Knowledge catalogue
arXiv:2607.12774v1 Announce Type: cross Abstract: This article presents our results for the 11th Affective Behavior Analysis in-the-Wild (ABAW) competition. For multi-task learning with simultaneous p
arXiv:2607.12630v1 Announce Type: cross Abstract: Visual Language Navigation (VLN) aims to enable an embodied agent to navigate complex environments by following natural language instructions. Recent
arXiv:2607.12924v1 Announce Type: new Abstract: In this paper, we study Reinforcement Learning in Parametrized Action Markov Decision Processes (PAMDP), where each decision consists of a symbolic acti
arXiv:2603.13356v2 Announce Type: replace Abstract: Robust reinforcement learning typically assumes that feedback sources are either globally trustworthy or corrupted within a fixed global budget. We
arXiv:2607.11976v1 Announce Type: new Abstract: Indexer-TopK, the operation to compute the scores and select the top-k candidates, is widely used by sparse attention kernels in large language models a
arXiv:2510.18939v2 Announce Type: replace Abstract: Long-horizon agentic search requires iteratively exploring the web over long trajectories and synthesizing information across many sources, enabling
arXiv:2607.12782v1 Announce Type: new Abstract: Hyperspectral foundation models learn transferable spectral-spatial representations from large-scale unlabeled data. They provide an effective paradigm
arXiv:2607.12418v1 Announce Type: new Abstract: Large-scale Vision-Language Models have demonstrated impressive transfer learning capabilities across a wide range of tasks. For few-shot classification
arXiv:2607.11327v2 Announce Type: replace-cross Abstract: Model editing keeps large language models (LLMs) up to date without retraining, but temporal facts expose a limitation of the prevailing locat
arXiv:2607.11019v2 Announce Type: replace Abstract: Enterprise data analysis is emerging as a distinct frontier for autonomous agents. Compared with general-purpose interaction and software engineerin
arXiv:2512.04144v3 Announce Type: replace Abstract: Targeted interventions on language models, such as unlearning or model editing, aim to modify specific information, but their effects often propagat
arXiv:2607.11897v1 Announce Type: new Abstract: Linear attention replaces softmax attention's growing KV cache with a fixed recurrent state, but this compression limits exact state tracking and long-c
arXiv:2607.12236v1 Announce Type: cross Abstract: Speculative execution accelerates LLM agents by using a smaller, cheaper model to predict and pre-launch the next step while the environment is idle.
arXiv:2607.12042v1 Announce Type: new Abstract: Visual generation is increasingly ubiquitous in diverse domains, from text-to-image/video synthesis to multimodal interactive creation. Yet prevailing m
arXiv:2607.12892v1 Announce Type: cross Abstract: Modern robot learning systems increasingly rely on dense progress or value signals to evaluate intermediate states, guide policy learning, and detect
arXiv:2607.12986v1 Announce Type: new Abstract: Plan evaluators can reward a strategic plan for becoming less explicit. This paper studies that failure in a staged expected-value scorer for LLM-genera
Last month we hosted an AI Nerd Meetup @Workato HQ featuring builders tackling the hardest problems in agentic AI. Highlights: @yuan_sihan of Fireworks - how open-source agents can use a frontier mode
Pinecone has introduced Nexus, a knowledge engine built specifically for AI agents. Instead of repeatedly searching documents with traditional RAG, Nexus compiles knowledge once from sources like data
arXiv:2607.08038v1 Announce Type: new Abstract: Diagnostic error is a major threat to patient safety, yet current large language model (LLM) systems often treat diagnosis as a one-shot prediction task
arXiv:2503.12999v4 Announce Type: replace-cross Abstract: Vision-Language Models (VLMs) have demonstrated exceptional performance in various multi-modal tasks. Recently, there has been an increasing i
arXiv:2607.08490v1 Announce Type: new Abstract: Biomedical language evolves rapidly as new discoveries emerge, causing traditional text models to lose semantic fidelity over time. Static embeddings an
arXiv:2607.08288v1 Announce Type: cross Abstract: In critical infrastructure, operational technology environments often cannot be actively scanned, and yet active system feedback is needed for risk as
arXiv:2607.08143v1 Announce Type: cross Abstract: We present the results of HIPE-OCRepair-2026, an ICDAR competition on LLM-assisted OCR post-correction of historical documents. OCR post-correction re
arXiv:2607.08724v1 Announce Type: new Abstract: Human decision-making is highly flexible -- some actions are taken immediately; others require longer deliberation. Language models have exhibited a sim
arXiv:2604.01388v2 Announce Type: replace Abstract: Recent advancements in open-vocabulary 3D scene understanding heavily rely on 3D Gaussian Splatting (3DGS) to register vision-language features into
New research from Meta. (bookmark it) It's on how to fix agents that forget previously made decisions. It's well know that long-horizon agents keep forgetting decisions they already made. Meta researc
arXiv:2607.08395v1 Announce Type: cross Abstract: Persistent AI agents extend large language models (LLMs) beyond single-turn interaction into long-lived software systems. Unlike traditional chat assi
arXiv:2607.07846v1 Announce Type: new Abstract: VectorizationLLM is a specialized Large Language Model based on Google open-weight LLMs. The model is designed to assist students to learn smart vectori
arXiv:2607.08489v1 Announce Type: cross Abstract: Vision-language models excel at video captioning, yet typically generate descriptions that fail to capture individual viewers' attention. We propose V
arXiv:2607.07282v1 Announce Type: new Abstract: The Prasthanatrayi -- the ten principal Upanisads, the Brahmasutra, and the Bhagavadgita, with Sankara's commentaries (bhasya) -- is the foundational co
arXiv:2607.07707v1 Announce Type: cross Abstract: Limited memory language models (LMLMs) externalize factual knowledge during pretraining to a knowledge base (KB), rather than memorizing it in their w
arXiv:2607.06964v1 Announce Type: cross Abstract: Bridging the gap between human pilot intent and autonomous flight operation is critical for real-world electric vertical takeoff and landing (eVTOL) a
arXiv:2607.07302v1 Announce Type: new Abstract: This paper reports an empirical study evaluating the relevance of several RAG metrics. The experiment is based on a question-answering dataset created b
arXiv:2607.07626v1 Announce Type: cross Abstract: Reliable confidence estimation is essential for deploying large language models (LLMs) in confidence-aware systems, where downstream decisions such as
arXiv:2607.06839v1 Announce Type: cross Abstract: Existing NAS benchmarks (e.g., NAS-Bench, NATS-Bench) cover only narrow, task-specific regions of the architectural design space and lack cross-domain
arXiv:2607.07673v1 Announce Type: new Abstract: Medicine is inherently multimodal, requiring clinicians to synthesize information across diverse data streams. Yet the development of multimodal foundat
arXiv:2607.06974v1 Announce Type: new Abstract: Large language models (LLMs) increasingly improve their reasoning at test time via additional computation, yet most existing works treat each problem in
arXiv:2607.07391v1 Announce Type: new Abstract: Mathematical reasoning benchmarks typically provide all facts needed to solve each problem, while interactive benchmarks often mix reasoning with tools,
arXiv:2607.06618v1 Announce Type: cross Abstract: Following the CMIVQA, MMI-VQA, and M4IVQA challenges in NLPCC 2023--2025, we introduce the Difficulty-Aware Medical Instructional Video Question Answe
arXiv:2601.00086v3 Announce Type: replace Abstract: Large language models (LLMs) often struggle to use tools reliably in domain-specific settings, where APIs may be idiosyncratic, under-documented, or
DigitalOcean has released a public preview of Managed Weaviate, a fully managed vector database service that enables users to scale AI and machine learning applications without managing infrastructure
arXiv:2607.07135v1 Announce Type: new Abstract: Contrastive Language-Image Pre-training (CLIP) relies on softmax-based self-attention, a strictly positive distribution that assigns probability mass to
arXiv:2607.07386v1 Announce Type: new Abstract: Linear attention models allow a fixed state size and a fixed amount of compute per token. However, due to their limited state size, linear attention mod
arXiv:2607.07388v1 Announce Type: new Abstract: Large Language Models (LLMs) store factual knowledge and domain-specific patterns implicitly in dense Transformer parameters, making knowledge expansion
arXiv:2607.07706v1 Announce Type: new Abstract: The quadratic cost of causal self-attention severely bottlenecks long-context transformer inference. While numerous post hoc linearization pipelines exi
arXiv:2607.07548v1 Announce Type: new Abstract: Large language model based search agents increasingly adopt multi-agent architectures in which a main agent decomposes a complex question into sub-queri
arXiv:2510.17476v2 Announce Type: replace Abstract: Equitable access to reliable health information is vital when integrating AI into healthcare. Yet, information quality varies across languages, rais
arXiv:2606.13566v2 Announce Type: replace Abstract: Current discussions of AI in scientific discovery are often dominated by two visible capabilities: search over existing knowledge and execution thro
arXiv:2607.05750v1 Announce Type: new Abstract: Computer-aided design (CAD) for industrial components requires long-horizon procedural modeling, robust feature dependencies, editable parametric geomet
arXiv:2607.05409v1 Announce Type: cross Abstract: Introductory programming instruction relies on hands-on practice and short learning activities to support mastery of foundational concepts. Although m
As organizations scale modern workloads — from high-throughput databases and network/security appliances to real-time analytics and AI/ML inference — network and block storage performance can quickly
LlamaIndex is inviting users to process large-scale document collections through its cloud platform at cloud.llamaindex.ai. The post, from LlamaIndex founder Jerry Liu, highlights the platform's capab
arXiv:2607.06482v1 Announce Type: cross Abstract: Current benchmarks for evaluating Large Language Models (LLMs) in data analysis often fail to reflect real-world settings. They typically focus on fac
arXiv:2607.05965v1 Announce Type: cross Abstract: Vascular computed tomography datasets are commonly annotated only once per scan, yielding the pervasive yet under addressed problem of single mask ann
arXiv:2607.06565v1 Announce Type: cross Abstract: Unified 3D foundation models aspire to generate 3D assets and reason about them in language within a single backbone, but their text-3D interaction re
arXiv:2607.06341v1 Announce Type: cross Abstract: Formal verification offers the strongest guarantee of software correctness, but it does not scale: the proofs demanded by interactive theorem provers
arXiv:2607.05956v1 Announce Type: new Abstract: The integration of Large Language Models (LLMs) into scientific research workflows, particularly for bibliographic discovery and literature synthesis, r
arXiv:2512.23236v4 Announce Type: replace-cross Abstract: Making deep learning recommendation model (DLRM) training and inference fast and efficient is important. However, this presents three key syst
arXiv:2603.11044v2 Announce Type: replace Abstract: In this paper, we propose LingDT-VL-OCR, a document parsing system tailored to financial-domain documents, transforming ultra-long financial PDFs in
// Memory becomes an action space // Great paper on long-term memory for agents. (bookmark it) In short, it's discusses the use of a learned policy for using memory at the right granularity. Most memo