KASI AI Paper Reports 2026-07
1 Executive Summary
KASI's AI adoption stands at just 6.7% (114/1694 papers) and is declining (-0.3 pp/yr, from 8.2% peak in 2023 to 5.4% in 2025), while global astro-AI adoption on arXiv surged from ~5% to ~18% over the same window — a widening 3x gap. KASI's portfolio is concentrated in Tier 1 classical ML (59 papers) with zero Tier 4 foundation-model work, no simulation-based inference (SBI) papers, and only nascent transformer usage (1 paper), leaving it exposed as peers like Flatiron CCA (SBI) and CfA (LLM pipelines) build AI-native infrastructure. Critically, AI papers show no impact premium (median IF 5.36 vs 5.40 non-AI), signaling that current AI use is incremental rather than transformative. With Rubin LSST/SKA demanding AI-native pipelines and Korean national assets (KISTI Neuron GPU cluster, KAIS, K-SSA) available, KASI must urgently pivot from classical ML toward SBI and foundation models via targeted talent hubs already present (Tier 3 leaders Hong, Jeong, Park, Lee).
We collected 1694 KASI research paper records (2021–2025) from the institute's internal system, each stored as a structured YAML file containing title, abstract, author metadata, journal metrics, and instrument usage. Every paper was then classified by a large language model (Gemini 2.5 Flash Lite) across eight dimensions — AI maturity tier (0–4), centrality, specific techniques, subfield, and confidence score — producing a fully machine-readable enriched dataset. Aggregate statistics, year-over-year trends, departmental breakdowns, and a co-authorship network were computed from the classified data, and a synthesis model (Claude Opus 4.8) generated the strategic narrative presented in this report.
2 Key Findings
- 1 AI adoption (6.7%) is stagnant-to-declining, dropping from a 2023 peak of 8.2% to 5.4% in 2025, diverging sharply from the global rise to ~18% by 2024.
- 2 The maturity curve is bottom-heavy: 59 Tier 1 (classical ML), 30 Tier 2 (standard DL), only 15 Tier 3 (physics-tailored), and 0 Tier 4 (foundation models).
- 3 Zero simulation-based-inference (SBI) papers (first→last: 0→0) despite SBI becoming mainstream globally and being Flatiron CCA's flagship strength.
- 4 AI papers deliver no impact advantage (median IF 5.36 vs 5.40 for non-AI) and no collaboration boost (49.1% vs 49.2% intl), indicating AI is applied incrementally.
- 5 Technique trends are flat or shrinking: classical-ML 9→7, CNN 8→7, GAN 1→0, normalizing-flow 1→0; only modest growth in gaussian-process (2→4) and transformer (0→1).
- 6 AI expertise is heavily concentrated: 태양권관측센터 (27.6%, 29 papers) and 기초천문연구본부 (30.0%, 10 papers) lead by rate, while large departments like 이론천문센터 (238 papers) sit at only 14.3%.
- 7 A functional collaboration network exists (176 researchers, 682 edges) with 4 of 5 top hubs at Tier 3, providing a ready nucleus for advanced-AI capability building.
Key Metrics
| Metric | Value |
|---|---|
| Total papers (2021–2025) | 1,694 |
| AI/ML papers | 114 (6.7%) |
| Tier 3+ (physics-tailored) papers | 18 (15.8%) |
| KASI-led AI papers | 44 (38.6%) |
| Year-over-year growth | +-0.3 pp/yr |
| AI median impact factor | 5.36 |
| Non-AI median impact factor | 5.4 |
| Coverage period | 2021–2025 |
3 Adoption Trends
4 Technique Evolution
5 Operational Gap Map
6 Department & Collaboration
7 Impact Factor & Instruments
8 Author Network & Internal Champions
183 researchers · 704 co-authorship edges. Drag nodes to explore, scroll to zoom, hover for details. Color = tier.
Top AI Research Hubs
9 SWOT Analysis
Strengths
- Established Tier 3 physics-tailored expertise via hubs Sungwook E. Hong, Se-Heon Jeong, Eunsu Park, Joongoo Lee, plus Tier 1 leader Arman Shafieloo (cosmology).
- Dense internal AI network of 176 researchers and 682 co-author edges enabling rapid internal diffusion.
- Domain leadership pockets: 태양권관측센터 at 27.6% AI rate (29 papers) and 기초천문연구본부 at 30.0% — solar/heliophysics is a defensible AI niche.
- KASI leads (first-author) on 38.6% of its AI papers (44 papers), showing genuine capability ownership rather than pure participation.
Weaknesses
- Declining adoption trajectory (-0.3 pp/yr; 8.2%→5.4% 2023→2025) versus global growth to ~18%.
- Zero Tier 4 foundation-model papers and zero SBI papers — absent from the two fastest-growing global frontiers.
- No measurable quality dividend from AI: IF 5.36 vs 5.40 non-AI, and intl collaboration essentially identical (49.1% vs 49.2%).
- AI capacity is siloed; theory-heavy 이론천문센터 (238 papers, only 14.3% AI) and 변광천체그룹 (132 papers, 11.4%) underutilize AI at scale.
Opportunities
- Leverage KISTI Neuron national GPU cluster to train foundation models and run SBI pipelines without owning hardware.
- Align with Rubin LSST and SKA AI-native pipeline needs for time-domain and radio survey science (변광천체그룹, transients subfield ready).
- Join national/regional programs — KAIS, A3 Net, and the K-SSA opportunity — to co-develop AI-native space-situational-awareness and astronomy tooling.
- Build an SBI/foundation-model partnership with peers (Flatiron CCA on SBI, CfA on LLM pipelines, NAOJ AI observatory programs) to leapfrog the capability gap.
Threats
- Global peers are entrenching AI-native pipelines (Flatiron SBI, STScI AI task force, MPIA GRAVITY+ ML, CfA LLMs) while KASI adoption stalls at 6.7%.
- Rubin LSST/SKA data volumes will make non-AI pipelines uncompetitive; KASI risks being a data consumer, not a discovery leader.
- Talent flight risk: with only 15 Tier 3 and 0 Tier 4 researchers, top AI hubs may be recruited by better-resourced international programs.
- The 3x and widening gap (6.7% vs 18%) compounds annually, making catch-up progressively more expensive and slower.
10 3–5 Year Strategic Roadmap
Establish an SBI & Foundation-Model Capability
High priority 0-1 yrStand up a dedicated AI-methods unit anchored on existing Tier 3 hubs (Hong, Park, Jeong, Lee) to close the SBI (currently 0 papers) and foundation-model (0 papers) gaps. Secure KISTI Neuron GPU allocation and partner with Flatiron CCA for SBI and CfA for LLM pipelines.
KPIs
- ≥5 SBI papers within 24 months (from 0)
- First Tier 4 foundation-model paper published
- Secured KISTI Neuron allocation with ≥3 active projects
Reverse the Adoption Decline via Departmental Diffusion
High priority 1-3 yrDeploy internal training and embedded ML specialists into large under-adopting departments (이론천문센터 14.3%/238 papers, 변광천체그룹 11.4%/132 papers) to lift institute-wide adoption back above the 2023 peak. Use the 682-edge network to seed AI methods across subfields.
KPIs
- Institute AI adoption ≥12% by 2027 (from 6.7%)
- 이론천문센터 AI rate ≥25% (from 14.3%)
- Tier 2+ papers doubled (from 45 to 90)
AI-Native Survey Pipeline Readiness (LSST/SKA/K-SSA)
High priority 1-3 yrBuild production AI pipelines for time-domain (변광천체그룹, transients) and solar/heliophysics (태양권관측센터, 27.6%) tied to Rubin LSST, SKA, and the K-SSA opportunity. Position KASI as Korea's AI-native survey analysis hub via KAIS and A3 Net.
KPIs
- ≥2 deployed AI-native pipelines for LSST/SKA/K-SSA
- K-SSA AI role formalized with funding
- ≥3 A3 Net joint AI publications
Convert AI into Impact Advantage
Medium priority 1-3 yrTarget high-impact, AI-enabled discovery science so that AI papers outperform rather than match the baseline (currently IF 5.36 vs 5.40). Prioritize novel physics-tailored methods over incremental classical-ML applications.
KPIs
- AI-paper median IF exceeds non-AI by ≥0.5
- AI intl-collaboration rate ≥60% (from 49.1%)
- Tier 3+ share of AI papers ≥40% (from 13%)
11 Strategic Moonshots
KASI Solar Foundation Model (Heliophysics LLM/Transformer)
태양권관측센터's leading 27.6% AI rate (29 papers) and long solar observation archives give KASI a unique domain-data advantage to train the first solar/heliophysics foundation model — a niche no global peer dominates.
Korean Astro-SBI National Facility
KASI has cosmology strength (Shafieloo) and Tier 3 physics-tailored talent but zero SBI papers; establishing a national SBI service on KISTI Neuron via KAIS would let Korea leapfrog directly to the mainstream frontier Flatiron CCA leads.