This paper introduces KGERMAR, a framework that constructs dynamic, context-specific knowledge graphs during inference for long-context language modeling, achieving lower perplexity and better memory…

The paper introduces Sherpa.ai, a multi-party Private Set Union (PSU) protocol that enables privacy-preserving entity alignment for Vertical Federated Learning (VFL) without disclosing shared sample i…

This paper proposes a lightweight encoder-based MEL solution called FAST-MEL that meets three objectives: high linking accuracy, computational efficiency, and storage efficiency.

The paper introduces Semantic Triplet Restoration (STR), a novel protocol that converts complex table structures into atomic semantic triplets, improving table question answering by providing explicit…

EviLink addresses the ambiguity of schema linking in Text-to-SQL by treating it as an uncertainty-aware inference over multiple plausible SQL paths, significantly improving recall and efficiency.

The paper introduces Sophrosyne, a system that moderates LLM agent exploration in relational data systems, significantly reducing over-exploration and boosting SQL generation accuracy by guiding the a…

PhishSigma++ is a novel entity-relation-based detector that improves malicious email detection by focusing on invariant functional relationships between typed entities, significantly outperforming tex…

The paper investigates how language models perform entity tracking across state changes and finds that LMs use a non-incremental, parallel aggregation strategy rather than maintaining a true internal…

The paper demonstrates that LLMs generate correlated, non-existent character ensembles (ghost couples) whose co-occurrence rates are highly predictable and model-specific, leading to the creation of f…

The paper introduces 'bundesrecht,' an open-source, end-to-end pipeline for processing complex German statutory references, which parses, normalizes, and resolves raw citation strings into structured,…

The paper identifies and quantifies 'zombie linkages' in various DNS integrations, demonstrating that persistent, outdated mappings pose significant security risks across different naming ecosystems.

The paper introduces Open-Domain Tabular Question Answering for Future Data Forecasting and Reasoning, a new dataset and framework that enables LLMs to perform time-series forecasting and reasoning on…

Frontier LLM-based agents can effectively overcome the manual bottleneck of phenotype annotation by achieving consistency comparable to human experts, significantly outperforming existing NLP tools.

The paper introduces Autonomous Agentic Data Engineering, demonstrating that LLMs can autonomously plan and optimize end-to-end data curation pipelines, leading to substantial performance gains in spe…

OmniRetrieval introduces a unified framework that handles natural language queries across diverse, heterogeneous knowledge sources (text, relational, graphs) by dispatching source-native queries witho…

The paper introduces Entity-Collision, a rigorous protocol that separates genuine retrieval lift from simple lexical overlap, demonstrating that embedder performance depends critically on the query ty…

SpecDB is a novel system that uses LLMs to synthesize highly customized, purpose-built relational databases, achieving performance comparable to commercial systems while significantly reducing code si…

The study compares agentic data retrieval using unstructured web data versus structured, semantically-annotated datasets, concluding that semantic metadata remains essential for high-precision, reliab…

The paper introduces Hyperparam, a set of lightweight JavaScript libraries designed to enable direct, model-aware querying of unstructured data (like agent traces) within client-side AI applications.

The paper defines AI Identity as the correspondence between an agent's declared state and its observed behavior, concluding that current infrastructure and standards are fundamentally inadequate for g…