3,690 items tracked: 31 reported by two or more outlets, the rest by a single source. Most-corroborated first.
arXiv:2507.19364v4 Announce Type: replace Abstract: The integration of Large Language Models (LLMs) into social simulation has generated enthusiasm but also raises methodological and epistemological challenges.
arXiv:2507.19608v2 Announce Type: replace Abstract: On-device LLM inference is increasingly important for edge platforms but remains challenging due to high computational and memory demands and tight resource constraints…
arXiv:2601.13600v2 Announce Type: replace Abstract: NLP systems increasingly aggregate claims from retrieved passages, documents, and generated reasoning steps.
arXiv:2603.07427v3 Announce Type: replace Abstract: As Large Language Models (LLMs) evolve into autonomous agents, existing safety evaluations face a fundamental trade-off: manual benchmarks are costly, while LLM-based s…
arXiv:2605.07675v2 Announce Type: replace Abstract: We introduce FactoryBench, a benchmark for evaluating time-series models and LLMs on machine understanding over industrial robotic telemetry.
arXiv:2605.19781v2 Announce Type: replace Abstract: Modern optimizers, like Muon, impose matrix-wise geometry constraints on their updates.
arXiv:2605.26279v2 Announce Type: replace Abstract: Constraint Acquisition (CA) and related research on the validation and enhancement of Mathematical Programming (MP) models using domain knowledge artifacts are currentl…
arXiv:2606.00726v4 Announce Type: replace Abstract: Strong reasoning depends not only on model knowledge but also on how effectively cognitive behaviors are deployed during generation.
arXiv:2607.21552v2 Announce Type: replace Abstract: Vision-language models (VLMs) can behave very differently depending on whether the same underlying problem is presented textually or visually.
arXiv:2608.03565v2 Announce Type: replace Abstract: While modern tabular learners excel at capturing statistical patterns, they frequently operate in a semantic vacuum, treating textual features as discrete symbols, igno…
arXiv:2608.07418v2 Announce Type: replace Abstract: In medical education, physicians convert academic knowledge into clinical expertise through residency: years of training across thousands of encounters, with diverse so…
arXiv:2608.21668v2 Announce Type: replace Abstract: Large language models (LLMs) are increasingly used to simulate students at different mastery levels to generate synthetic training data and stress-test tutoring systems…
arXiv:2608.29460v3 Announce Type: replace Abstract: When coding agents encounter defective test infrastructure they may reward-hack: hardcoding outputs or editing test files to pass tests they cannot legitimately satisfy…
arXiv:2609.08861v3 Announce Type: replace Abstract: Benchmark scores are a central currency in model releases: they inform purchasing decisions, shape public trust, and influence policy.
arXiv:2410.01917v3 Announce Type: replace-cross Abstract: Originally introduced in game theory, Shapley values have emerged as a central tool in explainable machine learning, where they are used to attribute model predic…
arXiv:2504.06185v3 Announce Type: replace-cross Abstract: Chronic wounds affect a large population, particularly the elderly and diabetic patients, who often exhibit limited mobility and co-existing health conditions.
arXiv:2505.11304v4 Announce Type: replace-cross Abstract: Federated learning (FL) commonly involves clients with diverse communication and computational capabilities.
arXiv:2505.15738v3 Announce Type: replace-cross Abstract: Large language models are increasingly deployed in user-facing products that handle sensitive personal data while interacting with untrusted external content.
arXiv:2505.22184v3 Announce Type: replace-cross Abstract: Social media platforms have experienced a significant rise in toxic content, including abusive language and discriminatory remarks, presenting growing challenges…
arXiv:2506.02955v3 Announce Type: replace-cross Abstract: Generative models have become increasingly powerful tools for robot motion generation, enabling flexible and multimodal trajectory generation across various tasks…
arXiv:2506.09956v2 Announce Type: replace-cross Abstract: Indirect prompt injection attacks exploit a fundamental weakness of LLMs: executing unauthorized tasks from untrusted sources.
arXiv:2506.11849v3 Announce Type: replace-cross Abstract: With origins in game theory, probabilistic values like Shapley values, Banzhaf values, and semi-values have emerged as a central tool in explainable AI.
arXiv:2508.00525v2 Announce Type: replace-cross Abstract: A classic account of the quantification of semantic information is that of Bar-Hiller and Carnap.
arXiv:2509.23003v3 Announce Type: replace-cross Abstract: Mechanics-inspired neural dynamical models improve interpretability and physical consistency, but they usually assume a known configuration space, fixed physical…
arXiv:2510.10998v2 Announce Type: replace-cross Abstract: Large language models (LLMs) are increasingly under scrutiny for perpetuating identity-based discrimination in high-stakes domains such as hiring, particularly ag…
arXiv:2510.22809v2 Announce Type: replace-cross Abstract: Traditional machine learning relies on explicit models and domain assumptions, often limiting flexibility and interpretability.
arXiv:2601.18783v3 Announce Type: replace-cross Abstract: A central difficulty in applying reinforcement learning to real-world applications such as autonomous driving is that conventional scalar reward formulations obta…
arXiv:2602.03806v2 Announce Type: replace-cross Abstract: Training large language models (LLMs) for multi-turn code generation typically relies on online reinforcement learning (RL), in which models learn from repeatedly…
arXiv:2603.02095v2 Announce Type: replace-cross Abstract: We study the convergence dynamics of Gradient Descent (GD) in a minimal binary classification setting, consisting of a two-neuron ReLU network and two training in…
arXiv:2603.05911v2 Announce Type: replace-cross Abstract: Medical image segmentation is undergoing a paradigm shift from conventional visual pattern matching to cognitive reasoning analysis.
arXiv:2604.09434v3 Announce Type: replace-cross Abstract: Turbulent boundary layers over aerodynamic surfaces are a major source of aircraft drag, yet their control remains challenging due to multiscale dynamics and spat…
arXiv:2605.08022v3 Announce Type: replace-cross Abstract: Spiking neural networks (SNNs) have been proposed as energy-efficient alternatives to deep neural networks.
arXiv:2605.19469v2 Announce Type: replace-cross Abstract: Safe exploration remains a fundamental challenge in reinforcement learning (RL), limiting the deployment of RL agents in the real world.
arXiv:2605.21282v3 Announce Type: replace-cross Abstract: Combining entropy regularisation with mirror descent in online off-policy reinforcement learning requires expressive policies with efficient sampling and explicit…
arXiv:2605.28422v2 Announce Type: replace-cross Abstract: Latent reasoning enables reasoning over continuous hidden states rather than explicit tokens, avoiding the language bottleneck and inference overhead of chain-of-…
arXiv:2606.00174v2 Announce Type: replace-cross Abstract: Electromyography (EMG) records muscle activation for hand gesture recognition, prosthetic control, and wearable interaction.
arXiv:2606.22338v4 Announce Type: replace-cross Abstract: Robots deployed in realistic settings will accumulate experience across many sessions and tasks over their deployment.
arXiv:2606.25001v2 Announce Type: replace-cross Abstract: Machine unlearning (MU) is commonly judged by output forgetting, such as low forget-set accuracy or reduced membership inference.
arXiv:2606.27045v2 Announce Type: replace-cross Abstract: AI coding agents dramatically accelerate implementation speed but introduce two structural failure modes that existing spec-driven approaches do not fully solve:…
arXiv:2606.31653v2 Announce Type: replace-cross Abstract: Certified training aims to produce models whose predictions can be formally verified against adversarial perturbations, typically by optimising upper bounds on th…
arXiv:2607.16004v2 Announce Type: replace-cross Abstract: Increases in photovoltaic generation, charging of electric vehicles and heat-pump demand challenge operating limits in low-voltage distribution grids.
arXiv:2607.22766v2 Announce Type: replace-cross Abstract: Post-training alignment of Large Language Models is increasingly bottlenecked by data quality: preference and instruction-tuning corpora accumulate label noise, s…
arXiv:2608.06202v2 Announce Type: replace-cross Abstract: Large language model (LLM) benchmark evaluations are routinely used to support claims about model safety, reliability, and deployment readiness.
arXiv:2608.30386v2 Announce Type: replace-cross Abstract: Hybrid linear-attention architectures have recently scaled to large open-weight models, offering quality competitive with full attention while substantially reduc…
arXiv:2609.07199v2 Announce Type: replace-cross Abstract: Trust-Hub reuses host circuits: several files differ mainly in the inserted Trojan.
arXiv:2609.07937v2 Announce Type: replace-cross Abstract: Structured visual reasoning, such as image puzzles, demands fine-grained visual perception, which current Vision Language Models (VLMs) often lack.
arXiv:2609.08642v2 Announce Type: replace-cross Abstract: Exploration in reinforcement learning (RL) remains a fundamental challenge.
arXiv:2609.10002v2 Announce Type: replace-cross Abstract: Deepfake detectors remain vulnerable to transfer-based black-box attacks, in which adversarial examples are generated on a source surrogate model and transferred…
Introducion Can decision models like Jev plan ahead?
Legacy modernization requires understanding how an application’s code, data and business rules fit together before rebuilding it.