arXiv:2602.21255v1 Announce Type: new Abstract: We establish a general equilibrium theory for systems of large language model (LLM) agents operating under centralized orchestration. The framework is a production economy in the sense of Arrow-Debreu (1954), extended to infinite-dimensional commodity spaces following Bewley (1972). Each LLM agent is modeled as a firm whose production set Y a $subset$ H = L 2 ([0, T ], R R ) represents the feasible metric trajectories determined by its frozen model weights. […]

Ver mais

So I am currently pursuing my undergrad and want to create an adaptive honeypot using RL(specifically DQN) and Cowrie Honeypot as my project. But I don’t have any idea on how to start or what to do and not do. I have beginner level knowledge of Q-Learning and Deep Q-Learning. Any help will be appreciated… submitted by /u/dn874 [link] [comments]

Ver mais

arXiv:2602.13275v1 Announce Type: new Abstract: Alignment research focuses on making individual AI systems reliable. Human institutions achieve reliable collective behaviour differently: they mitigate the risk posed by misaligned individuals through organisational structure. Multi-agent AI systems should follow this institutional model using compartmentalisation and adversarial review to achieve reliable outcomes through architectural design rather than assuming individual alignment. We demonstrate this approach through the Perseverance Composition Engine, a multi-agent system for document composition. The Composer drafts text, the Corroborator […]

Ver mais

arXiv:2601.12286v1 Announce Type: new Abstract: The increasing prevalence of Large Language Models (LLMs) demands effective safeguards for their operation, particularly concerning their tendency to generate out-of-context responses. A key challenge is accurately detecting when LLMs stray from expected conversational norms, manifesting as topic shifts, factual inaccuracies, or outright hallucinations. Traditional anomaly detection struggles to directly apply within contextual semantics. This paper outlines our experiment in exploring the use of Representation Engineering (RepE) and One-Class Support Vector Machine (OCSVM) […]

Ver mais

arXiv:2601.14062v1 Announce Type: cross Abstract: Healthcare sector indices consolidate the economic health of pharmaceutical, biotechnology, and healthcare service firms. The short-term movements in these indices are closely intertwined with capital allocation decisions affecting research and development investment, drug availability, and long-term health outcomes. This research investigates whether historical open-high-low-close (OHLC) index data contain sufficient information for predicting the directional movement of the opening index on the subsequent trading day. The problem is formulated as a supervised classification task […]

Ver mais

arXiv:2603.24699v1 Announce Type: new Abstract: Tiny palm-sized aerial robots possess exceptional agility and cost-effectiveness in navigating confined and cluttered environments. However, their limited payload capacity directly constrains the sensing suite on-board the robot, thereby limiting critical navigational tasks in Global Positioning System (GPS)-denied wild scenes. Common methods for obstacle avoidance use cameras and LIght Detection And Ranging (LIDAR), which become ineffective in visually degraded conditions such as low visibility, dust, fog or darkness. Other sensors, such as RAdio […]

Ver mais

arXiv:2602.03921v1 Announce Type: new Abstract: Mixture-of-Experts (MoE) models enable sparse expert activation, meaning that only a subset of the model’s parameters is used during each inference. However, to translate this sparsity into practical performance, an expert caching mechanism is required. Previous works have proposed hardware-centric caching policies, but how these various caching policies interact with each other and different hardware specification remains poorly understood. To address this gap, we develop textbf{SpecMD}, a standardized framework for benchmarking ad-hoc cache […]

Ver mais

Training deep neural networks with noisy labels remains a significant challenge, often leading to degraded performance. Existing methods for handling label noise typically rely on either transition matrix, noise detection, or meta-learning techniques, but they often exhibit low utilization efficiency of noisy samples and incur high computational costs. In this paper, we propose a self-training label correction framework using decoupled bilevel optimization, where a classifier and neural correction function co-evolve. Leveraging a small clean dataset, our method employs […]

Ver mais

arXiv:2601.16382v1 Announce Type: new Abstract: While the filtered-x normalized least mean square (FxNLMS) algorithm is widely applied due to its simple structure and easy implementation for active noise control system, it faces two critical limitations: the fixed step-size causes a trade-off between convergence rate and steady-state residual error, and its performance deteriorates significantly in impulsive noise environments. To address the step-size constraint issue, we propose the switched mbox{step-size} FxNLMS (SSS-FxNLMS) algorithm. Specifically, we derive the mbox{mean-square} deviation (MSD) […]

Ver mais

There is a scene in Plato that contains, in miniature, the catastrophe of Athenian public life. Two men meet at a courthouse. One is there to prosecute his own father for the death of a slave. The other is there to be indicted for indecency. [1] The prosecutor, Euthyphro, is certain he understands what decency requires. The accused, Socrates, is not certain of anything, and says so. They talk. Euthyphro’s confidence is striking. His own family thinks it […]

Ver mais