arxiv: 2604.10110 · v1 · submitted 2026-04-11 · 💻 cs.AI

Recognition: unknown

Trust Your Memory: Verifiable Control of Smart Homes through Reinforcement Learning with Multi-dimensional Rewards

Kai-Yuan Guo , Jiang Wang , Renjie Zhao , Tianyi Wang , Wandong Mao , Yu Gao , Mou Xiao Feng , Yi Xu

Authors on Pith no claims yet

Pith reviewed 2026-05-10 16:44 UTC · model grok-4.3

classification 💻 cs.AI

keywords reinforcement learningsmart homesmemory managementmulti-dimensional rewardsbenchmarksdevice controlMemHome

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The pith

Multi-dimensional rewards in reinforcement learning provide intermediate feedback that improves memory-driven device control in smart homes over conventional outcome-only methods.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper shows that standard reinforcement learning fails in smart-home memory tasks because it only rewards final success, leaving agents without guidance on the separate steps of adding, updating, deleting, or using stored information. The authors address this by designing rewards that score each memory subtask separately and by releasing MemHomeLife, a dataset drawn from real long-term user logs, plus the MemHome benchmark that tests these subtasks directly. This matters for smart-home assistants because reliable memory is needed to personalize device behavior across days or weeks without repeated user instructions. If the approach holds, RL agents can maintain accurate memory states and execute more complex control sequences with fewer local failures.

Core claim

Conventional RL methods that rely only on outcome-based supervision produce sub-optimal results in fine-grained memory management tasks such as adding, updating, deleting, and utilizing memory for device control. Multi-dimensional rewards supply intermediate feedback on each subtask, and the new MemHome benchmark, constructed from anonymized real-world long-term interaction logs in MemHomeLife, enables systematic evaluation of memory-driven smart-home control.

What carries the argument

Multi-dimensional rewards that assign separate signals to memory subtasks (adding, updating, deleting, utilizing) to supply intermediate supervision inside reinforcement learning.

If this is right

RL agents receive guidance on each memory operation instead of only final task completion, reducing local failures in long sequences of device control.
The MemHome benchmark allows separate measurement of performance on adding, updating, deleting, and utilizing memory, supporting targeted method improvements.
Models can maintain consistent memory states across extended user interactions, enabling more reliable personalization of smart-home devices.
Evaluation gaps between immediate device control and general memory retrieval are closed for smart-home scenarios.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

The same reward decomposition could be applied to other RL settings that require fine-grained state tracking, such as multi-turn dialogue or personal scheduling assistants.
Automatically learning the weights or structure of the multi-dimensional rewards might reduce the manual design burden while preserving the intermediate feedback benefit.
Combining the RL memory component with large language models could be tested to determine whether the added supervision improves the factual accuracy of LLM-based smart-home controllers.

Load-bearing premise

Multi-dimensional rewards can be designed to give effective intermediate feedback on memory subtasks without introducing new biases or requiring extensive manual tuning.

What would settle it

A controlled comparison in which agents trained with the multi-dimensional rewards show no gain, or a drop, in success rate on the memory subtasks of the MemHome benchmark relative to standard outcome-only RL.

Figures

Figures reproduced from arXiv: 2604.10110 by Jiang Wang, Kai-Yuan Guo, Mou Xiao Feng, Renjie Zhao, Tianyi Wang, Wandong Mao, Yi Xu, Yu Gao.

**Figure 2.** Figure 2: MemHome: From Real Long-Term Dialogues to [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗

**Figure 3.** Figure 3: Overview of the Product Reward In Smart Memory. [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗

**Figure 4.** Figure 4: Ablation study on reward design with Qwen3-4B. [PITH_FULL_IMAGE:figures/full_fig_p008_4.png] view at source ↗

read the original abstract

Large Language Models (LLMs) have become a key foundation for enabling personalized smart home experiences. While existing studies have explored how smart home assistants understand user queries to control devices in real time, their ability to perform memory-driven device control remains challenging from both evaluation and methodological perspectives. In terms of evaluation, existing benchmarks either focus on immediate device control or general open-domain memory retrieval tasks, and therefore cannot effectively evaluate a model's ability to perform memory-driven device control. Methodologically, while memory-driven device control can be approached using Reinforcement Learning, conventional RL methods generally rely on outcome-based supervision (i.e., whether the final task is achieved). This lack of intermediate feedback can lead to sub-optimal performance or local failures in fine-grained memory management tasks (adding, updating, deleting, and utilizing). To address these issues, we first release MemHomeLife, built from anonymized real-world long-term user interaction logs. To enable more fine-grained evaluation of different memory-related subtasks, we further construct MemHome, the first benchmark designed to systematically evaluate memory-driven device control in smart home scenarios.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

The paper releases a real-user dataset and a new benchmark for memory-driven smart home device control, then tries multi-dimensional rewards in RL to handle add/update/delete/utilize subtasks better than outcome-only supervision.

read the letter

The main thing to know is that they built MemHomeLife from anonymized long-term smart home logs and turned it into MemHome, the first benchmark that breaks memory-driven control into those four subtasks. The claim is that ordinary RL, which only sees whether the final device action succeeded, misses the intermediate memory steps and ends up with local failures, so they add separate reward dimensions to give feedback on each memory operation. That setup is new for this exact use case and the real logs give it more grounding than synthetic testbeds usually have. The benchmark construction itself looks like a useful step for anyone who wants to measure memory handling separately from raw control accuracy. The soft spots sit in the reward design and the missing evidence. If the multi-dimensional terms are hand-crafted to match the logs or the task definitions, any reported gains could be artifacts of shaping rather than a robust fix, and the abstract gives no numbers, ablations, or comparisons to show otherwise. Without those details it is difficult to tell whether the approach generalizes or simply fits this one benchmark. The work stays narrow to smart-home personalization, so it does not reshape broader RL or memory research. Readers who build agents for IoT or who need concrete memory benchmarks will get the most out of the data release. It is worth sending to peer review because a new dataset and a targeted evaluation setup can still be valuable even if the RL variant requires more controls and clearer reporting on how the rewards were chosen.

Referee Report

3 major / 2 minor

Summary. The paper claims that existing benchmarks are inadequate for evaluating memory-driven device control in smart homes, and that conventional outcome-based RL leads to sub-optimal performance on fine-grained memory subtasks (add, update, delete, utilize). It releases MemHomeLife (anonymized real-world logs) and constructs the MemHome benchmark to enable systematic evaluation, while proposing RL with multi-dimensional rewards to supply intermediate feedback and improve verifiable control.

Significance. If the multi-dimensional rewards can be shown to deliver robust, low-bias intermediate signals without heavy per-task tuning, the work would meaningfully advance RL applications for personalized, memory-aware smart-home agents. The public release of a real-log-derived dataset and a dedicated benchmark is a concrete contribution that could support future reproducible research in this area.

major comments (3)

[Methods / Reward Design] The central methodological claim—that multi-dimensional rewards overcome the limitations of outcome-based supervision—rests on the specific reward functions for the four memory subtasks. No evidence is provided that these functions are robust to formulation choices or free of bias introduced by hand-engineering from the MemHomeLife logs; without such validation (e.g., sensitivity analysis or ablation on reward components), reported gains may be artifacts of shaping rather than a general advance.
[Experiments / Results] The abstract states that conventional RL yields sub-optimal performance or local failures on fine-grained memory tasks, yet no quantitative comparison (success rates, failure modes, or learning curves) between outcome-based and multi-dimensional reward variants is referenced. This comparison is load-bearing for the paper’s motivation and must be shown on the MemHome benchmark.
[Benchmark Construction] The MemHome benchmark construction is described only at a high level; it is unclear how the four subtasks are operationalized into episodes, what state representation is used for the RL agent, and whether the benchmark includes held-out users or temporal splits that would test generalization beyond the training logs.

minor comments (2)

[Abstract / Introduction] The title refers to “verifiable control,” but the abstract does not define what verification mechanism is used or how the RL policy is made verifiable; this should be clarified early in the introduction.
[Abstract] The abstract contains a minor grammatical issue: “their ability to perform memory-driven device control remains challenging from both evaluation and methodological perspectives” would read more clearly as “remains challenging both from an evaluation and from a methodological perspective.”

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the thoughtful and constructive feedback on our manuscript. We believe the comments will help improve the clarity and rigor of the work. We address each major comment below and plan to incorporate revisions as indicated.

read point-by-point responses

Referee: [Methods / Reward Design] The central methodological claim—that multi-dimensional rewards overcome the limitations of outcome-based supervision—rests on the specific reward functions for the four memory subtasks. No evidence is provided that these functions are robust to formulation choices or free of bias introduced by hand-engineering from the MemHomeLife logs; without such validation (e.g., sensitivity analysis or ablation on reward components), reported gains may be artifacts of shaping rather than a general advance.

Authors: We agree that demonstrating the robustness of the multi-dimensional reward design is crucial for the central claim. In the revised manuscript, we will add a sensitivity analysis varying the weights and formulations of the reward components across the four subtasks, together with ablations that isolate individual reward dimensions. These additions will provide evidence that performance gains are not artifacts of the specific hand-engineered functions derived from the logs. revision: yes
Referee: [Experiments / Results] The abstract states that conventional RL yields sub-optimal performance or local failures on fine-grained memory tasks, yet no quantitative comparison (success rates, failure modes, or learning curves) between outcome-based and multi-dimensional reward variants is referenced. This comparison is load-bearing for the paper’s motivation and must be shown on the MemHome benchmark.

Authors: We acknowledge that the abstract claim requires direct supporting evidence. We will revise the manuscript to prominently present and reference the quantitative comparisons between outcome-based and multi-dimensional reward RL variants, including success rates, failure modes, and learning curves evaluated on the MemHome benchmark. The abstract will be updated to cite these results explicitly. revision: yes
Referee: [Benchmark Construction] The MemHome benchmark construction is described only at a high level; it is unclear how the four subtasks are operationalized into episodes, what state representation is used for the RL agent, and whether the benchmark includes held-out users or temporal splits that would test generalization beyond the training logs.

Authors: We will expand the benchmark construction section with concrete details. The revision will specify how the add, update, delete, and utilize subtasks are operationalized as RL episodes, describe the precise state representation provided to the agent, and clarify the use of held-out users together with temporal splits to evaluate generalization beyond the training logs. revision: yes

Circularity Check

0 steps flagged

No circularity: contributions are benchmark release and empirical RL proposal without self-referential derivations

full rationale

The paper introduces MemHomeLife (from real logs) and MemHome benchmark for memory-driven device control, then proposes multi-dimensional rewards to supply intermediate feedback for subtasks (add/update/delete/utilize) where outcome-only RL fails. No equations, uniqueness theorems, fitted parameters renamed as predictions, or self-citation chains appear in the provided text. The central claim—that multi-dimensional rewards improve fine-grained performance—is an empirical hypothesis tested on the new benchmark, not a reduction to inputs by construction. This is the expected non-finding for a data-release + method-proposal paper.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Only abstract available; no specific free parameters, axioms, or invented entities described. The approach assumes multi-dimensional rewards can be meaningfully defined for memory subtasks without further detail.

pith-pipeline@v0.9.0 · 5509 in / 1013 out tokens · 50338 ms · 2026-05-10T16:44:24.722121+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

27 extracted references · 20 canonical work pages · 8 internal anchors

[1]

Qingyao Ai, Yichen Tang, Changyue Wang, Jianming Long, Weihang Su, and Yiqun Liu. 2025. MemoryBench: A Benchmark for Memory and Continual Learning in LLM Systems.arXiv preprint arXiv:2510.17281(2025)

work page internal anchor Pith review Pith/arXiv arXiv 2025
[2]

Prateek Chhikara, Dev Khant, Saket Aryan, Taranjeet Singh, and Deshraj Ya- dav. 2025. Mem0: Building production-ready ai agents with scalable long-term memory.arXiv preprint arXiv:2504.19413(2025)

work page internal anchor Pith review arXiv 2025
[3]

Zhaopei Huang, Qifeng Dai, Guozheng Wu, Xiaopeng Wu, Kehan Chen, Chuan Yu, Xubin Li, Tiezheng Ge, Wenxuan Wang, and Qin Jin. 2025. Mem-PAL: Towards Memory-based Personalized Dialogue Assistants for Long-term User- Agent Interaction.arXiv preprint arXiv:2511.13410(2025)

work page arXiv 2025
[4]

Evan King, Haoxiang Yu, Sangsu Lee, and Christine Julien. 2024. Sasha: creative goal-oriented reasoning in smart homes with large language models.Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies8, 1 (2024), 1–38

2024
[5]

Harrison Lee, Samrat Phatale, Hassan Mansoor, Thomas Mesnard, Johan Ferret, Kellie Lu, Colton Bishop, Ethan Hall, Victor Carbune, Abhinav Rastogi, and Sushant Prakash. 2024. RLAIF vs. RLHF: scaling reinforcement learning from human feedback with AI feedback. InProceedings of the 41st International Con- ference on Machine Learning(Vienna, Austria)(ICML’24)...

2024
[6]

Silin Li, Yuhang Guo, Jiashu Yao, Zeming Liu, and Haifeng Wang. 2025. HomeBench: Evaluating LLMs in Smart Homes with Valid and Invalid Instruc- tions Across Single and Multiple Devices.arXiv preprint arXiv:2505.19628(2025)

work page arXiv 2025
[7]

Aixin Liu, Bei Feng, Bing Xue, Bingxuan Wang, Bochao Wu, Chengda Lu, Cheng- gang Zhao, Chengqi Deng, Chenyu Zhang, Chong Ruan, et al. 2024. Deepseek-v3 technical report.arXiv preprint arXiv:2412.19437(2024)

work page internal anchor Pith review Pith/arXiv arXiv 2024
[8]

Adyasha Maharana, Dong-Ho Lee, Sergey Tulyakov, Mohit Bansal, Francesco Barbieri, and Yuwei Fang. 2024. Evaluating Very Long-Term Conversational Memory of LLM Agents. InProceedings of the 62nd Annual Meeting of the Associ- ation for Computational Linguistics (Volume 1: Long Papers), Lun-Wei Ku, Andre Martins, and Vivek Srikumar (Eds.). Association for Com...

work page doi:10.18653/v1/2024.acl-long.747 2024
[9]

Charles Packer, Vivian Fang, Shishir_G Patil, Kevin Lin, Sarah Wooders, and Joseph_E Gonzalez. 2023. MemGPT: Towards LLMs as Operating Systems. (2023)

2023
[10]

Hao Peng, Yunjia Qi, Xiaozhi Wang, Bin Xu, Lei Hou, and Juanzi Li. 2025. VerIF: Verification Engineering for Reinforcement Learning in Instruction Following. InProceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, Christos Christodoulopoulos, Tanmoy Chakraborty, Carolyn Rose, and Violet Peng (Eds.). Association for Comput...

work page doi:10.18653/v1/2025.emnlp-main.1542 2025
[11]

Dmitriy Rivkin, Francois Hogan, Amal Feriani, Abhisek Konar, Adam Sigal, Xue Liu, and Gregory Dudek. 2024. Aiot smart home via autonomous llm agents. IEEE Internet of Things Journal(2024)

2024
[12]

Zhihong Shao, Yuxiang Luo, Chengda Lu, ZZ Ren, Jiewen Hu, Tian Ye, Zhibin Gou, Shirong Ma, and Xiaokang Zhang. 2025. Deepseekmath-v2: Towards self- verifiable mathematical reasoning.arXiv preprint arXiv:2511.22570(2025)

work page arXiv 2025
[13]

Zhihong Shao, Peiyi Wang, Qihao Zhu, Runxin Xu, Junxiao Song, Xiao Bi, Haowei Zhang, Mingchuan Zhang, YK Li, Yang Wu, et al. 2024. Deepseekmath: Pushing the limits of mathematical reasoning in open language models.arXiv preprint arXiv:2402.03300(2024)

work page internal anchor Pith review Pith/arXiv arXiv 2024
[14]

Yi Su, Dian Yu, Linfeng Song, Juntao Li, Haitao Mi, Zhaopeng Tu, Min Zhang, and Dong Yu. 2025. Crossing the Reward Bridge: Expanding RL with Verifiable Rewards Across Diverse Domains.arXiv preprint arXiv:2503.23829(2025)

work page arXiv 2025
[15]

Haoran Tan, Zeyu Zhang, Chen Ma, Xu Chen, Quanyu Dai, and Zhenhua Dong
[16]

InFindings of the Association for Computational Linguistics: ACL 2025

MemBench: Towards More Comprehensive Evaluation on the Memory Trust Your Memory: Verifiable Control of Smart Homes through Reinforcement Learning with Multi-dimensional Rewards of LLM-based Agents. InFindings of the Association for Computational Linguis- tics: ACL 2025, Wanxiang Che, Joyce Nabende, Ekaterina Shutova, and Moham- mad Taher Pilehvar (Eds.). ...

work page doi:10.18653/v1/2025.findings-acl.989 2025
[17]

Yunhao Tang, Sid Wang, Lovish Madaan, and Rémi Munos. 2025. Beyond Verifi- able Rewards: Scaling Reinforcement Learning for Language Models to Unverifi- able Data.arXiv preprint arXiv:2503.19618(2025)

work page arXiv 2025
[18]

Kimi Team, Yifan Bai, Yiping Bao, Guanduo Chen, Jiahao Chen, Ningxin Chen, Ruijue Chen, Yanru Chen, Yuankun Chen, Yutian Chen, et al . 2025. Kimi k2: Open agentic intelligence.arXiv preprint arXiv:2507.20534(2025)

work page internal anchor Pith review arXiv 2025
[19]

Qwen Team. 2024. Qwen2.5: A Party of Foundation Models. https://qwenlm. github.io/blog/qwen2.5/

2024
[20]

Jinpeng Wang, Chao Li, Ting Ye, Mengyuan Zhang, Wei Liu, and Jian Luan. 2025. ICPO: Intrinsic Confidence-Driven Group Relative Preference Optimization for Efficient Reinforcement Learning.arXiv preprint arXiv:2511.21005(2025)

work page arXiv 2025
[21]

Yu Wang, Ryuichi Takanobu, Zhiqi Liang, Yuzhen Mao, Yuanzhe Hu, Julian McAuley, and Xiaojian Wu. 2025. Mem-{\alpha}: Learning memory construction via reinforcement learning.arXiv preprint arXiv:2509.25911(2025)

work page arXiv 2025
[22]

Di Wu, Hongwei Wang, Wenhao Yu, Yuwei Zhang, Kai-Wei Chang, and Dong Yu. 2024. Longmemeval: Benchmarking chat assistants on long-term interactive memory.arXiv preprint arXiv:2410.10813(2024)

work page internal anchor Pith review arXiv 2024
[23]

Sikuan Yan, Xiufeng Yang, Zuchao Huang, Ercong Nie, Zifeng Ding, Zonggen Li, Xiaowen Ma, Kristian Kersting, Jeff Z Pan, Hinrich Schütze, et al. 2025. Memory- r1: Enhancing large language model agents to manage and utilize memories via reinforcement learning.arXiv preprint arXiv:2508.19828(2025)

work page internal anchor Pith review arXiv 2025
[24]

An Yang, Anfeng Li, Baosong Yang, Beichen Zhang, Binyuan Hui, Bo Zheng, Bowen Yu, Chang Gao, Chengen Huang, Chenxu Lv, et al. 2025. Qwen3 technical report.arXiv preprint arXiv:2505.09388(2025)

work page internal anchor Pith review Pith/arXiv arXiv 2025
[25]

Ziqi Yin, Mingxin Zhang, and Daisuke Kawahara. 2024. Harmony: A home agent for responsive management and action optimization with a locally deployed large language model.arXiv preprint arXiv:2410.14252(2024)

work page arXiv 2024
[26]

Tianyu Yu, Bo Ji, Shouli Wang, Shu Yao, Zefan Wang, Ganqu Cui, Lifan Yuan, Ning Ding, Yuan Yao, Zhiyuan Liu, et al. 2025. RLPR: Extrapolating RLVR to General Domains without Verifiers.arXiv preprint arXiv:2506.18254(2025)

work page arXiv 2025
[27]

打开空调，就要设置25度

Wanjun Zhong, Lianghong Guo, Qiqi Gao, He Ye, and Yanlin Wang. 2024. Memo- rybank: Enhancing large language models with long-term memory. InProceedings of the AAAI Conference on Artificial Intelligence, Vol. 38. 19724–19731. Kai-Yuan Guo et al. A Ablation on Reward Design. The detailed results of ablation studies under different prefix reward weights and ...

2024