HAM-VLN: Harnessing Hierarchical Agentic Memory for Zero-Shot Vision-and-Language Navigation

Vision-and-language navigation (VLN) enables robots to follow instructions in previously unseen environments. Recently, a training-free paradigm has emerged: the robot queries a multimodal LLM to understand its observations and plan the next action. However, long-horizon navigation based on either image streams or dense map inevitably introduces a growing memory and reasoning bottleneck. We present HAM-VLN, a decision-coupled, agent-authored memory that equips the robot with a persistent, depth-grounded world graph. In the same model call used to select the next action, HAM-VLN also records semantic and reflective information---including room type, objects, navigation progress, and failure notes. Recent waypoints remain verbatim within a bounded window, while older history re-enters the context only through retrieval scored by relevance, recency, and salience, together with one-hop topological expansion. This design requires no additional LLM calls beyond the per-waypoint decision. Compared to previous methods, HAM-VLN not only improves various navigation metrics but also reduces the context length by more than 65%. Specifically, HAM-VLN achieves 61.0% Success Rate (SR) on VLN-CE R2R, 52.7% SR on VLN-CE RxR, and 79.7% SR on HM3D-v2 ObjectNav without any training.

SR-Agent: An Experience-Driven Agentic Framework for Post-Ranking Strategy Refinement in E-Commerce Recommendation

User experience is a first-class objective in industrial e-commerce recommender systems (RS). Post-ranking strategies, which govern diversity, similarity, and exposure over a ranked list, are widely deployed in industrial RS for their simplicity and low serving cost. However, as the online recommendation environment evolves continuously, these statically configured strategies gradually become stale, thereby degrading the user experience. Refining them typically relies on manual inspection, diagnosis, and updates, making it slow, costly, and difficult to scale or reuse. Although recent LLM-based agents (e.g., RecUserSim, SimUSER, and Self-EvolveRec) offer promising directions, none of them close the full loop of automated, self-evolving strategy refinement. To bridge this gap, we introduce SR-Agent, which, to the best of our knowledge, is the first agentic framework deployed to refine post-ranking strategies in industrial RS. SR-Agent unifies three components: (i) a UserSim agent that applies inspection skills to surface user-perceived bad cases; (ii) an Analysis agent that consolidates recurring bad cases into structured, reusable diagnoses; and (iii) a constrained Strategy Refinement Harness that maps diagnoses to typed and bounded actions, gated by a four-stage reward pipeline with reversible rollback. Deployed on the Kuaishou e-commerce platform, SR-Agent continuously runs this refinement loop and, in a one-month online A/B test, increases order volume by 0.71%, browsing depth by 0.34%, and clicked-category diversity by 0.48%, while markedly shortening the refinement cycle and lowering operational cost.

Fused Constrained Policy Reuse Optimization for Wireless Resource Allocation

Deep reinforcement learning (DRL) has been widely adopted for wireless resource allocation due to its model-free adaptability. However, online exploration is costly, as randomly initialized policies may violate long-term constraints before sufficient data are collected. Future wireless systems must cope with increasingly dynamic traffic, fluctuating channel conditions, and stringent energy efficiency requirements, demanding algorithms that can learn quickly with minimal environment interactions to reduce both energy consumption and signaling overhead. We develop Fused-CPRO, a knowledge-fused constrained policy reuse optimization method addressing these challenges. Fused-CPRO constructs the allocation policy as a mixture of a learnable target policy, source policies from related scenarios, and domain-knowledge (DK) policies from expert rules, jointly optimizing the target policy and reuse probabilities under a constrained Markov decision process (CMDP). This fusion of heterogeneous priors accelerates convergence and enhances robustness. Constrained stochastic successive convex approximation (CSSCA) handles non-convex objectives and constraints, while a critic trained from mixed offline-online data improves sample efficiency by reusing pre-collected experience. We prove almost-sure convergence to a Karush-Kuhn-Tucker (KKT) point. Simulations on delay-constrained multi-user multiple-input multiple-output (MU-MIMO) power control and Cramer-Rao bound (CRB)-constrained multiple-input multiple-output integrated sensing and communication (MIMO-ISAC) beamforming demonstrate that Fused-CPRO improves empirical performance and converges substantially faster than representative baselines.

Message Passing Based Two-Timescale Bayesian Learning for Joint Channel and Memory Hardware Impairments Tracking

Hardware impairments in massive multiple-input multiple-output (MIMO) receivers introduce inter-symbol memory and inter-element coupling, severely degrading channel estimation. This paper employs a residual recurrent gated unit (RGRU) to model the intra-slot memory of the hardware impairments and proposes a message-passing-based two-timescale Bayesian deep learning (MP-TTBDL) framework for joint channel and impairment tracking. Owing to small-scale fading, the wireless channel varies rapidly across slots, whereas hardware impairments drift slowly due to hardware aging and environmental variations. To capture these distinct physical timescales, a fastvarying Markov prior and a slow-varying Gaussian Markov prior are assigned to the sparse channel and the network parameters, respectively. Based on a multi-slot factor graph formulation, a message-passing algorithm is developed. Specifically, the inter-slot messages admit closed-form updates, while the intra-slot factor graph, due to its complex recurrent structure, is partitioned into a channel tracking module and an impairments calibration module. The channel tracking module performs sparse channel estimation via turbo orthogonal approximate message passing (Turbo-OAMP), and the impairments calibration module updates the impairment parameters via a specially designed deep approximate message passing (DAMP) procedure, with the two modules iteratively exchanging extrinsic information through expectation propagation (EP) until convergence. Simulation results show that the proposed framework robustly achieves lower channel estimation error than conventional compensators followed by channel estimation across different online impairment scenarios and signal-to-noise ratio (SNR) conditions.

LoopGuard: Breaking Self-Reinforcing Attention Loops via Dynamic KV Cache Intervention

Through systematic experiments on long-context generation, we observe a damaging failure mode in which decoding can collapse into persistent repetition loops. We find that this degeneration is driven by collapsed attention patterns, where a subset of heads locks onto a narrow suffix of the history, and is further stabilized by inference-time KV cache reuse. Crucially, since many existing KV cache policies rely on attention-based importance, this collapse can produce spuriously high scores for repetitive tokens, causing cache management to inadvertently amplify repetition. To study this phenomenon in a controlled and reproducible manner, we introduce LoopBench, a benchmark with explicit loop-inducing conditions and loop-oriented metrics that quantify repetition severity and generation instability beyond downstream task scores. Building on these insights, we propose LoopGuard, a lightweight, plug-in KV cache guard that detects loop onset online and disrupts the feedback cycle by pruning repetitive tail spans under a fixed cache budget. Experiments on LoopBench show that LoopGuard reduces loop incidence by over 90 percentage points, while restoring output diversity and reducing token waste.

GS-CLIP: Zero-shot 3D Anomaly Detection by Geometry-Aware Prompt and Synergistic View Representation Learning

Zero-shot 3D Anomaly Detection is an emerging task that aims to detect anomalies in a target dataset without any target training data, which is particularly important in scenarios constrained by sample scarcity and data privacy concerns. While current methods adapt CLIP by projecting 3D point clouds into 2D representations, they face challenges. The projection inherently loses some geometric details, and the reliance on a single 2D modality provides an incomplete visual understanding, limiting their ability to detect diverse anomaly types. To address these limitations, we propose the Geometry-Aware Prompt and Synergistic View Representation Learning (GS-CLIP) framework, which enables the model to identify geometric anomalies through a two-stage learning process. In stage 1, we dynamically generate text prompts embedded with 3D geometric priors. These prompts contain global shape context and local defect information distilled by our Geometric Defect Distillation Module (GDDM). In stage 2, we introduce Synergistic View Representation Learning architecture that processes rendered and depth images in parallel. A Synergistic Refinement Module (SRM) subsequently fuses the features of both streams, capitalizing on their complementary strengths. Comprehensive experimental results on four large-scale public datasets show that GS-CLIP achieves superior performance in detection. Code can be available at https://github.com/zhushengxinyue/GS-CLIP.

Variable-Length Wideband CSI Feedback via Loewner Interpolation and Deep Learning

In this paper, we propose a variable-length wideband channel state information (CSI) feedback scheme for Frequency Division Duplex (FDD) massive multiple-input multipleoutput (MIMO) systems in U6G band (6425MHz-7125MHz). Existing compressive sensing (CS)-based and deep learning (DL)- based schemes preprocess the channel by truncating it in the angular-delay domain. However, the energy leakage effect caused by the Discrete Fourier Transform (DFT) basis will be more serious and leads to a bottleneck in recovery accuracy when applied to wideband channels such as those in U6G. To solve this problem, we introduce the Loewner Interpolation (LI) framework which generates a set of dynamic bases based on the current CSI matrix, enabling highly efficient compression in the frequency domain. Then, the LI basis is further compressed in the spatial domain through a neural network. To achieve a flexible trade-off between feedback overhead and recovery accuracy, we design a rateless auto-encoder trained with tail dropout and a multi-objective learning schedule, supporting variable-length feedback with a singular model. Meanwhile, the codewords are ranked by importance, ensuring that the base station (BS) can still maintain acceptable reconstruction performance under limited feedback with tail erasures. Furthermore, an adaptive quantization strategy is developed for the feedback framework to enhance robustness. Simulation results demonstrate that the proposed scheme could achieve higher CSI feedback accuracy with less or equal feedback overhead, and improve spectral efficiency compared with baseline schemes.

Robust Beamforming for Multiuser MIMO Systems with Unknown Channel Statistics: A Hybrid Offline-Online Framework

Robust beamforming design under imperfect channel state information (CSI) is a fundamental challenge in multiuser multiple-input multiple-output (MU-MIMO) systems, particularly when the channel estimation error statistics are unknown. Conventional model-driven methods usually rely on prior knowledge of the error covariance matrix and data-driven deep learning approaches suffer from poor generalization capability to unseen channel conditions. To address these limitations, this paper proposes a hybrid offline-online framework that achieves effective offline learning and rapid online adaptation. In the offline phase, we propose a shared (among users) deep neural network (DNN) that is able to learn the channel estimation error covariance from observed samples, thus enabling robust beamforming without statistical priors. Meanwhile, to facilitate real-time deployment, we propose a sparse augmented low-rank (SALR) method to reduce complexity while maintaining comparable performance. In the online phase, we show that the proposed network can be rapidly fine-tuned with minimal gradient steps. Furthermore, a multiple basis model-agnostic meta-learning (MB-MAML) strategy is further proposed to maintain multiple meta-initializations and by dynamically selecting the best one online, we can improve the adaptation and generalization capability of the proposed framework under unseen or non-stationary channels. Simulation results demonstrate that the proposed offline-online framework exhibits strong robustness across diverse channel conditions and it is able to significantly outperform state-of-the-art (SOTA) baselines.

Cross-Sparsity-Enabled Multipath Perception via Structured Bayesian Inference for Multi-Target Estimation

In this paper, we investigate a multi-target sensing system in multipath environment, where inter-target scattering gives rise to first-order reflected paths whose angles of departure (AoDs) and angles of arrival (AoAs) coincide with the direct-path angles of different targets. Unlike other multipath components, these first-order paths carry structural information that can be exploited as additional prior knowledge for target direction estimation. To exploit this property, we construct a sparse representation of the multi-target sensing channel and propose a novel cross sparsity structure under a three-layer hierarchical structured (3LHS) prior model, which leverages the first-order paths to enhance the prior probability of the direct paths and thereby improve the estimation accuracy. Building on this model, we propose a structured fast turbo variational Bayesian inference (SF-TVBI) algorithm, which integrates an efficient message-passing strategy to enable tractable probabilistic exchange within the cross sparsity, and a two-timescale update scheme to reduce the update frequency of the high-dimensional sparse vector. Simulation results demonstrate that leveraging the proposed cross sparsity structure is able to improve the target angle estimation accuracy substantially, and the SF-TVBI algorithm achieves estimation performance comparable to that of the Turbo-VBI, but with lower computational complexity.

GBV-SQL: Guided Generation and SQL2Text Back-Translation Validation for Multi-Agent Text2SQL

While Large Language Models have significantly advanced Text2SQL generation, a critical semantic gap persists where syntactically valid queries often misinterpret user intent. To mitigate this challenge, we propose GBV-SQL, a novel multi-agent framework that introduces Guided Generation with SQL2Text Back-translation Validation. This mechanism uses a specialized agent to translate the generated SQL back into natural language, which verifies its logical alignment with the original question. Critically, our investigation reveals that current evaluation is undermined by a systemic issue: the poor quality of the benchmarks themselves. We introduce a formal typology for "Gold Errors", which are pervasive flaws in the ground-truth data, and demonstrate how they obscure true model performance. On the challenging BIRD benchmark, GBV-SQL achieves 63.23% execution accuracy, a 5.8% absolute improvement. After removing flawed examples, GBV-SQL achieves 96.5% (dev) and 97.6% (test) execution accuracy on the Spider benchmark. Our work offers both a robust framework for semantic validation and a critical perspective on benchmark integrity, highlighting the need for more rigorous dataset curation.