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版本更新 · v2arXiv:2609.22231

EvalMem: An Operation-Level Diagnostic Framework for Long-Term Memory Systems

Zeyu Liu, Jian Zhong, Rongduo Han, Ziyang Wu, Shunye Tang, Chenghao He, Yaxuan Yang, Yihang Qiu 等 15 位作者

原文摘要

Long-horizon interactions with LLM-based assistants require memory systems that preserve and update user states, preferences, and interaction histories. Existing evaluations report end-to-end QA accuracy and cannot determine whether errors arise from encoding, retrieval, or generation. We introduce EvalMem, an operation-level diagnostic framework with three parallel Examiners. For each query, the Encoding Examiner checks whether the target fact is stored, the Retrieval Examiner assesses whether the native retriever returns usable evidence, and the Generation Examiner tests whether the model can answer from oracle evidence. Their outputs form fine-grained multi-label defect codes. To improve store-level diagnosis, we adapt agentic RAG with a recall-first strategy that searches using both the query and source evidence, increasing recall of present evidence on LoCoMo from 70.2% to 95.6%. Evaluations of seven memory systems on LoCoMo, LongMemEval-S, and dynamic DynaMem-Bench identify retrieval as the most frequently attributed failure layer; in default LoCoMo, retrieval defects reach 22.1%, compared with 7.7% for encoding and 6.5% for generation. Guided by this diagnosis, MemWiki, a search-friendly auxiliary structure built from each system's memory export, improves mean accuracy by 2.5 and 2.3 percentage points on LoCoMo and LongMemEval-S, respectively.

cs.CL

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.37147

MAPLE-Sec: Mobility-Aware Partitioned Language-Model Engine for Roaming IoT Security

Yuhang Qiu, Ziwei Li, Jinfeng Xu, Jialiang Xie, Fangmin Ren, Shirong Guo, Jing Yang, Sunil Prajapat 等 9 位作者

原文摘要

Mobile Internet-of-Things (IoT) endpoints such as connected vehicles, wearables, and drones continuously cross heterogeneous network domains, creating handover-driven attack surfaces that static, cloud-anchored detectors cannot follow. However, three obstacles block real-time detection on such roaming endpoints: detection state is lost at every inter-domain handover; transmitting raw telemetry to a fixed detector exhausts the uplink and the device battery; and full-depth language model inference cannot meet millisecond latency budgets on constrained hardware. To tackle these challenges, we propose MAPLE-Sec, a mobility-aware partitioning framework that splits a compact, security-tuned language model across a roaming device and its nearest edge node. First, a trajectory-conditioned partition scheduler recomputes the split point from predicted handover timing, residual battery, and uplink quality. Second, a token-budgeted early-exit mechanism emits a verdict at the shallowest layer that meets a confidence threshold. Finally, a learned activation-compression codec migrates the intermediate inference state across edge nodes during handover without retransmitting raw telemetry. We formalize the partition-and-migration decision as an on-device constrained optimization solved in sub-millisecond time. Extensive experiments across the CIC-IoT and Edge-IIoTset corpora under both IID and non-IID roaming conditions demonstrate that MAPLE-Sec sustains detection across mobility events, improving the converged F1-score by up to 9.3\% relative to Cloud-only and by 2.9 percentage points over the strongest edge-collaborative baseline (EdgeShard), while reducing end-to-end latency by up to 2.5$\times$. Ablation confirms that the activation-compression codec reduces migration energy by 2.4$\times$ versus the raw-activation migration variant.

cs.NI

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.30293

EgoHand: Monocular 3D Hand Reconstruction from Egocentric Video

Chao-Yeh Chen, Tiffany Chen, Lin Duan, Tianyi Hu, Yu-Hsiang Huang, Umar Iqbal, Jiefeng Li, Edy Lim 等 15 位作者

原文摘要

We introduce EgoHand, a monocular 3D hand reconstruction model that achieves state-of-the-art performance on egocentric video. Central to our approach is the curation of a high-quality training corpus of 14 million images, combining synthetic data, in-the-wild video, and multi-camera motion capture to balance scene diversity and supervision quality. The model adopts SOMA-Hand as its parametric representation and processes 2D hand crops with a transformer encoder, incorporating camera cues and an auxiliary 2D branch to improve geometric consistency and image alignment. Zero-shot evaluations on HOT3D, ARCTIC, and H2O demonstrate strong out-of-domain generalization, with lower joint-position, root-translation, and acceleration errors than competing baselines at an inference latency of approximately 9.01 ms. A blinded user preference study further shows that participants favor EgoHand's reconstructed hand poses over those from Apple EgoDex and Ropedia Xperience-10M. We release the EgoHand model and source code under the OpenMDW license, allowing free commercial use of the model.

cs.CV · cs.LG

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2411.00852

EF-LLM: A Continual Learning-Based Framework for AI-Assisted Automation, Enhanced Sparse Prediction, and Hallucination Detection

Zihang Qiu, Jayashri Ravishankar, Anam Malik, Zhongju Wang, Renyou Xie

原文摘要

Accurate prediction helps achieve supply-demand balance in energy systems,thereby supporting operational decision-making and scheduling.Traditional models,which lack AI-assisted automation,often rely heavily on expert knowledge,incur high labor costs,and perform poorly under sparse-data conditions.To address these challenges,this work proposes an Energy Forecasting Large Language Model (EF-LLM),which integrates domain knowledge and temporal data for time-series forecasting while supporting both pre-forecast operations and post-forecast decision-making.EF-LLM's human-artificial intelligence (AI) interaction capabilities further lower the entry barrier for forecasting tasks and reduce the need for additional expert involvement.To support these functions, a continual learning framework with updatable Low-Rank Adaptation (LoRA) and a multi-channel architecture is developed to align heterogeneous multimodal data, enabling EF-LLM to continuously acquire and adapt multimodal knowledge.In addition,EF-LLM improves prediction accuracy under sparse-data conditions by leveraging its ability to process and fuse multimodal information.A Fusion Parameter-Efficient Fine-Tuning (F-PEFT) method is further introduced to effectively exploit both time-series data and textual information.EF-LLM is also the first energy-specific LLM to detect hallucinations and quantify their occurrence rate, achieved via multi-task learning and semantic similarity analysis.This work has achieved success in energy prediction scenarios for load, PV, and wind power forecast.

cs.LG · cs.AI

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arXiv ↗PDF ↗