Topic - Edition
arXiv:2609.10922v1 Announce Type: new Abstract: Auto-research agents have shown the potential to automate hypothesis generation, experiment execution, and iterative refinement. However, scaling this paradigm to…
arXiv:2607.06451v3 Announce Type: replace-cross Abstract: Single-server private information retrieval (PIR) schemes are known to require linear query time. Recent works circumvent these classical lower bounds by…
arXiv:2604.16379v3 Announce Type: replace Abstract: Industrial B2B applications (e.g., construction site risk prediction, material procurement) face extreme data sparsity yet feature rich textual interactions. In such…
arXiv:2508.15304v3 Announce Type: replace Abstract: Multimodal recommendation combines the user historical behaviors with the modal features of items to capture the tangible user preferences, presenting superior…
arXiv:2603.20278v2 Announce Type: replace Abstract: Training deep research agents requires long-horizon trajectories that interleave search, evidence aggregation, and multi-step reasoning. However, existing data…
arXiv:2609.11414v1 Announce Type: cross Abstract: Large language models exhibit complementary strengths, motivating routing methods that dispatch each query to the most suitable model. Although existing routers are…
arXiv:2608.29652v3 Announce Type: replace Abstract: Generative Retrieval (GR) is promising for e-commerce search, yet existing methods struggle to maintain query-intent consistency throughout the training pipeline.…
arXiv:2604.19664v2 Announce Type: replace Abstract: Efficient semantic access to industrial product data is a key enabler for factory automation and emerging LLM-based agent workflows, where both human engineers and…
arXiv:2609.11572v1 Announce Type: new Abstract: Although large language models (LLMs) and retrieval-augmented generation (RAG) have advanced open-domain question answering (QA), they remain unreliable when documents…
arXiv:2604.04036v2 Announce Type: replace Abstract: Novice math teachers often encounter students' mistakes that are difficult to diagnose and remediate. Misconceptions are especially challenging because teachers must…
arXiv:2609.10862v1 Announce Type: cross Abstract: This report presents results from Project Qualia, an ongoing effort to determine whether experiential similarity between songs, a structure not captured by genre or…
arXiv:2609.11390v1 Announce Type: new Abstract: State-of-the-art retrieval-augmented generation (RAG) methods exploit document structures to acquire sufficient evidence, but often incur substantial token costs. To…
arXiv:2609.11758v1 Announce Type: cross Abstract: Allowing large language models (LLMs) to retrieve information from a set of trusted documents can increase reliability and reduce hallucination. However, recent work has…
arXiv:2608.27912v2 Announce Type: replace Abstract: Deep-research agents answer complex user questions through an iterative sequence of search steps, where the agent autonomously formulates sub-queries to retrieve the…
arXiv:2609.11209v1 Announce Type: cross Abstract: Retrieval-augmented generation (RAG) improves knowledge-intensive large language model (LLM) applications by conditioning generation on retrieved documents, but longer…
arXiv:2603.24204v2 Announce Type: replace Abstract: Large Language Models (LLMs) have demonstrated superior performance in listwise passage reranking task. However, directly applying them to rank long-form documents…
arXiv:2609.10750v1 Announce Type: new Abstract: LLM agents increasingly rely on external skills retrieved at runtime, making skill selection from large repositories a critical challenge. We present a production skill…
arXiv:2601.18570v2 Announce Type: replace Abstract: Federated recommendation systems commonly protect user privacy by keeping user parameters on local devices, while exchanging item parameters for collaborative model…
arXiv:2609.11190v1 Announce Type: cross Abstract: AI shopping assistants increasingly redirect consumer discovery, creating an urgent need for tools that support seller-side competitive decision-making. We present a…
arXiv:2609.11808v1 Announce Type: new Abstract: Late-interaction retrieval is the state-of-the-art for visual document search, but it pays for its accuracy in storage. Existing compression methods retain a subset or…
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