Daily edition · Information Retrieval
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34 articles · 6 sources · 32 papers ·
Top topics: Information Retrieval · AI Research · AI
The easiest performance win in React isn't always your JavaScript. Sometimes it's your images. I used to think performance optimization meant splitting bundles, memoizing components, and chasing Lighthouse scores. Then…
The impact of AI has led to a shift in interest from Search Engine Optimisation (SEO) to Answer Engine Optimisation The post “Twenty years of brand building simply froze in time”: How coding agents select their tools of…
arXiv:2609.04915v1 Announce Type: new Abstract: Many long-horizon LLM deployments face tight prompt budgets: latency, cost, and context limits make full-context prompting impractical as interaction length grows. The key…
arXiv:2609.04767v1 Announce Type: new Abstract: Large language models (LLMs) increasingly rely on information retrieval (IR) systems, such as Retrieval-Augmented Generation (RAG), to incorporate domain-specific…
arXiv:2606.08151v3 Announce Type: replace Abstract: Modern large language model (LLM) agents do not simply need longer contexts; they need decision-relevant evidence at the moment of action. We study decision-aware…
arXiv:2609.04999v1 Announce Type: new Abstract: This paper describes the participation of the BIT.UA team from the University of Aveiro in the 14th edition of the BioASQ Task B challenge on biomedical question…
arXiv:2609.04579v1 Announce Type: cross Abstract: Grounded language-model pipelines can be divided into three stages: selecting an object, retrieving passages for it, and using that evidence to answer. If the selected…
arXiv:2609.04391v1 Announce Type: cross Abstract: In this work, a novel evaluation scheme built on a generalized variant of the Rand Index measure, namely, the H\"ullermeier-Rifqi Index, is proposed in order to assess…
arXiv:2609.05025v1 Announce Type: cross Abstract: Hallucination-where a language model generates outputs that are factually incorrect or unsupported by the source-is a major challenge for both prompted and fine-tuned…
arXiv:2608.25546v3 Announce Type: replace Abstract: LLM-based recommendation has scaled along model capacity and sequence length, yet each position encodes only text, semantic IDs, or a few categorical features,…
arXiv:2609.05339v1 Announce Type: cross Abstract: Model upgrades are routine; memory migrations are not. An agent can keep the same memory store and still forget: a new model may interpret old notes differently, mixed…
arXiv:2609.05059v1 Announce Type: new Abstract: Whether repeated identical buying questions exhaust a language model's brand recommendations depends on retrieval. Across 300 question-engine cells (50 questions, six…
arXiv:2609.04255v1 Announce Type: new Abstract: Dense document images often contain many fine-grained visual and textual entities whose relevance depends on a user query. Standard vision-language retrievers encode…
arXiv:2609.04981v1 Announce Type: cross Abstract: Recent structured RAG methods leverage tree- or graph-based reasoning structures to improve multi-hop QA. However, they face key limitations in evidence-intensive QA,…
arXiv:2609.05160v1 Announce Type: cross Abstract: The study investigates students' interest and expectations in a Big Data Engineering course integrated with a Master curricula, as well as ethical implications of using…
arXiv:2609.04645v1 Announce Type: new Abstract: Multimodal Vision-Language Models (VLMs) have demonstrated remarkable capabilities in cross-modal understanding, yet a fundamental challenge persists when applying them to…
arXiv:2609.04961v1 Announce Type: new Abstract: E-commerce search often suffers from vocabulary mismatch between user queries and merchant-authored product titles, since short titles cannot fully cover diverse user…
arXiv:2609.03290v2 Announce Type: replace Abstract: Unified modeling has become a major direction for industrial click-through rate (CTR) prediction. Existing approaches typically unify sequential and non-sequential…
arXiv:2609.04269v1 Announce Type: cross Abstract: Deciding whether two supplier records belong to the same corporate family is a prerequisite for spend consolidation, credit exposure aggregation and sanctions screening.…
arXiv:2609.04574v1 Announce Type: new Abstract: Multimodal Graph Neural Networks have become standard for recommendation by augmenting sparse interaction data with content features. Yet current architectures face two…
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