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Introduces Contrastive Reflection, an iterative prompt-optimization framework for agentic IR workflows that uses structured traces to identify error-anchored behavioral slices and applies contrastive repair via a Teacher LLM, achieving significant improvements on HotpotQA.
This paper benchmarks 17 deep learning models for first-stage recall in large-scale code-to-code retrieval, evaluating their precision, efficiency, and scalability across multiple programming languages and datasets. It introduces LLM-based code normalization and query rewriting schemes that improve precision for lower-performing models.
MMed-Bench-IR is a heterogeneous benchmark for multilingual medical information retrieval across six languages, evaluating cross-lingual alignment, concept discrimination, and evidence retrieval. It reveals severe performance drops for non-English queries, highlighting gaps in existing English-only evaluations.
This article provides an in-depth introduction to the design philosophy behind two memory modules in EverOS: Knowledge Wiki and Reflection. The former manages external references through a three-layer structure and deterministic classification, while the latter integrates conversational experience through offline reflection, emphasizing memory governance, traceability, and gradual disclosure.
This paper introduces DR-DCI, a retriever-steered framework for scaling direct corpus interaction by dynamically expanding a local workspace, achieving improved accuracy and efficiency in agentic search over large corpora.
This paper presents the first systematic analysis of how large language models can learn to adapt query formulation strategies for different retrievers using reinforcement learning, revealing distinct optimal query styles and introducing a branching-based rollout technique for multi-retrieval-step training stability.
The article reflects on the complexities of AI agent memory beyond simple storage, highlighting challenges such as determining truthfulness, priority changes, distinguishing decisions from noise, and appropriate timing for surfacing context.
LEDGER is a new benchmark for evaluating long-context capabilities of LLMs on corporate annual reports, providing 4,999 digitized reports with 31 financial KPIs and three evaluation tasks spanning retrieval and extraction.
TACHIOM, a multivector retrieval system with token-aware clustering and hierarchical indexing, has been integrated into the PyLate ecosystem. It achieves up to 247x faster clustering and 9.8x retrieval speedup over state-of-the-art systems while maintaining comparable effectiveness.
This paper identifies 'vector search dilution' in RAG systems when scaling to large, heterogeneous document collections, and proposes MASDR-RAG, a domain-scoped retrieval approach that significantly improves retrieval accuracy by leveraging organizational metadata.
Introduces Infini Memory, a maintainable text-based persistent memory architecture for LLM agents that uses topic-structured documents and iterative retrieval to improve long-term memory usage, achieving 64.7% on MemoryAgentBench.
This paper proposes an attention expansion mechanism to enhance keyphrase extraction from long documents by augmenting PLM token representations with out-of-context information, achieving consistent improvements over state-of-the-art models without requiring full-document attention or expensive LLM inference.
REAL is a reasoning-enhanced graph framework for long-term memory management of LLMs that uses temporal and confidence-aware directed property graphs with non-destructive temporal updates and hybrid beam search retrieval, achieving an average improvement of 22.72%.
Didact is a cross-domain capability discovery system for defence that integrates defence reports and policy documents with a knowledge graph from research publications, using a composite RAG pipeline for natural language conversations and an interactive Evidence Rail for source visualization.
This paper proposes a training-free, CPU-only retrieval method that fuses BM25 lexical scores with late-interaction dense scores for conversational memory retrieval, achieving up to +17.2 points improvement on LoCoMo Hit@1 over late interaction alone across six encoders. The study provides controlled ablations on pooling operators, reranker effects, and benchmark robustness, framing the gain as a division of labor between dense and lexical signals.
QO-Bench is a diagnostic benchmark for query-operator question answering over typed event tuples, covering 22,984 news articles and 614 corporate events across 18 query templates. It evaluates RAG, ReAct RAG, GraphRAG, and extraction-to-SQL systems, finding that operator execution—not just retrieval—is a core bottleneck that stronger models alone cannot resolve.
This paper introduces Fine-grained Fragment Retrieval (FFR), a new task for locating semantically coherent multi-modal fragments (text and images) within long-form dialogues. The authors propose F2RVLM, a generation-based retrieval model trained with reinforcement learning, and FFRS, a two-stage retrieval system, along with a new dataset MLDR for evaluation.
OBLIQ-Bench introduces a suite of five oblique search tasks that expose a gap between retrieval and verification: reasoning LLMs easily recognize relevant documents once surfaced, but even state-of-the-art retrievers fail to surface them, highlighting overlooked bottlenecks in modern retrieval systems.
A new benchmark, OBLIQ-Bench, is introduced to test retrieval models and long-context LLMs on harder search queries, moving beyond saturated and expired benchmarks.
Researchers extract indexable, BM25-ready sparse features from frozen dense retrievers using reconstruction-trained sparse autoencoders.