Reassessed, no change — Fragmenting
FRAGMENTING remains the correct pressure state, and IN-007 is best read as confirmation rather than a new direction. The three simultaneous pressures AS-001 identified — capability gains continuing, the path bifurcating architecturally, the target migrating — have each continued through 2025–26 without converging. Field-wide commentary now describes 2026 progress as inference- and tooling-driven rather than training-scale-driven, and academic work documents diminishing (though not zero) returns on pure training-compute scaling. Simultaneously, frontier labs' continued tens-of-billions-dollar commitments to training-scale infrastructure through 2025 show the industry has not abandoned the original path either. No single development in IN-007 resolves OQ-001 (can a claim with a migrating target reach a stable assessment state) or OQ-002 (is this dissolution or collapse) — if anything, two more years of continued three-way fragmentation without resolution is itself mild evidence that this claim may be heading toward dissolution rather than either confirmation or collapse, which is exactly the distinction OQ-002 asks the Observatory to make a governance decision about.
Sourced from: Medium, "The State of Large Language Models: Latest Updates & Trends (2025–2026)" (Feb 2026); aimultiple.com summary of 2026 RL post-training scaling-laws research describing a "latent saturation trend"; Metaintro coverage of continued frontier-lab capital expenditure commitments through 2025 (Dec 2025). All three are secondary roundups rather than primary papers — adequate for establishing the shape of the 2025–26 debate, not for citing specific benchmark or expenditure figures as precise.
Verification Stage: VS-03 after ratified review (stored code VS-03 preserved).
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