Knowledge catalogue
THIS GUY LOST 200 IN ONE DAY BECAUSE THE STRING 'HERMES.md' WAS IN HIS GIT COMMITS HERMES.md is a real convention used in AI agent projects. it's a system prompt specification file. not some obscure e
arXiv:2604.20932v1 Announce Type: cross Abstract: Retrieval-augmented generation (RAG) systems are increasingly deployed in sensitive domains such as healthcare and law, where they rely on private, do
arXiv:2604.20924v1 Announce Type: new Abstract: Timely and interpretable early warning of sepsis remains a major clinical challenge due to the complex temporal dynamics of physiological deterioration.
arXiv:2602.11871v2 Announce Type: replace Abstract: Large Language Models (LLMs) are a powerful tool for statistical text analysis, with derived sequences of next-token probability distributions offer
arXiv:2604.21254v1 Announce Type: cross Abstract: LLM architecture research generally aims to maximize model quality subject to fixed compute/latency budgets. However, many applications of interest su
arXiv:2502.20769v3 Announce Type: replace Abstract: Developing interpretable models for neurodevelopmental disorders (NDDs) diagnosis presents significant challenges in effectively encoding, decoding,
arXiv:2604.21370v1 Announce Type: new Abstract: We present a systematic study of multilingual polarization detection across 22 languages for SemEval-2026 Task 9 (Subtask 1), contrasting multilingual g
arXiv:2603.12845v2 Announce Type: replace Abstract: Predicting enzyme kinetic parameters quantifies how efficiently an enzyme catalyzes a specific substrate under defined biochemical conditions. Canon
arXiv:2604.21889v1 Announce Type: cross Abstract: Real-time detection and mitigation of technical anomalies are critical for large-scale cloud-native services, where even minutes of downtime can resul
arXiv:2604.21840v1 Announce Type: cross Abstract: Modern phishing campaigns increasingly evade snapshot-based URL classifiers using interaction gates (e.g., checkbox/slider challenges), delayed conten
arXiv:2604.21435v1 Announce Type: new Abstract: Ultra-High-Resolution (UHR) imagery has become essential for modern remote sensing, offering unprecedented spatial coverage. However, detecting small ob
arXiv:2604.20129v1 Announce Type: new Abstract: The Synergistic Collapse occurs when scaling beyond 100 agents causes superlinear performance degradation that individual optimizations cannot prevent.
arXiv:2403.18151v2 Announce Type: replace-cross Abstract: Objective: Cytology plays a crucial role in lung cancer diagnosis. Pulmonary cytology involves cell morphological characterization in the spec
arXiv:2602.15861v2 Announce Type: replace-cross Abstract: Text analysis of tabular data relies on two core operations: summarization for corpus-level theme extraction and tagging for row-level labelin
arXiv:2604.20021v1 Announce Type: cross Abstract: As Large Language Models (LLMs) become increasingly popular, caching responses so that they can be reused by users with semantically similar queries h
arXiv:2604.19835v1 Announce Type: cross Abstract: Mixture-of-Experts (MoE) has become the dominant architecture for scaling large language models: frontier models routinely decouple total parameters f
arXiv:2509.20138v2 Announce Type: replace Abstract: Minimax-based search algorithms with alpha-beta pruning and transposition tables are a central component of classical game-playing engines and remai
arXiv:2604.20109v1 Announce Type: cross Abstract: The quadratic assignment problem (QAP) is a fundamental NP-hard task that poses significant challenges for both traditional heuristics and modern lear
arXiv:2604.20560v1 Announce Type: new Abstract: Automatically filling Case Report Forms (CRFs) from clinical notes is challenging due to noisy language, strict output contracts, and the high cost of f
arXiv:2604.20317v1 Announce Type: new Abstract: GAN-based facial attribute editing is widely used in virtual avatars and social media but often suffers from attribute entanglement, where modifying one
arXiv:2604.20627v1 Announce Type: new Abstract: The temporal lag between actions and their long-term consequences makes credit assignment a challenge when learning goal-directed behaviors from data. G
arXiv:2604.20764v1 Announce Type: cross Abstract: This paper presents a personalized Battery Electric Vehicle (BEV) energy consumption estimation framework that integrates map-based contextual feature
arXiv:2604.19998v1 Announce Type: new Abstract: Evaluating AI-generated reviews by verdict agreement is widely recognized as insufficient, yet current alternatives rarely audit which concerns a system
arXiv:2508.16676v2 Announce Type: replace-cross Abstract: Transformer architecture gradually dominates the LLM field. Recent advances in training optimization for Transformer-based large language mode
arXiv:2604.19636v1 Announce Type: new Abstract: Synthesizing human--object interaction (HOI) videos has broad practical value in e-commerce, digital advertising, and virtual marketing. However, curren
arXiv:2508.14170v2 Announce Type: replace Abstract: The increasing deployment of large language models (LLMs) in natural language processing (NLP) tasks raises concerns about energy efficiency and sus
arXiv:2604.19351v1 Announce Type: new Abstract: The quadratic computational complexity of the standard attention mechanism constitutes a fundamental bottleneck for large language models in long-contex
arXiv:2510.23156v2 Announce Type: replace-cross Abstract: The growing demand for smart home interfaces has increased interest in non-intrusive sensing methods like vibration-based gesture recognition.
arXiv:2604.19186v1 Announce Type: cross Abstract: Heterophily is a prevalent property of real-world graphs and is well known to impair the performance of homophilic Graph Neural Networks (GNNs). Prior
arXiv:2604.18913v1 Announce Type: new Abstract: Knowledge graphs (KGs) are increasingly integrated with large language models (LLMs) to provide structured, verifiable reasoning. A core operation in th
arXiv:2604.19623v1 Announce Type: cross Abstract: Edge-cloud hybrid inference offloads difficult inputs to a powerful remote model, but the uplink channel imposes hard per-request constraints on the n
arXiv:2604.19048v1 Announce Type: cross Abstract: The combination of Mixture-of-Experts (MoE) and Low-Rank Adaptation (LoRA) has shown significant potential for enhancing the multi-task learning capab
arXiv:2604.18758v1 Announce Type: new Abstract: Low-resource machine translation requires methods that differ from those used for high-resource languages. This paper proposes a novel in-context learni
arXiv:2604.19377v1 Announce Type: new Abstract: The emergence of sixth-generation (6G) technologies has introduced new challenges and opportunities for machine learning (ML) applications in Internet o
arXiv:2412.01782v4 Announce Type: replace-cross Abstract: DETR and its variants have emerged as promising architectures for object detection, offering an end-to-end prediction pipeline. In practice, h
This week at Google Cloud Next ‘26, we are sharing the evolution of Google Kubernetes Engine (GKE), delivering leading performance, efficiency, security, and scale for your most demanding and complex
AI is evolving from answering questions to reasoning and taking action. Companies who want to lead in today’s agentic era require computing infrastructure designed and optimized for these new requirem
arXiv:2604.16333v1 Announce Type: new Abstract: Knee osteoarthritis frequently exhibits discordance between structural damage observed in imaging and patient-reported symptoms such as pain. This misma
arXiv:2508.17412v4 Announce Type: replace Abstract: Conventional regularization is designed to control variance, but in small-data regression it can also aggravate underfitting when predictive signal
arXiv:2604.17255v1 Announce Type: new Abstract: Accurate comprehension and controllable generation of emotion and rhetoric are pivotal for enhancing the reasoning capabilities of large language models
arXiv:2510.23807v5 Announce Type: replace-cross Abstract: Despite their successes in vision and language, foundation models have stumbled in pathology, revealing low accuracy, instability, and heavy c
arXiv:2604.17693v1 Announce Type: new Abstract: In cooperative teams where agents act in a fixed order and share a single team reward, it is hard to know how much each agent contributed, and harder st
arXiv:2604.16364v1 Announce Type: cross Abstract: Health systems are rapidly deploying large language models (LLMs) that use clinical notes for clinical decision support applications. However, modern
arXiv:2412.00069v3 Announce Type: replace-cross Abstract: Mixture-of-Experts (MoE) has garnered significant attention for its ability to scale up neural networks while utilizing the same or even fewer
arXiv:2604.16366v1 Announce Type: cross Abstract: Artificial intelligence (AI) tutors have become increasingly popular in learning environments. In this study, we propose an AI agent prototype framewo
arXiv:2601.03154v2 Announce Type: replace Abstract: Reasoning-tuned LLMs utilizing long Chain-of-Thought (CoT) excel at single-answer tasks, yet their ability to model Human Label Variation--which req
arXiv:2601.03559v2 Announce Type: replace Abstract: Chain-of-Thought (CoT) reasoning improves multi-step mathematical problem solving in large language models but remains vulnerable to exposure bias a
arXiv:2604.16979v1 Announce Type: cross Abstract: High-quality and diverse multimodal data are essential for improving vision-language models (VLMs), yet existing datasets often contain noisy, redunda
arXiv:2604.16783v1 Announce Type: new Abstract: Vehicle trajectory prediction is central to highway perception, but deployment on roadside edge devices necessitates bounded, deterministic end-to-end l
arXiv:2604.18075v1 Announce Type: new Abstract: We investigate recently introduced domain-class incremental learning scenarios for vision-language models (VLMs). Recent works address this challenge us
arXiv:2412.02904v2 Announce Type: replace Abstract: Large language models (LLMs) have revolutionized the field of natural language processing with their impressive reasoning and question-answering cap
arXiv:2601.13684v2 Announce Type: replace Abstract: The linear memory growth of the KV cache poses a significant bottleneck for LLM inference in long-context tasks. Existing static compression methods
arXiv:2604.17935v1 Announce Type: new Abstract: The key-value (KV) cache is the dominant memory bottleneck during Transformer inference, yet little is known theoretically about how aggressively it can
arXiv:2604.17650v1 Announce Type: new Abstract: LLMs are increasingly deployed in dynamic, real-world settings, where the distribution of user prompts can shift substantially over time as new tasks, p
arXiv:2604.17245v1 Announce Type: new Abstract: High-DOF dexterous hands require compact actuation, rich sensing, and reliable thermal behavior, but conventional designs often occupy valuable in-hand
arXiv:2601.21257v2 Announce Type: replace Abstract: Advancing beyond single monolithic language models (LMs), recent research increasingly recognizes the importance of model collaboration, where multi
QIMMA (قِمّة) is a quality-focused leaderboard for evaluating Arabic Large Language Models, developed by TIIUAE. The leaderboard benchmarks Arabic LLMs across various performance metrics and tasks to
arXiv:2506.18141v3 Announce Type: replace Abstract: We identify semantically coherent, context-consistent network components in large language models (LLMs) using coactivation of sparse autoencoder (S
arXiv:2604.18210v1 Announce Type: cross Abstract: Success in association football relies on both individual skill and coordinated tactics. While recent advancements in spatio-temporal data and deep le
arXiv:2604.17318v1 Announce Type: new Abstract: Vision-Language Models (VLMs) are increasingly used in clinical diagnostics, yet their robustness to adversarial attacks remains largely unexplored, pos