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Dual-view time-series representations unlock significant gains in temporal reasoning by preserving both absolute measurements and invariant temporal structure, outperforming dedicated TSLMs.
LLMs falter on Romanized Code Mixing tasks, revealing a critical gap in their multilingual instruction-following abilities.
Autonomous research agents are surprisingly unreliable, with existing systems hallucinating references 21% of the time and failing to align methods with code as often as 80%, but a new "Chain-of-Evidence" approach can fix this.
Generating consistent visual narratives is now possible: CANVAS outperforms existing methods by explicitly planning character, background, and scene continuity across multiple shots.