Graph Retrieval-Augmented Generation (GraphRAG) has remarkably enhanced large language models on complex reasoning by leveraging structured entity topologies. However, existing frameworks heavily rely on standard autoregressive language models where the nature of inherent sequential generation severely hinders overall inference efficiency. Inspired by Diffusion Language Models (DLMs) that offer massive parallelism via continuous refine-in-parallel decoding, we aim to accelerate GraphRAG in the discrete space. However, it remains non-trivial for two challenges. First, partially denoised drafts are highly dynamic and uncertain, making dynamic graph grounding non-trivial. Second, raw denoising states are inherently noisy and unstable, making synchronous graph retrieval and multi-hop aggregation computationally prohibitive. To this end, we present LADDER, a novel framework that bridges diffusion language modeling with GraphRAG through graph-guided parallel decoding. Specifically, (i) we propose an event-driven self-clocking retrieval, inspired by our key insight that 88% of target entities emerge early in the partially denoised state, leading final commitment by an average of 5.7-9.6 steps. This mechanism dynamically triggers graph retrieval only when the set of graph-linkable entities expands, yielding an asynchronous self-clocking policy that bypasses learned gates or heuristic thresholds. (ii) An incomplete-query graph propagation module is designed to process the newly emerging entity queries using a specialized graph foundation model, continuously aggregating multi-hop evidence to sharpen parallel predictions and accelerate overall decoding convergence. Extensive experiments on three challenging multi-hop QA benchmarks show that LADDER raises average exact match from 39.6% to 45.2% while achieving a 4.1x latency reduction.
Figures & tables
Figure 1: Entities surface before their spans commit, early enough to be useful. (a) Fraction of entities still uncommitted x steps after surfacing in Et . (b) Fraction of final entities already detected after a given trajectory fraction. (c) Fraction of triggers with all gold documents already in memory; labels give the mean remaining steps. Section 4.4 targets the coverage loss visible here on long reasoning chains.
Figure 2: Overview of Ladder . The DLM exposes a partially denoised response state at each step. Newly matched graph entities trigger GFM propagation and retrieve top- K evidence, which conditions subsequent denoising updates. Equation 12 defines the complete retriever-training objective.
HotpotQA
2WikiMultihopQA
MuSiQue
Method
R@2
R@5
R@7
R@2
R@5
R@7
R@2
R@5
R@7
Autoregressive agent (Qwen2.5-7B)
RAPTOR ( Sarthi et al., 2024 )
30.1
56.8
65.2
43.8
66.1
73.5
17.6
31.9
39.1
LightRAG ( Guo et al., 2025 )
28.4
54.1
62.8
41.6
63.4
71.1
16.1
29.8
36.9
GraphRAG ( Edge et al., 2025 )
26.7
51.2
60.1
39.5
60.6
68.4
14.9
27.6
34.7
HippoRAG 2 ( Gutiérrez et al., 2025a )
32.6
59.6
68.1
46.7
70.8
77.9
21.6
37.6
41.8
Table 1: End-to-end retrieval recall (%). Autoregressive baselines and GFM-RAG (IRCoT) use multi-round thought-level retrieval; LADDER uses event-driven refreshes within one denoising trajectory. Bold marks the best value in each column.
HotpotQA
2WikiMultihopQA
MuSiQue
Average
Method
EM
F1
Time ↓
EM
F1
Time ↓
EM
F1
Time ↓
EM
F1
Time ↓
Autoregressive agent (Qwen2.5-7B)
RAPTOR
41.0
46.9
6.35
58.3
63.5
6.72
15.3
27.0
7.18
38.2
45.8
6.75
LightRAG
36.5
43.1
7.46
54.2
59.8
7.83
13.8
23.8
8.51
34.8
42.2
7.93
GraphRAG
33.7
40.6
9.72
51.4
56.9
10.16
12.5
21.9
11.24
32.5
39.8
10.37
HippoRAG 2
43.8
50.1
10.86
62.4
67.2
11.42
17.6
30.4
12.67
41.3
49.2
11.65
Table 2: End-to-end multi-hop QA performance. Autoregressive baselines and GFM-RAG (IRCoT) use multi-round thought-level retrieval; LADDER uses event-driven refreshes within one denoising trajectory. EM and F1 are percentages; time is per-question wall-clock seconds ( ↓ ). Bold marks the best value in each column.
Figure 3: Main results. Top: average exact match versus average per-question latency across the three datasets (Table 2 ), quality increasing upward and latency leftward. Middle: quality, latency, and convergence steps as a function of the commitment threshold τc (the vertical line marks τc=0.85 ). Bottom: convergence under four evidence conditions on 2WikiMultihopQA, with (left) positions committed per denoising step and (right) the distribution of steps to convergence.
Table 6
Appendix figures & tables9 assets
Supplementary material from the paper’s appendix.
Appendix
Dataset
#Entities
Mean
Median
P90
Lead>0
2WikiMultihopQA
1,892
5.71
3.0
15
88.8%
HotpotQA
3,090
9.35
5.0
24
88.0%
MuSiQue
4,524
9.62
6.0
24
88.5%
Appendix
Table 5: Entity span-level lead time, in denoising steps, over 1,000 test questions per dataset. An entity is committed when all of its token positions are independently finalised by the sampler. Reproduced across nine shards (three per dataset), where the Lead>0 rate stays within 85.8% – 88.8% .
Dataset
Triggers/q
Useful-trigger rate
HotpotQA
3.17
53.3%
2WikiMultihopQA
2.07
79.0%
MuSiQue
4.35
42.6%
Appendix
Table 6: Trigger quality across benchmarks. Triggers/q is the mean number of real graph retrievals per question; Useful-trigger rate is the fraction, among triggers before full gold coverage, that increase the number of gold documents in the top- K list.
HotpotQA
2WikiMultihopQA
MuSiQue
Seed policy
R@7
EM
R@7
EM
R@7
EM
New-seed only ( ΔEt )
69.1
46.8
82.7
63.9
39.2
17.1
Cumulative ( Et )
72.9
49.4
86.9
67.4
41.4
18.8
Appendix
Table 7: Cumulative versus new-only seeding of the GNN start mask, per dataset, with the trigger and all other settings fixed. Cumulative ( Ladder ’s default) seeds every entity confirmed so far, Et ; new-seed uses only the entities that triggered the current event, ΔEt .
HotpotQA
2WikiMultihopQA
MuSiQue
Schedule
Time ↓
R@7
EM
F1
Time ↓
R@7
EM
F1
Time ↓
R@7
EM
F1
Retrieve-once ( t=0 )
2.28
61.9
42.1
51.8
1.75
71.2
57.6
62.4
3.62
33.6
13.9
24.1
Fixed, every 10 steps
2.41
66.8
45.4
55.1
1.85
76.8
61.2
66.3
3.80
36.2
15.4
26.3
Fixed, every 5 steps
2.67
69.7
47.2
57.0
2.05
80.5
63.8
69.1
4.21
38.5
16.8
28.2
Per-step
3.94
72.4
48.8
58.9
3.02
84.1
66.2
72.0
6.18
41.0
18.4
30.3
Random, firing rate 15%
2.52
66.1
44.8
54.5
1.94
76.1
60.6
65.7
3.97
36.8
15.9
26.9
Appendix
Table 8: Single-trajectory refresh schedules under the same Dream-7B backbone and full event-conditioned GFM retriever. Time is per-question end-to-end wall-clock seconds ( ↓ ). These ablations isolate refresh policy and use a different execution unit from the multi-round IRCoT baseline in Table 2 . Bold marks the best value in each column.
HotpotQA
2WikiMultihopQA
MuSiQue
Objective
R@7
R disc
EM
F1
R@7
R disc
EM
F1
R@7
R disc
EM
F1
(1)
Lretrieve only
66.4
42.7
45.2
54.0
78.9
50.2
63.4
68.5
40.6
26.1
17.2
30.0
(2)
+Llookahead
70.8
53.1
48.1
57.8
84.2
62.8
66.1
71.8
41.1
33.4
18.3
30.4
(3)
Full ( Ladder )
72.9
55.8
49.4
59.6
86.9
65.7
67.4
73.5
41.4
34.9
18.8
30.7
(4)
Full −Lanchor
71.2
54.2
48.3
58.1
85.1
64.0
66.3
72.1
39.8
32.7
17.9
29.6
Appendix
Table 9: Training objectives, per dataset. Bold marks the best value in each column.
Dataset
K
EM
F1
R@ K (final)
Triggers/q
Time ↓
2WikiMultihopQA
5
59.0
64.6
80.1
2.07
1.56
7
67.4
73.5
86.9
2.07
1.88
10
73.0
76.3
92.1
2.08
2.70
15
71.0
75.7
93.9
2.09
3.55
MuSiQue
5
15.0
25.1
37.2
4.14
3.17
7
18.8
30.7
41.4
4.35
3.95
Appendix
Table 10: Sensitivity of Ladder to the retrieval budget K on 2WikiMultihopQA and MuSiQue, with the generator, retriever, and trigger policy held fixed. EM and F1 are percentages; R@ K is the final-state recall at the evaluated budget; Triggers/q is the mean number of real graph retrievals per question; latency is per-question wall-clock seconds ( ↓ ).
Dataset
Evidence
Mean
SD
Median
Paired
2WikiMultihopQA
No retrieval
41.70
15.68
36.0
—
Random passages
24.05
11.30
23.0
—
Graph retrieval
15.24
12.57
11.0
—
Oracle evidence
12.76
8.43
10.0
97%
HotpotQA
No retrieval
37.91
11.37
37.0
—
Random passages
31.00
13.54
31.0
—
Appendix
Table 11: Steps to convergence by evidence condition. Paired is the share of questions on which oracle evidence converges in strictly fewer steps than no retrieval.
Figure 4: Convergence on HotpotQA, in the format of Figure 3 .
Step
Event
Retrieval
Denoising state ut
Gold
0
—
fwd
(question only)
1/2
1
{college, Oxford, University}
fwd
Charlotte Brewer is at H Soulsford College, Oxford. (2): …
1/2
2
—
✓
Charlotte Brewer is at professor offord College, Oxford. (2): …
1/2
3
—
✓
(wording drifts further; entity set unchanged)
1/2
4 – 5
—
∅
(proxy text identical to step 3 )
1/2
6
—
✓
Charlotte Brewer is at professor offord College, Oxford. (2): … (still wrong)
1/2
Appendix
Table 12: Case study: “What larger educational institution is the university where Charlotte Brewer went, a part of?” Gold documents: Charlotte Brewer and Hertford College, Oxford . Bold tokens in the denoising state are independently committed positions; the remainder is the proxy’s argmax fill of still-masked positions. GNN marks a forward pass; ✓ marks a scheduled retrieval call served by cached graph scores; and ∅ marks a step where the unchanged query skips the retrieval call. Gold is the number of gold documents in the retrieved top- K out of the total.