Knowledge catalogue
arXiv:2606.01149v1 Announce Type: new Abstract: Video Moment Retrieval (MR) and Highlight Detection (HD) are crucial tasks in video analysis that aim to localize specific moments and estimate clip-wis
arXiv:2606.01413v1 Announce Type: cross Abstract: It is crucial for modern on-device AI systems that rely on retrieval-augmented inference to release and share datastores without compromising individu
arXiv:2606.00775v1 Announce Type: cross Abstract: Video Moment Retrieval (VMR) task requires accurately localizing temporal boundaries aligned with natural language queries, but many models suffer fro
arXiv:2606.00629v1 Announce Type: cross Abstract: Sound design workflows frequently oscillate between time-consuming library searches and the complexity of procedural synthesis, with practitioners typ
arXiv:2510.24870v2 Announce Type: replace Abstract: We introduce MiRAGE, an evaluation framework for retrieval-augmented generation (RAG) from multimodal sources. As audiovisual media becomes a preval
arXiv:2606.00021v1 Announce Type: cross Abstract: Speculative Decoding (SD) accelerates Large Language Model (LLM) inference by employing a lightweight draft model to propose candidate tokens, which a
arXiv:2606.00014v1 Announce Type: cross Abstract: Although studies have demonstrated that Large Language Models (LLMs) can perform well on Out-of-Distribution (OOD) tasks, their advantage tends to dim
arXiv:2605.30497v1 Announce Type: new Abstract: RAG-based legal assistants have been growing in popularity, but LLM hallucinations remain a key issue and potentially undermines justice. While benchmar
arXiv:2605.29004v1 Announce Type: new Abstract: Reported retrieval scores for training-free shape descriptors conflate local signal design, normalization, aggregation, codebook fitting, and metric cho
arXiv:2605.29886v1 Announce Type: cross Abstract: Retrieval-augmented generation (RAG) improves knowledge-intensive question answering by incorporating external evidence. However, existing RAG methods
arXiv:2605.29630v1 Announce Type: cross Abstract: End-to-end agent-memory benchmarks report a single hit@k per retriever, confounding lexical leakage (uncontrolled query/gold/distractor entity overlap
arXiv:2605.29158v1 Announce Type: new Abstract: Protein homology search underlies function annotation, structure prediction, and evolutionary analysis, but remains challenging in the 'twilight zone,'
arXiv:2605.27444v1 Announce Type: cross Abstract: The rapid expansion of space activities has led to an unprecedented accumulation of technical documentation, operational guidelines, and scientific li
arXiv:2605.27380v1 Announce Type: cross Abstract: Cross-lingual biomedical entity linking (BEL) maps mentions in any language to unique identifiers in a biomedical knowledge base (KB), supporting clin
arXiv:2605.27700v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used to generate scientific reports, but they can produce references that appear plausible while contain
arXiv:2604.08304v2 Announce Type: replace-cross Abstract: Retrieval-augmented generation (RAG) extends large language models (LLMs) with external knowledge, but this access path also introduces securi
arXiv:2605.26385v1 Announce Type: cross Abstract: Large-scale search, recommendation, and retrieval-augmented generation (RAG) systems typically employ a two-stage architecture: an early-stage ranker
Great to see a GBrain x ActiveGraph crossover here Great retrieval pairs well with almost anything you want to do with agents Gbrain is what an agent *knows* — a durable markdown/git knowledge substra
arXiv:2510.13217v2 Announce Type: replace-cross Abstract: Search systems are increasingly used for reasoning-intensive queries, where what makes a document relevant requires understanding or reasoning
this is a fantastic example of a robust agentic memory system built on langgraph! there's 4 key parts 1. retrieval (rag) 2. storage, fragmented by memory type 3. reasoning* 4. learning (often called d
arXiv:2605.24454v1 Announce Type: new Abstract: Large language models (LLMs) have shown strong performance in the legal domain, demonstrating notable potential in Legal Question Answering (LQA). Howev
arXiv:2512.08508v2 Announce Type: replace-cross Abstract: Identifying enzymes that catalyze target biochemical reactions is a key step in computational enzyme discovery and biocatalyst design. Recent
arXiv:2605.25020v1 Announce Type: new Abstract: Chronic dermatologic diseases such as pemphigus require long-term follow-up, generating extensive longitudinal clinical documentation that is difficult
arXiv:2605.26019v1 Announce Type: cross Abstract: Online Terms of Service often function as contracts of adhesion, creating asymmetries that may expose consumers to potentially abusive clauses. In Chi
arXiv:2605.24524v1 Announce Type: cross Abstract: In non-invasive neural language decoding, results can be inflated by sources that are not stimulus-evoked neural evidence: decoder priors, embedding-b
arXiv:2605.20425v1 Announce Type: new Abstract: Designing multi-agent workflows is especially difficult in open-ended scientific settings where tasks lack curated training sets, reliable scalar evalua
arXiv:2601.20107v2 Announce Type: replace-cross Abstract: Recent Vision-Language Models (e.g., ColPali) enable fine-grained Visual Document Retrieval (VDR) but incur prohibitive multi-vector index sto
arXiv:2605.22718v1 Announce Type: new Abstract: Autoregressive video diffusion models have enabled real-time, action-conditioned world generation. However, sustaining a persistent world, where revisit
arXiv:2605.18772v1 Announce Type: cross Abstract: Retrieval-Augmented Generation (RAG) improves the factual accuracy of large language model (LLM) outputs by grounding generation in external knowledge
arXiv:2605.18850v1 Announce Type: cross Abstract: We introduce KadiAssistant, a privacy-by-design AI assistant integrated into the Kadi research data ecosystem, enabling researchers to efficiently acc
arXiv:2605.16896v1 Announce Type: new Abstract: Contextual Automatic Speech Recognition (ASR) faces challenges with large-scale keyword dictionaries, as excessive irrelevant candidates introduce noise
arXiv:2605.16352v1 Announce Type: cross Abstract: Repository-level coding agents must first localize the files and symbols relevant to a task; failures at this stage can cascade across downstream obje
arXiv:2503.14800v3 Announce Type: replace-cross Abstract: Effective long-term memory management is crucial for language models handling extended contexts. We introduce the Enhanced Ranked Memory Augme
arXiv:2605.16331v1 Announce Type: cross Abstract: Protein language models are increasingly used to guide experimental and clinical decisions, yet it is often unclear whether a confident prediction ref
arXiv:2605.17630v1 Announce Type: new Abstract: Here's a trimmed version under 1920 characters: Open-vocabulary segmentation models such as SAM3 achieve strong performance through concept-level text p
arXiv:2605.18029v1 Announce Type: new Abstract: Multimodal product retrieval (MPR) underpins checkout-free retail and automated inventory systems, yet it demands fine-grained SKU discrimination that s
arXiv:2605.15937v1 Announce Type: new Abstract: Accurate multi-step port-of-call sequence prediction is vital for tactical resource orchestration and logistical efficiency. However, existing methods s
arXiv:2605.16113v1 Announce Type: cross Abstract: Large language models (LLMs) have achieved unprecedented success due to their exceptional generative capabilities. However, because they depend on kno
Real question: what is the actual latest state-of-the-art for file search and retrieval? - Actual grep over filesystem - Virtualized grep / BM25 over a db (what @mintlify did) - Vector search over a d
Releasing my first kernel on @huggingface: MaxSim Late-interaction retrieval (ColBERT / PyLate) bottlenecks on materializing the full similarity matrix. This kernel avoids it by using tiled scoring wi
arXiv:2605.15505v1 Announce Type: new Abstract: In enterprise operations, the context required for an AI agent task is scattered across systems of record, static information stores, and communication
arXiv:2605.14581v1 Announce Type: cross Abstract: Visual RAG has offered an alternative to traditional RAG. It treats documents as images and uses vision encoders to obtain vision patch tokens. Howeve
arXiv:2510.15849v2 Announce Type: replace Abstract: Accurate tongue segmentation is crucial for reliable TCM analysis. Supervised models require large annotated datasets, while SAM-family models remai
arXiv:2605.12988v1 Announce Type: new Abstract: Students learning algorithms often need support as they interpret traces, debug reasoning errors, and apply procedures across unfamiliar problem instanc
arXiv:2605.13277v1 Announce Type: cross Abstract: Visual evidence selection is a critical component of multimodal retrieval-augmented generation (RAG), yet existing methods typically rely on semantic
arXiv:2510.06719v2 Announce Type: replace-cross Abstract: Retrieval-Augmented Generation (RAG) enhances large language models (LLMs) by grounding them in external knowledge. However, its application i
arXiv:2605.12335v1 Announce Type: cross Abstract: Electronic Health Records (EHR) contain rich longitudinal patient information and are widely used in predictive modeling applications. However, effect
arXiv:2605.11591v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) have shown strong performance in multi-image cross-modal retrieval, yet suffer from severe position bias, where
A beginner-friendly guide to building a simple RAG (RETRIEVAL-AUGMENTED GENERATION) AI Agent with Pinecone + n8n 🧵 If you’re learning AI automation and you’re confused about Pinecone, knowledge bases,
arXiv:2605.08421v1 Announce Type: new Abstract: Visual Document Retrieval (VDR) models mostly rely on late interaction architectures, in which documents are represented by a set of local patch embeddi
arXiv:2605.08117v1 Announce Type: cross Abstract: Inertial Measurement Unit (IMU)-based Human Activity Recognition (HAR) aims to interpret and classify user behaviors from temporal motion signals. Rec
arXiv:2605.10155v1 Announce Type: new Abstract: Legal information in India remains largely inaccessible due to the complexity of legal language and the sheer volume of legal documentation involved in
arXiv:2605.08468v1 Announce Type: cross Abstract: Local LLM-based coding agents increasingly work in settings where correctness is earned through execution feedback, persistent state, and bounded repa
arXiv:2605.08857v1 Announce Type: new Abstract: Recent advances in uncertainty quantification for time series forecasting show that conformal prediction can provide reliable prediction intervals, yet
arXiv:2605.09012v1 Announce Type: new Abstract: Large language models are increasingly capable at closed-world mathematical reasoning, but research assistance also requires source-grounded use of the
arXiv:2605.09420v1 Announce Type: cross Abstract: In this study, we tackle Generalized Category Discovery (GCD) via a Relational Retrieval perspective, explicitly coupling labeled and unlabeled data t
arXiv:2605.10862v1 Announce Type: new Abstract: This paper demonstrates RUBEN, an interactive tool for discovering minimal rules to explain the outputs of retrieval-augmented large language models (LL
arXiv:2601.17644v3 Announce Type: replace-cross Abstract: The growing adoption of multimodal Retrieval-Augmented Generation (mRAG) pipelines for vision-centric tasks (e.g., visual QA) introduces impor
arXiv:2605.04116v1 Announce Type: cross Abstract: We show that remotely hosted applications employing in-context learning when augmented with a retrieval function to select in-context examples can be
arXiv:2605.03675v1 Announce Type: new Abstract: Long-running autonomous AI agents suffer from a well-documented memory coherence problem: tool-execution success rates degrade 14 percentage points over