Daily Digest — Oct 3

Knowledge-Based Zero-Replay Debugging of Multi-Agent LLM Traces

• A new knowledge-based approach frames multi-agent LLM trace debugging as a decision-support problem, compiling traces into structured event knowledge graphs. • The BranchPoint-Latent predictor, calibrated against a replay oracle on 37 trace families, improves per-trace localization (Branch Recall@5) from 0.73 to 0.93 at zero replay cost.

Knowledge-Guided Manipulation Using Multi-Task Reinforcement Learning

• The Knowledge Graph based Massively Multi-task Model-based Policy Optimization (KG-M3PO) framework unifies perception, knowledge, and policy for robotic manipulation in partially observable settings by augmenting vision with an online 3D scene graph.

GLiNER-Relex: A Unified Framework for Joint Named Entity Recognition and Relation Extraction

• GLiNER-Relex introduces a unified architecture that extends the GLiNER framework to perform both named entity recognition (NER) and relation extraction (RE) within a single model. • The model leverages a shared bidirectional transformer encoder and enables zero-shot extraction of arbitrary entity and relation types specified at inference time.

The Provenance Gap in Clinical AI: Evidence-Traceable Temporal Knowledge Graphs for Rare Disease Reasoning

• Frontier LLMs exhibit a 'Provenance Gap,' fabricating citations, with the best model achieving only 15.3% relevant PubMed identifiers even when prompted. • The HEG-TKG system, built from 4,512 PubMed records and curated sources, achieves 100% evidence verifiability with 203 inline citations for rare disease reasoning, matching baseline clinical feature coverage.