Knowledge catalogue
arXiv:2602.05437v2 Announce Type: replace Abstract: Vision-language models (VLMs) can achieve high accuracy while still accepting culturally plausible but visually incorrect interpretations. Existing
arXiv:2506.18186v3 Announce Type: replace Abstract: The restless multi-armed bandit (RMAB) framework is a popular approach to solving resource allocation problems in networked systems. In this paper,
arXiv:2604.00555v2 Announce Type: replace Abstract: Enterprise adoption of Large Language Models (LLMs) is constrained by hallucination, domain drift, and the inability to enforce regulatory complianc
Perplexity's reward design system prioritizes correctness as a foundational requirement, then evaluates user preference and efficiency only when answers meet correctness standards. This hierarchical a
arXiv:2604.18943v1 Announce Type: new Abstract: With the rise in capabilities of large language models (LLMs) and their deployment in real-world tasks, evaluating LLM alignment with human preferences
arXiv:2604.19738v1 Announce Type: cross Abstract: We establish central and non-central limit theorems for sequences of functionals of the Gaussian output of an infinitely-wide random neural network on
arXiv:2510.18333v2 Announce Type: replace-cross Abstract: Despite progress in watermarking algorithms for large language models (LLMs), real-world deployment remains limited. We argue that this gap st
arXiv:2604.19079v1 Announce Type: cross Abstract: Unification of automatic speech recognition (ASR) systems reduces development and maintenance costs, but training a single model to perform well in bo
arXiv:2604.18893v1 Announce Type: cross Abstract: A series of high-profile tragedies involving companion chatbots has triggered an unusually rapid regulatory response. Several jurisdictions, including
arXiv:2604.19099v1 Announce Type: cross Abstract: Education is not merely the transmission of information or the optimisation of individual performance; it is a fundamentally social, constructive, and
arXiv:2604.19593v1 Announce Type: cross Abstract: The importance of clear and correct text in legal documents cannot be understated, and, consequently, a grammatical error correction tool meant to ass
arXiv:2604.19638v1 Announce Type: new Abstract: Multimodal Large Language Models are increasingly adopted as autonomous agents in interactive environments, yet their ability to proactively address saf
arXiv:2506.14387v3 Announce Type: replace Abstract: Adapting LLMs with new knowledge is increasingly important, but standard fine-tuning often erodes aligned epistemic abstention: the ability to ackno
arXiv:2601.02993v4 Announce Type: replace Abstract: Retrieval-Augmented Generation (RAG) has become a key paradigm for reducing factual hallucinations in Large Language Models (LLMs), yet little is kn
The future is taking shape in Las Vegas this week, where the world’s leading startups are showcasing their groundbreaking AI work at Google Cloud Next. Whether they need the top AI models, infrastruct
arXiv:2604.18632v1 Announce Type: new Abstract: Stomata play a crucial role in regulating plant physiological processes and reflecting environmental responses. However, accurate and high-throughput st
arXiv:2604.19591v1 Announce Type: new Abstract: Fine-grained high-resolution remote sensing mapping typically relies on localized visual features, which restricts cross-domain generalizability and oft
arXiv:2604.18939v1 Announce Type: new Abstract: Table annotation is crucial for making web and enterprise tables usable in downstream NLP applications. Unlike textual data where learning semantically
arXiv:2604.19245v1 Announce Type: cross Abstract: Repair, an important resource for resolving trouble in human-human conversation, remains underexplored in human-LLM interaction. In this study, we inv
arXiv:2604.19212v1 Announce Type: new Abstract: Graph neural networks (GNNs) are the standard for learning on graphs, yet they have limited expressive power, often expressed in terms of the Weisfeiler
The Never Ending Lore of Harness w/@Vtrivedy10⚡️ Viv brought some amazing perspective around harness design, evals, file systems, RL Envs and so much. 0:00:00 - INTRO 0:03:30 - PhD at Temple Universit
The first wave of AI changed how we find information; the next wave is changing how we get work done. Today, we’re enhancing our most powerful AI tools and bringing them together under one roof. Gemin
arXiv:2604.18718v1 Announce Type: cross Abstract: Agentic security systems increasingly audit live targets with tool-using LLMs, but prior systems fix a single coordination topology, leaving unclear w
arXiv:2604.19070v1 Announce Type: new Abstract: Zero-shot reasoning on text-rich networks (TRNs) remains a challenging frontier, as models must integrate textual semantics with relational structure wi
Vibe coders are not going to like this. UC San Diego just published the first real field study of experienced developers using AI agents. They watched 13 of them code in the wild and surveyed 99 more.
arXiv:2601.08620v2 Announce Type: replace Abstract: Retrieval-Augmented Generation (RAG) pipelines must address challenges beyond simple single-document retrieval, such as interpreting visual elements
arXiv:2411.18275v2 Announce Type: replace Abstract: Vision-language models (VLMs) have significantly advanced autonomous driving (AD) by enhancing reasoning capabilities. However, these models remain
From vibe-coded and large-scale apps to AI models and agents, Cloud Run delivers on-demand compute with zero overhead and pay-per-use pricing for all of your workloads. Last year, the number of extern
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
While generative AI sparked a revolution, the true paradigm shift is the rapid evolution from standalone AI models to multi-agent autonomous systems. In this new era, the network transcends basic conn
arXiv:2512.02304v2 Announce Type: replace Abstract: Large language models (LLMs) can act as both problem solvers and solution verifiers, where the latter select high-quality answers from a pool of sol
arXiv:2604.18586v1 Announce Type: cross Abstract: Vaccination remains a cornerstone of global public health, yet the COVID-19 pandemic exposed how online misinformation, political polarization, and de
arXiv:2603.24472v2 Announce Type: replace Abstract: Self-distillation has emerged as an effective post-training paradigm for LLMs, often improving performance while shortening reasoning traces. Howeve
arXiv:2604.18570v1 Announce Type: cross Abstract: Modern medicine generates vast multimodal data across siloed systems, yet no existing model integrates the full breadth and temporal depth of the clin
arXiv:2604.17130v1 Announce Type: cross Abstract: The present study aims to investigate a cluster cleaning algorithm that is both computationally simple and capable of solving the PU classification wh
arXiv:2602.18835v2 Announce Type: replace Abstract: Food waste management is critical for sustainability, yet inorganic contaminants hinder recycling potential. Robotic automation accelerates sorting
arXiv:2502.13570v4 Announce Type: replace-cross Abstract: Two-sample hypothesis testing-determining whether two sets of data are drawn from the same distribution-is a fundamental problem in statistics
arXiv:2604.18348v1 Announce Type: new Abstract: Video diffusion transformers (DiTs) suffer from prohibitive inference latency due to quadratic attention complexity. Existing sparse attention methods e
Replit's Agent feature can automatically scan for security issues and publish the findings, allowing users to resolve all detected problems with a single 'Fix all with Agent' action. This functionalit
arXiv:2604.18292v1 Announce Type: cross Abstract: Large language models are increasingly expected to serve as general-purpose agents that interact with external, stateful tool environments. The Model
arXiv:2604.18293v1 Announce Type: new Abstract: Surprisal theory hypothesizes that the difficulty of human sentence processing increases linearly with surprisal, the negative log-probability of a word
arXiv:2506.12606v2 Announce Type: replace Abstract: While Mamba has demonstrated strong performance in language modeling, its potential as a speech self-supervised learning (SSL) model remains underex
arXiv:2604.18137v1 Announce Type: cross Abstract: Processing-in-Memory (PIM) architectures offer a promising solution to the memory bottlenecks in data-intensive machine learning, yet often overlook t
arXiv:2510.15339v3 Announce Type: replace Abstract: Building effective knowledge graphs (KGs) for Retrieval-Augmented Generation (RAG) is pivotal for advancing question answering (QA) systems. However
arXiv:2604.17065v1 Announce Type: new Abstract: Human Activity Recognition (HAR) involves the automatic identification of user activities and has gained significant research interest due to its broad
arXiv:2511.03180v2 Announce Type: replace Abstract: As multilingual Large Language Models (LLMs) gain traction across South Asia, their alignment with local ethical norms, particularly for Bengali, sp
arXiv:2604.17108v1 Announce Type: new Abstract: Coreference Resolution (CR) is a fundamental NLP task critical for long-form tasks as information extraction, summarization, and many business applicati
arXiv:2604.17008v1 Announce Type: new Abstract: Large Language Models (LLMs) are increasingly used to generate narrative content, including children's stories, which play an important role in social a
arXiv:2604.17794v1 Announce Type: new Abstract: The democratization of ubiquitous AI hinges on deploying sophisticated reasoning capabilities on resource-constrained devices. However, Small Language M
arXiv:2509.20823v5 Announce Type: replace-cross Abstract: Time series captioning, the task of describing time series in natural language, requires numeric and temporal reasoning, trend interpretation,
arXiv:2604.16372v1 Announce Type: new Abstract: Multimodal sarcasm detection has recently garnered significant attention. However, existing benchmarks suffer from coarse-grained annotations and limite
arXiv:2603.15421v2 Announce Type: replace Abstract: Large language model agents heavily rely on external memory to support knowledge reuse and complex reasoning tasks. Yet most memory systems store ex
arXiv:2506.01732v2 Announce Type: replace Abstract: Large Language Models (LLMs) are pre-trained on large data from different sources and domains. These datasets often contain trillions of tokens, inc
arXiv:2604.18356v1 Announce Type: new Abstract: Developing compassionate interactive systems requires agents to not only understand user emotions but also provide diverse, substantive support. While r
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:2604.17215v1 Announce Type: new Abstract: Large language models require continuous adaptation to new tasks while preserving safety alignment. However, fine-tuning on even benign data often compr
arXiv:2604.18197v1 Announce Type: cross Abstract: Qualitative methods are important to use alongside quantitative methods to improve Human-Robot Interaction (HRI), yet they are often applied in static
arXiv:2604.17398v1 Announce Type: new Abstract: We present a methodological framework to discover linguistic and discursive patterns associated to different social groups through contrastive synthetic
arXiv:2604.17761v1 Announce Type: cross Abstract: Interpretability tools are increasingly used to analyze failures of Large Language Models (LLMs), yet prior work largely focuses on short prompts or t
arXiv:2510.06133v2 Announce Type: replace Abstract: Diffusion large language models (dLLMs) generate text through iterative denoising. In commonly adopted parallel decoding schemes, each step confirms