The integration of large language models into wireless communication has shown promising results for individual tasks. However, existing approaches are typically designed for single-task scenarios and rely on supervised fine-tuning that fails to optimize for long-term decision quality. In this paper, we propose WirelessLLM-Agent, a unified LLM-based agent framework for multi-task wireless communication decision-making. Our framework integrates a semantic state serialization module that transforms heterogeneous wireless states into structured textual representations, a multi-task adapter architecture based on MoE-LoRA […]

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arXiv:2602.15859v1 Announce Type: new Abstract: Building reliable conversational AI assistants for customer-facing industries remains challenging due to noisy conversational data, fragmented knowledge, and the requirement for accurate human hand-off – particularly in domains that depend heavily on real-time information. This paper presents an end-to-end framework for constructing and evaluating a conversational AI assistant directly from historical call transcripts. Incoming transcripts are first graded using a simplified adaptation of the PIPA framework, focusing on observation alignment and appropriate response […]

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Cooperation in multi-agent reinforcement learning (MARL) benefits from inter-agent communication, yet most approaches assume idealized channels and existing value decomposition methods ignore who successfully shared information with whom. We propose CLOVER, a cooperative MARL framework whose centralized value mixer is conditioned on the communication graph realized under a realistic wireless channel. This graph introduces a relational inductive bias into value decomposition, constraining how individual utilities are mixed based on the realized communication structure. The mixer is a GNN […]

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I realized one day that software alone became mechanical. I developed a taste for simplicity and problems started becoming less like learning experiences, and turned into mechanical exercises that resembled busy work more than true exploration. I realized I was working always in the abstract. I took detours, sure. I got multiple cyber security/pentesting certs, and sure it was interesting. But, it didn’t scratch that itch in the end. I realized I needed to start touching the physical […]

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Deep Reinforcement Learning systems are highly sensitive to the learning rate (LR), and selecting stable and performant training runs often requires extensive hyperparameter search. In Proximal Policy Optimization (PPO) actor–critic methods, small LR values lead to slow convergence, whereas large LR values may induce instability or collapse. We analyse this phenomenon from the behavior of the hidden neurons in the network using the Overfitting-Underfitting Indicator (OUI), a metric that quantifies the balance of binary activation patterns over a […]

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This document was adapted from Style Directives in Jack Bradshaw’s Monorepo. Effective documentation is the cornerstone of sustainable development; however, the inevitability of team growth makes it hard to write documentation as team that isn’t fragmented and inconsistent; therefore, some degree of coordination between contributors is necessary. Even solo engineers, who once had a free pass, must now contend with the inconsistency of AI-generated documentation. I myself use AI to generate some of the documentation in my repository, […]

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arXiv:2507.09905v3 Announce Type: replace-cross Abstract: In multi-source learning with discrete labels, distributional heterogeneity across domains poses a central challenge to developing predictive models that transfer reliably to unseen domains. We study multi-source unsupervised domain adaptation, where labeled data are available from multiple source domains and only unlabeled data are observed from the target domain. To address potential distribution shifts, we propose a novel Conditional Group Distributionally Robust Optimization (CG-DRO) framework that learns a classifier by minimizing the worst-case […]

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How are you, hacker? 🪐Want to know what’s trending right now?: The Techbeat by HackerNoon has got you covered with fresh content from our trending stories of the day! Set email preference here. ## Crawl, Walk, Run, Fly – The Four Phases of AI Agent Maturity By @denisp [ 5 Min read ] Don’t let your AI fly before it can walk. Why jumping straight to fully autonomous systems is a recipe for disaster (and what to do […]

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arXiv:2601.22240v1 Announce Type: new Abstract: The rapid advancement and widespread adoption of generative artificial intelligence (GenAI) and large language models (LLMs) has been accompanied by the emergence of new security vulnerabilities and challenges, such as jailbreaking and other prompt injection attacks. These maliciously crafted inputs can exploit LLMs, causing data leaks, unauthorized actions, or compromised outputs, for instance. As both offensive and defensive prompt injection techniques evolve quickly, a structured understanding of mitigation strategies becomes increasingly important. To […]

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