LLM

Future-testable claims, typed verification, and lineage-aware correction for streaming omni-modal understanding.

Aug 7, 2026

From similarity to semantic verification Real image-search requests are rarely one-dimensional. “Find this shirt in pink” asks the system to preserve the object and style, change one attribute, and ignore incidental factors such as the background. A single similarity score hides these separate requirements; free-form chain-of-thought may omit constraints or claim visual evidence that was never verified.

Jul 31, 2026

Why trajectory faithfulness matters A multimodal agent does more than emit an answer. It observes images, calls tools, retrieves evidence, forms intermediate claims, and sometimes repairs its own trajectory. Final-answer accuracy compresses all of this into one bit: correct or incorrect. It cannot reveal whether the answer was grounded, guessed from a language prior, or reached through errors that happened to cancel out.

Jul 30, 2026

Abstract Graph Foundation Models need large, diverse graph corpora, but real-world graphs are often small, private, or expensive to annotate. GraphMaster turns graph synthesis into a coordinated agent workflow: four specialized LLM agents iteratively expand a text-attributed graph while preserving both its meaning and topology.

Mar 6, 2025