Daily edition · Information Retrieval
TILens turns technical updates into a focused daily brief: official releases, trusted reporting, and practitioner analysis, deduplicated and organized by topic.
31 articles · 1 source · 31 papers ·
Top topics: Information Retrieval · AI · AI Research
arXiv:2609.00844v1 Announce Type: cross 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 cost for…
arXiv:2603.29093v3 Announce Type: replace-cross Abstract: LLM agents rerun full reasoning for every task, even one they solved moments earlier. We introduce \textbf{APEX-EM}, a non-parametric experience memory that…
arXiv:2609.00165v1 Announce Type: new Abstract: Two-sided digital platforms are inherently dynamic: user preferences shift, item popularity evolves, and reviews both reflect and drive these changes. Yet most sequential…
arXiv:2609.01067v1 Announce Type: new Abstract: Reinforcement learning for user-centric agents is limited by the cost, latency, and risk of collecting online feedback, as well as by the lack of counterfactual…
arXiv:2609.00618v1 Announce Type: new Abstract: We investigate the role of conversational context modeling in user preference tracking for Conversational Recommendation Systems (CRSs). In this regard, we propose DREAMS,…
arXiv:2608.15213v2 Announce Type: replace-cross Abstract: Crowd counting must recover reliable local density under severe variations in perspective, head scale, occlusion, and background clutter. Although modern…
arXiv:2609.01556v1 Announce Type: cross Abstract: We evaluate embedding retrieval where surface form and meaning are pulled apart on purpose: retrieving items that share underlying structure but not wording, in two…
arXiv:2606.19719v4 Announce Type: replace Abstract: Semantic caching cuts LLM inference costs by serving a cached response to semantically similar queries. Standard practice evaluates these systems using PR-AUC, a…
arXiv:2609.00638v1 Announce Type: new Abstract: Retrieval is the first stage of modern search and advertising systems, selecting a candidate set from a large item universe for downstream ranking and auction. Recent work…
arXiv:2609.00364v1 Announce Type: new Abstract: Embedding models are trained and evaluated as if retrieval were exact; in production they serve behind approximate indexes -- HNSW, IVF, product quantization, or the…
arXiv:2609.01325v1 Announce Type: cross Abstract: Neural ranking models have become core components of modern information retrieval systems and important building blocks of AI systems such as retrieval-augmented…
arXiv:2609.01316v1 Announce Type: new Abstract: Retrieval over visually rich documents has a representation problem: important content often lives in tables, charts, figures, and layout relations that plain OCR…
arXiv:2609.00913v1 Announce Type: new Abstract: Interactions with cold items negatively impact real-time personalization of ID-based recommender systems. This is because the use of such interactions degrades user…
arXiv:2609.00808v1 Announce Type: cross Abstract: As far-right actors increasingly exploit online platforms to disseminate ideology and mobilize supporters, civil society organizations (CSOs) play a vital yet…
arXiv:2609.00313v1 Announce Type: new Abstract: Scientific discovery depends on finding prior literature that shapes what comes next. Existing retrieval systems optimize for relevance and popularity, often favoring…
arXiv:2608.30130v2 Announce Type: replace Abstract: Retrieval-augmented language models can fail to respect negative constraints when the retriever supplies evidence about concepts the user explicitly excluded. Beyond…
arXiv:2609.01030v1 Announce Type: new Abstract: Price extraction from websites is a key task for market monitoring, price comparison, and business analytics in e-commerce. Existing approaches can be broadly divided into…
arXiv:2609.01240v1 Announce Type: new Abstract: Scaling Transformers has driven large gains in language modeling, but transplanting this to behavior-sequence modeling in production ranking is challenging: recommendation…
arXiv:2408.09199v2 Announce Type: replace Abstract: In the pursuit of enhancing domain-specific Large Language Models (LLMs), Retrieval-Augmented Generation (RAG) emerges as a promising solution to mitigate issues such…
arXiv:2609.00667v1 Announce Type: new Abstract: Large vision-language models used as listwise rerankers must jointly process visual tokens from tens of candidates per query, making token pruning essential for practical…
Showing 1 day · 31 items available
