PEAM: On-device Communication Agent for Personalized, Efficient and Autonomous Mobile Network Configuration

Haowen Wan; Shangzhuo Xie; Syue-Ju Syue; Xianyue Wu; Runhua Li; Qianqian Yang

cs.AIOct 10, 2026

PEAM: On-device Communication Agent for Personalized, Efficient and Autonomous Mobile Network Configuration

Authors: Haowen Wan, Shangzhuo Xie, Syue-Ju Syue, Xianyue Wu, Runhua Li, Qianqian Yang

Organizations: Polytechnic Institute, Zhejiang University · College of Information Science and Electronic Engineering, Zhejiang University · OPPO

Abstract

The growing deployment of compact large language models on smartphones is enabling autonomous on-device decision-making, such as emerging mobile GUI agents. However, many mobile decisions, such as network configuration when users switch apps or connectivity conditions change, must satisfy stringent latency constraints to preserve user experience. In this paper, we propose PEAM, an on-device LLM-based communication agent that autonomously generates personalized network-configuration rules executable within milliseconds. Specifically, PEAM first fine-tune the on-device LLM with a dataset constructed from manually annotated real-user data to integrate the communication knowledge into the local model. PEAM then operates in two phases. In the rule-generation phase, which is performed when the device is idle, such as overnight, PEAM analyzes historical network-configuration records in batches and reflects across batches to consolidate consistent evidence, reduce local bias, and derive robust configuration rules. To enable more accurate and stable rule generation over time, PEAM further constructs a user profile from high-confidence rules to capture the user's network preferences and incorporates this profile into each rule-generation cycle. In the rule-execution phase, PEAM is invoked when the communication context changes, such as when users switch apps or connectivity conditions change, and searches the rule library to identify and execute the appropriate rule. Experiments demonstrate that PEAM, based on a fine-tuned 4B model, achieves 99.13% correct network configuration decisions, while 92.50% of the rules generated by PEAM are valid, outperforming the traditional expert-rule based method by 4.41% and 84.50%, respectively.

Figures & tables

Figure 1: Modeling personalized mobile network configuration from historical decision records. PEAM infers why a user prefers a particular configuration and determines what configuration action should be executed.

Figure 2: Overview of PEAM. The framework contains four stages: (1) Record Acquisition collects user-initiated configuration records; (2) Periodic Update performs clustering, batch-wise record analysis, and cross-batch reflection to generate personalized rules; (3) User-Level Local Memory maintains the rule base and user profile; and (4) Real-Time Execution retrieves and executes matched rules without invoking the LLM.

Figure 3: Prompt template for Record Analysis. The yellow boxes contain fixed task and output constraints, the blue box contains the user profile generated in the previous update period, and the green box contains the semantic representations of the current records.

Method	Simulated User Dataset				Real User Dataset			Model Pricing
	REA	RCR	TRR	JMS	REA	RCR	TRR	Input	Output
Expert Rules	94.72	6.06	8.00	22.15	48.68	2.92	2.56	-	-
Qwen3.5-Flash	86.58	25.93	56.86	51.90	74.99	12.49	51.35 (19/37)	0.028	0.278
Gemini-3-Flash	92.24	54.79	80.19	64.55	72.51	15.47	49.02 (25/51)	0.500	3.000
GPT-5.2	87.56	57.25	87.47	55.72	71.87	20.96	38.89 (14/36)	1.750	14.00
PEAM	99.13	60.58	92.50	81.32	72.34	25.86	38.10 (24/63)	-	-

Table 1: End-to-end performance on the simulated-user and real-user datasets, together with API pricing for the cloud-based LLM baselines. REA, RCR, and TRR are reported as percentages. Values in parentheses denote the number of triggered rules over the total number of generated rules. The Improvement row reports the absolute gain over the strongest baseline for each metric. Input and output prices are reported in USD per million tokens. Bold and underlined values denote the best and second-best results, respectively.

Method	Record Analysis			Reflection			User Profiling
	SS	JMS	IT	SS	JMS	IT	SS	JMS	IT
Qwen3.5-Flash	0.7451	74.35	8.73	0.6577	66.91	7.34	0.8726	87.98	2.57
Gemini-3-Flash	0.6973	79.69	4.74	0.6433	64.88	17.31	0.8593	85.54	2.58
GPT-5.2	0.7418	81.06	13.80	0.8377	73.05	9.76	0.8582	89.89	6.59
PEAM	0.8361	86.68	15.64	0.8955	84.12	13.98	0.9112	87.10	5.31
Improvement	+0.0910	+5.62	-	+0.0578	+11.07	-	+0.0386	-	-

Table 2: Task-level performance on Record Analysis, Cross-Batch Consolidation, and User Profiling. IT is reported in seconds. The Improvement row reports the absolute gain over the strongest baseline for each metric. Bold and underlined values denote the best and second-best results, respectively.

Method	Record Analysis			Reflection			User Profiling			Real-time Execution
	SS	JMS	IT	SS	JMS	IT	SS	JMS	IT	Retrieval Time
PEAM	0.8413	84.06	35.53	0.8563	75.21	32.62	0.8952	84.64	11.87	-
PEAM-Q	0.8132	80.80	33.17	0.8608	72.09	59.29	0.7991	64.56	8.950	0.5730

Table 3: Task-level performance of AndesVL-based PEAM under two deployment configurations. PEAM denotes the full-precision model evaluated on an NVIDIA A100 GPU, whereas PEAM-Q denotes the quantized model deployed on a smartphone. Inference time (IT) is reported in seconds, while retrieval time is reported in milliseconds.

Variant	REA	RCR	TRR	JMS
PEAM	99.13	60.58	92.50	81.32
- w/o Clustering	98.61	49.57	91.79	65.80
- w/o Reflection	92.44	67.96	43.21	70.43
- w/o User Profiling	97.36	61.31	85.26	75.27

Table 4: Ablation results for the major components of PEAM. REA, RCR, and TRR are reported as percentages.

Appendix figures & tables13 assets

Supplementary material from the paper’s appendix.

Appendix

Intent	Condition
Optimize cellular network usage for smooth meeting scenarios.	app_scene=Meeting , WiFi_QoE $<4$ , cellular_score $\geq 80$
Optimize cellular network usage for smooth video call scenarios.	app_scene=VIDEO_CALL , WiFi_QoE $<4$ , cellular_score $\geq 80$
Optimize cellular network usage for smooth voice call scenarios.	app_scene=VOICE_CALL , WiFi_QoE $<4$ , cellular_score $\geq 80$
Optimize cellular network usage for smooth game-playing scenarios.	app_scene=GAME_PLAYING , WiFi_QoE $<4$ , cellular_score $\geq 80$
Optimize cellular network usage for smooth game lobby scenarios.	app_scene=GAME_LOBBY , WiFi_QoE $<4$ , cellular_score $\geq 80$
Save cellular data in live-streaming scenarios.	app_scene=LIVE_STREAM , WiFi_QoE $\geq 4$

Appendix

Table A1: Rule Conditions for Network Switching Intents

Figure A1: On-device deployment of PEAM.

Metric	Retrieval Time	Execution Time
		Cellular-to-Cellular	Cellular-to-Wi-Fi
Time (ms)	0.573	614.2	93.57

Appendix

Table A2: Runtime of strategy retrieval and execution. All times are reported in milliseconds.

Intent	Objective Evaluation	Subjective Evaluation
Interview Results
When using Douyin at two regular home locations, prefer connecting to the home Wi-Fi network; switch to cellular data when Wi-Fi is unavailable.	—	—
When using Nike Run Club at home, prefer connecting to Wi-Fi.	—	—
At home, prefer connecting to the home Wi-Fi network regardless of the app being used.	—	—
PEAM Analysis Results
Switch to the cellular network when using Douyin or watching short videos at specific locations such as POI-1 and POI-2 to ensure a smooth viewing experience.	Fully Compliant (55)	Fully Compliant

Appendix

Table A3: Comparison Between User Interview Results and PEAM Analysis Results for User 001

Intent	Objective Evaluation	Subjective Evaluation
Interview Results
Use the China Mobile app for faster login; switch to the corresponding carrier SIM when opening the app, and switch back to Wi-Fi after login.	—	—
Connect to Wi-Fi when downloading apps from the app store to save cellular data.	—	—
Switch to the SIM 1 cellular network before using Alipay to ensure network stability.	—	—
Connect to the commonly used Wi-Fi network when entering a regular location.	—	—
Switch back to a more stable cellular network when Wi-Fi quality is poor.	—	—

Appendix

Table A4: Comparison Between User Interview Results and PEAM Analysis Results for User 002

Intent	Objective Evaluation	Subjective Evaluation
Interview Results
When using Xiaohongshu in the first regular area at home, prefer manually connecting to the home Wi-Fi network.	—	—
When using Bilibili in the first regular area at the office, prefer manually connecting to an available Wi-Fi network; switch to cellular when the network quality is poor.	—	—
PEAM Analysis Results
Connect to the cellular network when entering the company location.	Partially Compliant (2)	Not Mentioned
Habitually switch to Wi-Fi when using Xiaohongshu at the company location POI-1.	Partially Compliant (1)	Not Mentioned

Appendix

Table A5: Comparison Between User Interview Results and PEAM Analysis Results for User 003

Intent	Objective Evaluation	Subjective Evaluation
Interview Results
Prefer using SIM 1 for Douyin short videos, possibly because SIM 1 has sufficient cellular data.	—	—
Switch to SIM 2 for Douyin and TeamTalk when the SIM 1 network quality is poor.	—	—
Switch to the cellular network when Wi-Fi quality is poor while using apps such as Youdao Dictionary and WeChat.	—	—
Proactively switch to the cellular network before leaving a regular location, likely to avoid lag caused by weak signals near the edge of Wi-Fi coverage.	—	—
Connect to another phone’s hotspot for internet access during phone data backup or data migration.	—	—

Appendix

Table A6: Comparison Between User Interview Results and PEAM Analysis Results for User 004

Intent	Objective Evaluation	Subjective Evaluation
Interview Results
When cellular data becomes stuck, prefer switching to a Wi-Fi network with better signal quality to continue using apps.	—	—
Manually connect to the in-building Wi-Fi network at POI-1.	—	—
Prefer using the company Wi-Fi network when using TeamTalk at the office.	—	—
Use cellular data to access Tencent News when Wi-Fi is unavailable.	—	—
When playing DNF Mobile, switch to cellular data if the connected Wi-Fi network has poor quality.	—	—

Appendix

Table A7: Comparison Between User Interview Results and PEAM Analysis Results for User 005

Intent	Objective Evaluation	Subjective Evaluation
Interview Results
When using Douyin, switch to cellular data if the connected Wi-Fi network becomes stuck.	—	—
PEAM Analysis Results
Switch to the SIM 2 cellular network when the Wi-Fi user experience score is poor or relatively low to obtain a stable network connection.	Fully Compliant (4)	Partially Compliant
Switch to the cellular network when watching Douyin live streams on holiday mornings to ensure smooth viewing and avoid lag.	Partially Compliant (2)	Partially Compliant
Connect to SSID-1 at POI-1.	Fully Compliant (9)	Not Mentioned

Appendix

Table A8: Comparison Between User Interview Results and PEAM Analysis Results for User 006

Category	Features
Spatiotemporal Context	Timestamp, POI, geofence
Foreground Application	App package name, app scene, app foreground dwell time, app-scene foreground dwell time
Network Access Status	Network access type, data SIM slot, PLMN, roaming status
Cellular Network	Cellular network type, RSRP, RSRQ, SINR, cellular signal level, network quality score
Wi-Fi Network	SSID, BSSID, frequency band, bandwidth, RSSI, QoE score, Wi-Fi signal level
Others	Traffic configuration, VPN configuration, battery status

Appendix

Table A9: Contextual Features Used for Network Intent Modeling

Figure A2: An example of the resulting standardized data representation

Figure A3: An example of the standardized semantic representation.

Figure A4: Clustering results of optimized parameters on simulated users.

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