MobileKGQA: On-Device KGQA System on Dynamic Mobile Environments
Authors: Junyong Ahn
Hyeongrok Han
Bong Gyun Kang
Jisoo Mok
Byunghan Lee
Sungroh Yoon
Venue: ICLR 2026 Poster (2026)
Keywords: Knowledge Graph Question Answering
Large Language Model
Primary area: applications to computer vision, audio, language, and other modalities

Abstract: Developing a mobile system capable of generating responses based on stored user data is a crucial challenge. Since user data is stored in the form of Knowledge Graphs, the field of knowledge graph question answering (KGQA) presents a promising avenue towards addressing this problem. However, existing KGQA systems face two critical limitations that preclude their on-device deployment: resource constraints and the inability to handle data accumulation. Therefore, we propose MobileKGQA, the first on-device KGQA system capable of adapting to evolving databases with minimal resource demands. MobileKGQA significantly reduces computational overhead through embedding hashing. Moreover, it successfully adapts to evolving databases under resource constraints through a novel annotation generation method. Its mobile applicability is validated on the NVIDIA Jetson Orin Nano edge-device platform, achieving 20.3% higher performance while using only 30.4% of the energy consumed by the SOTA (state-of-the-art). On standard KGQA benchmarks, using just 7.2% of the computation and 9% of the parameters, MobileKGQA demonstrates performance that is empirically indistinguishable from the SOTA and outperforms baselines under distribution shift scenarios.

Published as a conference paper at ICLR 2026
MOBILEKGQA: ON-DEVICE KGQA SYSTEM ON DYNAMIC MOBILE ENVIRONMENTS
Junyong Ahn', Hyeongrok Han?, Bong Gyun Kang', Jisoo Mok
Byunghan Lee Sungroh Yoon 1.2.f 'nteeriisciplinary Program in Artificial Intelligence, Scoul National University *Department of ECE, Seoul National University
$ DDepartment of EE&CS, Daegu Gyeongbuk Institute of Science and Technology *Department of EE, Scoul National University Science and Technology (t (t1d215, gksgudpp, luckypanda, sryoon) @snu. ac kr jmok @ edgist. itststt ac aee.acc.k. kr bhleeêseoul wewe@eseolleehh....m www..et................... 'Corresponding author
ABSTRACT
Developing a mobile system capable of generating responses based on stored user data is a crucial challenge. Since user data is stored in the form of Knowl- edge Graphs, the field of knowledge graph question answering (KGQA) presents a promising avenue towards addressing this problem. However, existing KGQA systems face two critical limitations that preclude their on-device deployment: resource constraints and the inability to handle data accumulation. Therefore, we propose MobileKGQA, the first on-device KGQA system capable of adapt- ing evolving databases with minimal resource demands. MobileKGQA signif- icantly reduces computational overhead through embedding hashing. Moreover, it successfully adapts to evolving databases under resource constraints through a novel annotation generation method. Its mobile applicability is validated on the NVIDIA Jetson Orin Nano edge-device platform, achieving 20.3% % higher per- formance while using only 30.4% of the energy consumed by the SOTA (state- of-the-art). On standard KGQA benchmarks, using just 7.2% of the computa- tion and 9% of the parameters. MobileKGQA demonstrates performance that is empirically indistinguishable from the SOTA and outperforms baselines under distribution shift scenarios.
INTRODUCTION
In mobile devices, vast amount of user information, such their social interactions and activity logs, is stored in knowledge graph (KG) databases (Novović et al., 2017). These databases con- tinuously accumulate new user information. By leveraging this user information in KG databases, on-device Large Language Models (LLMs) can deliver more customized responses to the user.
Knowledge Graph Question Answering (KGQA) (Sun al.) 2018 2019) thus naturally rises as a promising approach to improve user experience of on-device LLMs through the integration of KG databases. KGQA aims provide answers to queries by retrieving the information from KGs; recently, it has been shown to be effective at addressing limitations of LLMs, such as the difficulty in handling new information (Pan al., 2024), unstable reasoning (Luo al.) 2024b). the lack of explainability (Luo et al.] 2024c), and hallucinations (Wang et al.) 2023a Martino et al., 2023). Extending these advantages of KGQA systems to on-device I.LMss enables the integration of the information stored in KG databases, such that on-device LLMs better align with the user's demands.
Although KGQA systems hold immense potential to enhance the quality of on-device LLMs, they face critical limitation that precludes their deployment in mobile environments: poor adaptability to evolving KG databases. As users interact with their mobile devices, new information continu- ously added to the KG databases (c.g., when emerging trends introduce words when change of location updates location- based preferences). This continuous modification can induce substan- tial distribution shifts, and some studies have reported sharp shifts in real user data (Desiderio al.

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Table 1: Limitations of various KGQA models with respect mobile deployment. Favorable char- acteristics are highlighted in red (adaptability: adaptability to the distribution shift).
category
model
computtation
adaptability
Inteney tuning parameters
RaG-KBOA (re et
2022
443M
X
w/o LLM
De:AF Yo at.
20233
low 848M
X
L.M
RoG (uo
2024c
low
7B
X
(iintured)
CQA (Lub ci al. 2024a
low
7B
X
KB-BINDER [Li et 2023a
high
w/ LLM (frozen)
ToG (Sun al. 2024
high
O
MoDBcKGQA
low
0.136M
2023 Miller et al. 2015). Moreover, these shifts have also been shown to cause performance degra- dation of KGQA systems over time (Dai et al., (2255) (also see Table Therefore, current systems require frequent model retraining, which is typically performed on centralized servers, raising seri- ous concerns for data privacy.
Given the challenges of adaptability and data privacy, developing a KGQA system that can be trained directly on mobile devices is a key research topic. However, no such system has been proposed (Table and current mainstream approaches still rely on tuning billions of parameters. While some in-context learning (ICL)-based methods do not require training, they involve repeatedly invoking LLMs, resulting in high eeency-mmking them unsuitable as well. To address this challenge, we propose MobileKGQA, the first KGQA system that can be deployed and trained directly on mobile devices. This work is driven by two key research questions: (1) How can we reduce the training resources required by current KGQA systems to a level feasible on edge devices? and (2) How can generate annotations for supervised training without data leakage?
In response to the first question, MobileKGQA introduces a retrieval mechanism based on hash codes. This mechanism consists of two main components: the hashing module and the reason- ing module. The hashing module significantly reduces computational cost by compressing high- dimensional, floating-poin poaii LLM embeddings into binary hash codes h E {-1, 1, 1). It achieves this by maximizing the mutual information between the original embedding and the hash code to pre- serve semantic content. As result, unlike conventional approaches, MobileKGQA avoids the need to store gigabyte-scale embeddings for few megabytes of text (Li et al. 2025) or to regenerate em- beddings during training (Mavromatis & Karypis (025). The resulting hash codes are then passed to the reasoning module, which infers answer candidates with minimal computational and memory overhead. These candidates are used to retrieve the most plausible reasoning paths corresponding to the input question, allowing on-device I.LMs to generate responses grounded in the structured knowledge stored in the KG database. By leveraging this hashing- baased architecture, MobileKGQA dramatically reduces the computation and parameter count, paving the way for on-device adaptation.
To address the second research question-the absence of ground truth annotations (i.e., question- answer pairs) for newly accumulated knowledge the system must generate annotations through complex reasoning over recently integrated information. Previous approaches have attempted to solve this problem by employing multiple L.M invocations (Jiang et al. (023b) relying on cloud- based LLMs (Li et al. 2024). However, such strategies are unsuitable for mobile environments due to constraints in computational resources and concerns over data privacy. MobileKGQA overcomes these limitations through a novel annotation generation process based on sequential reasoning. This method incrementally integrates structured knowledge to construct logically coherent and semanti- cally consistent questions. By decomposing complex reasoning into simpler, interpretable steps, it not only enhances the quality of generated annotations but also significantly reduces the number of output tokens required, making it well-suited for resource-constrained mobile settings.
To thoroughly evaluate our proposed MobileKGQA model, we conducted experiments on the NVIDIA Jetson Orin Nano platform, which closely approximates a real mobile environment. The results show that MobileKGQA is the only model whose optimal configuration remains feasible on mobile devices, delivering 20.3% higher performance while consuming only 30.4% of the energy. In addition, evaluations on two standard KGQA benchmarks demonstrate that MobileKGQ. achieves performance comparable to the latest state-of-the-an art lightweight models while requiring just 7.2% of the computation and 9% of the parameters. Moreover, it exhibits superior adaptability to evolving data, consistently outperforming all baselines.
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2
RELATED WORK
2.1 KNOWLEDGE GRAPH QUESTION ANSWERING
Conventional KGQA research mainly falls into categories (Luo et al. (2022) ): Information Re-
trieval (IR)-based and Semantic Parsing (SP)-based methods. More recently, LLM-base approaches have emerged, Additional details not covered in this section are presented in Appendix B
IR-based methods construct subgraphs from knowledge graphs to extract information relevant to a given question. Then, the model learns reason about the answer using the retrieved graph and the question (Miller et al. 22166] Sun et al. (019). Research has focused on improving the reasoning process. For example, NSM (He et al.] (021) applied knowledge distillation method to provide the supervision signals in intermediate steps, while EmbedKGQA (Saxena et al. (020) used graph embeddings to improve reasoning performance on sparse graphs with many missing links.
SP-based methods infer answers by converting given questions to query languages (e.g.. SPARQL) that are compatible with a knowledge graph. The query language obtained either through query graph generation YYhh et al. 2016 Lan & Jiang 2in0]Jiang et al. 2023c) or by seq2seq model (Shu cet al. 2022 Yu et al., (023). For instance, QGG (Lan & Jiang 2020) generates a query subgraph
by taking extend, connect, and aggregate actions at each step. On the other hand, DecAF (Yu et al.
2023) leverages a seq2seq model-based joint decoding to generate both query language and natural language answers.
LLM-based methods can be categorized into two groups The first category employs In-context Learning (ICL), aiming retrieve relevant data. It utilizes prompt engineering and CoT reason- ing (Li et al. 2023a [Snn et al.) 2024; Gu et al. 2023). The second category fine-tunes LLMs to generate query languages reasoning paths from given queries. (Luo al. 2024a C).
2.2 KGQA FOR MOBILE ENVIRONMENT
Despite advances in KGQA systems, existing models face considerable challenges when deployed in mobile environments. IR- IR-based models, for instance, require high computation as they need to store high-dimensional embeddings (Saxena et al. 2020) and require repetitive computations for subgraph construction (Zhang et al. 2022). On the other hand, SP-based models struggle with query language generation, often producing non-executable logical forms when new relations introduced to the
database (Xie et al.] 2022 Zhang et al., 2023). Lastly, LLM-based models utilizing ICL exhibit high latency, driven by processes such multiple CoT reasoning (Sun et al.] (024) iterative logical form generation et. 2023a). Additionally, models requiring LLM fine-tuning tuning encounter significant computational burdens, especially under distribution shifts (Luo et al. 2024a
PRELIMINARY
Knowledge Graphs. A knowledge graph is defined as set of triples G r(h,r, h,t V.r R), where represents entity set and represents the relation set. triple (h, r, represents a knowledge where relation exists between the head entity h and the tail entity 1.
KGQA and Distribution Shift. Knowledge Graph Question Answering (KGQA) task that aims to answer natural language queries using information stored in knowledge graphs. Formally, given a query and a knowledge graph G, KGQA seeks to learn a function of that generates the correct answer
a a= f=
Following scenario commonly occurring in mobile devices, where new data is gradually added over time, we define the distribution shift in knowledge graphs as the accumulation of new data from different distribution:
Gaified
Guri
Gnew
Gun Pori & Gnew Pnew
where Gon represents the original knowledge graph following original distribution Pani and Gnew represents the newly introduced knowledge graph following different distribution Pre.. The function
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(1) Hashing Phase
Preserve Cosine Similarity
4, Which countries border the U.S.?
g=((h,r,0))
EM Embedding Space
Hashing Space
Colerdo Rier
wwsgrashy.feeerrree counties
Canasa
Embedding Model
4q
00.48
Hashing Module
h4, -113
localion portally pontaincdty
Z4
h4p
locaton couuntry form growrnmet
Proniderial
basc poodions
form, www.mmert Lourniee
Zr, 22.562.56
2.56
1.61
hr.
odvireerrtve_.ooo www...ddiisiiiaatteooooo porent
base
Earth
Kirwa
(2) Retrieval Phase
a Candicales
Answer
base locations countriss wis
Reasoning Module
Z4
hg -hr
location.co wttpiionccnntn///oooommo goverment
govermennt.coutiig
Zy
Final Answer
location lncation pertilly.comtairrdtty gegeraairriiiityyoggggggy river wwwpbbbweiiiieeeeee countrres
Figure 1: Pipeline of the hashing and retrieval phase: 1) The hashing module is trained to transform embeddings of questions and relations into binary hash codes while preserving their semantic infor- mation. 2) The reasoning module selects answer candidates and specifies query-relevant paths. 3) The LLM selects the optimal reasoning path from the retrieved paths and generates answers. E.M. and H.M. indicate embedding model and hashing module.
1.M is learned using dataset Dirain = [((Ti, (z) )on) i=1 Gahinea) and tested using dataset Diest H(gz. (g)atine / Ishimee).
Following prior research (Wang et al. 2023b Luo al. 2024cp, we refer question entities eg as words that are mentioned in the question, and assume that these entities are connected to answers through relations in the graph. Among the paths between question entities and answers, we refer reasoning paths as the paths that exhibit logical coherence with the given question.
METHODS
MobileKGQA consists of three phases: (1) hashing phase that minimizes the computational cost of the reasoning module, (2) retrieval phase that enables efficient data retrieval, and (3) an adaptation phase that addresses distribution shift, which operates when new data is introduced and the database is updated. The complete framework is illustrated in Figure and Our code is available at https: http:////oyyaaal/m/mmmeeeeoo
4.1 HASHING PHASE
In the hashing phase, we project embeddings obtained from the embedding model (EM) into low- dimensional hash codes while retaining semantic information. To achieve this, construct the embedding set Z for the questions Q and the relations R of the knowledge graph.
Z {z EM(x), x E Q o R
Since hashing involves discrete optimization over binary codes, we approximate this transformation two-stage mapping for tractable optimization: low-dimensional mapping & followed by binarization mapping v.
Maximize; I (z, subject to; ((((=))
To implement this transformation, we apply a low-dimensional mapping & using multi-layer per- ceptron (MLP). Then, batch normalization (BN) to enforce zero-centered distributions, which promotes a more uniform spread in the hashing space and decreases the chances that different rep- resentations share identical codes.
d (anL(BN((LLP h= sgn(d)
(2)
Based the above implementation, we propose the following notation and assumption.
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Notation. Let E RR be embedding and h {-1,1}4 hash code. We express them in polar coordinates as z = (rz. 02) and h (rk, 0n., where r denotes the norm and @ the orientation. Assumption (Independence Assumption), The norm of embeddings vs is independent of both the orientation of embeddings 0- and the orientation of hash codes On. i.e.,
P(r:, 02) ((O=)) and P(r., 0%) P(rr))
Given empirical studies L.LM representation (Pennington et al. 2014) and the feature-wise nor- malization of batch normalization, these assumptions provide reasonable approximation. Building on this, we formally present the conditions for maximizing mutual information.
Theorem (Conditions for Mutual Information Maximization). Suppose Assumption 1 holds, and both á and ve preserve the cosine similarity between any pair of embeddings in Z. Then, the mutual information (2, h) is maximized.
The proof of Theorem is provided in Appendix C To optimize our hashing module, we employ a log-ratio loss (Kim et al. 2019), which was originally proposed for regression problems in the computer vision domain. In our implementation, we establish the cosine similarities between em- beddings before each mapping regression targets, thereby training the mapping preserve cosine similarity. Specifically, the loss function formulated as follows, where a represents a hyperpa- rameter balance the mappings, sim serves cosine similarity function, and l denotes the log-ratio loss:
Lhash Lø(Zay Ziy 2z)) (day di; dl))
G((a,i7) log sim(f(a).f()) log sim(f(a).f()) sim((a.)
sim(a.
(4)
4.2 RETRIEVAL PHASE
During the retrieval phase, the reasoning module predicts answer candidates from the knowledge graph. It then extracts the paths linking the query entities to these candidates, effectively restrict- ing the range of plausible reasoning. The retrieved paths subsequently provided to the L.M to produce the final answer.
Reasoning Module To implement an answer node selection process effectively, we implement a reasoning module based on GNN, leveraging the ReaRev architecture (Mavromatis & Karypis 2022) which effectively utilizes graph structure as an inductive bias. This reasoning module RM takes three inputs the query hash code hy. the relation hash code hy, and the graph and generates node representations H of the graph.
H (ha) (Mg. hr:
(5)
The reasoning module is trained through KL-divergence loss, with answer nodes labels Q.
Treasson DKa(Q) softmax(WHH)
(6)
Given query entities eg and answer candidates { we retrieve the shortest paths set P that links the e, to åj. and LLM generates the final answer based on these paths.
p
{p"
a a
While each path nd captures meaningful relationships between the et and a it may not necessarily align with the ground-trun truth reasoning path. However, prior studies (Li et al.) 2025) Mavromatis & Karypis 2025 Luo et al., 2024c) demonstrated that the shortest paths can serve heuristic. Moreover, given the exponential growth of the search space with path length, focusing on the shortest paths can be practical way to optimize the retrieval budget.
effective
4.3 ADAPTING TO DISTRIBUTION SHIFTS
In the adaptation phase, annotation generation method enables efficient multi-hop reasoning by breaking complex reasoning into smaller steps. These substeps provide a clear structure for effective reasoning, reducing unnecessary token generation. The entire process is illustrated in Figure
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Sample and Filter
token-to-characte ratio
Graph
Kourtna Kardsehian people person Panelope
pecpleeparion person gender Non-entaypted (Keep)
1200 Decationlocation events Bamako person.place. birth Ournar Kone
Encrysted (Discard)
sampled quistion andly e, :Koutney Kardeahian
sampled answer a Penetope Sootland Disick
Kourtney Kardoshan -s pessle.person person.chidren - Penelope Scotand Disic
Penelope Scotand Disick caughter Kourtney Kardashian.
Perwlope Scotland Divikk va people person pender up Female
3 Merge
④
Replace
2
Verbalize
"eee person" the dauggttr of Kourtney Kardeshien.
5 Generate and Refine
Aircow
Kouriney Kandashian named Penelope
Penelope
female.
doughter Kourtney
Figure 2:
Adaptation Pipeline: stepwise generation of annotations. (Details in Sec. 4.3
Step 1. Sample and Filter Reasoning Paths
Reasoning paths are sampled randomly from the knowledge graph. Paths containing encrypted enti- ties are filtered out, identified by high token-to-character ratio after tokenization, which indicates non-natural character sequences.
Step 2. Verbalize Filtered Triples
Filtered triples are converted into natural language sentences, allowing LLMs to process them more effectively as text rather than structured data.
Step Construct a Merged Sentence
The verbalized triples are combined into single sentence describing the answer. This process helps the model identify relationships between the answer and other information, providing useful expres- sions for describing them before generating questions, enhancing the final output.
Step Replace Answer with Placeholder
The answer in the merged sentence is replaced with type-indicating placeholder, preventing its direct inclusion.
Step 5. Generate and Refine Question
The LLM generates a question from the masked sentence. If the question lacks the question en- tity-the starting point for reasoning- refinement step is applied to ensure its inclusion. The gen- erated annotations are then used to optimize the hashing and reasoning modules through supervised loss functions (Equations and (). Prompts for MobileKGQA and baseline methods, along with
detailed annotation examples, are in Appendices K
and M
5 EXPERIMENTS
Datasets. Following previous works (Mavromatis & Karypis 2022: Sun et al. 2024: Jiang et al.
2023a Luo al. 2024a), we tested our model with two standard KGQA datasets: WebQSP Yih et al. 2016), CWQ (Talmor & Berant 2018). More details on the datasets are in Appendix E
Baselines. We compared our method with baselines from categories: 1) IR-based model:
ReaRev (Mavromatis & Karypis (022). Nutrea (Choi et al.]
2023); 2) SP-based model: Uni-
fiedSKG (Xie et al. 2022): 3) LLM-based model: (fine-tuning): RoG (Luo et al.) 2024c). GNN-
RAG (Mavromatis & Karypis 2025), SubgraphRACG (Li et al. 2025); 4) LLM-based (ICL) model:
StructGPT (Jiang et al. 2023a). ToG (Sun et al. 2024). See Appendices F and D for more details.
Evaluation Metrics. Following the prior works (Mavromatis & Karypis 2025 Li et al. 2025), measured the performance of our method with Hit and Fl -ioocrre [
Additionally the hashing module
performance was evaluated using mAP, a widely used metric in hashing task (Luo et al. 2023). More details about the metric are in Appendix
5.1 PERFORMANCE WITHOUT DISTRIBUTION SHIFTS
To assess MobileKGQA both training and inference, we conducted an analysis against existing KGQA models, focusing computational resource consumption (see Table 2a) and performance (see Table 2b). One of the most notable observations is that MobileKGQA not only requires the shortest training time among leading lightweight models but also uses only 9% of the parameters
Some haselines claim Hits@1 for evaluation hut actually Hit, stated in Li et al. 2025

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Published as a conference paper at ICLR 2026
and 7.2% of the computational cost (measured under the GTE-large setting). Furthermore, it is the second most optimal model in terms of memory and storage efficiency.
Another key advantage of MobileKGQA is its consistent computational requirements, irrespective of the embedding model's size. Unlike MobilcKGQA, the current SOTA lightweight model, GNN- RAG, must repeatedly generate embeddings during training. Consequently, the embedding model size increases, GNN-RAG's computational cost can far exceed MobileKGQA's by a factor 41.8 (as measured with GTE-Qwen2-I.5D In stark contrast, MobileKGQA maintains a constant compu- tational cost regardless of the embedding model, enabling it to leverage large-scale embedding mod- els which are rapidly advancing for multilingual support and performance enhancement (Hugging Face 2025 even in mobile environments.
In terms of performance, MobileKGQA also demonstrates its effectiveness across various em- bedding models and on-device LLMs (Table (26). It shows that MobileKGQA achieves performance that empirically indistinguishable from the cur- rent SOTA model, GNN-RAG. Specifically, across all combinations reported, MobileKGQA achieved an average of 0.46 higher Hit and 0.16 lower Fl score compared to GNN-RAG. Even in the worst- case scenario, MobileKGQA recorded only 0.2 lower Hit and 1.2 lower Fl score. These results indicate that the performance difference between MobileKGQA and GNN-RAG is practically neg- ligible. Moreover, this consistency suggests that the hashing module in MobileKGQA can effec- tively compress embeddings from various embed- ding models. Compared to ICL-based methods, MobileKGQA demonstrated superior latency and performance across a wide range of LLMs, from mobile (0.5B) to desktop (14B) LLMs.
Comparative analysis of training cost and performance of various KGQA mod- els on the WebQSP (P: tuning parameters, Disk: storage, LMsr (110M), f: GTE- large (434M). f: GTE- Qwen2- red: best, underline: aecond-best MobileKGQA uti- lized 256bit hash code and its cost is in- dependent of the embeddings. Experimental results on CWQ are in Appendix
Table 2:
(a) Computational cost for training
Model
PIM)/FLOPs T(h) VRAM(GB) Disk(GB)
NuTrea*
1.2
10.32
3.3
8.6
UnifiedSKG
2850
10
0OM
3
RoG
6738
10"
0OM
32
GNN-RAG*
0.8
2.4
1.85
4.9
0.54
GNN-RAGf
8.3
2.10
0.54
GNN-RAGi
1.7
25.1
2.22
26.3
0.54
SubgraphRAGf
4.2
5,3
1.74
1.8
18.12
MobileKGQA
0.1
0.6
1.63
3.5
0.6
(b) Performance on inference
Base Model
Model
Token Time(s)
Hit F1
To more rigorously evaluate the mobile applica- bility of MobileKGQA. we evaluated it on the NVIDIA Jetson Orin Nano, which provides resource-constrained environment closely resem- bling real-world edge-device conditions (Table 3 Under the platform, the 256-bit hash-code-based MobileKGQA could be trained with its optimal hy- perparameter configuration, but GNN-RAG could not be trained with the reported optimal config- uration due to computational limitations. Accord- ingly, we tuned GNN-RAG to train within the con- straints and selected DI of WebQSP (see Section 5.2) as a dataset to improve the training stability of GNN-RAG (Details in the Appendix H). Ex- perimental results demonstrate that MobileKGQA can be trained using only 30.6% of the training time and 30.4% of the energy consumption required by GNN-RAG. This efficiency allows on-device LLM to incorporate new information more rapidly, while also reducing battery drain and enhancing device safety. Moreover, unlike GNN-RAG, Mo- bileKGQA completed training in both 7W and 15W modes without triggering any throttling, underscor- ing its stability for mobile deployment. Addition- ally. MobileKGQA exhibits higher GPU utilization and temperature, attributable to the lightweight hash codes that allow faster data loading from the storage and computation. In terms of performance, MobileKGQA further achieved 20.3% improvement over ENN-RAG, showing that simply down-
Sentence
ReaRev
N/A
0.08
N/A 70.9
NuTrea
0.12
72.7
Ta-le
UnifiedSKG
N/A
OOM
N/A 73.9
Ilama (7B)
RoG
88.5
0OM
85.7
70.8
StructGPT
56.8
0.67
24.6
ToG
2379.2
27.1
12.6
N/A
Qwen
GNN RAG
68.1
0.74
68.2
43.8
(0.5B, 4bit)
SubgrahhR
100.9
MobileKGQA
68.6
0.72
70.6
44.2
StructGPT
48.6
1.02
53.8
N/A
ToG
2816.3
53.7
31.6
GNN-RAG
22.6
0.38
79.5 66.7
GNN-RAG
21.9
0.37
80.0
Gemma (.6B, 4bit)
GNN-RAGE
21.6
0.36
78.5 65.6
SubgraphRAGt
218.1
3.72
41.0
12.3
MobilcGGQA
23,5
0.39
79.5
65.7
MohileKCQQA
22.7
0.37
79.8 67.0
MohileKGQA
21.6
0.36
79.8
67.0
StructGPT
84.2
2.68
53.7
TuG
2426.5
65.2
51.4
N/A
Llama 3.1 (8B, 4bi0)
RAG
33.8
1.19
80.1
62.0
SubgrphRRAH
151.5
4.97
57.7
42.8
MobileKGQA
33,4
1.13
80.4
60.8
StructGPT
341.6
16.92
46.4
ToG
2458.8
96.0
38.2
N/A
Phi (14B, 4hit)
GNN-RAG*
49.2
2.43
82.1
62.3
SubgraphRAGtt
268.6
14.1
54,5
42.1
MobileKGQA*
49,7
2.46
83.0
62.2

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Table 3: Resource usage and KGQA performance of MobileKGQA vs. GNN-RAG under 7W and 15W power modes on the NVIDIA Jetson platform (*: Whether the best configuration determined on server can be applied, t: CPU-GPU unified memory, f: reasoning- module performance, Mo- bileKGQA utilized 256-bit bit hash codes. Red indicates MobileKGQA outperforms, blue otherwise.)
Mode
Model
Best Config
Training Time (h)
RAME (GB)
(P C))
C))
Energy (Wh)
Throttle
RM
Usage
Temp.
Usage
Temp.
Hit
F1
GNN RAG
impossible
5.9
7.3
25.7
49.3
55.7
49.7
28.7
No
61.7
54.3
MobileKOIAA
possible
2.1
5.0
70.1
11.8
74.2
62.9
comparison
Ours better
64.4%%
31.5%
| 8.6% | 0.8%
25.9% 1 0.8%1 1 58.9% ]
Tic
20.3%
15.8%
GNN-RAG
impossible
3.6
7.3
19.9
50.9
50.4
50.0
22.5
Yes
61.7
54.3
15W
MobileKGQA
possible
1.1
5.0
18.4
49.6
55,5
51.4
6.84
No
74.2
62.9
compurison
Ours better
69.4%
31,5%]
|
7.5%
2.6%
10.1%
2.8%
69.6%
20.3%
15.8%
sizing by changing hyperparameters of the model cannot preserve reasoning module performance. These findings highlight the necessity of the proposed hashing strategy for the KGQA system on mobile devices.
5.2 PERFORMANCE WITH DISTRIBUTION SHIFTS
To evaluate the proposed adaptation method under distribution shift caused by data accumulation, designed the following experiment. The dataset was split into three distinct domains, D1, D2, and D3, each further divided into train, validation, and test sets (details in Appendix[].. The domain shift was simulated three stages: the model was first trained on DI only; then adapted using annotations from DI and graph data from DI and D2; and finally adapted again using DI annotations and graph data from all domains. Table demonstrates that the proposed adaptation method effective in a domain shift scenario. The model was first trained only on DI using the train set of D1, and the evaluation results on the test set of DI are shown in the first column (S1). The first adaptation then performed on newly accumulated D>2 using query-answer pairs generated through annotation generation (see Section 4.3). The results of the adapted model on the test set of D1 and the test set of D2 are shown in the second (S (PA)) and third (T2) columns, respectively. The results of the above model using both the test set of DI and D2 are shown in the fourth (2) column. Subsequently, the second adaptation performance of model on the test sets DI and D2, as well as the test set of D3, are shown in the fifth (S (PA)) and sixth (Tp) columns, respectively. Finally, the results obtained from evaluating the model on the test sets of D1, D2, and D3 are presented in the last column (total).
performed on the newly accumulated D3. After the second adaptation, the
Table 4: Hit score of various KGQA models on graph datasets for WebQSP and CWQ datasets across different domains based on Gemma 2 (2B, 4bit) model. (red: best results)
Dataset
WebQSP
CWQ
S(D1) T(D2)
S(D1+D2) T(D3)
S(DI) T(D2)
S(DI+D2) T(D3)
Model
8, (PA)) T2
S12 S12(PA) Ts total
S: (PA)) T.
S12 S1²(PA) Ts total
ToG
31.3
30.9
31.1
32.1
31.6
29.3
32.1
30.9
29.9 30.6
ReaRev
76.2
30.0
58.1
20.4
41.4
41.0
15.7
26.4
14.7 22.4
Nu'ree
69.6
N/A
25.4
52.3
N/A
12.5
34.7
37.3
N/A
9.62
21.3
N/A
23,5 22.1
GNN-RAG
79.2
36.4
62.5
27.4
47.0
57.9
31.6
42.7
32.1 39.1
SubgraphRAAC AG
22.9
15.6
20.0
14.3
17.5
30,1
10.3
18.7
5.1 14.0
MobileKGQA
79.1
78.9
43.2
64.9
63.4
30.4
48.8
57.8
60.2
34.0
45.1
45.6
32,5
41.0
Examining the experimental results, we find that MobileKGQA outperforms the baselines, high- lighting the effectiveness of our annotation generation method. Notably, in the CWQ dataset, per- formance on the original domain improved after adaptation (i.e., S1 vs. S1(PA), S12 vs. S12(PA)). suggesting that annotations generated for the new domain successfully transferred knowledge. These results confirm the effectiveness of our adaptation strategy. Additionally, comparison with few-shot learning methods and results under data deletion scenarios are in appendices [.2 and 1.3 Moreover, the adaptation cost measured on the NVIDIA Jetson platform is in Appendix 1.4
8

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Published as a conference paper at ICLR 2026
6 ANALYSIS
6.1 IMPACT OF HASHING DIMENSION ON PERFORMANCE AND COMPUTATIONAL COST
Table 5: Changes in Performance and training costs based on hashing dimensions (Using Gemma
(2B, 4bit) and LMsr models, RM: Reasoning Module only, Disk: storage for embeddings)
WebQSP
CWQ
Metric
FP32 768
256bit
128bit
64bit
FP32
768
256bit
128bit
64bit
mAP
0.907
0.877
0.827
0.887
0.845
0.774
Hit
79.5
79.5
79.1
78.4 (1.3%
54.4
54.4
53.8
5A.5 (2.9%
Hi (RM)
77.6
77.5
76.6
75.5 (2.7%))
41.4
41.3
40.6
40.3 (2.7%
GB))
6.2
0.065
0.032
0.016
89.3
0.93
0.46
0.23
PFLOPs
1.46
0.36
0.29
0.26
8.50
2.63
2.32
Pa)ams(M)
0.239
0.136
0.127
0.291
0.189
0.179
We conducted analysis demonstrate that the hashing module effectively preserves semantics while reducing the model's computational costs. Table Table shows the performance and computational cost for different hashing dimensions in MobileKGQA, Specifically, FP32 768 represents the origi- nal 768-dimensional embedding generated by the LMsr model (Zhang et al. 2022). Consistent with previous experiments, MobileKGQA and its retrieval module show minimal degradation- less than 2.9%-at at 64 bits, while maintaining near-identical performance up to 256 bits on both datasets. This demonstrates that our hashing method effectively maintains performance while reducing computa- tional resources. Furthermore, on the WebQSP dataset, the 64-bit hash code achieves strong mAP of 0.75, indicating high-quality hashing. Compared to the original embedding, hashing reduces stor- age by 99.75%. and reduces the Reasoning Module's parameters and PFLOPs by 46.9% and 82.2%, respectively. Additional experiments on hashing module training costs and annotation generation costs are in Appendix 1.5 and 1.6
6.2 QUALITY ANALYSIS ON GENERATED QUERIES
Table 6: Quality analysis on the generated annotations using various LLMs and WebQSP dataset. (red: best, blue: second-best. Full table is provided in Appendix I.7)
Gemma2 (2B, 4bit)
(14B, 4bit)
Method
ROUGE-L(S))
BERTScoref &))
Tokens Time(s)
ROUGE-L(S)
BERTScorc(%)
Tokens
Time(s)
RLM
17.0
36.4
25401
6.8
12.1
18.8
54045
16.9
CoT
32.3
37.6
328687
53.2
26.0
32,5
424289
118.2
Ours
42,8
48.7
97855
25.5
41.9
48.4
89471
30.2
To evaluate the quality of annotations generated by MobileKGQA, we compared the method with two existing approaches: ReasoningL (RLM) (Jiang et al. 2023b and Chain of Thought (CoT) (Wei et al. 2022) (prompt in Appendix D. Specifically, we used human-generated questions in WebQSP references and compared them with questions generated by various methods. As shown in Table 66 MobileKGQA outperformed all baselines in both ROUGE-L and BERTScore, clearly demonstrating the effectiveness of our method. Moreover, MobileKGQA required only 21 % of the tokens and 26% of the time compared to CoT (measured using Phi-4), demonstrating its efficiency across various models-free from mobile-scale (2B) desktop-scaa -cale (14B).
6.3 ABLATION STUDY
Table 7: Ablation study on the annotation generation method
To evaluate the effectiveness of our proposed annotation genera- tion method, we compared its performance against RLM and COT. Table 7 presents the adaptation performance when adapting from DI to D2 in WebQSP. The results show that MobileK consis- tently achieved the highest performance across all domains, clearly demonstrating the effectiveness our method. Experiments were conducted on the WebQSP dataset using the Gemma (2B, 4-bit).
Hit
Method
S
S(PA)
T
total
RIM
79.1
78.1
37.8
62.3
CoT Ours
79.1
73.6
42.4
61.4
79.1
78.9
43,2
64.9

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Published as a conference paper at ICLR 2026
7 CONCLUSION
In this paper, we present MobileKGQA, on-device knowledge -aatggee-rpphhho question answering sys- tem designed for on-device training. To reduce resource demands, a hashing module converts high- dimensional embeddings into hash codes, while stepwise reasoning method enables annotation generation with minimal tokens. Mobile applicability is demonstrated on the NVIDIA Jetson Orin Nano, where the system delivers 20.3% higher performance and uses only 30.4% of the energy of the state-of-the of the SOTA baseline, MobileKGQA attains comparable performance and outperforms baselines in evolving databases. This work establishes a foundation for practical, privacy-preserving KGQA systems for dynamic mobile environments.
model. Moreover, using just 9% of the parameters and 7.2% of the computation
ACKNOWLEDGEMENTS
This research was supported by the National Research Foundation of Korea (NRF) funded by the Korean government (MSIT) under Grant Nos. 2022R IA3B 1077720, 2022R 0922R1557703990 and RS- 2022-NR067569 (Pioneer Research Center Program). This work was also supported by the BK21 FOUR Program (Education and Research Program for Future ICT Pioneers) Scoul National Uni- versity in 2025. In addition, this research was supported by the Institute of Information & Com- munications Technology Planning & Evaluation (ITTP) grant funded by MSIT (RS-2021-1121 Artificial Intelligence Graduate School Program at Seoul National University). Furthermore, this work was conducted as part of the Sovereign AI Foundation Model Project (Data Track) organized by MSIT and supported by the National Information Society Agency (NIA), Republic of Korea (Grant No. 2026-AIData WI101). Moreover, this work was supported by HUINNO AIM Company through HA-Rnd2325 2AA-Rnd233-preeiiccChnlllllll ppedicCCCiicalleeeeriiooioo
REPRODUCIBILITY STATEMENT
To enhance the reproducibility of this study, provide detailed proofs, dataset descriptions, and comprehensive implementation details with hyperparameters, in Appendices E respectively.
10 ETHICS STATEMENT
This work relies solely on publicly available datasets for evaluation. The proposed on-device frame- work aims to enhance privacy by enabling local processing of user data, thereby reducing reliance on centralized servers. However, in practical deployments, appropriate safeguards should be imple- mented to ensure the secure handling of potentially sensitive information. As the system incorporates LLM-based reasoning and automated annotation generation, it may produce inaccurate or mislead- ing outputs; therefore, is not intended for use in high-staker or safety-critical applications without appropriate human oversight. We also acknowledge that personalization systems may introduce un- intended biases depending on the data, and future work will focus on mitigating such risks.
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Appendix for MobileKGQA: On-Device KGQA System on Dynamic Mobile Environments
A DETAILS ABOUT THE USE OF LARGE LANGUAGE MODELS
In this study, we utilized large language models (LLMs) for manuscript preparation, specifically for refining sentences and checking grammar and spelling. The authors carefully reviewed and edited the content generated by the LLMs to ensure accuracy and consistency. Additionally, the LLMs did not influence the research ideas or interpretation results.
B ADDITIONAL RELATED WORK
B. -R-BE METHODS
GRAFT-Net (Sun et al.. 2018) leverages graph representation learning to answer questions by extracting information from hybrid subgraph that integrates both knowledge base and entity-linked text resources.
PullNet (Sun et al., 2019) is an iterative framework that constructs subgraph through graph convolutional network-based retrieval process, enabling multi- hop reasoning across both knowledge base and corpus resources.
NSM (He et al. 2021 utilizes teacher-student framework where a bidirectional (for- ward/backward) reasoning teacher network effectively guides the student network's inter- mediate step learning in knowledge base question answering.
EmbedKGQA (Saxena et al. 2020) leverages knowledge graph embedding techniques address the challenge of incomplete knowledge graphs in multi-hop question answering, eliminating the need for external text resources neighborhood constraints.
TransferNet (Shi et al. 2021) performs multi-hop reasoning by sequentially transferring entity scores through activated relations, supporting both knowledge graph labels and text relations while maintaining interpretability through transparent intermediate steps.
ReaRev Mavromatis & Karypis 2022) enhances knowledge graph question answer- ing through adaptive instruction refinement and graph neural network-based breadth-fire first search execution, enabling flexible reasoning that adapts to available knowledge graph in- formation.
NuTrea (Choi et al. 2023) is tree search-based graph neural network that enhances multi- hop knowledge graph question answering by incorporating both forward- -loking message passing and global context through novel node embedding scheme.
B.2 SP-BASED METHODS
QGG Lan & Jiang 2020) enhances knowledge base question answering by integrating constraint processing early in the query graph generation process, utilizing a staged ap- proach that efficiently handles both multi-hop relations and constrained queries simultane- ously.
modular framework that combines retriever for knowl- edge base item selection, transducer for logical form generation, and a checker for verifi- cation, enabling efficient question-anse answering over large-scale knowledge bases.
ReTraCk (Chen al. 202
UnifiedSKG (Xie et al., 2022) proposes unified text-to-text framework that standardizes diverse structured knowledge grounding tasks for more systematic and compatible research across different domains.
TIARA (huu et al. 2022) enhances knowledge base question answering by combining pre- trained language models with multi multi-grained retrieval and constrained decoding, enabling improved semantic understanding and logical form generation.
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DecAF (Yu al. 2023) presents a hybrid framework that combines logical form generation and direct answer prediction for knowledge base question answering while simplifying the process through free-text retrieval.
UniKGQA (Jiang et al. 2023c) presents a unified approach that combines Knowledge Graph retrieval and reasoning processes using a pre-trained language model and matching information propagation for multi-hop question answering.
B.3 LLM-BASED METHODS
StruetGPT Diang et al. 2023a) presents a framework called StructGPT that enhances language models' reasoning on structured data through an iterative two-step approach of collecting evidence via specialized interfaces before performing targeted reasoning to grad- ually reach the correct answer.
Pangu Gu et al. 2023) presents a grounded language understanding framework that lever- ages language models' discriminative abilities rather than generative capabilities by com- bining a symbolic agent for environment exploration with neural language model that evaluates plan plausibility.
KB-BINDER (Li et al. 2023a) enables few-shot knowledge base question answering by using large language models to generate logical forms based on demonstrations, then grounding them executable queries through BM25 score matching.
ToG (Sun et al., 2024 presents interactive agent-based approach that enables large language models perform verifiable reasoning by iteratively exploring and leveraging knowledge graphs through beam search to find optimal reasoning paths and generate reli- able answers.
FlexKBQA (Li al. 2024) presents a few-shot framework called FlexKBQA that lever- ages large language models to translate sampled knowledge base programs into natural language questions, then uses execution-guided self-training with unlabeled user queries to train lightweight specialized model.
SymKGQA (Agarwal et al. 2024) is a few-shot KGQA framework that uses in-context learning create step-by-step symbolic logical forms. stands out from previous methods by not requiring the LLM to have prior knowledge of KoPL. To connect these logical forms to the knowledge graph, SymKGQA includes retrieval-augmenter module called QUACK, which provides grounding based on the specific question.
C PROOF OF THEOREM
Assumption 1 (Independence Assumption). The norm of LLM embeddings Ts is independent of both the orientation of embeddings O2 and the orientation hash codes On, i.e.,
P(rz, 02) P(rz) P(02)
P(r., 0%) P(r.)
Theorem (Conditions for Mutual Information Maximization) Suppose Assumptions and hold, and both & and sb preserve the cosine similarity between any pair of embeddings in Z. Then, the mutual information I [(2, h) is maximized.
Proof. By definition, the mutual information between and is given by:
I(2,h) = H(z) H(= h).
Expressing in polar coordinates,
h2 H(r,, 02) H(r,, 0z h).
Applying the first condition of Assumption 1,
H(rz; H(r=)) + H(2.),
and similarly,
H (rr 02 1h) H(r= h) H H(= h).
(10)
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Thus, rewrite mutual information
I[z, h) = H(r)) + H(0)) - H(r= (h) H h).
(11)
Using the definition of mutual information,
I(2,h) = I(r., h) + h))
(12)
Now, consider the hash code h {-1, 1]dd Since each element of h takes values in {-1, 13, Th is
constant. Consequently,
T(2, h) [(r,, 0n.) I(02, 0%)
(13)
Applying the second condition of Assumption 1,
I(z, h) I(02,
(14)
By the definition of mutual information,
[(z, hi) H(O.) ( 1%)) & H(2)). Since both & and i preserve cosine similarity, the 0, determines Oz, ensuring
(15)
H(0= 0n. 0.
(16)
Thus, mutual information is maximized:
T(2,h) H(O.).
(17)
D IMPLEMENTATION DETAILS
D.1 HYPERPARAMETER SETTINGS
Table 8: Hyperparameters for Hashing Module
Embedding
Dataset
Bit
Ir
ak
64bit
1.0 x 10-5
0.01
WebQSP
128bit
1.0 x 10
0.01
256bit
1.0 x 10-5
0.01
LMsr
64bit
5.0 x 10
0.01
CWQ
128hit
5.0 x 10-5
0.01
256bit
1.0 10
0.01
64hit
5.0 10-5 0.01
GTE-large
WebQSP
128bit
5.0 x 10
0.01
256bit
5.0 10
0.01
64bit
1.0 10 10
0.01
GTE--wwen-I.5BB
WebQSP
128bit
5.0 x 10 -5
0.01
256bit
3.0 x 10
0.01
Table g presents the learning rate (Ir) and balancing parameter (a)) settings used for hashing mod- ule across various LLMs on the WebQSP and CWQ datasets. Table provides the hyperparameter settings for the reasoning module the WebQSP and CWQ datasets, including the number of itera- tions (# iter.), instructions (# ins.), GNN layers (# gnn), and embedding dimensions (dim). In this conducted three times, and the average results presented. The random seeds 0, 1, and were used for the repetitions. All experiments were conducted on NVIDIA Tesla V100 GPU.
study, all reported experiments
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Table 9: Hyperparameters for Reasoning Module
Embedding
Bit
Ir.
# iter.
# ins.
# gnn
dim
WebQSP
LMsr
64bit
0.0009
3
2
50
LMsr
128bit
0.0009
3
2
2
50
LMsr
256bit
0.0007
2
50
GTE-lane GaE-large
256bit
0.0004
3
50
GTE- GTE-Qwent Qwent-wennne 1.5B
256bit
0.0005
2
3
50
CWQ
LMsr
64hit 0.0007
2
3
50
LMsr
128hit 0.0007
50
L.srr
256hit 0.0005
2
3
3
50
D.2 UTILIZED QUANTIZED LLMS IN THIS STUDY
In this study, we evaluated the performance of MobileKGQA in comparison with baseline mod- els across various large language models. Our experiments revealed that among the open-source quantized large language models, those provided by Ollama exhibited the best text generation per- formance while enabling optimized computations across diverse local machines. Consequently, we employed Ollama's models for the text generation stage in both the baseline models and Mo- bileKGQA's LLM. Specific Ollama models used in this study can be found at the following link.
Qwen2-0.5B hittepss//ollmaescccoom/////r///
Gemma2-2B /tt::////OLLaaa.cccmm///aa////g2
Llama3.1881 (ttp::///blaaaa.co//bbaaaa/////
Phi4-14B (https://ollemt) camee.commlli htt.:///aaa.cmmmmmrrrr////////
E
DATASET DETAILS
Table 10: Statistics of datasets. Each row represents the number of Questions, Questions in trann/valiation/nestt set, total entities, entities per question, total relations, relations per question, total triples, triples per question, followed by the knowledge base that the datasets originated from.
Attribute
WebQSP
CWQ
Total Questions
4,700
34,689
Train
2826
27,639
Validation
246
3,519
Test
1,628
3,531
Total Entities
1,298,306
2,259,510
Entities per Question
2,461
11,422
Total Relations
6,094
6,649
Relations Question
628
845
Total Triples
19,986,134
138,785,703
Triples per Question
4,252
4,001
Knowledge Base
Freebase
Freebase
In this study, we evaluate our approach using two prominent KGQA datasets: WebQSP (Yih al.) 2016) and CWQ (Talmor & Berant 2018). both constructed from the Freebase knowledge graph. The knowledge graphs contain 1.29 million and 2.25 million entities, respectively. To ensure fair comparison with existing research, we maintain the established train-dev-test splits from previous
work (Mavromatis & Karypis 2022 Yu et al. 2023 Sun al.) 2024). Specifically, utilize the
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preprocessed version of the datasets from Luo et al. (2024c), which is available through the Hugging Face platform (WebQSH c The detailed statistics these datasets are presented in Table 10. WebQSP cxhibits varying levels of reasoning complexity: 65.49% can be answered using a single fact, while 34.51% % of questions require aggregation across two facts. The maximum reasoning depth necessary is limited two hops within the graph. On the other hand, CWQ surpasses WehQSP in complexity and requires more sophisticated reasoning patterns. The questions are categorized as 45% composition, 45% conjunction, 5% comparative, and 5% superlative. In terms of reasoning depth, 40.91 % of questions are answerable with single fact, 38.34% require aggregation across two facts, and 20. 75% involve reasoning over more than three facts. The maximum reasoning depth extends to four hops within the graph.
F BASELINE SELECTION CRITERIA AND DETAILS
To rigorously evaluate our model, selected baselines based on the following criteria: in scenarios without distribution shift, we compared our model to high-performance, large-parameter fine-tuned models (Xie et al. 2022 Luo et al. 2024c In scenarios with distribution shifts, we chose the top-performing models with lightweight design, either capable of fine-tuning in mobile environ- ments (Mavromatis & Karypis 2025 Li al. 2025 or not requiring fine-tuning all (Jiang al., 2023a Sun et al. 2024). Moreover, to reflect the fast-growing on-device LLM field, we selected four newly released on-device LLM models: Qwen2-0.5BB Gemma2-22 2B, Llama 3.1- and Phi4- Gemma2-2B is an on-device LLM that can operate on mobile devices without restriction. Addition- ally, to account for advancements on-device L.LM inference speed, we utilized Qwen2-0.5B. and to reflect improvements in on-device LLM performance, we employed Llama 3.1-8B and Phi4-14B, which cannot operate on mobile devices without restrictions yct. As embedding models, we used the LMsr (Zhang al. 2022) and GTE-large (Zhang et al. 2024). which were employed in GNN-RAG and SubgraphRAG (Mavromatis Karypis, 2025; Li et al., 2025). Additionally, to account for the rapidly increasing scale of embedding models, we utilized the GTE-Que OTTE-Qwen2:1.5B model (Li al.
2023b), which is the current SOTA embedding model MTEB dataset (Hugging Face 2025
G EXPLANATION OF THE METRICS
To evaluate the question-answering answering performance of MobileKGQA, we utilize two widely used met- rics in previous research (Li et al., 2025): Fl score and Hit. Moreover, to evaluate the performance of the hashing module, mean average precision (mAP) is utilized.
F1 Score. The Fl score the harmonic mean of precision and recall, providing a balanced measure of model's 's accuracy when both false positives and false negatives are critical. It is defined as:
Precision x Recall
Fl
x
(18)
Precision + Recall
Hit. Hit measures the proportion of queries for which at least one correct answer appears in the model's output. It is defined as:
1 Q Σ 1 1
Hit
Ø]
(19)
where Q is the number of queries, A is the set of predicted answers for the i-th query, A, is the set of ground truth answers, and 1[-] is an indicator function that returns if the condition is met and otherwise.
Mean Average Precision (mAP) is a widely used metric for evaluating information retrieval sys- tems, as it considers both precision and the ranking of relevant items. In our evaluation, mAP mea- sures how well the hashing module preserves similarity between representtaannnnnnnnnnn mAP is based
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on Precision @ k, which quantifies the proportion of relevant items among the top-k retrieved results. Given a query let R& denote the set of top-k retrieved items and G be the set of ground-truth relevant items. Then, Precision @ k is defined as:
Precision k R& o gl
(20)
k
Since Precision @ k only measures performance specific cutoff, the relevance indicator function rel(k) is utilized to capture whether a retrieved item contributes to precision:
1, the k-th retrieved item is relevant otherwise
rel(k) =
(21)
0,
Using these definitions, we compute Average Precision (AP), which measures the average of preci- sion values at positions where relevant items appear. It is given by:
AP
Precision @ rel(k)
(22)
where N is the total number of retrieved items. Finally, define Mean Average Precision (mAP) as the mean of AP values across multiple queries. Given a set of queries {4), :.,...;,.. 92). mAP is expressed as:
1 Q
mAP-
AP:
(23)
1=1
where AP; is the average precision for query q:-
To compute mAP in our experiments, we randomly sampled 100 queries from the test dataset and repeated 30 times. For each sampled set, we computed the mAP score and averaged the results to obtain final evaluation metric. For defining relevant items, we used the five most similar representa- tions among the entire set based on cosine similarity. These top-5 nearest neighbors were considered to belong the same class for each query.
H DETAILS ABOUT THE EXPERIMENTS ON THE NVIDIA JETSON ORIN NANO
Table 11: Key specifications of the NVIDIA Jetson Orin Nano (8 GB)
Component
Specification
GPU
1024 core NVIDIA Ampere architecture with 32 Tensor Cores
CPU
6-core ARM Cortex-A78AE v8.2 64-bit CPU 1.5 MB L2 4 MB L3
Memory
8 GB 128-bit LPDDRS @ 68 GB/s
Power Modes
Configurable
15
The NVIDIA Jetson Orin Nano GB was employed as the on-device Al platform for our study. We chose as proxy for real-world mobile and edge devices due to its limited electrical power, small thermal envelope, and compact physical dimensions. Its configurable 7W and 15W power modes en- abled experiments under two representative operating conditions. The 7W mode approximates the thermal and power constraints of smartphones or small hand-held robots, where continuous opera- tion is critical. In contrast, the 15W mode serves as proxy for larger edge platforms such as drones, service robots, or industrial IoT gateways that have slightly higher power and thermal budgets but remain well below desktop-cll calsssp-class levels. By combining these tightly controlled power settings with a full GPU-CPU system-on-mane module,, the board effectively reproduces the resource limitations (low power, restricted cooling, and confined form factor) of real-world mobile environments.
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To enable fair comparison between GNN-RAG and MobileKGQA the Jetson platform, carefully reproduced the experimental conditions reported in the original GNN-RAG paper. We em- ployed LMsr the embedding model, since it achieved the best performance in their experiments, and used the authors' official evaluation code without modification. However, the configuration de- scribed by the GNN-RAG authors requires substantially provide, making training infeasible. To overcome this limitation, we modified their dataloader to reduce memory and re-tuned the model enable training within the limited memory constraints. Moreover, the memory demand for relation embeddings in GNN-RAG scales with the size of the dataset. Therefore, we trained the model on DI, subset of the WebQSP dataset, to keep the overall memory requirements manageable. After these adjustments, we successfully configured the model to operate within the Jetson's 7.6 GB memory limit, occupying 7.3 GB, as summarized Table T2
memory than the Jetson platform can
Table 12: Hyperparameter Settings for GNN-RAG the NVIDIA Jetson platform
Embedding batch size lr.
iter.
# ins.
# gnn dim
LMsr
0.0005
3
2 10
I ADDITIONAL EXPERIMENTS
1.1 PERFORMANCE OF MOBILEKGQA ON CWQ AND METAQA DATASET WITHOUT DISTRIBUTION SHIFT.
Table 13: Performance comparison various KGQA models on the CWQ dataset. (red: best, blue:
second-best best, MobileKGQA utilized 256bit hash codes and LMsr embedding model.)
Base Model
Model
Token Time(sec.)
Hit
F1
Sentence
ReaRev
0.08
N/A 41.4
N/A
BERT
NuTrea
0.12
49.5
T5 (3B)
UnifiedSKG
N/A
OOM
N/A 73.3
Llama 2 (7B)
RoG
89.6
OOM
62.6 56.2
ToG
3010.1
36.1
15.2 N/A
Qwen 2 (0.5B, 4bit)
GNN-RAG
83.3
0.98
47.0 29.5
SubgraphR
86.0
0.97
17.8 6.4
MobileKGQA
85.1
0.93
47.5 30.1
ToG
2534.6
53.2
31.9 N/A
Gemma 2 (2B, 4bit)
GNN-RAG
16.9
0.33
54.4 40.7
SubgraphR AG
186.9
3.49
32.7 14.0
MobileKGQA
17.0
0.32
54.4 40.8
ToG
3540.4
83.6
39.4 N/A
Llama 3.1 (8B, 4bit)
GNN-RAG
44.0
1.17
57.6 37.3
SubgraphRAG
269.0
6.33
40.2 30.7
MobileKGQA
43.7
1.01
57.8 37.1
ToG
3402.3
166.5
18.4 N/A
Phi (14B, 4bit)
GNN-RAG
88.9
4.41
64.2 39.0
SubgraphRAG
404.5
21.1
39.3 33.2
MobileKGQA
90.4
4.47
63.8 38.8
In the main paper, we evaluated the computational cost and performance of various KGQA models on the WebQSP dataset. In this section, we additionally report experimental results on the CWQ dataset. (See Table 13). Consistent with the findings on WebQSP, MobileKGQA achieves perfor- mance that is nearly identical to the state-of-the-art lightweight model, GNN-RAG, in the CWQ dataset. The average performance gap between the two models is only 0.075 in both Hit score and F1 score. Furthermore, MobileKGQA requires the least amount of resources in terms of latency and token generation, demonstrating its practical efficiency.
We further evaluate MobileKGQA on the MetaQA benchmark, which comprises multi-ho hop question answering tasks, and compare its performance against the GNN-RAG baseline. As shown in Ta- ble 14 MobileKGQA achieves comparable results across 1-hop, 2-hop, and 3-hop settings, closely
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matching the baseline scores. These findings indicate that the proposed hashing- based architecture effectively preserves the semantic information necessary for multi-hop reasoning and maintains sta- ble retrieval quality when extended to additional knowledge graph benchmarks.
Table 14: Fl Score Comparison MetaQA Datasets
Model
MetaQA 1-hop
MetaQA 2-hop
MetaQA 3-hop
GNN-RAG
95.9 + 0.53
96.7 + 0.78 96.7 + 0.63
88.7 + 0.86 88.6 + 0.92
MobileKGQA
95.8 + 0.68
1.2 PERFORMANCE COMPARISON WITH FEW-SHOT LEARNING METHOD
Table 15: Comparison of MobileKGQA and Few-Shot Learning Methods WebQSP (Mo-
bilcKGQA utilized 256bit hash codes; Hit score reported. *; p-value & 0.0001
On-device LLM
Model
Gemma (2B, 4bit)
Phi-4 (14B, 4bit)
SymKGQA
10.6 +: 0.4
12.3 + 0.2
MobileKGQA
37.3 1 0.3
39.2 1 0.4
To further analyze the adaptability of MobileKGQA its shifted distribution, conducted com- parison with SOTA few-shot learning methods based on cloud-based LLMs: symKGQA (Agarwal et al. 2024). Since obtaining large number (e.g., over 100) of ground truth annotations is often im- practical in real-world scenarios, we evaluated all models under zero-shot setting to reflect practical constraints. Additionally, the experiments was replaced with an on-device LLM.
conducted in setting where the cloud-based LLM
Experimental results show that MobileKGQA consistently outperforms symKGQA on both on- device LLMs. (See Table T5) This is because conventional few-shot learning methods rely solely on few-shot prompting to translate between natural language and logical forms (e-g., SPARQL, KoPL), which require lengthy reasoning process or high-capacin cloud- bbaedd LLMs. As a result, these methods often fail to execute the translation process effectively resource-constrained mobile en- vironments, producing irrelevant questions nonsensical logical forms. In contrast, MobileKGQA decomposes the complex translation process into smaller substeps that are more manageable for on-device LLMs, enabling strong performance even in resource-constrane settings.
1.3 CONSIDERATION DISTRIBUTION SHIFT CAUSED BY DATA DELETION
Table 16: Hit scores of GNN-RAG and MobileKGQA on the WebQSP dataset under varying data deletion ratios. (MobileKGQA used bi6-bit hash codes. *; & 0.01, **; & 0.001.)
Deletion Ratio
GNN-RAG
MobileKGQA
20%
84.2 + 0.2
87.2 1 0.4 *
40%
77.2 + 0.6
84.9 1 0.8
60%
70.0 + 0.2
83.3 0.6
80%
56.7 + 0.3
71.4 + 0.3 **
Data deletion common on mobile devices and can induce distribution shifts that degrade the performance of KGQA systems. Nevertheless, valid annotations for the updated database still be generated using cule-based methods. Consequently, when data deletion occurs, MobileKGQA can efficiently adapt through on-device supervised training with only minimal computational overhead. In contrast, the current trat-of-thee-a model, GNN-RAG, must either undergo resource-intedd -intesieinteessve retraining (see Table 2) accept noticeable decline in performance.
Table 16 presents experimental results supporting this claim, showing the performance of models trained the original WebQSP dataset and evaluated version with randomly deleted triples. As
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shown in the table, MobileKGQA consistently outperforms GNN-RAG across all deletion scenarios, and even when 80% the edges are removed, it exhibits only an 18.1% performance drop. This demonstrates that MobileKGQA effectively adapt under data deletion conditions.
1.4 COST OF MODEL ADAPTATION ON THE NVIDIA JETSON ORIN NANO
Table 17: Adaptation cost of MobileKGQA on NVIDIA Jetson Orin Nano (based on Gemma
(2B, 4bit) model, WebQSP dataset)
CPU
GPU
Mode
Condition
Energy (Wh) Time
RAM (GB) Throttle
Usage (%)
Temp (CC)
Usage (%)
Temp ('C
7W
$ (DI-D2)-T (D3)
(DDI
11.6
14.2
2.1
2.5
24.1
23.8
48.7
49.4
70.5
69.8
50.5
50.3
5.0
No
(DI) (D2)
8.2
1.3
18.5
50.5
55.1
51.9
15
5.0
(DDI+D2)- (D3)
6.7
18.3
51.2
56.0
51.7
To evaluate the adaptation cost in real mobile environment, conducted the WebQSP experiment described in Section 5.2 on the NVIDIA Jetson Orin Nano. As summarized in Table 17 adapting from DI D2 required only 14.2 Wh of energy-well within the capacity of current smartphones (e.g. the OnePlus 13 battery is rated at 22.92 Wh). Notably, this measurement was obtained on the large WebQSP D2 dataset containing 4,354,04 444.048 triples, so the energy demand for typical on-device adaptation is expected to be substantially lower. Extrapolating from these results, adapting 10,000 triples consumes roughly 0.05 Wh, which is less than 0.2% of a typical smartphone battery (based on the OnePlus 13). This extremely low adaptation cost indicates that MobileKGQA can be routinely retrained on-device as users interact with their devices, enabling the embedded LLM to incorporate new knowledge almost in real time.
1.5 TRAINING COST FOR HASHING MODULE
Table 18: Changes in computation costs for training hashing module based on hashing dimensions.
dimension
Dataset
Metric
512bit
256bit
128bit
64bit
Params(M)
1.18
0.59
0.30
0.15
GFLOPs
2.83
1.18
0.59
0.41
WebQSP
Time(s)
15
VRAM(GB)
2.60
2.38
2.35
2.33
Table 18 summarizes the computational resources required for training the hashing module with 2304-dimensional embedding model. The results show that the training process is highly efficient in terms of resource usage, with all configurations completed in under 20 seconds an NVIDIA Tesla V100 GPU. The low parameter count and computational cost indicate that the module requires minimal memory and processing power. Even with the largest hash dimension (51 (512-bit), training completes within 16 seconds, and VRAM usage remains below 2.6 GB. Given these characteristics, our hashing module is well-suited for deployment in resource-constraine environments, including mobile devices, and can be easily integrated into real-time applications without significant compu- tational overhead.
1.6 IMPACT OF GENERATED QUERIES PERFORMANCE AND COMPUTATIONAL COST
To demonstrate that MobileKGQA can adapt to distribution shifts using only the computational re- sources available on mobile devices, we analyzed how performance and computational requirements change with the number of generated annotations. Table 19 presents the performance improvements observed when adapting MobileKGOA from DI to D2 in WebQSP, based on the number of annota- tions generated by Gemma2 (2B) with token generation speed of 40 tokens per second on mobile devices (OctoML 2023). The results show that MobileKGQA requires just 35 minutes to generate 800 annotations mobile device, consuming less than 0.5 MB of storage. This demonstrates that
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Table 19: Computational cost and performance after adaptation based on generated annotations.
Number of generated annolations
Datasct
Metric
0
200
400
600
800
Token(K)
0
14.0
22.9
38.3
50.9
WebQSP (D1-D2)
FIf
49.3
50.1
50.8
50.6
51.7
Time(min)f
0
10.1
16.3
27.2
35.3
storage(MB)
0.12
0.24
0.36
0.48
MobileKGQA is already feasible for mobile deployment. The Fl score was computed the com- bined DI + D2 dataset, and the inference time was measured on mobile device at processing rate of 40 tokens per second.
1.7 QUALITY ANALYSIS ON GENERATED ANNOTATIONS
Table 20: Quality analysis on the generated annotations using various LLMs and WebQSP dataset. (red: best, blue: second-best)
Metrics
LLM
Generation Method
ROUGE-L (%)
BERTScore (%)
Tokens
Time (min)
ReasoningLMM
9.7
10.7
85220
13.7
Qwen (0.5B, 4bit)
Chain of Thought
18.5
18.7
73847
12.2
MobileKGQA's
30.2
33.8
116842
21.3
ReasoningLMMY
17.0
36.4
25401
6.8
Gemma 2 (2B, 4bit)
Chain of Thought
32.3
37.6
328687
53.2
MobileKGQA's
42.8
48.7
97855
25.5
ReasoningLMM
12.9
26.3
29095
7.5
Llama 3.1 (8B, 4bit)
Chain of Thought
31.2
37.7
540194
74.2
MobileKGQA's
42.4
48.9
83742
24.2
ReasoningL.M
12.1
18.8
54045
16.9
Phi 4 (14B, 4bit)
Chain of Thought
26.0
32.5
424289
118.2
MobileKGQA's
41.9
48.4
89471
30.2
To evaluate the quality and efficiency of annotations generated by our method, we conducted an analysis based on four key metrics: ROUGE-L, BERTScore, number of generated tokens, and generation time. ROUGE-L and BERTScore assess how closely the generated questions resemble human-annotated questions, while the number of tokens and time reflect the generation efficiency. For this analysis, we utilized human-annotated questions from the WebQSP dataset. To generate annotations, relevant triples were first identified through SPARQL queries corresponding to each human-annotated question, and one triple combination was then randomly sampled for generation. Instances excluded if appropriate triples could not be retrieved due issues such manual SPARQL queries and incorrect relation information. After this filtering process, a total of 2, 101 an- notations were evaluated. We compared our method against two baseline approaches (1) Reason- ingLM (RLM) (Jiang et al. (2023b), which employs few-shot prompting, and (2) Shain-of-Thoughe (CoT) (Wei et al., (2022), which enables step-by-step reasoning-across four different LLM settings, as reported in Table 20 For RLM, we used the prompt exactly as presented in the original paper, while for CoT, we utilized the prompt from Appendix All experiments were conducted on NVIDIA Tesla V100 GPU.
Across all model configurations from lightweight models like Qwen2 (0.5B, 4-bit) to larger ones such as Phi-4 (14B, 4-bit), which are still difficult to deploy mobile devices-Moffe Mobilee con- sistently achieved the highest ROUGE-L and BERTScore. also demonstrates that requires only 28.4% of the tokens compared to COT, which shows the second-best performance. When applying CoT to Qwen2-0.5B, we observed that the model often failed to follow the CoT prompt and instead produced short, dummy text. As result, it generated fewer tokens and required less time than other on-device LLMs. These results demonstrate that our proposed method is both more effective and efficient than existing approaches. Furthermore, its robust performance across varying L.LMs high-
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lights its applicability not only to devices with more limited capabilities than today's smartphones, but also to future perffrrrfee on-device LLMs.
1.8 COMPARISON WITH OTHER HASHING METHODS
To evaluate the effectiveness of the proposed hashing module, we compared MobileKGQA against several alternative hashing method, including K-means-based quantization, PCA-based hashing, and locality-sensitive hashing (LSH). For fair evaluation, we replaced only the hashing compo- nent while keeping all other parts of the MobileKGQA framework unchanged. Experiments were conducted on WebQSP and CWQ using 256-bit hash codes, and all reported scores are the mean = standard deviation over three runs. As summarized Table 21 MobileKGQA consistently outper- forms all alternative hashing techniques.
Table 21: Performance Comparison WebQSP and CWQ
Method
WebQSP
CWQ
K-means Quantization
67.9 + 0.05
30.7 + 0.17
PCA
65.5 + 0.16
29.2 + 0.21
LSH
68.2 + 0.08
29.3 = 0.19
MobileKGQA
69.4 : 0.08
33.3 + 0.18
To better understand these findings, we analyzed why the baseline hashing methods perform subop- timally in our setting:
K-means Quantization Product quantization (PQ) is widely used K-means-based method that compresses embeddings by partitioning a vector into multiple subvectors, each mapped to its near- est centroid. Because global 256-bit codebook would require an exponentially large number of centroids, subvector partitioning is unavoidable for PQ. However, this partitioning fails to retain cross-subvector porcllttions-infrmmann that encodes important semantic dependencies learned by the reasoning module. As result, retrieval performance degrades when PQ is used for hashing in KGQA.
PCA-based Hashing PCA identifies directions of high variance but does not consider the bina- rization step that follows. When PCA outputs are thresholded to generate binary codes, substantial quantization errors occur, discarding semantically informative dimensions. This mismatch between projection and quantization leads to reduced retrieval quality in KGQA tasks, where semantically precise embeddings are crucial.
Locality-Sensitivy Hashing (LSH) LSH hashes embeddings using randomly sampled hyper- planes and preserves angular similarity in expectation. However, transformer embeddings encode semantic information along specific "privileged basis" directions (Team (023). and these data- dependent structures are further shaped by user-specific knowledge bases. Because LSH relies on distribution-agnosicc random projections, fails to capture such informative directions, resulting in suboptimal retrieval quality in our setting.
Overall, these findings demonstrate the advantage of MobileKGQA's learned hashing module, By training data-aware, task-specific transformations, it preserves semantic structure more effectively and reduces the quantization error inherent in traditional hashing approaches.
1.9 ANALYSIS OF HASH COLLISIONS AND HASHING FAILURE CASES
We evaluate the robustness of the hashing module in MobileKGQA by analyzing both (i) the likeli- hood of hash collisions during retrieval and (ii) potential failure cases arising from insufficient hash capacity or distribution shifts.
Collision Analysis. To assess whether hashing introduces ambiguity during retrieval, we measure collision rates across datasets with different graph densities and hop lengths. Table 22 reports the
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collision rate together with mAP. For each query category, the collision rate and mAP calcu- lated as the probability that duplicated 256-bit hash codes appeared among the codes generated for retrieval. Across all settings, the collision rate remained zero, even in dense graphs such as MetaQA 3-hop and multi-hop reasoning tasks. This indicates that hash collisions are highly unlikely in the practical KGQA environments targeted by MobileKGQA.
This observation reflects fundamental difference between KGQA hashing and conventional appli- cations. While typical hashing (e.g., indexing, integrity checking) does not require reconstructing semantic information, KGQA specifically relies on hash representations to preserve semantic con- tent for reasoning. This requirement forces KGQA hashing to retain richer information, making collisions significantly less likely in practice. For this reason, we adopt mAP as the primary metric, as it directly measures semantic preservation.
Table 22: Hash Collision Rates Across Graph Types, Densities, and Hop Lengths
Graph Type
Density
Hop
mAP
Collision Rate
92.9
WebQSP
11.7
2
88.3
92.5
CWQ
53.7
2
87.7
0
>
86.2
MetaQA-Ihop 2548.8
89.9
MetaQA-2hop
3108.7
2
89.7
0
MetaQA-3hop
3141.7
3
89.7
Failure Case Analysis. In MobileKGQA, hashing failures may stem from two sources-insufficit hash capacity or distribution shift-but neither poses a meaningful challenge in practice. Regarding the first case, although retrieval performance could degrade if the data complexity exceeds the expressiveness of the hash space, our experiments on large and structurally rich benchmarks (WebQSP, CWQ) show that compact 256-bit code can reliably handle millions of entities without any loss in performance. Furthermore, even when we intentionally reduced the extreme level (down 16 bits) to induce hashing failures, the performance dropped by only 9.5%. These results suggest that capacity-related failures are unlikely to pose practical concern real-world settings.
hashing dimension
Table 23: Performance Changes According to Hashing Dimension on WebQSP
Metric
256-bit
128-bit
64-bit
32-bit
16-bit
mAP
0.907
0.877
0.827
0.690
0.567
Hit (RM)
77.5
76.6
75.5
72.3
70.1
(9.5%
For the second failure mode, although distribution shifts may affect accuracy, our hashing module is extremely lightweight and can be retrained on-device with minimal overhead (Appendix I.5, Table 17), enabling rapid and efficient adaptation whenever the user's s data distribution changes. As shown the table below for WebQSP, the hashing module maintains stable performance throughout the entire adaptation process, demonstrating that retraining reliably preserves hashing quality under distribution shifts.
Table 24: Domain-wise Performance of the Hashing Module during Adaptation WebQSP
Metric
D1
D1
D2
D1 D2 + D3
mAP
0.913
0.881
0.893
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J DETAILS ABOUT DOMAIN SPLIT
This section describes the details of how each domain is defined proposed to model distribution shifts. The questions in the dataset were embedded using embedding model based on the Llama 3.1 8B model provided by Ollama, and the K-nearest neighbor algorithm was applied to cluster them into three distinct domains. Each domain was then randomly split into training (50%), validation (10%), and test (40%) sets. Since each dataset consists of queries and their associated graphs, split- ting based solely on queries still ensures that the corresponding graphs are appropriately distributed across different domains. Table 25 presents the statistics of queries included in each domain, while Figures 3a and 3b illustrate the distribution of domain domain-specific queries in WebQSP, CWQ.
Table 25: Statistics of each domain split
Dataset
Attribute
Domain1
Domain2 Domain3
question
1596
1024
2080
train
798
512
1040
WebQSP
validation
159
102
208
# test
639
410
832
question
9601
13068
12020
train 4800
6534
6010
CWQ
validation
960
1306
# test
3841
5228
4808
40
D1
D2
40
D2
20
D3
D3
20
400
-40
-60
-20
40
-60
-70
(a) WebQSP
(b) CWQ
Figure 3: PCA visualization of question embeddings across different domains
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K
PROMPTS FOR MOBILEKGQA
Answer Generation Prompt
Reasoning Paths: {paths}
Please answer the question below by referencing the reasoning paths above. Construct your answer using the exact wording from the reasoning paths whenever possible. Provide all possible answers comma-separater list, outputting only the list.
Question: {question]
Query Generation Prompt (Verbalization)
Your task is to convert single knowledge triple into simple and grammatically correct English sentence.
Important instructions:
Do NOT add any external knowledge interpretation.
Use the original words from the triple.
Generate exactly one sentence.
Do not include any additional explanation.
Example:
Triple: (Marfan syndrome, medicine.rist seffiiieerisk.factooriseeese Myxomatous degeneration)
Sentence: Marfan syndrome is risk factor for Myxomatous degeneration.
Triple: (Monotheism, religion.religion religioo..blliefff beliefs, Sikhism)
Sentence:
Monotheism is belief of Sikhism.
Now perform the task with the following input: Triple: {triples}
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Query Generation Prompt (Merge)
You are a language model that integrates multiple factual sentences into single English sentence that describes the answer, using only the provided information.
Instructions:
Given input sentences, write one fluent and grammatically correct English sentence that describes the answer.
Make sure all facts from the input sentences are included in the output sentence to describe the answer.
Output only the final merged sentence. Do not provide any explanation or reasoning. imput sentences include encoded identifiers (e.g.. m. m.0j2sdtx), replace it with an appropriate sype-indicating phrase such as the person, the location, or the film, depending on the context.
### Example
Input Sentences:
LLampddusa Airport is located in Italy.
The capital of Italy is Rome.
Italy established on 2 June 1946.
Answer: Rome
Output: Rome is the capital of Italy, country that established 2 June 1946 and where Lampedusa Airport is located.
#### Example 2
Input Sentences:
-Grald Baron Cohen is accountant.
EEran Baron Cohen the parent of Gerald Baron Cohen.
Erran Baron Cohen worked the music for Grimsby.
Answer: Gerald Baron Cohen
Output: Gerald Baron Cohen is accountant and the of Erran Baron Cohen, who worked on the music for Grimsby.
#### Example 3
Input Sentences:
m.046csbd is the discoverer of the Euler Lagrange equation.
Mikhail Golovin was student of m. m..44csbbd.
Answer: Mikhail Golovin
Output: Mikhail Golovin was a student of the person who discovered the Euler- equation.
Now perform the task with the following input:
Input Sentences:
{sentences}
Answer: {answer)
Output:
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Query Generation Prompt (Placeholder Generation)
Your task is to classify target phrase by replacing it with type-indicating placeholder (e.g.. "the person", "the record label", "the ideology") that best reflects its semantic category.
Instructions:
You given target phrase and sentence where it appears, which may provide helpful context.
Your job determine the semantic type of the target phrase and replace with an appropriate placeholder.
Output only the type-indicating placeholder. Do not include any explanation.
#### Example
Sentence: Kurt Gödel developed Gödel's completeness theorem.
Target phrase: Gödel's s completeness theorem
placeholder: the theorem
#### Example 2
Sentence: A&M Octone Records signed Dropping Daylight as one their artists. Target phrase: A&M Octone Records placeholder: the record label
#### Example 3
Sentence: David Wylie advocated for the issue of nonviolence. Target phrase: nonviolence
placeholder: the ideology
### Example
Sentence: Gerald Baron Cohen is accountant and the son of Erran Baron Cohen, who worked on the music for Grimsby. Target phrase: Gerald Baron Cohen placeholder: the person
Now perform the task with the following input:
Sentence: [merged_sentene sentence]
Target phrase: {answer) placeholder:
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Query Generation Prompt (Masking)
Your task is to replace a specific phrase in a sentence with a sype-indicating placeholder (c.g., "the person", "the record label", "the ideology") that semantically describes the original phrase.
Instructions:
You are given sentence and target phrase that appears the sentence.
Replace the target phrase with a semantic placeholder phrase that accurately reflects its type (e.g., "the person" " for a person's name, "the country" for location, "the concept" for theory).
Output only type-indicating placeholder. Do not include any explanation or commentary.
#### Example
Sentence:
Kurt Gödel developed Gödel's completeness theorem.
Target phrase: Gödel's 's completeness theorem
Place holder: *the theorem
Output: Kurt Gödel developed *the theorem*
#1## Example 2
Sentence: A&M Octone Records signed Dropping Daylight one of their artists. Target phrase: A&M Octone Records
Place holder: *The record labe!"
Output: *The record label signed Dropping Daylight as of their artists.
#### Example 3
Sentence: David Wylie advocated for the issue of nonviolence.
Target phrase: nonviolence
Place holder: *the ideology*
Output: David Wylie advocated for the issue of *the ideology
#*# Example 4
Sentence: Gerald Baron Cohen is an accountant and the son of Erran Baron Cohen, who worked on the music for Grimsby.
Target phrase: Gerald Baron Cohen
Place holder: *the person
Output: *the person* is an accountant and the son of Erran Baron Cohen, who worked on the music for Grimsby.
Now perform the task with the following input:
Sentence: [merged}
Target phrase: {a_entity)
Place holder: (place_holder)
Output:
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Query Generation Prompt (Question Generation)
Your task is to generate natural-sounding question based sentence in which the answer has already been masked with a semantic placeholder (e.g., the person, the process, the river). You also provided with generated question.
question entities, which must be included in the
Instructions:
Convert the given sentence into a question where the part marked with asterisks is the answer.
You must include question entities in the question.
Output only the question without additional explanation.
### Example
Sentence:
*the person*, accountant, is the son of Erran Baron Cohen, who worked on the music for Grimsby.
Question entity: accountant, Grimsby
Question: Who is the accountant whose parent worked on the music for Grimsby?
#*## Example 2
Sentence: *the process* is the process through which plants convert sunlight into energy. Question entity: sunlight, plants
Question: What is the process through which plants convert sunlight into energy?
### Example 3
Sentence; *the river which flows through South America, is one of the longest rivers in the world.
Question entity: South America
Question: What is the longest river in the world that flows through South America?
Now perform the task with the following input:
Sentence: [masked]
Question entity: {question.entity)
Question:
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Query Generation Prompt (Refinement)
Your task is to revise the given question to ensure that all specified question entities explicitly included in it.
The original answer of the question, represented by the placeholder (e.g., the person, the
process), must not change in the revised question.
Instructions:
You are given sentence containing placeholder, a list of question entities, and question.
If any of the question entities are missing from the question, revise it to include them
naturally and meaningfully.
You must preserve the original answer implied by the placeholder. Do not change the type
identity of what the question is asking for.
Your output should be the revised question only without explanation.
Example
Sentence: *the person", who is an accountant, is the son of Erran Baron Cohen, who worked the music for Grimsby.
Question entity: accountant, Grimsby
Question: Who is the person whose parent worked on the music?
Revised question: Who is the accountant whose parent worked on the music for Grimsby?
Example
Sentence: *the process* the process through which plants convert sunlight into energy.
Question entity: sunlight, plants
Question: What is the process through which energy is produced?
Revised question: What is the process through which plants convert sunlight into energy?
Example
Sentence: *the river which flows through South America, is one of the longest rivers in the
world.
Question entity: South America
Question: What is one the longest rivers in the world? Revised question: What is the longest river in the world that flows through South America?
Example 4
Sentence: *the event* marked the beginning of the French Revolution.
Question entity: French Revolution
Question: What event marked the beginning of the revolution?
Revised question: What event marked the beginning of the French Revolution?
Now perform the task with the following input:
Sentence: (masked)
Question entity: inreettio_entityy
Question: {question}
Revised question:
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L PROMPTS FOR ANNOTATION GENERATION USING CHAIN OF THOUGHT
Query Generation Prompt (Chain of Thought)
Given triples, create question that illustrates relationships between multiple entities, ensuring that the question entity is explicitly mentioned the question and that the answer entity the correct answer to the generated question.
Follow these steps:
1. Find triple containing the Answer Entity. Based on this triple, form initial question asking about the Answer Entity.
Check if there are other triples involving the Answer Entity. If found, incorporate relevant information into the question to make it more specific. If there are no more triples involving the Answer Entity, select triple that contains any entity already mentioned in the current question. Add its information to further enrich the question.
4. Continue enriching the question following step 3 until no relevant triples remain.
Example:
Given Triples:
Accountant people..erson.professii Gerald Baron Cohen
Gerald Baron Cohen people.person.paee Erran Baron Cohen
Erran Baron Cohen film.flmm.mnn Grimsby
Question Entity:
Accountant, Grimsby
Answer Entity:
Gerald Baron Cohen
Generation Steps:
1. Identify triple containing the answer entity:
Triple: Accountant
Gerald Baron Cohen
Question: "Who is the accountant?"
Check for additional triples involving answer entity:
-'rriple: Gerald Baron Cohen people.perseeeeeeeeeeeeee pewpll.peroo..pooe parents- Erran Baron Cohen
Quession: "Who is the accountant whose parent Erran Baron Cohen?"
Check other triples already mentioned in the current question:
-Triple: Erran Baron Cohen film.fil..n mulmi Grimsby
Question: "Who is the accountant whose parent composed the music for the film Grimsby?"
4. No more triples to process. Finalize the generation process
Now perform the task with the following input:
Given Triples: (triples)
Question Entity: (question_enii e
Answer Entity: seiiswer.enttty")
Output format:
Generation Steps:
(Step-by-step analysis showing which triples are used and how the question is enriched)
Question: (generated question)
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M ANNOTATION GENERATION EXAMPLES
Generation Steps
Example
triples:
1. KITT film.film portade filmmss-------- Knight Rider 2000
2. Knight Rider 2000 fim.performance. actor William Daniels
Sampled Information
3. Knight Rider 2000 plim
Voice
question entity: KITT
answer entity: William Daniels
sentences:
1. KITT was portrayed in films as a character from Knight Rider 2000.
Verbalization
2. William Daniels performed in Knight Rider 2000.
3. Knight Rider 2000 features a voice performance.
merged sentence about the answer:
Merge
William Daniels, who played the character KITT in Knight Rider 2000, featured a voice performance the film.
Placeholder
Placeholder for the answer: the person
Generation
masked sentence:
Masking
*the person*, who played the character KITT in Knight Rider 2000, fea- tured a voice performance the film.
generated question:
Question Generation
Who featured voice performance in the film as KITT in Knight Rider 2000?
Since the question entity KITT is already included in the question, the refinement step skipped.
Refinement
Generation Steps
Example
triples:
1. General Motors automotive automotive.company company Vauxhall
Sampled Information
2. Vauxhall common hopic.notable Automobile Make
question entity: General Motors
answer entity: Vauxhall
sentences:
Verbalization
1. General Motors makes Vauxhall vehicles.
2. Vauxhall is a notable type of automobile make.
merged sentence about the answer: Vauxhall is notable type of automobile make made by General Motors.
Merge
Placeholder
Placeholder for the answer: the automobile make
Generation
masked sentence:
Masking
*the automobile make* is notable type of automobile make made by general motors.
generated question:
Question Generation
what is the notable type of automobile make made by general motors?
Since the question entity general motors already included in the ques- tion, the refinement step is skipped.
Refinement
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Reviews:
[{"review_id": "vnMN3HLy0s", "reviewer": "Reviewer_DB7Z", "summary": "The paper introduces MobileKGQA, an on-device KGQA system that trains and runs fully on mobile/edge hardware. It combines a hashing-based retriever (compressing LLM embeddings to binary codes) with a lightweight reasoning module, plus a stepwise on-device annotation generation to handle evolving KGs under tight compute/privacy constraints. On WebQSP/CWQ and a Jetson Orin Nano, it reports comparable SOTA accuracy while using much less params, and on-device tests beats baselines.", "questions": "See weaknesses.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. This paper is studying a practical and important problem, i.e., to make the on device KGQA. \n2. Although the reviewer does not think the proposed the algorithm is very novel, these algorithms are sound and supported by theoretical verification and solves the problem nicely. \n3. This paper has thorough and solid empirical evidence to support the effectiveness of the proposed method. Especially, this paper really runs the algorithm on a Jetson, which is more practical.", "weaknesses": "1. For the hashing algorithm, this seems to work similar as quantization to binary with cosine constraints, so the reviewer is wondering if a simple kmeans quantization can work better? \n2. The privacy preserving is an important aspect the paper is asserting, is there any privacy leakage when the LLM are generating the final answer?", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "aguDq4k5DO", "reviewer": "Reviewer_i4Rc", "summary": "The paper proposes MobileKGQA, the first KGQA system that supports both inference and on-device training under strict resource constraints. To reduce memory and computation, the authors introduce a hashing module that compresses high-dimensional embeddings into compact binary codes while preserving semantic similarity. To adapt to continuously evolving knowledge graphs without cloud retraining, the paper further introduces a stepwise annotation generation method that constructs question–answer pairs locally. Experiments on WebQSP and CWQ show that MobileKGQA achieves performance comparable to state-of-the-art lightweight KGQA systems while using only 7.2% of computation and 9% of parameters.", "questions": "Q1: Can the proposed system also adapt when triples are removed from the KG, or is the current adaptation mechanism designed only for newly added knowledge?\n\nQ2: Is there any scenario where hashing leads to ambiguous collisions that harm reasoning? Are there failure cases or examples?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "S1: The paper presents the first KGQA system that supports on-device training and adaptation to evolving knowledge graphs, demonstrating clear practical value for privacy-sensitive mobile environments.\n\nS2: The proposed hashing module achieves large reductions in computation and storage while preserving semantic similarity, leading to near-SOTA performance with only a small fraction of the parameters and energy consumption. Real-device experiments further strengthen the credibility of the claims.", "weaknesses": "W1: The theoretical justification of the hashing module relies on simplifying assumptions (e.g., independence between embedding norm and orientation), and the paper lacks deeper empirical analysis of potential failure cases, such as hash collisions or cold-start relations with limited semantic context.\n\nW2: Although the system claims full on-device adaptation, it relies on pre-trained embedding models to encode newly added triples. The paper does not analyze whether these embedding models can run efficiently on mobile hardware, making it unclear if the entire pipeline is truly on-device in practical scenarios.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "6BUVYuAD8H", "reviewer": "Reviewer_RLPG", "summary": "This paper addresses the critical challenges of deploying Knowledge Graph Question Answering (KGQA) systems on mobile devices, specifically the significant obstacles posed by resource constraints and continuous data accumulation. The authors propose MobileKGQA, the first on-device KGQA system specifically designed to adapt to evolving knowledge graphs with minimal resource requirements. The system employs embedding hashing to compress high-dimensional embeddings into compact binary codes, significantly reducing computational and storage overhead. It also introduces a novel annotation generation method based on sequential reasoning to handle distribution shifts caused by newly accumulated data. Extensive experiments on the NVIDIA Jetson Orin Nano platform demonstrate that MobileKGQA achieves 20.3% higher performance while consuming only 30.4% of the energy of the state-of-the-art (SOTA) model. On standard KGQA benchmarks, it uses only 7.2% of the computation and 9% of the parameters of the SOTA model while maintaining comparable performance.", "questions": "- Regarding Hash Collisions: Did you evaluate the hash collision rate? If collisions exist, how does the system mitigate their potential negative impact on the retrieval of multi-hop reasoning paths? \n- Regarding Generalization Capability: Do you have plans, or could you provide results, extending your evaluation to knowledge graphs beyond Freebase (e.g., Wikidata)? For dense graphs where semantic relations highly overlap, does your method require specific adjustments or optimizations?\n- Regarding Method Comparison: Could you provide comparative results against simpler hashing baselines (e.g., LSH)? This would help the community better understand the added value brought by the \"maximizing mutual information\" design choice.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "This paper demonstrates significant strengths across several key dimensions, presenting a compelling case for its contribution to the field of on-device AI and knowledge graph reasoning.\n- This work exhibits high originality by tackling the relatively unexplored problem of on-device training and adaptation for KGQA systems, not just on-device inference. It creatively combines ideas from different domains: Embedding Hashing for KGQA, Stepwise Annotation Generation for Adaptation\n- The study is executed with high quality and methodological rigor. The experimental evaluation is comprehensive and convincing.\n- The paper is generally well-structured and clearly written. The logical presentation of the three-phase pipeline (Hashing, Retrieval, Adaptation) is clear. Figures 1 and 2 effectively illustrate the core workflows. The use of tables to summarize comparative results and ablation studies is clear and facilitates comparison.\n- This work is highly important for both academic research and practical applications. It directly addresses the growing need for privacy-preserving, efficient, and adaptive AI on personal devices. By enabling on-device training, it avoids data privacy concerns associated with cloud-based model retraining. The proposed techniques pave the way for a new generation of mobile systems that can continuously learn from user data without compromising performance or battery life. The provided prompts and methodology serve as a valuable resource for subsequent research in the community.", "weaknesses": "- Insufficient Analysis of Hashing Module Limitations: \n\nThe hashing module is central to MobileKGQA's efficiency claims, but the paper lacks a deeper analysis of its inherent limitations – particularly regarding hash collisions. Compressing high-dimensional embeddings into low-dimensional binary codes is inherently a lossy process. The paper does not discuss the possibility of different entities or relations being mapped to the same or very similar hash codes (i.e., hash collisions) and their potential impact. This weakens the claim of the system's robustness. If the collision rate increases as the knowledge graph expands, it could contaminate the answer candidate set, leading to degraded retrieval accuracy and final QA performance.\n\n- Limited Diversity in Experimental Datasets Affects Generality Claims:\n\nThe paper's experiments are validated only on two datasets (WebQSP and CWQ) derived from Freebase. To claim broad applicability, testing on knowledge graphs with different structural characteristics is necessary. For example, graphs like Wikidata are denser with more complex relations, while some domain-specific graphs might be sparser. The performance of the hashing module might vary with graph properties. In sparse graphs, with less semantic information, the discriminative power of hash codes might be insufficient; in dense graphs, with high semantic overlap, the risk of hash collisions increases. The current experimental setup cannot demonstrate the system's effectiveness across these diverse scenarios.\n\n- Lack of Comparison with Simpler Hashing or Dimensionality Reduction Baselines:\n\nThe paper fails to adequately compare its proposed mutual information maximization-based hashing method against simpler, more established baseline techniques. There is no comparison against classical dimensionality reduction/hashing techniques like Locality-Sensitive Hashing (LSH) or Principal Component Analysis (PCA). It is therefore difficult to ascertain to what extent the performance gains stem from the paper's sophisticated hashing objective function versus merely the act of \"using hashing\" itself. This makes it challenging to accurately assess the innovative contribution of this module.\n\n- Imprecisions in Result Presentation:\n\nIn Table 2a, GNN-RAG's disk usage is reported as 0.54GB, while MobileKGQA's is 0.6GB. However, the narrative in the main text might lead readers to perceive MobileKGQA as optimal across all dimensions.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)