Mnemis: Dual-Route Retrieval on Hierarchical Graphs for Long-Term LLM Memory

AI Memory, specifically how models organizes and retrieves historical messages, becomes increasingly valuable to Large Language Models (LLMs), yet existing methods (RAG and Graph-RAG) primarily retrieve memory through similarity-based mechanisms. While efficient, such System-1-style retrieval struggles with scenarios that require global reasoning or comprehensive coverage of all relevant information. In this work, We propose Mnemis, a novel memory framework that integrates System-1 similarity search with a complementary System-2 mechanism, termed Global Selection. Mnemis organizes memory into a base graph for similarity retrieval and a hierarchical graph that enables top-down, deliberate traversal over semantic hierarchies. By combining the complementary strength from both retrieval routes, Mnemis retrieves memory items that are both semantically and structurally relevant. Mnemis achieves state-of-the-art performance across all compared methods on long-term memory benchmarks, scoring 93.9 on LoCoMo and 91.6 on LongMemEval-S using GPT-4.1-mini.

Subspace-Based Super-Resolution Sensing for Bi-Static ISAC with Clock Asynchronism

Bi-static sensing is an attractive configuration for integrated sensing and communications (ISAC) systems; however, clock asynchronism between widely separated transmitters and receivers introduces time-varying time offsets (TO) and phase offsets (PO), posing significant challenges. This paper introduces a signal-subspace-based framework that estimates decoupled angles, delays, and complex gain sequences (CGS)-- the target-reflected signals -- for multiple dynamic target paths. The proposed framework begins with a novel TO alignment algorithm, leveraging signal subspace or covariance, to mitigate TO variations across temporal snapshots, enabling coherent delay-domain analysis. Subsequently, subspace-based methods are developed to compensate for TO residuals and to perform joint angle-delay estimation. Finally, leveraging the high resolution in the joint angle-delay domain, the framework compensates for the PO and estimates the CGS for each target. The framework can be applied to both single-antenna and multi-antenna systems. Extensive simulations and experiments using commercial Wi-Fi devices demonstrate that the proposed framework significantly surpasses existing solutions in parameter estimation accuracy and delay resolution. Notably, it uniquely achieves a super-resolution in the delay domain, with a probability-of-resolution curve tightly approaching that in synchronized systems.

Advanced Codebook Design for SCMA-aided NTNs With Randomly Distributed Users

In this letter, a novel class of sparse codebooks is proposed for sparse code multiple access (SCMA) aided non-terrestrial networks (NTN) with randomly distributed users characterized by Rician fading channels. Specifically, we first exploit the upper bound of bit error probability (BEP) of an SCMA-aided NTN with large-scale fading of different users under Rician fading channels. Then, the codebook is designed by employing pulse-amplitude modulation constellation, user-specific rotation and power factors. To further reduce the optimization complexity while maintaining the power diversity of different users, an orthogonal layer-assisted joint layer and power assignment strategy is proposed. Finally, unlike existing SCMA codebook designs that treat all users as one super-user, we propose to minimize the BEP of the worst user to ensure user fairness. The simulation results show that the proposed scheme is capable of providing a substantial performance gain over conventional codebooks.