Protocol specifications rarely justify executable behavior in one place: a transition may inherit its source state from a remote control block, name its events only by number, and remain subject to a later correction. Recovering the nearby clause is therefore insufficient; the system must assemble a source-linked certificate and determine whether any stronger rule applies. We introduce LASER, a proof-carrying search method for sparse, defeasible protocol evidence. LASER represents missing support and unresolved corrections as explicit obligations, searches typed evidence routes, and lets an occurrence-aware symbolic checker alone decide whether a certificate warrants, refutes, or leaves a behavior unresolved. On 91 BehaviorCheck families under a matched 2K budget, LASER improves Joint Edge+Proof over the strongest local reader on two model families (Δ=+.132, p=.017; Δ=+.198, p<.001) while cutting false discoveries by 2--3×. Controlled replays attribute the gain and its limits: neither reference routing nor deterministic citation expansion alone exceeds the local baseline; whole-document reading (29--65K tokens), two-round iterative retrieval, and even oracle minimal-certificate evidence all fail to reach LASER, localizing the bottleneck in typed obligation closure rather than evidence access. An authority-corruption replay shows source-link errors only cause abstention, while the only unsafe-warrant mechanism is mis-recording a Held erratum as Verified, confined to that erratum's family. LASER thus improves long-range dependency recovery while converting incomplete evidence and uncertain authority into explicit residual obligations rather than unsupported behavior.
Figures & tables
Figure 1: Local evidence versus certificate closure.
Figure 2: Sparse obligations behind a warranted edge.
Figure 4: Learned search ordering with checker-exclusive warrant.
Reader
Edge recovery
Proof quality
Decision
P
R
F1
Macro F1
Exact
Evid. F1
nDCG
Joint
FDR ↓
Abstain
Local context
Previous context
.647
.219
.327
.302
.297
.406
.412
.220
.353
.538
BM25 + source neighbors
.581
.285
.382
.313
.308
.486
.474
.242
.419
.462
Occurrence-centered context
.582
.258
.358
.374
.363
.559
.554
.308
.418
.407
Long-range evidence routing
Table 1: Behavior recovery under a matched 2K evidence budget.
Figure 5: Component ablation and evidence-budget response.
Removed component
Evaluation
Δ correct/closed
Δ expansions
Safety effect
Proof paths
25 applicable errata
−25 Closed@1
+0.87
No unsafe change
Checker feedback
277 controlled routes
0
0.00
0/167 false routes
Diverse beam
277 controlled routes
0
0.00
0/167 false routes
Validated union
277 controlled routes
0
+0.18 path; +0.90 atomic
0/167 false routes
Authority checking
20 paired families
−16 correct claims
N/A
+8 unsafe warrants
Table 2: Removal ablation across controlled evaluations.
Slice
n
Edge F1
Exact match
Joint Edge+Proof
Local
LASER
Δ
Local
LASER
Local
LASER
Dependency regime
Remote reference
28
.025
.726
+.701
.000
.643
.000
.321
Local route
63
.579
.688
+.109
.444
.587
.349
.460
Protocol
BGPv4
52
.188
.780
+.592
.173
.692
.096
.404
Table 3: Behavior recovery by dependency regime and protocol.
Routing
Expansion
Edge F1
Joint
Remote F1
Local
off
.382
.242
.025
Local
on
.382
.242
.025
Reference
off
.396
.253
.054
Reference
on
.700
.407
.719
Table 4: Attributing the remote-reference gains by replaying frozen completions through the same checker. Neither deterministic citation expansion without routing nor routing without the typed expansion closes remote references; the gain requires both components. Remote column: Edge F1 on the 28 reference-required families.
Method
Δ Joint
CI95
p
Δ Edge
Hybrid
+0.132 ∗
[0.033,0.231]
0.017
+0.256
Cue-gated
+0.110
[-0.011,0.220]
0.096
+0.230
Reference
+0.099
[-0.011,0.209]
0.121
+0.244
Expansion only
-0.066
[-0.143,0.011]
0.182
-0.061
Routing only
-0.055
[-0.132,0.022]
0.262
-0.023
Table 5: Family-paired LASER variants and attribution arms against the strongest local reader (occurrence-centered) on Joint Edge+Proof, 20,000 bootstrap and permutation rounds, family as the unit ( n=91 ); p : two-sided permutation test, ∗p<0.05 .
Protocol
Fam.
Fired
Req.
Prec.
Rec.
BGPv4
52
52
25
.48
1.00
DCCP
8
1
2
1.00
.50
TCP
31
0
1
.00
.00
All
91
53
28
.49
.93
Table 6: Public cue-gate transfer audit; Req.: reference required, Prec./Rec.: cue precision/recall. The gate is a fixed, prediction-blind pattern test; gold is used only to evaluate it. It misses two required references (one DCCP and one TCP complement variant) and over-fires on BGPv4, where reference routing never removes a correct local edge.
Reader (GLM-5.3, n=91 )
Evidence
Edge F1
Joint
Exact
Δ Joint
p
Iterative retrieval (2 rounds)
2K
.264
.176
.242
-0.099
0.065
Direct-full (whole document)
29–65K
.276
.209
.253
-0.066
0.240
Local BM25 + source neighbors
2K
.406
.275
.319
+0.000
1.000
Occurrence-centered (strongest local)
2K
.447
.275
.352
—
—
Oracle certificate evidence
≤ 2K
.496
.330
.407
+0.055
0.474
LASER (reference)
2K
.799
.484
.626
+0.209
< .001
Table 7: Full second-model replication on GLM-5.3 (all 91 families; same prompt, checker, and 2K budget). Δ Joint and p are family-paired against the strongest local reader. Whole-document reading, two-round iterative retrieval, and even oracle minimal-certificate evidence do not reach LASER: the bottleneck is typed obligation closure, not evidence access. Oracle uses private certificate evidence for selection only.
Corruption
Records
Unsafe
Lost
Held → Verified
8
16
0
Verified → Held
12
0
24
RFC binding corrupted
20
0
24
Stale text corrupted
20
0
24
Table 8: Joint authority/source-link corruption replay on the 20-family sealed authority suite (40 claims). Clean replay reproduces all 40 expected labels. Unsafe: unsafe warrants; Lost: lost closures. Source-link and binding corruption never creates an unsafe warrant; it degrades to Unknown . Mis-recording a Held erratum as Verified is the only unsafe-warrant mechanism, and it affects exactly the corrupted family.
Window
Warranted
Edges
Edge F1
Joint
± 64/32
59
92
.667
.418
± 128/64
67
121
—
—
± 192/96
68
122
.799
.484
± 384/192
None
None
.799
.484
± 768/384
68
122
—
—
no aliases
68
122
.799
.484
Table 9: Occurrence-checker sensitivity replay over frozen GLM completions; the deployed window is ± 384/192. Results saturate at ± 192/96 and are unchanged by the alias table or a doubled window; halving the window to ± 64/32 loses recall while keeping precision (.880), i.e., degradation is conservative.
LLM-based Lean proving systems increasingly organize a proof as a blueprint: a dependency graph of formal statements. We introduce BlueprintRepair, a repair interface that lets a model change this graph through ten schema-checked local operations. An operation names the node it edits, so the target theorem cannot be changed. Lean checks every applied change, and an accepted repair must declare every blueprint lemma its proof uses. We also construct BlueprintTrace, a benchmark of 142 controlled failures with complete accepted and rejected repair trajectories. We compare typed edits, exact source patches, and complete module rewrites under matched source, feedback, model, and budget, one episode per state and interface. With DeepSeek-V4-Flash, the three interfaces solve almost the same number of the benchmark's localized failures. Typed repair is the cheapest per solved state (patching is 1.30x as expensive, rewriting 2.06x), and within 10,000 completion tokens per task it reaches almost all of its final coverage, while both free-form interfaces are well behind. A second model, Qwen3.6-Flash, solves fewer states but keeps typed repair cheapest, puts it ahead on the proof-authoring states, and repeats the localized pattern.
Tools like Tamarin and ProVerif have achieved notable success in analyzing and verifying complex real-world protocols such as EMV, 5G, and WPA2, even detecting zero-day exploits. Despite these successes, verifying such protocols remains a time-consuming, challenging task, often requiring significant human effort and expertise. In this paper, we present a reinforcement learning (RL) framework inspired by AlphaZero and AlphaProof that implements a new style of proof search for Tamarin. We have developed a stateless API for Tamarin that acts as a classical RL environment. We guide a Monte Carlo Tree Search (MCTS) by a neural heuristic that learns from completed subproofs. We evaluate our framework on 16 case studies, ranging from classical protocol models to challenging state-of-the-art protocol models from recent publications. Our method finds more proofs automatically than Tamarin's standard search and produces shorter proofs than both the standard and human-engineered heuristics. Our pipeline is applicable out of the box to assist Tamarin users in active research, reducing the human effort required. Moreover, our standardized interface provides a programmatic way for users to interact with Tamarin. Finally, our work demonstrates the promising potential of adapting RL-based methods to the Tamarin domain.
Matthias Cosler, Cas Cremers, Bernd Finkbeiner +2
CISPA Helmholtz Center for Information Security, Saarbrücken, Germany · Technical University of Munich, Munich, Germany
Agent evaluations face two distinct evidentiary questions: whether a reported claim is recomputable from retained evidence (sufficiency), and whether the retained records cover the committed experiment set (coverage). Generic logs and hash-linked transcripts answer neither reliably. We introduce ClaimReceipt, a claim-relative receipt specification and selective verifier that binds typed transaction evidence to a signed experiment manifest and returns PASS, INVALID, or INCONCLUSIVE per claim. We freeze the specification before implementation (SHA-256 18d109...b81). On 1,392 historical buyer--seller records, a CR-2 verifier reproduces all five manually labeled audit verdicts, exactly replays 600 deterministic and 792 post-generation records, makes every one of 13 declared field groups non-redundant under tested ablations, and returns the expected result on 11/11 semantic faults with 0/8 false positives. We then run a separate prospective CR-3 epoch: 30 assignments are committed before inference, terminal receipts are signed and chained, and private evidence is encrypted for an auditor. Complete evidence yields coverage and accounting PASS; withholding one terminal receipt returns INCONCLUSIVE_COVERAGE, while withholding all private openings preserves coverage and protocol verification but makes economic claims inconclusive, exactly matching a preregistered prediction. Receipt instrumentation adds 0.021% of model-inference time and 9.9 KB per transaction. A specification-legibility probe indicates that our own frozen specification is not yet unambiguous to an independent reader. Claim verification therefore requires both claim-sufficient evidence and a committed universe against which omissions become visible.