Knowledge catalogue
arXiv:2604.20134v1 Announce Type: cross Abstract: Security Operations Centers (SOCs) increasingly encounter difficulties in correlating heterogeneous alerts, interpreting multi-stage attack progressio
arXiv:2510.21652v2 Announce Type: replace Abstract: AI agents hold the potential to revolutionize scientific productivity by automating literature reviews, replicating experiments, analyzing data, and
arXiv:2604.20070v1 Announce Type: cross Abstract: Advances in AI agent capabilities have outpaced users' ability to meaningfully oversee their execution. AI agents can perform sophisticated, multi-ste
arXiv:2412.03594v3 Announce Type: replace-cross Abstract: Large language models (LLMs) increasingly play an important role in a wide range of information processing and management tasks in industry. M
arXiv:2604.20051v1 Announce Type: new Abstract: Self-play has recently emerged as a promising paradigm to train Large Language Models (LLMs). In self-play, the target LLM creates the task input (e.g.,
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:2601.06606v2 Announce Type: replace-cross Abstract: We demonstrate CEDAR, an application for automating data science (DS) tasks with an agentic setup. Solving DS problems with LLMs is an underex
arXiv:2604.19772v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used in scientific writing but struggle with book-length tasks, often producing inconsistent structure a
Confidently wrong is worse than just wrong. When an AI searches one giant knowledge base for everything, it finds something—and serves it up like it's the answer. That's not a model problem. That's a
A developer describes building a persistent-state AI system using Ollama with three models (a 4B router model, a 9B primary model, and a 9B 'subconscious' model) running on dual RTX 3060 GPUs, demonst
arXiv:2604.20241v1 Announce Type: new Abstract: Battery research is a rapidly growing and highly interdisciplinary field, making it increasingly difficult to track relevant expertise and identify pote
arXiv:2603.07474v2 Announce Type: replace-cross Abstract: What is the interplay between semantic representations learned by language models (LM) from surface form alone to those learned from more grou
arXiv:2511.17265v2 Announce Type: replace-cross Abstract: Nowadays, we are witnessing an Artificial Intelligence revolution that dominates the technology landscape in various application domains, such
arXiv:2604.20306v1 Announce Type: cross Abstract: Medical Visual Question Answering (MedVQA) aims to generate clinically reliable answers conditioned on complex medical images and questions. However,
arXiv:2604.20548v1 Announce Type: cross Abstract: Scientific progress depends on the continual generation of innovative re-search ideas. However, the rapid growth of scientific literature has greatly
arXiv:2601.09871v2 Announce Type: replace Abstract: Human-AI complementarity is the claim that a human supported by an AI system can outperform either alone in a decision-making process. Since its int
arXiv:2604.19755v1 Announce Type: new Abstract: Anti-money laundering (AML) transaction monitoring generates large volumes of alerts that must be rapidly triaged by investigators under strict audit an
arXiv:2604.20570v1 Announce Type: new Abstract: Spatial intelligence is essential for multimodal large language models, yet current benchmarks largely assess it only from an understanding perspective.
LlamaIndex have a most excellent open source project called LiteParse, which provides a Node.js CLI tool for extracting text from PDFs. I got a version of LiteParse working entirely in the browser, us
arXiv:2512.15808v2 Announce Type: replace-cross Abstract: Foundation models (FMs) are driving a prominent shift in biomedical imaging from task-specific models to unified backbone models for diverse t
arXiv:2508.15411v3 Announce Type: replace-cross Abstract: Generative AI (GenAI) has emerged as a transformative technology, demonstrating remarkable capabilities across diverse application domains. Ho
arXiv:2604.17517v2 Announce Type: replace Abstract: Autonomous agent systems are governed by enforcement mechanisms that flag hard constraint violations at runtime. The Agent Control Protocol identifi
arXiv:2510.12817v3 Announce Type: replace-cross Abstract: Human Label Variation (HLV) refers to legitimate disagreement in annotation that reflects the diversity of human perspectives rather than mere
arXiv:2604.20452v1 Announce Type: cross Abstract: Retrieval-Augmented Generation (RAG) expands the knowledge boundary of large language models (LLMs) at inference by retrieving external documents as c
arXiv:2604.20329v1 Announce Type: cross Abstract: Recent works show that image and video generators exhibit zero-shot visual understanding behaviors, in a way reminiscent of how LLMs develop emergent
Enterprise AI scaling is accelerating as organizations shift from experimentation to full deployment, embedding intelligence into core workflows. At the same time, agentic systems are driving a broade
arXiv:2402.06266v2 Announce Type: replace Abstract: Multi-objective reinforcement learning (MORL) algorithms extend conventional reinforcement learning (RL) to the more general case of problems with m
arXiv:2506.00979v5 Announce Type: replace-cross Abstract: The rapid development of Artificial Intelligence Generated Content (AIGC) techniques has enabled the creation of high-quality synthetic conten
arXiv:2604.20487v1 Announce Type: cross Abstract: Large language models (LLMs) encode knowledge in parametric weights, making it costly to update or extend without retraining. Retrieval-augmented gene
arXiv:2510.26285v2 Announce Type: replace-cross Abstract: Prior work has shown that large language models (LLMs) often converge to accurate input embedding for numbers, based on sinusoidal representat
arXiv:2604.20652v1 Announce Type: new Abstract: Large language models trained on human feedback may suppress fraud warnings when investors arrive already persuaded of a fraudulent opportunity. We test
arXiv:2411.10109v2 Announce Type: replace Abstract: Machine learning can predict human behavior well when substantial structured data and well-defined outcomes are available, but these models are typi
AI investment is surging to manic heights, but C-suites have yet to see it translate into measurable business outcomes — a gap that is now one of the defining strategic challenges of the current techn
arXiv:2604.16756v2 Announce Type: replace-cross Abstract: Prompt-induced cognitive biases are changes in a general-purpose AI (GPAI) system's decisions caused solely by biased wording in the input (e.
arXiv:2506.02132v5 Announce Type: replace Abstract: Large transformer-based language models dominate modern NLP, yet our understanding of how they encode linguistic information relies primarily on stu
arXiv:2604.20733v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards (RLVR) has become a core post-training recipe. Introducing suitable off-policy trajectories into on-polic
arXiv:2508.09958v3 Announce Type: replace Abstract: As large language models (LLMs) become increasingly popular, there is a growing need to predict which out of a set of LLMs will yield a successful a
arXiv:2604.20666v1 Announce Type: cross Abstract: Effective retrieval-augmented generation across bilingual Greek--English applications requires embedding models capable of capturing both domain-speci
arXiv:2604.20816v1 Announce Type: cross Abstract: Reinforcement Learning (RL) post-training has become the standard for aligning generative models with human preferences, yet most methods rely on a si
arXiv:2602.20537v3 Announce Type: replace Abstract: Spatiotemporal predictive learning (STPL) aims to forecast future frames from past observations and is essential across a wide range of applications
arXiv:2604.20738v1 Announce Type: new Abstract: We introduce a benchmark dataset for question answering and translation in bilingual Latin and English settings, containing about 7,800 question-answer
arXiv:2604.19971v1 Announce Type: cross Abstract: Interactive spatial layouts empower users to synthesize information and organize findings for sensemaking. While Large Language Models (LLMs) can auto
arXiv:2604.20436v1 Announce Type: cross Abstract: Generative AI (GenAI) is reshaping software engineering by shifting development from manual coding toward agent-driven implementation. While vibe codi
arXiv:2509.15174v3 Announce Type: replace-cross Abstract: WARNING: This paper contains examples of offensive materials. To address the proliferation of toxic content on social media, we introduce SMAR
arXiv:2603.01168v2 Announce Type: replace-cross Abstract: Reliable decision-making in complex multi-agent systems requires calibrated predictions and interpretable uncertainty. We introduce SphUnc, a
arXiv:2604.20022v1 Announce Type: cross Abstract: Large language models are increasingly deployed as autonomous diagnostic agents, yet they conflate two fundamentally different capabilities: natural-l
arXiv:2604.20819v1 Announce Type: new Abstract: The scalability of long-context large language models is fundamentally limited by the quadratic memory cost of exact self-attention, which often leads t
arXiv:2604.19803v1 Announce Type: new Abstract: Agentic AI is rapidly transforming the way research is conducted, from prototyping ideas to reproducing results found in the literature. In this paper,
there are early signs of 5.5 being a competent ai research partner. several researchers let 5.5 run variations of experiments overnight given only a high level algorithmic idea, wake up to find comple
arXiv:2604.20614v1 Announce Type: new Abstract: Modern neural networks can achieve high accuracy while remaining poorly calibrated, producing confidence estimates that do not match empirical correctne
arXiv:2604.19934v1 Announce Type: new Abstract: We study how large language models recall relational knowledge during text generation, with a focus on identifying latent representations suitable for r
Unplugging completely! No WiFi and zero notifications. A great way to get deep focus on a project. Here is a walkthrough showing how to run Gemma 4 (26B A4B) fully offline with LM Studio & OpenCode to
arXiv:2602.22437v3 Announce Type: replace-cross Abstract: Fully Sharded Data Parallel (FSDP), also known as Zero Redundancy Optimizer (ZeRO), is widely used for large-scale model training, because of
arXiv:2603.23146v2 Announce Type: replace-cross Abstract: The widespread adoption of Large Language Models (LLMs) has made the detection of AI-Generated text a pressing and complex challenge. Although
arXiv:2602.04713v2 Announce Type: replace-cross Abstract: Aligning text-to-image generation with user intent remains challenging, as users frequently provide ambiguous inputs and struggle with model i
arXiv:2603.27889v2 Announce Type: replace Abstract: Framing theory posits that how information is presented shapes audience responses, but computational work has largely ignored audience reactions. Wh
arXiv:2604.19217v1 Announce Type: cross Abstract: Crop yield prediction is one of the most important challenge, which is crucial to world food security and policy-making decisions. The conventional fo
arXiv:2604.15034v2 Announce Type: replace Abstract: Recent advances in LLM based agent systems have shown promise in tackling complex, long horizon tasks. However, existing agent protocols (e.g., A2A
arXiv:2604.19724v1 Announce Type: cross Abstract: Despite the remarkable success of Vision Transformers (ViTs) across a wide range of vision tasks, recent studies have revealed that they remain vulner
arXiv:2604.18724v1 Announce Type: new Abstract: Users typically interact with and evaluate language models via single outputs, but each output is just one sample from a broad distribution of possible