ChronoSynth is a new framework for generating natural‑language questions from temporal knowledge graphs while preserving essential constraints such as answer field, time condition, reference event, and temporal order. It introduces a Temporal Intent State to make question semantics explicit, retrieves compatible typed patterns from a memory, adapts them to the input graph, and validates the output, using feedback to revise any inconsistencies. Experiments on CRONQUESTIONS and MultiTQ show that ChronoSynth achieves state‑of‑the‑art performance, with ablation studies confirming the effectiveness of its components.
arXiv:2607. 14770v1 Announce Type: new Abstract: Knowledge graph question generation (KGQG) aims to generate natural-language questions from structured graph evidence.
arXiv:2608. 20083v1 Announce Type: cross Abstract: Question Answering over Temporal Knowledge Graphs (TKGQA) requires reasoning over time-sensitive facts, yet existing embedding-based methods struggle with multi-step queries due to single-pass reasoning pipelines.
Question Answering over Temporal Knowledge Graphs (TKGQA) requires reasoning over time-sensitive facts, yet existing embedding-based methods struggle with multi-step queries due to single-pass reasoning pipelines. We propose SABET-QA, a framework that iteratively refines reasoning states across multiple hops via a bidirectional entity-temporal scoring mechanism and a slot-aware contextualization module that aligns question semantics with temporal KG embeddings.
arXiv:2609.22213v1 Announce Type: new Abstract: Temporal Knowledge Graph Question Answering (TKGQA) requires answer inference from evidence that is both structurally valid and temporally admissible....
arXiv:2610.28176v1 Announce Type: cross Abstract: Large Language Models (LLMs) often struggle with temporally grounded reasoning because their static parametric memory is misaligned with the dynamic...
arXiv:2607. 26520v1 Announce Type: cross Abstract: Conversational AI agents commonly lack persistent memory across sessions.
arXiv:2603.28773v2 Announce Type: replace-cross Abstract: Large language models (LLMs) frequently generate confident yet factually incorrect content when used for language generation (a phenomenon of...
arXiv:2608. 16224v1 Announce Type: cross Abstract: By leveraging large-scale pretraining, LLMs can interpret diverse temporal expressions and question formulations without task-specific training.
RDL-Bench is a new benchmark for task-aware question answering over relational databases, introduced in arXiv:2610.21382v1. It categorizes user questions into reasoning-oriented queries, which depend on the current database state, and prediction-oriented queries, which rely on future outcomes under temporal constraints. Each benchmark instance includes a natural-language question, task metadata, schema details, temporal context, executable evidence or target-construction logic, and denotation-level ground truth, supporting evaluation in both native solver and task-aware modular system settings.
Conversational AI agents commonly lack persistent memory across sessions. The obvious fixes like injecting full chat histories into the context window, or delegating to a third-party memory service, either exhaust the model's context budget or send personal data through infrastructure the user does not control.
arXiv:2606.13120v2 Announce Type: replace Abstract: Search Agents -- large language models augmented with search tools -- have intensified the need for future-proof evaluation benchmarks. Existing be...
arXiv:2601.07148v4 Announce Type: replace-cross Abstract: Tool use, such as web search, has become a standard capability even in freely available large language models (LLMs). However, existing bench...