Daily Digest — Oct 7

Natural Language Questions as an Interface for Knowledge Graphs: QRAKEN Graph Distillation and Semantic Self-Healing

• QRAKEN achieves strict F1 scores of 0.643 with GPT-4.1 mini and 0.652 with GPT-5.4 on the CK25 benchmark, delivering relative gains of 30% and 32% over the strongest recomputed participant.

Foresight-over-Graph: Reasoning Beyond Local Horizons for Knowledge Base Question Answering

• Foresight-over-Graph achieves state-of-the-art performance on knowledge base question answering benchmarks, improving Hit on the CWQ dataset by 16.58% while reducing language model calls and token usage.

MAWARITH: A Dataset and Benchmark for Legal Inheritance Reasoning with LLMs

• MAWARITH is a new dataset containing 12,500 Arabic legal inheritance cases, designed to train and evaluate LLMs on complex, multi-step reasoning for Islamic inheritance law. • The dataset supports the full reasoning chain, including identifying heirs, applying rules, and calculating exact shares, offering step-by-step solutions and justifications.

Language as a Wave Phenomenon: Semantic Phase Locking and Interference in Neural Networks

• The PRISM model, a complex-valued encoder, demonstrates that semantic relationships correlate with phase structure, with synonym pairs showing significantly higher phase coherence (R=0.198) than random pairs (R=0.072).

Geometric Mixture-of-Experts with Curvature-Guided Adaptive Routing for Graph Representation Learning

• The Geometric Mixture-of-Experts (GeoMoE) framework fuses node representations across diverse Riemannian spaces using Ollivier-Ricci Curvature (ORC) to model complex graph topologies. • GeoMoE employs a graph-aware gating network with node-specific weights, regularized by a curvature-guided alignment loss for interpretable and geometry-consistent routing.

JFTA-Bench: Evaluate LLM's Ability of Tracking and Analyzing Malfunctions Using Fault Trees

• The JFTA-Bench benchmark, containing 3130 entries and averaging 40.75 turns per entry, evaluates LLMs on malfunction localization using fault trees in multi-turn dialogues. • A novel textual representation of fault trees is proposed to enable direct processing by LLMs, aiding in malfunction tracking and analysis.