All papers

The paper constructs a semantic gain graph using offline model evaluations to retrieve complementary, non-redundant documents for multi-hop question answering.

The paper presents a bit-factorized document identifier design that allows generative retrieval models to generalize to unseen documents without retraining.

The paper evaluates query rewriting strategies to balance query fidelity and recall in conversational search systems.

The paper shows that data curation and model merging boost a small model's passage retrieval and ranking performance without architectural changes.

Presents a training-free framework that tracks changing conversational facts as versioned records to improve agent memory retrieval.

The authors evaluate offline graph-modified embeddings for vector retrieval, improving multi-hop evidence recovery without requiring query-time graph access.

The paper introduces a large language model-based metric for offline evaluation of user-product relevance in dynamic product advertising retrieval.

This paper introduces a self-improving synthetic data generation pipeline to train embedding models across diverse retrieval domains without extensive manual annotation.

Proposes a structured knowledge admission and verification system to prevent continual learning agents from storing harmful past experience entries.

This paper proposes SCOUT, a cascaded two-track framework combining visual candidate filtering, offline semantic summaries, and query refinement for document retrieval.

The paper introduces a threat modeling framework examining adversarial query demotion and graph edit attacks on graph-based RAG systems.

The paper guides knowledge-graph retrieval agents by evaluating the causal impact of evidence on answering questions rather than text similarity.

Creates a structured schema that converts research papers into connected scientific entities to improve retrieval-augmented generation for autonomous agents.

Combines language models and lightweight decision models to fact-check statements in peer reviews against original manuscripts.

The paper introduces ExecutionRAG to show how search drift causes apparent retrieval failures in multi-hop question answering.

Proposes dynamic sparse supervision to align language model representations with neural sparse retrieval systems.

Develops an information-gain calibrated distillation method to improve retrieval query generation in search agents.

The paper proposes adding an explicit copy-paste prediction head to language models to improve in-context retrieval and copying efficiency.

Proposes NEAR, a training-free framework using neighborhood-derived residual representations to reduce retrieval errors from shared non-target information in memory records.

The paper analyzes geometric properties of text embeddings to predict how many nearest neighbors an individual example loses under dimensionality reduction.

This paper uses limited human preference signals to calibrate automated evaluation metrics for biomedical retrieval-augmented question answering.

The paper presents an intervention method using Shapley values to assign credit to individual actions within multi-action agentic retrieval sets.

The paper proposes an agentic framework enabling multimodal models to identify information gaps and retrieve in-context demonstrations iteratively.

Introduces a set-level reinforcement learning objective to train language models to produce diverse and document-grounded scientific research ideas.

The paper proposes OpCredit, a policy learning framework that assigns individual operation credits for agentic knowledge graph question answering.

The paper surveys memory-centric compression techniques for managing inference resources and context windows in large language models.

Presents a multi-stage search system that retrieves and ranks relevant scientific literature directly from informal research ideas.

The paper introduces a dataset and citation recommendation system that retrieves relevant scientific papers without relying on citation graphs.

This paper introduces a benchmark of expert-annotated question-answer pairs to evaluate multimodal comprehension of long historical Chinese documents.

Introduces FASHION-RQ, an automated framework deriving hierarchical evaluation benchmarks for fashion retrieval directly from catalog structures.

JevGPT combines fast local evidence assessments with deliberative planning to improve autonomous research agents operating under fixed retrieval budgets.

Presents a multi-agent framework that decouples research planning, subquestion investigation, and factual verification via a shared semantic workspace.

The paper describes a modular software backend architecture for extracting energy domain information and answering grounded questions from web sources.

Presents SPARQL-LLM, a multilingual natural language to SPARQL question answering system evaluated in the TEXT2SPARQL 2026 challenge.

The paper introduces an educational tool combining Arabic knowledge graphs and passage retrieval to generate narrative-based questions.

The paper presents a framework that jointly learns query rule assignment and rule-constrained evidence retrieval over structured knowledge bases.

This paper introduces a multi-agent retrieval framework that coordinates search across legal statutes and precedent cases for grounded legal reasoning.

The paper presents a retrieval-aware denoising method for conversational user profiles to improve personalized dense retrieval.

The paper introduces a framework combining guidelines and agentic linking to ground medical concepts across diverse text types and ontologies.

This paper introduces UNIGRAPHE, a deterministic method to assemble clinical text into queryable knowledge graphs conforming to SNOMED CT grammar.

This paper introduces an interactive retrieval-augmented framework that combines knowledge graphs and text retrieval to explore digital cultural heritage archives.

The authors present a multilingual dense retrieval and reranking framework to link scientific web claims to their source publications.

The paper proposes a linear score correction method to recover retrieval quality in compressed late-interaction document representations.

The paper describes a system submission combining rules and language models for multilingual financial named-entity recognition and question answering.

Proposes PRISM, a multimodal clinical prognostic model combining patient records with retrieved population-level embedding memory banks.

The paper evaluates knowledge-graph-informed chunking and retrieval strategies over German legal code to support verifiable retrieval-augmented generation.

Presents a two-stage visual-textual alignment and retrieval-augmented framework for generating structured radiology reports from medical scans.

The paper proposes an event-centric memory framework that converts dialogue history into query-relevant graph working memory for conversational agents.

The paper evaluates structural segmentation strategies for hierarchical legal documents to improve dense retrieval performance in Brazilian legal texts.

This paper introduces a training-free retrieval-augmented generation framework for biomedical named entity linking to improve cross-domain adaptability.

The paper introduces an industrial generative retrieval framework featuring collaborative tokenization and preference optimization for WeChat search systems.

The paper investigates how indexed instruction wording affects the performance of lexical rankers for robot task retrieval in manipulation benchmarks.

Proposes an adaptive retrieval-augmented generation framework that dynamically expands clinical queries for medication recommendation.

This paper introduces GUIDE, a multi-agent copilot system that assists clinicians in drafting and updating evidence-based medical practice guidelines.

The paper analyzes the performance of retrieval-augmented generation architectures on open-domain question answering tasks involving culturally grounded South Asian narratives.

The paper proposes a reinforcement learning framework that decides when and what external knowledge to retrieve for machine translation.

The paper proposes a relation-semantic reasoning framework to resolve disconnects between answer reachability and valid paths in biomedical graphs.

XViewRank uses result-set overlap context to fuse BM25 rankings generated from distinct query perspectives created by language models.

TAGHN enhances automated subject indexing by reranking candidate descriptors using a text-attentive graph over thesaurus neighborhoods.

RCER reconstructs cross-evidence relations and entity dependencies directly across iterative retrieval rounds to support complex multi-hop question answering.