Recent advances in reinforcement learning for large language models have produced powerful agent frameworks that achieve strong performance on multi-turn tool use, interactive search, and complex reasoning. However, existing reinforcement learning frameworks for large language model agents face three critical limitations: difficulty in handling dynamic user interactions owing to reliance on pre-scripted queries, limited scalability across varying interaction horizons with fixed scaling schedules, and substantial reward engineering overhead requiring domain-specific manual tuning. We introduce Progressive Multi-Turn Reinforcement Learning […]

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arXiv:2601.00257v1 Announce Type: new Abstract: Despite the growing interest in low-altitude economy (LAE) applications, including UAV-based logistics and emergency response, fundamental challenges remain in orchestrating such missions over complex, signal-constrained environments. These include the absence of real-time, resilient, and context-aware orchestration of aerial nodes with limited integration of artificial intelligence (AI) specialized for LAE missions. This paper introduces an open radio access network (O-RAN)-enabled LAE framework that leverages seamless coordination between the disaggregated RAN architecture, open interfaces, and […]

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Generalisation to unseen subjects in EEG-based emotion classification remains a challenge due to high inter-and intra-subject variability. Continual learning (CL) poses a promising solution by learning from a sequence of tasks while mitigating catastrophic forgetting. Regularisation-based CL approaches, such as Elastic Weight Consolidation (EWC), Synaptic Intelligence (SI), and Memory Aware Synapses (MAS), are commonly used as baselines in EEG-based CL studies, yet their suitability for this problem remains underexplored. This study theoretically and empirically finds that regularisation-based CL […]

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TL;DR: We collected 87,871 observations showing AI epistemic self-assessment produces consistent, calibratable measurements. No consciousness claims required. The Conflation Problem When people hear “AI assesses its uncertainty,” they assume it requires consciousness or introspection. It doesn’t. Functional Measurement Phenomenological Introspection “Rate your knowledge 0-1” “Are you aware of your states?” Evaluating context window Accessing inner experience Thermometer measuring temp Thermometer feeling hot A thermometer doesn’t need to feel hot. An LLM evaluating knowledge state is doing the same […]

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Card tokenization in payment gateways is usuallyanchored on hardware security modules (HSMs) that store keysand execute sensitive cryptographic operations under PCI controls.This model is mature but difficult to scale elastically and not wellaligned with cloud native architectures that rely on horizontalgrowth on commodity servers. Trusted execution environments(TEEs), such as Intel SGX or confidential virtual machines, keeptokenization code and key material inside isolated enclaves whilethe surrounding gateway stack remains untrusted. This paperdesigns an enclave based token vault that sits […]

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arXiv:2601.17097v1 Announce Type: new Abstract: Real-time automatic speech recognition systems are increasingly integrated into interactive applications, from voice assistants to live transcription services. However, scaling these systems to support multiple concurrent clients while maintaining low latency and high accuracy remains a major challenge. In this work, we present SWIM, a novel real-time ASR system built on top of OpenAI’s Whisper model that enables true model-level parallelization for scalable, multilingual transcription. SWIM supports multiple concurrent audio streams without modifying […]

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I have been writing articles on freeCodeCamp for a while (20+ articles, 240K+ views). Recently, I completed my biggest project! I explain the math from an engineering perspective and connect how math solves real life problems and makes billion dollar industries possible. For example, in “Chapter 6: Probability & Statistics – Learning from Uncertainty” I explain how Markov chains allow the application of the Markov decision processes, which is the foundation for all RL and DRL. The chapters: […]

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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:2601.14349v1 Announce Type: new Abstract: Motivation: Developing high-performing bioinformatics models typically requires repeated cycles of hypothesis formulation, architectural redesign, and empirical validation, making progress slow, labor-intensive, and difficult to reproduce. Although recent LLM-based assistants can automate isolated steps, they lack performance-grounded reasoning and stability-aware mechanisms required for reliable, iterative model improvement in bioinformatics workflows. Results: We introduce MARBLE, an execution-stable autonomous model refinement framework for bioinformatics models. MARBLE couples literature-aware reference selection with structured, debate-driven architectural reasoning among […]

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This study introduces a hybrid point cloud compression method that transfers from octree-nodes to voxel occupancy estimation to find its lower-bound bitrate by using a Binary Arithmetic Range Coder. In previous attempts, we’ve shown that our entropy compression model based on index convolution achieves promising performance while maintaining low complexity. However, our previous model lacks an autoregressive approach, which is apparently indispensable to compete with the current state-of-the-art of compression performance. Therefore, we adapt an autoregressive grouping method […]

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