#retrieval

25 curated events

papersTODAY 04:00 UTC

Paper Details Option-Aware Retrieval and VLM Tuning for Offline Medical VQA

An arXiv paper describes a submission to the MedReason 2026 challenge that handles both multiple-choice and open-ended medical visual question answering with fully offline, containerized inference. The authors report that retrieval for multiple-choice questions needs to compare candidate options in a specific way, and they also adapt a vision-language model to the clinical task. The work is listed under both cs.AI and cs.CL.

medical-ai/vlm/retrieval/benchmark

papersTODAY 04:00 UTC

Paper Examines How First Query Shapes Agentic Deep Search

A new arXiv paper studies deep research agents that answer complex questions by repeatedly searching, reading, and reasoning. It argues that the quality of the initial search query is decisive, since well-tuned lexical retrieval can surface useful evidence early on benchmarks like BrowseComp-Plus. The authors frame the opening move as a strategic choice that shapes the rest of the search loop.

agents/search/retrieval/benchmarks

papersTODAY 04:00 UTC

Retrieval-Grounded Reasoning Approach Proposed for Universal Multimodal Embeddings

A new arXiv paper introduces a method that grounds chain-of-thought reasoning in retrieved evidence to improve universal multimodal embeddings, which aim to represent text, images and other modalities in one shared space. The authors argue that reasoning steps should be tied to retrieval so that only relevant information shapes the final embedding. The work targets a single model that can handle a range of cross-modal retrieval tasks.

multimodal/retrieval/embeddings/reasoning

papersTODAY 04:00 UTC

Agentic Visual RAG via Explicit Context Selection and Consolidation

A new arXiv paper proposes a visual retrieval-augmented generation approach that treats evidence gathering as an explicit agentic process. Instead of relying on a single retrieval step, the method selects and consolidates page images as context before reasoning over visually rich documents. The work targets settings where supporting evidence is sparse and spread across pages.

rag/vision-language/agents/retrieval

papersTODAY 04:00 UTC

HyperProve Uses Answer-Guided Hypergraph Expansion for Multi-Hop QA

A new arXiv paper introduces HyperProve, a method for multi-hop question answering that builds hypergraphs instead of treating retrieved passages as separate matches to the original question. The approach expands the evidence graph using the candidate answer as a guide, so that intermediate entities, relations, and constraints linking facts can be captured. The authors argue this better reflects how the information needed for complex questions is connected.

question-answering/retrieval/hypergraphs/research

papersTODAY 04:00 UTC

arXiv paper explores mixture-of-experts LLMs as efficient retrievers

A new arXiv preprint examines whether decoder-only language models built with mixture-of-experts layers can serve as effective first-stage retrievers. The authors note that retrieval quality typically rises with backbone size, but dense models force every query and document through all parameters. The work targets strong retrieval performance while limiting the computational cost per query.

retrieval/mixture-of-experts/llm/efficiency

papersTODAY 04:00 UTC

IROH Retrieval System Leads JOKER 2026 Humor Ranking Task

A team called VANGUARD describes IROH, a three-stage retrieval pipeline that blends sparse and dense retrieval with cross-encoder reranking and LLM judges distilled from rationales. The system placed first on the English JOKER 2026 Task 1 leaderboard with a MAP score of 0.6347. The work appears as an arXiv paper under the cs.CL category.

humor/retrieval/benchmarks/clef

papersTODAY 04:00 UTC

arXiv paper proposes property-registry contract for lattice design search

A new arXiv preprint describes a contract mechanism for searching thermal-mechanical lattice libraries that lets a design system retrieve a matching cell or explicitly refuse when no candidate meets the requirements. The work targets early-stage engineering queries, which are knowledge-heavy and frequently jointly unsatisfiable, such as demands for a cell that is simultaneously light, stiff, thermally conductive and inexpensive. The proposed property-registry approach is framed as a way to make refusal a legitimate outcome rather than returning an unsuitable match.

arxiv/materials/design/retrieval

papersTODAY 04:00 UTC

Paper Proposes Embedding Language Models and RAG Directly Into DuckDB

A new arXiv preprint describes a method for tightly coupling large language models and retrieval-augmented generation with DuckDB, an embedded analytical database. The work targets analytics tasks that need to draw on both structured tables and unstructured documents at the same time. According to the authors, building the retrieval layer into the database engine simplifies prototyping compared with wiring LLM calls on from the outside, though the abstract only briefly sketches the approach.

rag/duckdb/llm/retrieval

papersTODAY 04:00 UTC

DeepDiscovery: A Location-Inference Framework for Task-Level Repository Understanding

A new arXiv preprint introduces DeepDiscovery, a framework aimed at helping language models understand large industrial code repositories at the task level. The authors argue that current retrieval approaches surface only isolated fragments and miss broader repository context. The work targets software engineering tasks where whole-repository comprehension matters.

code-llm/retrieval/software-engineering/arxiv

papersTODAY 04:00 UTC

RA-CoA: Training-free Fashion Image Captioning via Retrieval-Augmented Chain-of-Attributes

Researchers propose RA-CoA, a training-free method for fashion image captioning that combines retrieval with a chain-of-attributes reasoning approach. The work targets e-commerce use cases, where captioning demands fine-grained visual analysis and correct domain-specific fashion terminology rather than generic scene description. It is published as an arXiv preprint in the cross-listed machine learning category.

image-captioning/retrieval/e-commerce/arxiv

papersSEP 10 04:00 UTC

Fortunate Recall: Ontology-Driven Memory Lifecycle Management for LLMs

A newly posted arXiv paper tackles the problem of AI assistants accumulating unstructured personal information until their long-term memory becomes cluttered and less reliable. The proposed method organizes stored facts using an ontology and applies lifecycle rules that determine which memories should be retained, updated, or discarded over time. The goal is to keep conversational agents internally consistent during extended interactions while improving the precision of memory retrieval.

memory/llm/retrieval/research

papersSEP 10 04:00 UTC

Research tackles omitted temporal expressions in Japanese news for retrieval systems

A new arXiv paper examines how news articles often reference dates incompletely, such as mentioning only a day or month, which requires knowledge of the publication date to interpret correctly. This becomes problematic when articles are indexed as standalone text in search and retrieval-augmented applications. The work, focused on Japanese news, proposes methods for reconstructing these omitted temporal expressions.

nlp/retrieval/japanese/temporal

papersSEP 10 04:00 UTC

Researchers propose minimal sufficient user profiles for personalized language models

A new arXiv paper examines how large language models personalize their outputs using records retrieved from user histories. Rather than prepending a fixed number of retrieved items, the authors learn minimal sufficient user profiles that retain only the information needed to produce accurate, preference-aligned responses.

personalization/llm/retrieval/research

papersSEP 10 04:00 UTC

Training trajectories determine circuit removability in annealable soft-prior Transformers

Researchers asked whether retrieval circuits that small Transformers learn with the help of soft positional priors keep functioning once that prior is taken away. They tested this using a model whose prior-based attention biases can be gradually annealed out during training. The results indicate that the specific training trajectory, not just the architecture, decides whether a learned circuit can stand on its own after the prior is removed.

interpretability/transformers/training-dynamics/retrieval

papersSEP 10 04:00 UTC

OntologyAligner pairs ontology-aware retrieval with LLM reranking for biomedical normalization

A new arXiv paper presents OntologyAligner, a method for mapping free-text biomedical phrases to standardized ontology concepts. It combines a retrieval stage aligned with the ontology's structure with a reranking step in which a large language model uses hierarchy cues to separate closely related candidates. The work is announced in cs.AI with a cross-listing in cs.CL.

llm/biomedical/retrieval/nlp

papersSEP 10 04:00 UTC

Study traces word-by-word embedding trajectories of Vietnamese legal headlines

A cs.CL paper on arXiv examines how dense retrieval models build question representations incrementally by encoding 2,144 held-out headlines from the Thu Vien Phap Luat Vietnamese legal library one word at a time. The authors track these embedding trajectories using Nemotron-3-Embed (8B/1B) and Qwen3-Embedding (8B/0.6B) models, offering insight into how vector representations evolve as words arrive. The v2 listing replaces the earlier version of the paper.

embeddings/retrieval/vietnamese/research

papersSEP 10 04:00 UTC

EviMem proposes evidence-gap-driven iterative retrieval for long-term conversational memory

Researchers present EviMem, a retrieval method for long-term conversational memory that identifies gaps in the evidence gathered so far and iteratively fetches additional material across past sessions. The approach targets temporal and multi-hop questions where a single retrieval pass typically fails to locate relevant information. The paper is available as a revised version (v2) on arXiv.

retrieval/memory/conversational-ai/research

papersSEP 10 04:00 UTC

LiteRAG: Cost-Efficient Graph-Based Retrieval-Augmented Generation

A new arXiv paper introduces LiteRAG, a graph-based retrieval method built to lower the query-time overhead that plagues existing graph-based RAG systems. The approach also keeps retrieved contexts compact and focused, which speeds up generation and supports better multi-hop question answering.

retrieval/rag/graph/research

papersSEP 10 04:00 UTC

When Does Low-Bit Quantization Preserve the Decisions of Vector Search?

A new arXiv study investigates why low-bit quantization delivers high recall on some embedding types while failing sharply on others, a difference that average distortion and global rank correlation cannot explain. The authors analyze quantized vector search at the level of individual comparisons to identify when compressed indexes still reproduce full-precision retrieval decisions.

quantization/vector-search/retrieval/embeddings

papersSEP 10 04:00 UTC

Study separates what language agents store in memory from what they use to answer

An arXiv paper examines memory management for persistent language agents, arguing that the experiences an agent keeps in storage should be treated separately from the evidence it retrieves for any individual question. The authors note that a superseded fact can mislead an answer about the current state while still being essential for queries about the past. The work frames forgetting as a deliberate design choice in agent architectures.

agents/memory/retrieval

papersSEP 10 04:00 UTC

Paper Evaluates Query Rewriting Benefits for RAG Under a Strong Retrieval Baseline

A new study tests whether rewriting a user's question into multiple search variants actually improves retrieval-augmented generation when the retrieval system is already highly capable. The authors compare several rewriting strategies within a single fixed evaluation setup to measure what each adds over a strong baseline.

rag/retrieval/query-rewriting/evaluation

papersSEP 12 04:00 UTC

MOSAIC proposes query-aware exploration policies for GraphRAG retrieval

A new arXiv paper introduces MOSAIC, a method that adapts how a system explores a knowledge graph depending on the query at hand. The authors argue that using one shared exploration strategy for all queries is a structural mismatch, since some questions need compact local evidence while others require broader traversal. MOSAIC is presented as a query-aware policy adaptation approach for Graph Retrieval-Augmented Generation.

graphrag/retrieval/research/agents

papersSEP 12 04:00 UTC

Study examines catastrophic forgetting in skill retrieval for LLM agents

A new arXiv paper studies how synthetic data affects the ability of LLM agents to select the right external skill from large repositories. The authors describe a deployed skill router covering 34,396 skills and run a large-scale evaluation of retrieval under limited data conditions. The findings point to catastrophic forgetting as a risk when synthetic data is used for training these routers.

agents/synthetic-data/retrieval/llm-agents

papersSEP 10 04:00 UTC

CARRE framework prescribes explainable retention actions for at-risk customers

A new paper introduces CARRE, a three-stage framework that moves churn analysis beyond simply flagging customers likely to leave. It retrieves feasible counterfactual interventions and evaluates the reasoning behind each recommendation, so that retention suggestions come with a justified rationale rather than a risk score alone.

explainability/counterfactual/retrieval/churn