A reality check on the AI jobs hysteria
Haven’t you heard? White-collar jobs are going away, decimated by AI. Waves of layoffs in the tech sector (most recently at Coinbase and Meta and Cisco) are said to presage what will soon come for all
Knowledge catalogue
Haven’t you heard? White-collar jobs are going away, decimated by AI. Waves of layoffs in the tech sector (most recently at Coinbase and Meta and Cisco) are said to presage what will soon come for all
arXiv:2605.24436v1 Announce Type: cross Abstract: Selecting the most suitable algorithm for a given problem instance remains a challenging task, particularly in online or dynamic environments where pr
arXiv:2605.25446v1 Announce Type: new Abstract: Electrocardiography (ECG) is central to cardiovascular care, but conventional AI models are often restricted to common arrhythmias and may generalize po
arXiv:2605.25156v1 Announce Type: cross Abstract: Prediction models trained under the source distribution do not generalize well to a different target distribution. A valid inference about an unseen d
arXiv:2605.25499v1 Announce Type: new Abstract: Importance weighting (IW) is a golden solver for joint distribution shift, where the joint distributions differ between the training and test data. To s
arXiv:2605.26059v1 Announce Type: cross Abstract: Bayesian inverse design provides a principled framework for inferring aerodynamic geometries from sparse flow observations while quantifying uncertain
arXiv:2605.23945v1 Announce Type: new Abstract: Reinforcement Learning from Human Feedback (RLHF) has become a key post-training paradigm for improving model quality. However, the synchronous three-st
arXiv:2605.24301v1 Announce Type: new Abstract: Bidirectional thrust grants quadrotors a second equilibrium condition and increased control authority, expanding the envelope of possible aggressive man
arXiv:2605.26072v1 Announce Type: new Abstract: Efficient learning of user preferences is crucial for many modern decision making systems but typically requires costly labeled data. Active learning re
arXiv:2506.04805v2 Announce Type: replace Abstract: Loss spikes commonly emerge during neural network training with the Adam optimizer across diverse architectures and scales, yet their underlying mec
arXiv:2602.13203v2 Announce Type: replace-cross Abstract: Telecommunication networks experience complex failures such as fiber cuts, traffic overloads, and cascading outages. Existing monitoring and d
arXiv:2605.25377v1 Announce Type: cross Abstract: Large Vision-Language Models (LVLMs) have advanced multimodal understanding, yet their reliability is limited by hallucination, where generated conten
arXiv:2605.24673v1 Announce Type: cross Abstract: We generalize finite-sample bounds for convex clustering to the setting where affinity weights appearing in the objective correspond to a general conn
arXiv:2605.24542v1 Announce Type: cross Abstract: This paper examines the erosion of Public Key Cryptography (PKC) security under adaptive adversarial optimisation driven by artificial intelligence. T
arXiv:2605.23905v1 Announce Type: cross Abstract: We show that AI-driven investment strategies are inherently self-defeating at scale. As AI adoption rises, three mutually reinforcing channels -- sign
arXiv:2605.23978v1 Announce Type: new Abstract: In algorithmic markets, predictive models become part of the data-generating process they aim to forecast. Once their outputs are converted into trades,
arXiv:2605.25303v1 Announce Type: cross Abstract: The 2 rightarrow q norm of a matrix X in R^{n imes d} is defined as lVert X rVert_{2 rightarrow q} = sup_{lVert v rVert_2 = 1} lVert Xv rVert_q. We gi
arXiv:2605.24742v1 Announce Type: new Abstract: Obtaining consistent explanations for machine learning on molecular graphs requires predictions and attributions to be aligned with chemical identity. H
arXiv:2602.17234v2 Announce Type: replace Abstract: Backtesting LLMs on resolved events assumes models reason only from pre-cutoff knowledge, yet pretrained models inevitably leak post-cutoff knowledg
arXiv:2601.03191v3 Announce Type: replace-cross Abstract: Multimodal medical large language models have shown substantial progress in chest X-ray interpretation but continue to face challenges in spat
arXiv:2011.10331v4 Announce Type: replace-cross Abstract: Multi-view clustering has wide applications in many image processing scenarios. In these scenarios, original image data often contain missing
arXiv:2508.11307v3 Announce Type: replace-cross Abstract: The Universal Thermal Climate Index (UTCI) is a measure of thermal comfort that quantifies how humans experience environmental conditions. Due
arXiv:2605.23920v1 Announce Type: cross Abstract: Real-effort tasks, in which participants perform cognitively costly activities whose outcomes depend on actual performance, are widely used in experim
arXiv:2605.24576v1 Announce Type: new Abstract: Artificial intelligence-enabled electrocardiography (AI-ECG) can detect heart failure (HF), including disease not captured by left ventricular ejection
arXiv:2605.24921v1 Announce Type: new Abstract: A central challenge in electroencephalography (EEG) foundation modeling is learning transferable representations across recordings with diverse tasks, m
arXiv:2605.24420v1 Announce Type: cross Abstract: Batch Normalization (BN) is widely adopted to enable faster convergence and more stable training of deep neural networks. However, its impact on priva
arXiv:2605.24983v1 Announce Type: new Abstract: Conformal prediction is a useful and versatile alternative to model calibration in machine learning classification. It replaces single-class prediction
arXiv:2605.24998v1 Announce Type: new Abstract: Self-improvement training enables the large reasoning models (LRMs) to improve themselves by self-generating reasoning trajectories as training data wit
arXiv:2605.24546v1 Announce Type: new Abstract: Process modeling is a sub-domain of Business Process Management (BPM) focused on the translation of process artifacts into formal models. This task trad
arXiv:2605.24611v1 Announce Type: new Abstract: Classical Hopfield networks are limited to static patterns due to symmetric weights, whereas asymmetric networks can encode temporal sequences via limit
arXiv:2605.25143v1 Announce Type: new Abstract: Test-time scaling improves language model reasoning by spending additional compute to explore multiple solution trajectories. The key challenge is to ma
arXiv:2605.25451v1 Announce Type: new Abstract: Training multimodal large language models (MLLMs) is challenged by both model and data heterogeneity. Existing systems redesign the training pipeline to
arXiv:2605.24743v1 Announce Type: cross Abstract: While LLMs excel at single-turn generation, they struggle with long-horizon, multi-turn interactions. Offline reinforcement learning (RL) offers a sca
arXiv:2605.25427v1 Announce Type: cross Abstract: Despite success on standard benchmarks, vision language models display persistent failures on tasks involving processing of multi-object scenes, inclu
arXiv:2605.24053v1 Announce Type: new Abstract: Large Language Models (LLMs) are predominantly governed by probabilistic frameworks in which the sum of outcome probabilities is constrained to unity. T
arXiv:2511.16449v4 Announce Type: replace-cross Abstract: Vision-Language-Action (VLA) models have shown great potential for embodied AI by integrating visual perception, language understanding, and a
arXiv:2511.03529v2 Announce Type: replace Abstract: Federated Learning (FL) enables clients to collaboratively train a global model without sharing their private data. However, the presence of malicio
arXiv:2605.24437v1 Announce Type: new Abstract: We present a novel framework for embedding hard constraint satisfaction into neural network (NN) architectures, specifically feedforward neural networks
arXiv:2605.25419v1 Announce Type: new Abstract: Effective learning support requires understanding not only what learners know but also how accurately they perceive their own understanding. This metaco
arXiv:2602.15811v2 Announce Type: replace-cross Abstract: Clinical deployment of chest radiograph classifiers requires models that can be updated as new datasets become available without retraining on
arXiv:2605.23925v1 Announce Type: cross Abstract: Intelligent tutoring systems increasingly provide automated feedback on student work, but robust feedback requires assessing reasoning, not only final
arXiv:2605.24076v1 Announce Type: cross Abstract: Modern Artificial Intelligence achieves remarkable predictive power by optimizing statistical risk functionals over vast corpora. Yet a gap separates
arXiv:2605.26089v1 Announce Type: cross Abstract: We present Channel-wise Vector Quantization (CVQ), a novel image tokenization paradigm that replaces patch-wise tokens with channel-wise tokens. Unlik
arXiv:2605.24210v1 Announce Type: new Abstract: What functions can Neural Processes represent? We analyze the representational capacity of popular NP architectures: Conditional Neural Processes (CNPs)
arXiv:2410.19248v2 Announce Type: replace Abstract: Quality of Service (QoS) is an important metric to measure the performance of network services. Nowadays, it is widely used in mobile edge environme
arXiv:2605.25831v1 Announce Type: cross Abstract: Large language models (LLMs) define a distribution over text, which can be viewed as a probabilistic representation of uncertainty: sampling K respons
arXiv:2506.17629v2 Announce Type: replace-cross Abstract: Embodied Visual Reasoning (EVR) seeks to follow complex, free-form instructions based on egocentric video, enabling semantic understanding and
arXiv:2605.25662v1 Announce Type: new Abstract: Graph neural networks for node classification are typically trained by gradient descent over hundreds or thousands of epochs. Recent work has shown that
arXiv:2605.24872v1 Announce Type: new Abstract: Conformal prediction provides distribution-free coverage guarantees, but in many-class classification it may still under-cover specific classes or subpo
arXiv:2605.24422v1 Announce Type: cross Abstract: Stochastic Dominance (SD) theory provides a rigorous framework for selecting superior assets tailored to the asset allocation needs of investors with
arXiv:2605.24460v1 Announce Type: cross Abstract: Automatically mapping and segmenting global mining footprints using remote sensing and deep learning is critical for monitoring the socio-environmenta
arXiv:2605.25672v1 Announce Type: new Abstract: In this paper, we address the challenge of performing non-prehensile pushing operations with a compliant robotic manipulation system. To ensure safe ope
arXiv:2310.04981v2 Announce Type: replace-cross Abstract: Vision-language models (VLMs) transform environment percepts into vision-language semantics interpretable by LLMs. However, completing complex
arXiv:2605.26067v1 Announce Type: cross Abstract: Conditionally positive definite (CPD) kernels are defined with respect to a function class F. It is well known that such a kernel K is associated with
arXiv:2605.26045v1 Announce Type: cross Abstract: Activation oracles aim to make the activations of other models legible to humans and yield promising results compared to white-box interpretability te
arXiv:2605.23909v1 Announce Type: new Abstract: We investigate the calibration of large language models' (LLMs') confidence across diverse tasks. The results of our preregistered study show that the c
arXiv:2605.25235v1 Announce Type: cross Abstract: We give an attribution method for neural combinatorial-optimisation (CO) policies that (i) decomposes a decision by constraint families via LP-relaxat
arXiv:2605.25354v1 Announce Type: new Abstract: While LLMs excel at reasoning over prompts using static pretrained knowledge, they struggle significantly with context learning-the ability to dynamical
arXiv:2605.23928v1 Announce Type: new Abstract: We present Context, the intelligence layer of the Magarshak Architecture, which replaces reactive query-response chatbots with proactive goal-directed a
arXiv:2605.24602v1 Announce Type: cross Abstract: Multimodal large language models (MLLMs) frequently suffer from object hallucinations, yet the visual perceptual mechanism underlying this failure rem