{
 "landscape_reviewed": "2026-07",
 "policy": "Public specifications only. 'Not identified in public specification' never means absent — it means not described in the sources reviewed. This manifest maps every comparison statement on /related-work to its primary source.",
 "items": [
  {
   "name": "Reality Mining (Eagle & Pentland, MIT Media Lab)",
   "cluster": "sensing-programs",
   "verification": "verified",
   "primary_source": "https://link.springer.com/article/10.1007/s00779-005-0046-3",
   "venue_year": "Personal and Ubiquitous Computing 10:255-268, 2006 (online Nov 2005)",
   "confidence": "high",
   "matrix": {
    "longitudinal_evidence": "Yes",
    "persistent_individual": "Yes",
    "future_prediction": "Partial",
    "prospective_sealing": "Not central",
    "probabilistic_calibration": "Not identified in public specification",
    "population_baseline": "Not central",
    "own_routine_baseline": "Not central",
    "wrong_target_specificity": "Not identified in public specification",
    "evidence_attribution": "Not central",
    "observation_cost_reporting": "Partial"
   },
   "notes": "Dataset released via MIT Reality Commons; 2275+ citations on Springer page. Routine predictability explored (entropy, HMM inference) but formal future-forecast scoring arrived in the 2009 eigenbehaviors follow-up, still retrospective. Battery/logging feasibility discussed in paper."
  },
  {
   "name": "Digital phenotyping (Onnela & Rauch; Torous et al., Harvard / Beiwe)",
   "cluster": "sensing-programs",
   "verification": "verified",
   "primary_source": "https://www.nature.com/articles/npp20167",
   "venue_year": "Neuropsychopharmacology 41:1691-1696, 2016; platform paper Torous, Kiang, Lorme & Onnela, JMIR Mental Health 3(2):e16, 2016",
   "confidence": "high",
   "matrix": {
    "longitudinal_evidence": "Yes",
    "persistent_individual": "Yes",
    "future_prediction": "Partial",
    "prospective_sealing": "Not identified in public specification",
    "probabilistic_calibration": "Not identified in public specification",
    "population_baseline": "Not central",
    "own_routine_baseline": "Partial",
    "wrong_target_specificity": "Not identified in public specification",
    "evidence_attribution": "Not central",
    "observation_cost_reporting": "Partial"
   },
   "notes": "Nature page tags include 'Predictive markers', supporting Partial on future_prediction (e.g., later relapse-anomaly studies use each patient's own baseline). Data collection is prospective but sealed forecast registration is not part of the public specification. Battery/burden/privacy tradeoffs disc"
  },
  {
   "name": "StudentLife (Wang et al., Dartmouth)",
   "cluster": "sensing-programs",
   "verification": "verified",
   "primary_source": "https://dl.acm.org/doi/10.1145/2632048.2632054",
   "venue_year": "ACM UbiComp 2014 (Proc. 2014 ACM Int. Joint Conf. on Pervasive and Ubiquitous Computing)",
   "confidence": "high",
   "matrix": {
    "longitudinal_evidence": "Yes",
    "persistent_individual": "Yes",
    "future_prediction": "Not central",
    "prospective_sealing": "Not central",
    "probabilistic_calibration": "Not identified in public specification",
    "population_baseline": "Not central",
    "own_routine_baseline": "Not central",
    "wrong_target_specificity": "Not identified in public specification",
    "evidence_attribution": "Partial",
    "observation_cost_reporting": "Partial"
   },
   "notes": "Crossref confirms authors (Wang, Chen, Chen, Li, Harari, Tignor, Zhou, Ben-Zeev, Campbell) and venue. Feature-level attribution is cohort-level (which sensed behaviors correlate with outcomes), not per-claim evidence. Dataset publicly released; spawned successors (CrossCheck, later trajectory-predic"
  },
  {
   "name": "GLOBEM (Xu et al., UW)",
   "cluster": "sensing-programs",
   "verification": "verified",
   "primary_source": "https://arxiv.org/abs/2211.02733",
   "venue_year": "NeurIPS 2022 Datasets & Benchmarks Track; platform paper IMWUT 6(4), 2022/2023 (doi 10.1145/3569485), UbiComp 2023 Distinguished Paper",
   "confidence": "high",
   "matrix": {
    "longitudinal_evidence": "Yes",
    "persistent_individual": "Partial",
    "future_prediction": "Partial",
    "prospective_sealing": "Not central",
    "probabilistic_calibration": "Not identified in public specification",
    "population_baseline": "Partial",
    "own_routine_baseline": "Partial",
    "wrong_target_specificity": "Not identified in public specification",
    "evidence_attribution": "Not central",
    "observation_cost_reporting": "Not central"
   },
   "notes": "GitHub (UW-EXP/GLOBEM) and PhysioNet hosting verified. Suite includes personalized/routine-based algorithms (Xu 2019, Xu 2021) and one onset-prediction method (Chikersal 2021), supporting Partial on future_prediction and own_routine_baseline; overlapping-user setup supports Partial on persistent_ind"
  },
  {
   "name": "PULSE",
   "cluster": "pulse-systems",
   "verification": "verified",
   "primary_source": "https://arxiv.org/abs/2605.17679",
   "venue_year": "arXiv preprint, 2026 (2605.17679)",
   "confidence": "high",
   "matrix": {
    "longitudinal_evidence": "Yes",
    "persistent_individual": "Yes",
    "future_prediction": "Partial",
    "prospective_sealing": "Not identified in public specification",
    "probabilistic_calibration": "Not identified in public specification",
    "population_baseline": "Yes",
    "own_routine_baseline": "Yes",
    "wrong_target_specificity": "Not identified in public specification",
    "evidence_attribution": "Partial",
    "observation_cost_reporting": "Partial"
   },
   "notes": "Authors incl. Zhiyuan Wang, Subigya Nepal, Philip Chow, Laura E. Barnes (UVA / ubicomp health-AI). Exact match to the queried system: agents selecting modalities and time windows over phone passive sensing for person-level prediction. 'calibrate' in the abstract refers to RAG population comparison, "
  },
  {
   "name": "Health-LLM",
   "cluster": "pulse-systems",
   "verification": "verified",
   "primary_source": "https://arxiv.org/abs/2401.06866",
   "venue_year": "arXiv preprint, 2024 (2401.06866)",
   "confidence": "high",
   "matrix": {
    "longitudinal_evidence": "Yes",
    "persistent_individual": "Partial",
    "future_prediction": "Partial",
    "prospective_sealing": "Not identified in public specification",
    "probabilistic_calibration": "Not identified in public specification",
    "population_baseline": "Not central",
    "own_routine_baseline": "Not central",
    "wrong_target_specificity": "Not identified in public specification",
    "evidence_attribution": "Partial",
    "observation_cost_reporting": "Not central"
   },
   "notes": "MIT Media Lab-led. Not agentic (prompting + fine-tuning). Qualifies under 'LLMs over longitudinal personal sensing data for individual-level prediction'; many of its 10 tasks are current-state assessment rather than horizon forecasting."
  },
  {
   "name": "PH-LLM (Personal Health Large Language Model)",
   "cluster": "pulse-systems",
   "verification": "verified",
   "primary_source": "https://arxiv.org/abs/2406.06474",
   "venue_year": "arXiv preprint, 2024 (2406.06474)",
   "confidence": "high",
   "matrix": {
    "longitudinal_evidence": "Yes",
    "persistent_individual": "Yes",
    "future_prediction": "Yes",
    "prospective_sealing": "Not identified in public specification",
    "probabilistic_calibration": "Not identified in public specification",
    "population_baseline": "Not central",
    "own_routine_baseline": "Partial",
    "wrong_target_specificity": "Not identified in public specification",
    "evidence_attribution": "Partial",
    "observation_cost_reporting": "Not central"
   },
   "notes": "Google Health AI; fine-tuned Gemini. A more agentic sibling exists (PHIA: 'Transforming Wearable Data into Personal Health Insights using LLM Agents', arxiv.org/abs/2406.06464); PH-LLM chosen here for its explicit individual-level future-outcome prediction."
  },
  {
   "name": "Ego4D Forecasting benchmark (locomotion, hand movement, short-term object interaction anticipation, long-term action anticipation)",
   "cluster": "ego4d-finerob",
   "verification": "verified",
   "primary_source": "https://arxiv.org/abs/2110.07058",
   "venue_year": "CVPR 2022 (Grauman et al., 'Ego4D: Around the World in 3,000 Hours of Egocentric Video')",
   "confidence": "high",
   "matrix": {
    "longitudinal_evidence": "Partial",
    "persistent_individual": "Not central",
    "future_prediction": "Yes",
    "prospective_sealing": "Partial",
    "probabilistic_calibration": "Not central",
    "population_baseline": "Not identified in public specification",
    "own_routine_baseline": "Not central",
    "wrong_target_specificity": "Not central",
    "evidence_attribution": "Not central",
    "observation_cost_reporting": "Not identified in public specification"
   },
   "notes": "Forecasting tasks confirmed via official ego4d-data.org/docs/benchmarks/forecasting and EGO4D/forecasting GitHub. Clip-level JSON schema (clip_uid/clip_frame) confirms unit is video clip, not persistent individual. Held-out labels are challenge-server retrospective test, not real-time prospective se"
  },
  {
   "name": "FineRob (fine-grained real-user behavior simulation benchmark)",
   "cluster": "ego4d-finerob",
   "verification": "verified",
   "primary_source": "https://arxiv.org/abs/2412.03148",
   "venue_year": "arXiv 2024 preprint (Kun Li, Chenwei Dai, Wei Zhou, Songlin Hu; 'Fine-Grained Behavior Simulation with Role-Playing Large Language Model on Social Media')",
   "confidence": "high",
   "matrix": {
    "longitudinal_evidence": "Yes",
    "persistent_individual": "Yes",
    "future_prediction": "Partial",
    "prospective_sealing": "Not identified in public specification",
    "probabilistic_calibration": "Not central",
    "population_baseline": "Not identified in public specification",
    "own_routine_baseline": "Not identified in public specification",
    "wrong_target_specificity": "Not central",
    "evidence_attribution": "Partial",
    "observation_cost_reporting": "Not identified in public specification"
   },
   "notes": "FineRob DOES exist publicly (contrary to the possibility it might not). It is the dataset introduced in arXiv:2412.03148, corroborated across multiple secondary sources. Caveat: it is a behavior-*simulation* / role-play benchmark, closer to imitation fidelity than to prospective calibrated forecasti"
  },
  {
   "name": "KnowMe-Bench",
   "cluster": "memory-benchmarks",
   "verification": "verified",
   "primary_source": "https://arxiv.org/abs/2601.04745",
   "venue_year": "arXiv preprint, January 2026 (2601.04745); no peer-reviewed venue identified",
   "confidence": "high",
   "matrix": {
    "longitudinal_evidence": "Yes",
    "persistent_individual": "Yes",
    "future_prediction": "Not central",
    "prospective_sealing": "Not central",
    "probabilistic_calibration": "Not identified in public specification",
    "population_baseline": "Not identified in public specification",
    "own_routine_baseline": "Not identified in public specification",
    "wrong_target_specificity": "Not identified in public specification",
    "evidence_attribution": "Yes",
    "observation_cost_reporting": "Not identified in public specification"
   },
   "notes": "Publicly listed on arXiv as claimed. Author overlap with CloneMem (Sen Hu, Huacan Wang, Ronghao Chen) — same group, complementary benchmarks. Matrix from abstract; full PDF has more detail."
  },
  {
   "name": "LoCoMo",
   "cluster": "memory-benchmarks",
   "verification": "verified",
   "primary_source": "https://arxiv.org/abs/2402.17753",
   "venue_year": "ACL 2024 (arXiv February 2024)",
   "confidence": "high",
   "matrix": {
    "longitudinal_evidence": "Yes",
    "persistent_individual": "Partial",
    "future_prediction": "Not central",
    "prospective_sealing": "Not central",
    "probabilistic_calibration": "Not central",
    "population_baseline": "Not identified in public specification",
    "own_routine_baseline": "Not identified in public specification",
    "wrong_target_specificity": "Not identified in public specification",
    "evidence_attribution": "Partial",
    "observation_cost_reporting": "Not identified in public specification"
   },
   "notes": "Persistent-individual is Partial: personas persist across sessions but are synthetic dialogue pairs, not a tracked real individual. Evidence attribution Partial: QA items carry annotated evidence turns used for retrieval evaluation, not a scored attribution output."
  },
  {
   "name": "LongMemEval",
   "cluster": "memory-benchmarks",
   "verification": "verified",
   "primary_source": "https://arxiv.org/abs/2410.10813",
   "venue_year": "ICLR 2025 (arXiv October 2024); Wu et al.",
   "confidence": "high",
   "matrix": {
    "longitudinal_evidence": "Yes",
    "persistent_individual": "Partial",
    "future_prediction": "Not central",
    "prospective_sealing": "Not central",
    "probabilistic_calibration": "Partial",
    "population_baseline": "Not identified in public specification",
    "own_routine_baseline": "Not identified in public specification",
    "wrong_target_specificity": "Not identified in public specification",
    "evidence_attribution": "Partial",
    "observation_cost_reporting": "Not identified in public specification"
   },
   "notes": "Calibration marked Partial because abstention scores knowing-what-you-don't-know, not calibrated probability forecasts. Persistent-individual Partial: simulated user with consistent facts in compiled histories, not a longitudinally tracked person."
  },
  {
   "name": "Memora",
   "cluster": "memory-benchmarks",
   "verification": "verified",
   "primary_source": "https://arxiv.org/abs/2604.20006",
   "venue_year": "ACL 2026 Findings (arXiv April 2026); paper: From Recall to Forgetting",
   "confidence": "medium",
   "matrix": {
    "longitudinal_evidence": "Yes",
    "persistent_individual": "Partial",
    "future_prediction": "Not central",
    "prospective_sealing": "Not central",
    "probabilistic_calibration": "Not identified in public specification",
    "population_baseline": "Not identified in public specification",
    "own_routine_baseline": "Not identified in public specification",
    "wrong_target_specificity": "Not identified in public specification",
    "evidence_attribution": "Partial",
    "observation_cost_reporting": "Not identified in public specification"
   },
   "notes": "Exists publicly; benchmark name Memora confirmed in paper (Uddin, Shubham, Blanco, Baral, Wang). Conversations are simulated (LLM-generated with validation). Matrix rests on abstract-level description; recommending task applies preferences, not future-event forecasting."
  },
  {
   "name": "CloneMem",
   "cluster": "memory-benchmarks",
   "verification": "verified",
   "primary_source": "https://arxiv.org/abs/2601.07023",
   "venue_year": "ACL 2026 Long Papers (Anthology 2026.acl-long.1549; arXiv January 2026)",
   "confidence": "high",
   "matrix": {
    "longitudinal_evidence": "Yes",
    "persistent_individual": "Yes",
    "future_prediction": "Not identified in public specification",
    "prospective_sealing": "Not central",
    "probabilistic_calibration": "Not identified in public specification",
    "population_baseline": "Not identified in public specification",
    "own_routine_baseline": "Not identified in public specification",
    "wrong_target_specificity": "Partial",
    "evidence_attribution": "Not identified in public specification",
    "observation_cost_reporting": "Not identified in public specification"
   },
   "notes": "ACL Anthology confirms ACL 2026 Long; an OpenReview PDF surfaced labeled ICLR 2026 — cite the Anthology version. Same author group as KnowMe-Bench. Wrong-target Partial: cloning tasks are judged on fidelity to the specific individual, but no wrong-person control is publicly described. Code: github.c"
  },
  {
   "name": "LoCoMo-Plus",
   "cluster": "memory-benchmarks",
   "verification": "verified",
   "primary_source": "https://arxiv.org/abs/2602.10715",
   "venue_year": "arXiv preprint, February 2026 (Li, Guo, Zhang et al.); no peer-reviewed venue identified",
   "confidence": "medium",
   "matrix": {
    "longitudinal_evidence": "Yes",
    "persistent_individual": "Partial",
    "future_prediction": "Not central",
    "prospective_sealing": "Not central",
    "probabilistic_calibration": "Not identified in public specification",
    "population_baseline": "Not identified in public specification",
    "own_routine_baseline": "Not identified in public specification",
    "wrong_target_specificity": "Partial",
    "evidence_attribution": "Not identified in public specification",
    "observation_cost_reporting": "Not identified in public specification"
   },
   "notes": "Exists publicly (spelled 'Locomo-Plus' on arXiv). An evaluation framework inspired by, not a direct data extension of, Maharana et al.'s LoCoMo. Wrong-target Partial: constraint consistency penalizes generic responses that violate this user's latent constraints. Preprint only; matrix from abstract."
  },
  {
   "name": "LifeSim-Eval (LifeSim)",
   "cluster": "evolving-user",
   "verification": "verified",
   "primary_source": "https://arxiv.org/abs/2603.12152",
   "venue_year": "arXiv preprint 2603.12152 [cs.CL], March 2026 (no conference venue stated)",
   "confidence": "high",
   "matrix": {
    "longitudinal_evidence": "Yes",
    "persistent_individual": "Yes",
    "future_prediction": "Not central",
    "prospective_sealing": "Not central",
    "probabilistic_calibration": "Not identified in public specification",
    "population_baseline": "Not identified in public specification",
    "own_routine_baseline": "Not identified in public specification",
    "wrong_target_specificity": "Partial",
    "evidence_attribution": "Not identified in public specification",
    "observation_cost_reporting": "Not identified in public specification"
   },
   "notes": "Verified from full paper PDF. Users simulated (Qwen3-32B agent; 1M-persona pool from SocioVerse/AlignX). Prospective sealing not applicable: all trajectories synthetic, motivated by scarcity of real multi-year logs. Wrong-target 'Partial': persona alignment/profile recovery test specificity to the t"
  },
  {
   "name": "Limitless (limitless.ai Pendant)",
   "cluster": "commercial",
   "verification": "verified",
   "primary_source": "https://www.limitless.ai/",
   "venue_year": "Official site, accessed 2026-07; acquired by Meta, announced 2025-12-05",
   "confidence": "high",
   "matrix": {
    "longitudinal_evidence": "Yes",
    "persistent_individual": "Yes",
    "future_prediction": "Not central",
    "prospective_sealing": "Not identified in public specification",
    "probabilistic_calibration": "Not identified in public specification",
    "population_baseline": "Not identified in public specification",
    "own_routine_baseline": "Not identified in public specification",
    "wrong_target_specificity": "Not identified in public specification",
    "evidence_attribution": "Not identified in public specification",
    "observation_cost_reporting": "Not identified in public specification"
   },
   "notes": "Official page: 'Limitless has been acquired by Meta' (Dan Siroker). Footer tagline 'Go Beyond'. Service withdrawn from EU, UK, Brazil, China, Israel, South Korea, Turkey. Acquisition corroborated by CNBC and TechCrunch (2025-12-05); team folded into Meta Reality Labs. Longitudinal/persistent rows re"
  },
  {
   "name": "Bee (bee.computer, Bee Pioneer)",
   "cluster": "commercial",
   "verification": "verified",
   "primary_source": "https://www.bee.computer/",
   "venue_year": "Official site, accessed 2026-07; Amazon acquisition announced 2025-07-22",
   "confidence": "high",
   "matrix": {
    "longitudinal_evidence": "Yes",
    "persistent_individual": "Yes",
    "future_prediction": "Partial",
    "prospective_sealing": "Not identified in public specification",
    "probabilistic_calibration": "Not identified in public specification",
    "population_baseline": "Not identified in public specification",
    "own_routine_baseline": "Not identified in public specification",
    "wrong_target_specificity": "Not identified in public specification",
    "evidence_attribution": "Not identified in public specification",
    "observation_cost_reporting": "Not identified in public specification"
   },
   "notes": "Footer now '©2026 Bluush LLC or its affiliates'; hiring banner shows a Bee + Amazon co-brand logo. Acquisition by Amazon reported by TechCrunch and CNBC (2025-07-22, confirmed by CEO Maria de Lourdes Zollo). future_prediction = Partial only via 'timely reminders' / 'suggested to-dos' proactivity wor"
  },
  {
   "name": "Omi (omi.me, Based Hardware Inc.)",
   "cluster": "commercial",
   "verification": "verified",
   "primary_source": "https://www.omi.me/",
   "venue_year": "Official site, accessed 2026-07",
   "confidence": "high",
   "matrix": {
    "longitudinal_evidence": "Yes",
    "persistent_individual": "Yes",
    "future_prediction": "Not central",
    "prospective_sealing": "Not identified in public specification",
    "probabilistic_calibration": "Not identified in public specification",
    "population_baseline": "Not identified in public specification",
    "own_routine_baseline": "Not identified in public specification",
    "wrong_target_specificity": "Not identified in public specification",
    "evidence_attribution": "Not identified in public specification",
    "observation_cost_reporting": "Not identified in public specification"
   },
   "notes": "Company: Based Hardware Inc., San Francisco; footer '© 2026 Based Hardware'. 'Open source. Own your data. Runs on your device.' GitHub: github.com/BasedHardware/omi. Active and selling (omi, omi Glass dev kit, accessories, Omi Enterprise); no acquisition or discontinuation indicated."
  },
  {
   "name": "Plaud (plaud.ai — Note, Note Pro, NotePin, NotePin S)",
   "cluster": "commercial",
   "verification": "verified",
   "primary_source": "https://www.plaud.ai/",
   "venue_year": "Official site (Plaud Inc.), accessed 2026-07",
   "confidence": "high",
   "matrix": {
    "longitudinal_evidence": "Partial",
    "persistent_individual": "Not central",
    "future_prediction": "Not central",
    "prospective_sealing": "Not identified in public specification",
    "probabilistic_calibration": "Not identified in public specification",
    "population_baseline": "Not identified in public specification",
    "own_routine_baseline": "Not identified in public specification",
    "wrong_target_specificity": "Not identified in public specification",
    "evidence_attribution": "Not identified in public specification",
    "observation_cost_reporting": "Not identified in public specification"
   },
   "notes": "Active, four devices ($159-$189) plus 'Plaud Intelligence' subscriptions ($99.99-$239.99/yr) and Team plan; 'Ask Plaud' queries across recordings (hence longitudinal Partial). Strong privacy positioning ('never used to train AI models'). Consent reminder on page. No acquisition; copyright '© 2026 Pl"
  },
  {
   "name": "TwinMind (twinmind.com)",
   "cluster": "commercial",
   "verification": "verified",
   "primary_source": "https://www.twinmind.com/",
   "venue_year": "Official site, accessed 2026-07; Ear-3 benchmark published 2025-09",
   "confidence": "high",
   "matrix": {
    "longitudinal_evidence": "Yes",
    "persistent_individual": "Yes",
    "future_prediction": "Partial",
    "prospective_sealing": "Not identified in public specification",
    "probabilistic_calibration": "Not identified in public specification",
    "population_baseline": "Partial",
    "own_routine_baseline": "Not identified in public specification",
    "wrong_target_specificity": "Not identified in public specification",
    "evidence_attribution": "Not identified in public specification",
    "observation_cost_reporting": "Not identified in public specification"
   },
   "notes": "population_baseline = Partial strictly because the public Ear-3 benchmark scores against competing ASR systems — a vendor baseline for transcription, not a population baseline for personal prediction. Privacy: real-time transcription, audio not stored by default. Free/Pro($15)/Max($30) tiers. Active"
  },
  {
   "name": "Personal AI (personal.ai)",
   "cluster": "commercial",
   "verification": "verified",
   "primary_source": "https://www.personal.ai/",
   "venue_year": "Official site, accessed 2026-07 (repositioned; page last published 2026-06-30)",
   "confidence": "high",
   "matrix": {
    "longitudinal_evidence": "Yes",
    "persistent_individual": "Yes",
    "future_prediction": "Not central",
    "prospective_sealing": "Not identified in public specification",
    "probabilistic_calibration": "Not identified in public specification",
    "population_baseline": "Not identified in public specification",
    "own_routine_baseline": "Not identified in public specification",
    "wrong_target_specificity": "Not identified in public specification",
    "evidence_attribution": "Not identified in public specification",
    "observation_cost_reporting": "Not identified in public specification"
   },
   "notes": "Major pivot from consumer 'train your own AI' to carrier infrastructure: meta description says 'telco-optimized stack for personal agents on the carrier network... no app to install'; site pitches tokens as a 'fourth primitive' beside Talk/Text/Data at '92% gross margin', CTA 'Book a Carrier Briefin"
  },
  {
   "name": "V-JEPA (Bardes et al., Meta AI)",
   "cluster": "world-models",
   "role": "adjacent architectural family — relationship, not a comparison-matrix row",
   "verification": "verified",
   "primary_source": "https://arxiv.org/abs/2404.08471",
   "venue_year": "arXiv:2404.08471, 2024 (preprint); 'Revisiting Feature Prediction for Learning Visual Representations from Video'",
   "confidence": "high",
   "notes": "Original video Joint-Embedding Predictive Architecture: learns by predicting in representation space rather than reconstructing pixels. Cited as the measurement intuition TargetSpace lifts to the entity level (preserve target-relevant structure, not the full record). JEPA-style systems are eligible participants in the grid, not competitors. V-JEPA 2 (arXiv:2506.09985, 2025) extends to understanding/prediction/planning."
  },
  {
   "name": "Genie (Bruce et al., Google DeepMind)",
   "cluster": "world-models",
   "role": "adjacent architectural family — relationship, not a comparison-matrix row",
   "verification": "verified",
   "primary_source": "https://arxiv.org/abs/2402.15391",
   "venue_year": "ICML 2024, PMLR 235:4603-4623; 'Genie: Generative Interactive Environments'",
   "confidence": "high",
   "notes": "Foundation world model learning action-controllable interactive environments from unlabelled internet video. Peer-reviewed (ICML 2024). Illustrates where prominent world-model work concentrates: physical/simulated scenes rather than persistent individuals."
  },
  {
   "name": "Genie 2 (Google DeepMind)",
   "cluster": "world-models",
   "role": "adjacent architectural family — relationship, not a comparison-matrix row",
   "verification": "verified",
   "primary_source": "https://deepmind.google/blog/genie-2-a-large-scale-foundation-world-model/",
   "venue_year": "DeepMind research announcement (blog/tech report, NOT a peer-reviewed paper), December 2024",
   "confidence": "high",
   "notes": "Large-scale foundation world model generating playable 3D environments from a prompt image. Cited as a research announcement with access date; no arXiv ID or DOI exists. Treated as evidence of the physical-scene concentration of world-model research."
  },
  {
   "name": "Prophet Arena (Yang et al.)",
   "cluster": "forecasting-platforms",
   "role": "adjacent instrument (event forecasting, not tracked-person forecasting) — relationship, not a comparison-matrix row",
   "verification": "verified",
   "primary_source": "https://arxiv.org/abs/2510.17638",
   "venue_year": "arXiv:2510.17638, 2025 (preprint); live leaderboard at prophetarena.co; 'LLM-as-a-Prophet: Understanding Predictive Intelligence with Prophet Arena'",
   "confidence": "high",
   "notes": "Live, contamination-resistant benchmark of LLM forecasting on real-world events with calibration and market-based metrics. Shares sealing/proper-scoring/calibration machinery with TargetSpace; differs in object: external public events, not the latent state of one consenting tracked person, so no R2 own-routine baseline and no wrong-target permutation are applicable. Preprint, not peer reviewed."
  },
  {
   "name": "KalshiBench (Nel)",
   "cluster": "forecasting-platforms",
   "role": "adjacent instrument (event forecasting, not tracked-person forecasting) — relationship, not a comparison-matrix row",
   "verification": "verified",
   "primary_source": "https://arxiv.org/abs/2512.16030",
   "venue_year": "arXiv:2512.16030, 2025 (preprint); 300 Kalshi prediction-market questions with post-cutoff resolution",
   "confidence": "high",
   "notes": "Measures LLM epistemic calibration against regulated prediction-market outcomes resolved after training cutoffs. Same measurement family (prospective, proper-scored, calibration-centric); no persistent tracked individual, so the target-specificity stack does not apply. Preprint, not peer reviewed."
  }
 ]
}