Tootfinder

NFL Week 8 betting power ratings: Why Packers and Eagles aren't among top six, plus ranking all 32 teams

https://www.cbssports.com/nfl/news/nfl-wee

QDER: Query-Specific Document and Entity Representations for Multi-Vector Document Re-Ranking
Shubham Chatterjee, Jeff Dalton
https://arxiv.org/abs/2510.11589 https://

QDER: Query-Specific Document and Entity Representations for Multi-Vector Document Re-Ranking
Neural IR has advanced through two distinct paths: entity-oriented approaches leveraging knowledge graphs and multi-vector models capturing fine-grained semantics. We introduce QDER, a neural re-ranking model that unifies these approaches by integrating knowledge graph semantics into a multi-vector model. QDER's key innovation lies in its modeling of query-document relationships: rather than computing similarity scores on aggregated embeddings, we maintain individual token and entity representa…

ModernBERT ColBERT: Enhancing biomedical RAG through an advanced re-ranking retriever
Eduardo Mart\'inez Rivera, Filippo Menolascina
https://arxiv.org/abs/2510.04757 https…

ModernBERT + ColBERT: Enhancing biomedical RAG through an advanced re-ranking retriever
Retrieval-Augmented Generation (RAG) is a powerful technique for enriching Large Language Models (LLMs) with external knowledge, allowing for factually grounded responses, a critical requirement in high-stakes domains such as healthcare. However, the efficacy of RAG systems is fundamentally restricted by the performance of their retrieval module, since irrelevant or semantically misaligned documents directly compromise the accuracy of the final generated response. General-purpose dense retrieve…

REGENT: Relevance-Guided Attention for Entity-Aware Multi-Vector Neural Re-Ranking
Shubham Chatterjee
https://arxiv.org/abs/2510.11592 https://arxiv.org/pd…

REGENT: Relevance-Guided Attention for Entity-Aware Multi-Vector Neural Re-Ranking
Current neural re-rankers often struggle with complex information needs and long, content-rich documents. The fundamental issue is not computational--it is intelligent content selection: identifying what matters in lengthy, multi-faceted texts. While humans naturally anchor their understanding around key entities and concepts, neural models process text within rigid token windows, treating all interactions as equally important and missing critical semantic signals. We introduce REGENT, a neural…

Por um mundo em que isto se estenda Š merda dos "rankings das escolas" que infestam a imprensa portuguesa.
https://mathstodon.xyz/@johncarlosbaez/115479917595681479

Towards Inference-time Scaling for Continuous Space Reasoning
Minghan Wang, Thuy-Trang Vu, Ehsan Shareghi, Gholamreza Haffari
https://arxiv.org/abs/2510.12167 https://

Towards Inference-time Scaling for Continuous Space Reasoning
Inference-time scaling through multiple sample generation in combination with Process- or Outcome-Reward Model (PRM or ORM) re-ranking has proven effective for text-based reasoning in large language models. This paper investigates whether such established techniques can be successfully adapted to reasoning in the continuous space, using COCONUT (Hao et al. 2024) continuous space reasoning LM as the backbone. We demonstrate the feasibility of generating diverse reasoning paths through dropout-ba…

Contrastive Retrieval Heads Improve Attention-Based Re-Ranking
Linh Tran, Yulong Li, Radu Florian, Wei Sun
https://arxiv.org/abs/2510.02219 https://arxiv.o…

Contrastive Retrieval Heads Improve Attention-Based Re-Ranking
The strong zero-shot and long-context capabilities of recent Large Language Models (LLMs) have paved the way for highly effective re-ranking systems. Attention-based re-rankers leverage attention weights from transformer heads to produce relevance scores, but not all heads are created equally: many contribute noise and redundancy, thus limiting performance. To address this, we introduce CoRe heads, a small set of retrieval heads identified via a contrastive scoring metric that explicitly reward…

Reproducing and Extending Causal Insights Into Term Frequency Computation in Neural Rankers
Cile van Marken, Roxana Petcu
https://arxiv.org/abs/2510.06728 https://

Reproducing and Extending Causal Insights Into Term Frequency Computation in Neural Rankers
Neural ranking models have shown outstanding performance across a variety of tasks, such as document retrieval, re-ranking, question answering and conversational retrieval. However, the inner decision process of these models remains largely unclear, especially as models increase in size. Most interpretability approaches, such as probing, focus on correlational insights rather than establishing causal relationships. The paper 'Axiomatic Causal Interventions for Reverse Engineering Relevance Comp…

Towards Long-Term User Welfare in Recommender Systems via Creator-Oriented Information Revelation
Xu Zhao, Xiaopeng Ye, Chen Xu, Weiran Shen, Jun Xu
https://arxiv.org/abs/2510.10511

Towards Long-Term User Welfare in Recommender Systems via Creator-Oriented Information Revelation
Improving the long-term user welfare (e.g., sustained user engagement) has become a central objective of recommender systems (RS). In real-world platforms, the creation behaviors of content creators plays a crucial role in shaping long-term welfare beyond short-term recommendation accuracy, making the effective steering of creator behavior essential to foster a healthier RS ecosystem. Existing works typically rely on re-ranking algorithms that heuristically adjust item exposure to steer creator…

Bridging Language Gaps: Advances in Cross-Lingual Information Retrieval with Multilingual LLMs
Roksana Goworek, Olivia Macmillan-Scott, Eda B. \"Ozyi\u{g}it
https://arxiv.org/abs/2510.00908

Bridging Language Gaps: Advances in Cross-Lingual Information Retrieval with Multilingual LLMs
Cross-lingual information retrieval (CLIR) addresses the challenge of retrieving relevant documents written in languages different from that of the original query. Research in this area has typically framed the task as monolingual retrieval augmented by translation, treating retrieval methods and cross-lingual capabilities in isolation. Both monolingual and cross-lingual retrieval usually follow a pipeline of query expansion, ranking, re-ranking and, increasingly, question answering. Recent adv…