AI RESEARCH
SCOUT: Active Information Foraging for Long-Text Understanding with Decoupled Epistemic States
arXiv CS.CL
•
ArXi:2605.04496v1 Announce Type: new Long-Text Understanding (LTU) at million-token scale requires balancing reasoning fidelity with computational efficiency. Frontier long-context LLMs can process millions of token contexts end-to-end, but they suffer from high token consumption and attention dilution. In parallel, specialized LTU agents often sacrifice fidelity through task-agnostic abstractions like graph construction or indexing.