ATOM

• Complete Architectural Redesign: ATOM now employs a three-module parallel pipeline for DTKG construction and updates.
• Atomic Fact Decomposition: A new first module splits text into minimal "atomic facts," addressing the "forgetting effect" where LLMs omit facts in longer contexts.
• Enhanced Exhaustivity and Stability: The new architecture achieves significant gains: ~31% in factual exhaustivity, ~18% in temporal exhaustivity, and ~17% in stability.
• Dual-Time Modeling: Implemented dual-time modeling (t_obs vs. t_start/t_end) to prevent temporal misattribution in dynamic KGs.
• Parallel 5-Tuple Extraction: Module-2 now directly extracts 5-tuples (subject, predicate, object, t_start, t_end) in parallel from atomic facts.
• Parallel Atomic Merge Architecture: Module-3 uses an efficient, parallel pairwise merge algorithm, achieving 93.8% latency reduction vs. Graphiti and 95.3% vs. iText2KG.
• LLM-Independent Resolution: Replaced slow LLM-based resolution with distance metrics (cosine similarity) for scalable, parallel merging.