Embedded AI Companion System on Edge Devices

• URL: http://arxiv.org/abs/2601.08128v1
• Date: Tue, 13 Jan 2026 01:50:06 GMT
• Title: Embedded AI Companion System on Edge Devices
• Abstract summary: We propose a memory paradigm that alternates between active and inactive phases.<n>During phases of user activity, the system performs low-latency, real-time dialog using lightweight retrieval over existing memories and context.<n>This design minimizes latency while maintaining long-term personalization under the tight constraints of embedded hardware.
• Score: 8.307883066152916
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Computational resource constraints on edge devices make it difficult to develop a fully embedded AI companion system with a satisfactory user experience. AI companion and memory systems detailed in existing literature cannot be directly used in such an environment due to lack of compute resources and latency concerns. In this paper, we propose a memory paradigm that alternates between active and inactive phases: during phases of user activity, the system performs low-latency, real-time dialog using lightweight retrieval over existing memories and context; whereas during phases of user inactivity, it conducts more computationally intensive extraction, consolidation, and maintenance of memories across full conversation sessions. This design minimizes latency while maintaining long-term personalization under the tight constraints of embedded hardware. We also introduce an AI Companion benchmark designed to holistically evaluate the AI Companion across both its conversational quality and memory capabilities. In our experiments, we found that our system (using a very weak model: Qwen2.5-7B-Instruct quantized int4) outperforms the equivalent raw LLM without memory across most metrics, and performs comparably to GPT-3.5 with 16k context window.

Related papers

• PASK: Toward Intent-Aware Proactive Agents with Long-Term Memory [83.06095498971682]
  We first propose DD-MM-PAS (Demand Detection, Memory Modeling, Proactive Agent System) as a general paradigm for streaming proactive AI agent.<n>We instantiate this paradigm in Pask, with streaming IntentFlow model for DD, a hybrid memory (workspace, user, global) for long-term MM, PAS infra framework.<n>We also introduce LatentNeeds-Bench, a real-world benchmark built from user-consented data and refined through thousands of rounds of human editing.
  arXiv  Detail & Related papers  (2026-04-09T09:06:13Z)
• D-Mem: A Dual-Process Memory System for LLM Agents [3.5426740232689604]
  We introduce D-Mem, a dual-process memory system.<n>It retains lightweight vector retrieval for routine queries while establishing an exhaustive Full Deliberation module as a high-fidelity fallback.<n>Experiments on the LoCoMo and RealTalk benchmarks using GPT-4o-mini and Qwen3-235B-Instruct demonstrate the efficacy of our approach.
  arXiv  Detail & Related papers  (2026-03-19T08:55:22Z)
• AMemGym: Interactive Memory Benchmarking for Assistants in Long-Horizon Conversations [61.6579785305668]
  AMemGym is an interactive environment enabling on-policy evaluation and optimization for memory-driven personalization.<n>Our framework provides a scalable, diagnostically rich environment for advancing memory capabilities in conversational agents.
  arXiv  Detail & Related papers  (2026-03-02T15:15:11Z)
• AgentLongBench: A Controllable Long Benchmark For Long-Contexts Agents via Environment Rollouts [78.33143446024485]
  We introduce textbfAgentLongBench, which evaluates agents through simulated environment rollouts based on Lateral Thinking Puzzles.<n>This framework generates rigorous interaction trajectories across knowledge-intensive and knowledge-free scenarios.
  arXiv  Detail & Related papers  (2026-01-28T16:05:44Z)
• EvolMem: A Cognitive-Driven Benchmark for Multi-Session Dialogue Memory [63.84216832544323]
  EvolMem is a new benchmark for assessing multi-session memory capabilities of large language models (LLMs) and agent systems.<n>To construct the benchmark, we introduce a hybrid data synthesis framework that consists of topic-initiated generation and narrative-inspired transformations.<n>Extensive evaluation reveals that no LLM consistently outperforms others across all memory dimensions.
  arXiv  Detail & Related papers  (2026-01-07T03:14:42Z)
• HiMeS: Hippocampus-inspired Memory System for Personalized AI Assistants [7.477189210398971]
  We propose HiMeS, an AI-assistant architecture that fuses short-term and long-term memory.<n>Inspired by the hippocampus-neocortex memory mechanism, we propose HiMeS, an AI-assistant architecture that fuses short-term and long-term memory.
  arXiv  Detail & Related papers  (2026-01-06T05:05:50Z)
• Memoria: A Scalable Agentic Memory Framework for Personalized Conversational AI [0.6840655769002751]
  Agentic memory is emerging as a key enabler for large language models (LLM)<n>We present Memoria, a modular memory framework that augments LLM-based conversational systems with persistent, interpretable, and context-rich memory.<n>We demonstrate how Memoria enables scalable, personalized conversational artificial intelligence (AI) by bridging the gap between stateless LLM interfaces and agentic memory systems.
  arXiv  Detail & Related papers  (2025-12-14T13:38:06Z)
• MEMTRACK: Evaluating Long-Term Memory and State Tracking in Multi-Platform Dynamic Agent Environments [6.12783571098263]
  MEMTRACK is a benchmark designed to evaluate long-term memory and state tracking in multi-platform agent environments.<n>Each benchmark instance provides a chronologically platform-interleaved timeline, with noisy, conflicting, cross-referring information.<n>Our benchmark tests memory capabilities such as acquistion, selection and conflict resolution.
  arXiv  Detail & Related papers  (2025-10-01T18:34:03Z)
• Memory Management and Contextual Consistency for Long-Running Low-Code Agents [0.0]
  This paper proposes a novel hybrid memory system designed specifically for LCNC agents.<n>Inspired by cognitive science, our architecture combines episodic and semantic memory components with a proactive "Intelligent Decay" mechanism.<n>Key innovation is a user-centric visualization interface, aligned with the LCNC paradigm, which allows non-technical users to manage the agent's memory directly.
  arXiv  Detail & Related papers  (2025-09-27T08:01:26Z)
• MemOS: A Memory OS for AI System [116.87568350346537]
  Large Language Models (LLMs) have become an essential infrastructure for Artificial General Intelligence (AGI)<n>Existing models mainly rely on static parameters and short-lived contextual states, limiting their ability to track user preferences or update knowledge over extended periods.<n>MemOS is a memory operating system that treats memory as a manageable system resource.
  arXiv  Detail & Related papers  (2025-07-04T17:21:46Z)
• MEM1: Learning to Synergize Memory and Reasoning for Efficient Long-Horizon Agents [84.62985963113245]
  We introduce MEM1, an end-to-end reinforcement learning framework that enables agents to operate with constant memory across long multi-turn tasks.<n>At each turn, MEM1 updates a compact shared internal state that jointly supports memory consolidation and reasoning.<n>We show that MEM1-7B improves performance by 3.5x while reducing memory usage by 3.7x compared to Qwen2.5-14B-Instruct on a 16-objective multi-hop QA task.
  arXiv  Detail & Related papers  (2025-06-18T19:44:46Z)
• InternLM-XComposer2.5-OmniLive: A Comprehensive Multimodal System for Long-term Streaming Video and Audio Interactions [104.90258030688256]
  This project introduces disentangled streaming perception, reasoning, and memory mechanisms, enabling real-time interaction with streaming video and audio input.<n>This project simulates human-like cognition, enabling multimodal large language models to provide continuous and adaptive service over time.
  arXiv  Detail & Related papers  (2024-12-12T18:58:30Z)
• An Efficient and Streaming Audio Visual Active Speaker Detection System [2.4515389321702132]
  We present two scenarios that address the key challenges posed by real-time constraints. First, we introduce a method to limit the number of future context frames utilized by the ASD model. Second, we propose a more stringent constraint that limits the total number of past frames the model can access during inference.
  arXiv  Detail & Related papers  (2024-09-13T17:45:53Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.