arXiv AI
BIO-MEMART: Biometric-Aware KV Cache Memory for Multi-User LLM Agents
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arXiv AI
Sep 10
Personalizing LLM Agent Memory Using Biometrics
The paper introduces Bio-Memory, a biometric-aware memory architecture for large language model agents that conditions memory retrieval on both semantic similarity and biometric matching. By augmenting each memory note with a biometric embedding, Bio-Memory filters retrieval candidates based on identity before ranking them semantically. Experiments on a 10-user shared-agent setting across face and palmprint benchmarks show significant improvements in distinguishing owner from non-owner queries, with up to a 27.29% F1 gain on CALFW and 25.75% on MS_Blue.
arXiv AI
Sep 11
Kernel-Managed Shared Memory for System-Wide Personalization
The paper introduces kernel‑managed shared memory, a system‑level abstraction that lets specialized agents write structured, tagged memories while the agent‑system kernel controls retrieval, privacy, and prompt injection. Implemented on AIOS, this design outperforms unmanaged external memory, standard retrieval‑augmented injection, and full context concatenation across GPT‑4o, Llama‑3.1:8B, and Qwen‑2.5:7B, improving personalization scores by 2.4‑4.0 points on a 5‑point scale and reducing latency and token usage by 15‑61%. The results show that centralizing memory management in the kernel delivers most personalization benefits at a fraction of the cost.
arXiv Machine Learning
Jul 30
InferScale: GPU-Native KV Injection for Personalized LLM Serving
arXiv:2607. 27090v1 Announce Type: cross Abstract: Large language models are increasingly deployed with persistent personalized context, such as accumulated memory profiles or long conversation histories, that is shared across a user's many requests.
arXiv AI
Aug 5
MemArena: An Ego-Centric Benchmark for On-Device Agentic Personal Memory Assistants at Scale
arXiv:2608. 02613v1 Announce Type: cross Abstract: Edge-deployed personal memory assistants must handle private interpersonal conversations on-device with open-weight models.
Hugging Face Trending Papers
Jul 29
InferScale: GPU-Native KV Injection for Personalized LLM Serving
Large language models are increasingly deployed with persistent personalized context, such as accumulated memory profiles or long conversation histories, that is shared across a user's many requests. Production memory systems (e.
arXiv Computation and Language
Sep 10
Fine-Tuning a KV Cache Concatenation-Aware Model or Recomputing KV Caches? Why Not Both?
The paper addresses the challenge of long input contexts in Retrieval-Augmented Generation (RAG) systems, where concatenating many retrieved chunks increases prefill workload and time to first token (TTFT). It proposes a dual strategy: fine‑tuning the model to be aware of KV cache concatenation and selectively recomputing only part of the KV caches. Experiments on the RULER benchmark show that for a 124k‑token input, this combined method boosts the RULER score by 9.7 points over a baseline that recomputes caches only, while cutting TTFT by 80% compared with full attention.