14.1
Appendix
Table 45: Detailed composition cells (CanItEdit). Hybrid-multi overall 22.9% is not a better editor: single-span 48.5% is inherited from direct str_replace . Rates are percentages.
Method
All 31
Routed 19
Fallback 12
Direct str_replace
10/31
7/19
3/12
Predicted scaffold → filler
8/31
5/19
3/12
Gold scaffold → same filler
13/31
7/19
6/12
Appendix
Table 46: Paired 31-case control on multi-span examples whose predicted <old> spans cover every gold hunk. Filler is aligned old-visible Stage 2 at step 1610. Routed = Hybrid-multi sent the example to Stage 2; fallback = predicted K=1 kept the str_replace reconstruction.
Relation
n
Direct
Pred. scaf.
Gold scaf.
Merged multiple gold spans
12
3/12
3/12
6/12
Strict superset
10
5/10
2/10
3/10
Exact boundaries
3
2/3
1/3
1/3
Strict subset
3
0/3
2/3
3/3
Overlap / fragmented
3
0/3
0/3
0/3
Predicted K= gold K
17
7/17
5/17
5/17
Appendix
Table 47: Boundary-relation and K -match slices of the 31 fully-covered examples. Relation rows use first-match precedence (merge > fragment > exact > superset > subset > residual overlap). The K rows are not exclusive of the relation rows; bins with n≤3 are not interpreted.
Figure 7: Overall CanItEdit pass@1 under gold-scaffold corruptions of the aligned old-visible filler. Missing required spans collapse performance; one extra hole is comparatively cheap; expanding gold boundaries is not. Drop-one / drop-25 / drop-50 overall rates mix emptied 1-span scaffolds; the locator-quality curve in Figure 4 uses multi-span pass@1. Diagnostic, not autonomous performance.
Scaffold
pass@1
Multi
Δ
SynErr
Appl./imposs.
Clean gold
27.1
25.4
—
27.1
210/0
Drop one gold span
4.3
6.3
−22.9
12.9
210/0
Drop 25% of gold spans
3.3
4.9
−23.8
11.0
210/0
Drop 50% of gold spans
0.5
0.7
−26.7
10.0
210/0
Expand ±1
15.2
7.7
−11.9
39.0
210/0
Expand ±2
13.8
7.0
−13.3
42.9
210/0
Appendix
Table 48: Full gold-scaffold corruption grid on the aligned old-visible filler (CanItEdit). Relative Δ is against clean gold 57/210 ( 27.1% ). Applied / impossible counts how often the operator changed the scaffold. Shift ±1 also changes coverage (mean recall 29.6% / 32.9% on the 142) and extra-span count.
Family
Mode
Recall
Extra
Multi
Overall
SynErr
gold
clean scaffold
100.0
0.00
25.4
27.1
27.1
missing-span
drop-one
66.0
0.00
6.3
4.3
12.9
missing-span
drop-25%
62.2
0.00
4.9
3.3
11.0
missing-span
drop-50%
45.5
0.00
0.7
0.5
10.0
shift
−1 line
29.6
2.62
9.9
11.9
15.2
shift
+1 line
32.9
2.46
4.9
8.1
32.4
Appendix
Table 49: Locator quality → multi-span pass@1 on the aligned old-visible filler. Recall and extra-span count are means on the 142 multi-span examples. Δ vs. gold and vs. 11.3 are multi-span points. Corruption rows are diagnostics; str_replace rows are autonomous references.
Method
Overall
1-span
Multi-span
Direct one-shot str_replace
23.3
48.5
11.3
One-edit prompt, max 5 rounds
14.8
35.3
4.9
First-valid executor, max 1 round
19.5
48.5
5.6
First-valid executor, max 2 rounds
18.1
38.2
8.5
First-valid executor, max 3 rounds
17.1
30.9
10.6
First-valid executor, max 4 rounds
19.0
36.8
10.6
Appendix
Table 50: Sequential str_replace on CanItEdit. One-edit prompt and first-valid executor use the same checkpoint as direct one-shot. Rates are percentages. Gold-location old-visible Stage 2 is a diagnostic, not a sequential baseline.
Stop reason
n
Rate
noop_edit
59
28.1%
Malformed edit structure
56
26.7%
repeated_edit
29
13.8%
max_rounds
27
12.9%
code_state_loop
22
10.5%
ambiguous_old
13
6.2%
Appendix
Table 51: First-valid sequential stop reasons (max 5 rounds). too_many_edits is the one-edit-prompt death mode (52.9%) and does not apply here.
Knowledge editing aims to update or correct factual knowledge in a language model. A widely used approach, locate-then-edit, first localizes a fact within the model and then edits the weights there. To date, such methods have been developed exclusively for autoregressive models (ARMs). Whether they work for masked diffusion models (MDMs), which model text bidirectionally and generate by iterative denoising rather than next-token prediction, remains an open question. We address it by transferring locate-then-edit to MDMs and comparing multiple MDMs with their matched ARMs. Our central finding has two parts. First, where an edit should be applied transfers between them: the same early-to-mid-layer MLP at the last subject token is most effective for both. Second, this shared location does not guarantee a shared outcome. Single-token edits succeed in both, but as targets grow longer, editing degrades far more sharply in the MDMs than in the ARMs. The failure stems from how the edited fact is generated: producing a multi-token target passes through intermediate states in which the target is partially unmasked, for which the edit was never optimized. Guided by this diagnosis, we introduce a simple correction that optimizes the edit including such states, substantially restoring multi-token performance. Our code is available at https://github.com/holi-lab/MDM-KE.
Haewon Park, Yohan Jo
Graduate School of Data Science, Seoul National University
Large language models (LLMs) have become ubiquitous tools for code generation and editing. However, development teams often use multiple LLM assistants. Different developers may prefer different models, and individual developers may switch between models across different coding sessions. Because of this, the edits any one model makes are frequently applied to foreign code originally generated by another model. These LLMs are often trained on different datasets, and as a result have different stylistic preferences. Do LLMs behave differently when they edit foreign code originally written by a different LLM with a different coding style? We find that models tend to make more, and often excessive, edits on foreign code. We introduce CROCODIL (Cross-model Code Editing with LLMs), a post-training framework for reducing excessive edits while preserving functional correctness. CROCODIL's similarity reward penalizes large changes, while its execution reward scores build and test success. We use the product of these two rewards to encourage the policy to decrease the edit size without decreasing the edit task success rate. CROCODIL is available at https://github.com/EngineeringSoftware/Crocodil.
Large language models (LLMs) are increasingly used to edit existing code, but correctness alone is not enough: useful repairs should also be minimal, reviewable, and faithful to the original implementation. We study over-editing, the tendency of a model to rewrite code beyond what is required to fix a bug. We construct an evaluation framework from 400 BigCodeBench problems by injecting controlled AST-level corruptions into reference solutions, giving each repair task a known minimal patch. Across frontier LLMs, over-editing is widespread even among strong models like GPT-5.5: high Pass@1 can coexist with unnecessarily large edits and added cognitive complexity. A preservation instruction substantially reduces this behavior, lowering average excess Levenshtein distance from 0.195 to 0.131, reducing added cognitive complexity by 26.6%, and increasing Pass@1 by 2.3 points. However, these gains do not simply follow from a larger reasoning budget or larger models. We next ask whether minimal editing can be learned directly during post-training. We observe that supervised fine-tuning overfits to seen corruption patterns, whereas reinforcement learning gives the best out-of-domain edit-fidelity and performance-retention trade-off. These results position edit fidelity as a distinct axis of code-repair quality and show that it can be measured and learned.