Large language models (LLMs) have demonstrated strong capabilities in question answering, yet they still frequently suffer from hallucinations on knowledge-intensive tasks. Knowledge graphs (KGs) provide LLMs with structured, interpretable, and updatable factual grounding, making them a promising external knowledge source for reliable reasoning. However, existing LLM-guided graph reasoning methods typically rely on hop-wise greedy or beam-style pruning during evidence retrieval. Such local decision processes are inherently myopic: evidence that appears weak near the source may become crucial only after deeper graph context is explored, causing answer-critical branches to be discarded prematurely and making the reasoning chain difficult to recover. To address this limitation, we propose Foresight-over-Graph (FoG), a foresight-aware evidence retrieval framework for knowledge base question answering (KBQA). FoG iteratively constructs a question-relevant evidence subgraph and uses far-to-near feedback to guide path exploration, and maintains a compact memory subgraph to support continued exploration. Extensive experiments on widely used KBQA benchmarks demonstrate that FoG achieves state-of-the-art performance, with a particularly large improvement of 16.58% in Hit on CWQ, while also reducing LLM calls and token usage. Our code is available at https://github.com/yhong7/FoG .
Figures & tables
Figure 1 : Limitations of short-sighted hop-by-hop KG traversal and the intuition behind FoG.
Figure 2 : The overview of our proposed FoG.
Type
Method
LLM
WebQSP
CWQ
Hit
Hit@1
F1
Hit
Hit@1
F1
Non-LLM
NSM
-
-
74.30
-
-
48.80
-
SQALER
-
-
76.10
-
-
-
-
KGT5
-
-
56.10
-
-
36.50
-
UniKGQA
-
-
77.20
72.20
-
51.20
49.40
LLM-only
Zero-shot
GPT-4o
58.24
58.24
42.57
35.82
35.82
31.49
Table 1 : Overall results on WebQSP and CWQ. The † and ‡ symbols denote graph retrieval-based methods and LLM-guided graph agents method, respectively. The ⋆ indicates the best-performing baseline. The best and second-best results are highlighted in bold and underlined , respectively.
WebQSP
CWQ
Method
Hit
Hit@1
Hit@5
F1
Hit
Hit@1
Hit@5
F1
FoG w/o FSE
90.42 ↓1.21
88.78 ↓0.88
90.19 ↓1.38
77.41 ↓3.87
85.22 ↓1.81
73.95 ↓6.26
82.70 ↓2.10
62.98 ↓8.36
FoG w/o FFP
89.27 ↓2.36
86.92 ↓2.74
88.74 ↓2.83
73.85 ↓7.43
78.61 ↓8.42
68.43 ↓11.78
76.02 ↓8.78
57.34 ↓14.00
FoG w/o MSM
90.19 ↓1.44
88.89 ↓0.77
90.04 ↓1.53
77.19 ↓4.09
79.73 ↓7.30
67.12 ↓13.09
75.84 ↓8.96
56.15 ↓15.19
FoG
91.63
89.66
91.57
81.28
87.03
80.21
84.80
71.34
Table 2 : Ablation results on WebQSP and CWQ. FSE, FFP, and MSM denote Foresight Subgraph Expansion, Foresight Feedback Propagation, and Memory Subgraph Manager, respectively.
Figure 5
Method
WebQSP
CWQ
Hit
Hit@1
F1
Hit
Hit@1
F1
FoG w/ GNN
89.92
88.13
78.75
84.06
76.79
67.27
FoG w/ FFP
91.63
89.66
81.28
87.03
80.21
71.34
Table 3 : Comparison between standard GNN propagation and FFP.
Dataset
Method
Call
Input
Output
Total
WebQSP
ToG
15.9
6031.2
987.7
7018.9
PoG
9.0
5234.8
282.9
5517.7
FoG
2.3
1837.3
248.9
2086.2
CWQ
ToG
22.6
8182.9
1486.4
9669.4
PoG
13.3
7803.0
353.2
8156.2
FoG
2.8
3461.7
295.4
4585.1
Table 4 : Efficiency comparison on WebQSP and CWQ: average LLM calls and token usage per question.
Figure 5 : Effect of τp on knowledge retrieval capacity (CWQ and WebQSP).
Figure 6 : Error distribution of existing graph-guided reasoning failures.
Appendix figures & tables4 assets
Supplementary material from the paper’s appendix.
Appendix
Figure 7 : The prompt template of question decomposition prompt.
Figure 8 : The prompt template of memory subgraph management.
Figure 9 : Case study 1: failure modes of existing methods.
Figure 10 : Case study 2: workflow of our method FoG.
Harbin Institute of Technology, Harbin, China · Beijing University of Posts and Telecommunications, Beijing, China · Joint Laboratory of HIT and iFLYTEK, Beijing, China +2
1Key Laboratory of AI Safety, Institute of Computing Technology, Chinese Academy of Sciences · 2Shandong University · 3Shandong University-Weihai Research Institute of Industrial Technology