Search: “retrieval”

arXiv:2609.05059v1 Announce Type: cross Abstract: Whether repeated identical buying questions exhaust a language model's brand recommendations depends on retrieval. Across 300 question-engine cells (5

I've been running Qwen3.8-27B as a local inference server for a production content intelligence pipeline (HVAC industry stuff, lots of long-context retrieval and structured extraction). I have been wa

arXiv:2609.03654v1 Announce Type: cross Abstract: The comparative analysis of banks' financial statements poses significant challenges for automated question answering systems due to their complexity,

arXiv:2609.03619v1 Announce Type: cross Abstract: Multi-agent debate (MAD) improves the reasoning capabilities of large language models by having multiple agents iteratively refine their responses thr

Jörg Schad is the VP of Engineering at @pinecone. He joins @kbal11 to discuss moving beyond RAG with precomputed context, context artifacts, semantic layers, and AI retrieval. @joerg_schad https://sof

arXiv:2609.02754v1 Announce Type: cross Abstract: Large language models now answer medical questions with expert-level performance. However, the context these systems act on can be misleading, and mis

arXiv:2609.00818v1 Announce Type: new Abstract: We argue that financial report generation should operate at the analytical rather than structural level, composing content from data-derived insights ra

arXiv:2609.00935v1 Announce Type: cross Abstract: Deep Research agents tackle knowledge-intensive tasks through multi-round retrieval and decision-oriented generation. However, these agents suffer fro

arXiv:2609.00551v1 Announce Type: cross Abstract: Multimodal memory offers a scalable interface for long-video question answering, but existing methods often retrieve captions, frames, transcripts, su

arXiv:2609.00099v1 Announce Type: new Abstract: Corrosion accounts for approximately 4% of global GDP, and reliable prediction is essential for timely mitigation. Machine learning effectively predicts

arXiv:2609.00342v1 Announce Type: new Abstract: Whole-slide images (WSIs) are challenging for vision-language reasoning because diagnostically relevant morphology is sparse, heterogeneous, and distrib

arXiv:2609.00844v1 Announce Type: new Abstract: Customer-service QA in an AI contact center (AICC) runs under deployment constraints that benchmark QA misses: tight voice-hotline latency and a high co

arXiv:2608.29285v1 Announce Type: new Abstract: Vision-Language Models align point clouds with image and text embeddings, enabling zero-shot recognition, retrieval, and open-vocabulary understanding o

arXiv:2603.16138v2 Announce Type: replace-cross Abstract: Generative search systems are increasingly replacing link-based retrieval with AI-generated summaries, yet little is known about how these sys

arXiv:2608.28699v1 Announce Type: new Abstract: Understanding long-form video remains a fundamental challenge for multimodal large language models (MLLMs). Sparse frame sampling fails to capture fine-

arXiv:2608.28645v1 Announce Type: cross Abstract: Low-resource languages without an adequate training corpus often use a related, higher-resource language as a scaffold for comprehension. Still, there

arXiv:2608.29088v1 Announce Type: new Abstract: Multimodal question answering remains sensitive to noisy, incomplete, and weakly grounded evidence. Long unstructured contexts can introduce redundancy

arXiv:2608.28696v1 Announce Type: new Abstract: Visual in-context learning (ICL) with multimodal large language models (MLLMs) is effective for fine-grained visual classification, but each retrieved i

arXiv:2510.09347v2 Announce Type: replace Abstract: Unlike Business-to-Consumer e-commerce platforms (e.g., Amazon), inexperienced individual sellers on Consumer-to-Consumer platforms (e.g., eBay) oft

arXiv:2608.30676v1 Announce Type: new Abstract: When medical AI systems hallucinate clinical reasoning, the consequences extend beyond incorrect answers: fabricated justifications that superficially r

arXiv:2606.20122v2 Announce Type: replace Abstract: Open-ended deep research (OEDR) requires systems to acquire knowledge through multi-round retrieval and generate coherent long-form reports. The out

arXiv:2608.29582v1 Announce Type: cross Abstract: Current evaluations of large language models (LLMs) primarily focus on factual knowledge retrieval, overlooking the fundamental challenge of navigatin

arXiv:2608.29179v1 Announce Type: cross Abstract: Generative recommendation encodes items as hierarchical semantic identifiers (SIDs) and retrieves the next item through autoregressive decoding. Stand

arXiv:2601.12075v2 Announce Type: replace Abstract: Language models used in retrieval-augmented settings must arbitrate between parametric knowledge stored in their weights and contextual information

arXiv:2608.28695v1 Announce Type: new Abstract: Image retrieval has traditionally been formulated as a point-wise matching problem, where each candidate image is scored in isolation. However, this ato

arXiv:2608.30725v1 Announce Type: new Abstract: Recent multilingual vision--language encoders cover hundreds of languages in a single model, yet on two state-of-the-art instances retrieval on low-reso

Learn how to build a multi-tenant agentic document chat application on Amazon Bedrock Managed Knowledge Base, where users upload documents and immediately ask grounded questions. This post covers the

arXiv:2608.26856v1 Announce Type: cross Abstract: Although Multimodal Large Language Models (MLLMs) have demonstrated impressive performance in Medical Visual Question Answering (Med-VQA), their relia

arXiv:2608.26744v1 Announce Type: new Abstract: Zero-shot classification needs efficient label retrieval and fine-grained visual reasoning, yet discriminative and generative vision-language models fai

arXiv:2608.26004v1 Announce Type: cross Abstract: Agentic LLM pipelines face escalating inference costs as context accumulates across retrieval, tool use, and multi-turn interactions. To control laten

arXiv:2608.25115v1 Announce Type: new Abstract: Existing methods for improving Retrieval-Augmented Generation (RAG) efficiency mainly optimize downstream LLM generation, such as context compression or

arXiv:2601.16296v3 Announce Type: replace Abstract: Video-to-video diffusion models achieve impressive single-turn editing performance, but practical editing workflows are inherently iterative. When e

arXiv:2608.24010v1 Announce Type: new Abstract: Supervised proxy-based deep cross-modal hashing has become the dominant paradigm for large-scale retrieval. However, prevalent methods model class proxi

arXiv:2510.14249v2 Announce Type: replace-cross Abstract: Understanding and modeling the relationship between language and sound are essential for applications such as music information retrieval, tex

arXiv:2608.22830v1 Announce Type: new Abstract: Deploying LLMs for enterprise Text-to-SQL is bottlenecked less by the model than by what context reaches it: business logic spans thousands of tables, a

arXiv:2608.23471v1 Announce Type: cross Abstract: Memory is becoming a default subsystem in deployed LLM agents to provide persistent personalization and continuity. This naturally prompts a question:

arXiv:2608.23419v1 Announce Type: cross Abstract: Information Retrieval (IR) systems seek to identify relevant documents within a collection. In practical applications, collections are dynamic, with d

arXiv:2608.21796v1 Announce Type: cross Abstract: Knowledge-based Visual Question Answering (KB-VQA) aims to answer queries that necessitate reasoning over external knowledge sources beyond the visual

arXiv:2608.21375v1 Announce Type: new Abstract: Heterogeneous agentic retrieval-augmented generation (RAG) systems increasingly orchestrate external APIs, internal databases, vector stores, and graph

arXiv:2608.20357v1 Announce Type: cross Abstract: Deep search is brittle on underspecified user queries: missing constraints such as time, location, scope, or definitions can lead to retrieval drift a

Input tokens are often a meaningful part of the cost of running Retrieval Augmented Generation (RAG) at scale. This post describes a query-aware context compression pattern on Amazon Bedrock: after re

arXiv:2608.19029v1 Announce Type: new Abstract: Accurate and responsible medical question answering (QA) is important in healthcare, where complex cases require factual knowledge and nuanced reasoning

arXiv:2608.17050v1 Announce Type: cross Abstract: Methods for improving knowledge use in large language models typically fall into two regimes. Non-parametric retrieval offers flexible access to exter

arXiv:2608.17665v1 Announce Type: new Abstract: LLM-driven agents can autonomously exchange opinions on online platforms and form communities. Such agent-operated social platforms raise a new security

arXiv:2608.14587v1 Announce Type: new Abstract: Background: Recent advances in information retrieval (IR) leverage both dense and sparse representations, large language models (LLMs), and specialized

arXiv:2608.15749v1 Announce Type: new Abstract: Existing 3D editing methods have made notable progress in controllability, yet they remain limited in several important ways. Most approaches rely on te