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108,318 candidate doc → node links pending adjudication. Each was auto-generated by cosine similarity ≥ 0.55 between document and prediction embeddings (bge-base-en-v1.5, 768-dim). Showing page 10 of 62, 50 rows by similarity. Adjudicating updates doc_node_links.reviewed=true with the chosen polarity, writes per-link rows to audit_log, and removes the row from this queue. Phase 4 inference will use confirmed corroborates/contradicts links as Bayesian evidence.
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Confirm or label (corroborates / contradicts) all unreviewed links above a similarity threshold in one transaction. Each affected row writes a per-link audit_log entry. Capped at 1,000 links per call. Use Preview first.
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| Sim | Doc | Source | Pred | Domain | Prior | |
|---|---|---|---|---|---|---|
| 0.64 | github_release 2023-07-20 | 247_057 Parameter scaling race is over; frontier labs plateauing at 10T parameters Alex Wissner-Gross | AI | 28% | ||
| 0.64 | github_release 2026-03-13 | 241_037 Chinese AI strategy will stay open source / open weights Eric Schmidt | AI | 49% | ||
| 0.64 | github_release 2026-01-29 | 231_001 Anthropic model family is closest to embodying the singularity and recursive self-improvement today. Alex Wissner-Gross | AI | 41% | ||
| 0.64 | github_release 2022-03-10 | CMQ_026 NVIDIA silicon roadmap: Blackwell (2025) → Vera Rubin (2026) → Vera Rubin Ultra (2027) → Feynman (2028) — annual architectural cadence. Jensen Huang | Semis | 83% | ||
| 0.64 | github_release 2026-04-02 | CMQ_056 Small Language Model (SLM) optimizations and model-distillation techniques will enable localized humanoid reasoning with extreme power efficiency — embedded AI without cloud dependency. Dario Amodei | AI/Compute | 19% | ||
| 0.64 | github_release 2026-03-27 | 235_037 Auto-regressive transformers and diffusion models will consolidate into one unified architecture. Alex Wissner-Gross | AI | 35% | ||
| 0.64 | github_release 2024-07-02 | 246_036 Terafab will deliver 1 terawatt/year AI compute, 50x current global output of 20 gigawatt. Peter Diamandis | AI | 46% | ||
| 0.64 | github_release 2024-06-18 | 246_036 Terafab will deliver 1 terawatt/year AI compute, 50x current global output of 20 gigawatt. Peter Diamandis | AI | 46% | ||
| 0.64 | github_release 2026-02-20 | COD_TECH_001 A16/N2-class TSMC process availability materially supports 2027 AI accelerator ramps Codex Research Pack | Semis | 50% | ||
| 0.64 | github_release 2024-12-27 | 240_021 Post-transformer architecture will be even more specialized than GPUs Alex Wissner-Gross | AI | 35% | ||
| 0.64 | github_release 2024-09-13 | 229_047 Figure's 3,000 B200 GPU cluster is coming online for Helix pre-training with much larger GPUs planned. Brett Adcock | AI | 77% | ||
| 0.64 | github_release 2026-05-22 | 234_012 Anthropic revenue will cross OpenAI revenue in middle of 2026 Peter Diamandis | Markets/Stocks | 67% | ||
| 0.64 | github_release 2020-09-18 | 240_020 New architecture won't map to current NVIDIA architecture; will create next Anthropic/OpenAI Dave Blundin | AI | 46% | ||
| 0.64 | github_release 2026-06-10 | 235_008 Anthropic/OpenAI will be forced to release first-party OpenClaw competitor in next couple months. Alex Wissner-Gross | AI | 42% | ||
| 0.64 | github_release 2022-08-22 | CMQ_056 Small Language Model (SLM) optimizations and model-distillation techniques will enable localized humanoid reasoning with extreme power efficiency — embedded AI without cloud dependency. Dario Amodei | AI/Compute | 19% | ||
| 0.64 | github_release 2020-11-11 | CYB_009 Hardware-software alignment will force widespread foundational-codebase rewrites — exemplified by Midjourney's strategic migration from Google TPU architecture to native GPU framework, enabling hyper-fast personalization, HD generation, and massive par... David Holz | AI | 77% | ||
| 0.64 | github_release 2026-04-22 | CMQ_026 NVIDIA silicon roadmap: Blackwell (2025) → Vera Rubin (2026) → Vera Rubin Ultra (2027) → Feynman (2028) — annual architectural cadence. Jensen Huang | Semis | 83% | ||
| 0.64 | github_release 2026-03-17 | SEM_022 FP4 / ternary-weight architectures decouple AI capability from raw transistor density — embargoed nations maintain competitive development. Dave Blundin | AI/Architecture | 65% | ||
| 0.64 | github_release 2026-06-24 | 241_037 Chinese AI strategy will stay open source / open weights Eric Schmidt | AI | 49% | ||
| 0.64 | github_release 2024-12-01 | TK11 Autonomous Regulatory Block (Level 4 Halt) | — | 10% | ||
| 0.64 | github_release 2023-08-25 | CMQ_056 Small Language Model (SLM) optimizations and model-distillation techniques will enable localized humanoid reasoning with extreme power efficiency — embedded AI without cloud dependency. Dario Amodei | AI/Compute | 19% | ||
| 0.64 | github_release 2026-02-10 | 235_008 Anthropic/OpenAI will be forced to release first-party OpenClaw competitor in next couple months. Alex Wissner-Gross | AI | 42% | ||
| 0.64 | github_release 2026-04-01 | 235_008 Anthropic/OpenAI will be forced to release first-party OpenClaw competitor in next couple months. Alex Wissner-Gross | AI | 42% | ||
| 0.64 | github_release 2022-12-13 | INF_027 AI infrastructure applied to structural biology will compress drug-development timelines from approximately a decade to weeks — potentially eradicating many major diseases within 10 years. Requires localized high-speed InfiniBand networking inside the DC. Demis Hassabis | Biotech/Longevity | 38% | ||
| 0.64 | github_release 2026-05-05 | 240_013 Sam Altman predicts another architecture breakthrough as big as transformers over LSTMs Sam Altman | AI | 41% | ||
| 0.64 | github_release 2026-04-02 | INF_072 There is approximately a 50/50 chance that simply scaling existing methodologies (transformer architecture + more data + more compute) will be enough to reach AGI — though "nowhere near" human-level AGI currently. Demis Hassabis | AI | 40% | ||
| 0.64 | github_release 2026-01-08 | 238_071 Future AI models may compress all human knowledge into megabytes via post-transformer breakthroughs Alex Wissner-Gross | AI | 35% | ||
| 0.64 | github_release 2025-12-01 | 240_015 Post-transformer architectures will make a 1000x cost reduction look like child's play Alex Wissner-Gross | AI | 42% | ||
| 0.64 | github_release 2025-10-03 | INF_072 There is approximately a 50/50 chance that simply scaling existing methodologies (transformer architecture + more data + more compute) will be enough to reach AGI — though "nowhere near" human-level AGI currently. Demis Hassabis | AI | 40% | ||
| 0.64 | github_release 2024-07-11 | COD_TECH_001 A16/N2-class TSMC process availability materially supports 2027 AI accelerator ramps Codex Research Pack | Semis | 50% | ||
| 0.64 | github_release 2026-05-06 | 235_005 AI capability will grow 100x this year in raw parameter count as lower bound. Dave Blundin | AI | 49% | ||
| 0.64 | github_release 2024-03-15 | 240_021 Post-transformer architecture will be even more specialized than GPUs Alex Wissner-Gross | AI | 35% | ||
| 0.64 | github_release 2024-03-15 | CMQ_058 Localized hardware setups (multiple Apple Mac Studios, dedicated 'AI Max 300' silicon) will allow developers to run powerful inference workloads directly on-premises — reducing cloud dependency. Alex Finn | AI/Compute | 59% | ||
| 0.64 | github_release 2026-06-16 | 234_017 OpenAI codex lead predicts current coding agents will seem primitive in 10 weeks OpenAI Codex Lead | AI | 33% | ||
| 0.64 | github_release 2026-06-17 | 241_037 Chinese AI strategy will stay open source / open weights Eric Schmidt | AI | 49% | ||
| 0.64 | github_release 2024-02-20 | FUT_024 XPT 2022 tournament assigned mere 2.3% probability to AI achieving gold-medal performance in International Mathematical Olympiad by 2025 — actual achievement empirically reached forcing systemic re-evaluation within forecasting community. Historical tr... Superforecaster Community | AI | 100% | ||
| 0.64 | github_release 2024-02-20 | S_ASI_SLOW_2040PLUS ASI slow: post-2040 / soft takeoff | asi_recursive_self_improvement | 60% | ||
| 0.64 | github_release 2023-10-10 | CMQ_026 NVIDIA silicon roadmap: Blackwell (2025) → Vera Rubin (2026) → Vera Rubin Ultra (2027) → Feynman (2028) — annual architectural cadence. Jensen Huang | Semis | 83% | ||
| 0.64 | github_release 2023-06-14 | 248_032 First-generation neural uploads will be destructive; 2nd-4th generation will be non-destructive. Alex Wissner-Gross | Biotech/Longevity | 42% | ||
| 0.64 | github_release 2026-03-13 | 234_017 OpenAI codex lead predicts current coding agents will seem primitive in 10 weeks OpenAI Codex Lead | AI | 33% | ||
| 0.64 | github_release 2026-01-27 | 239_004 xAI/Grok will catch up and exceed competitors on coding by mid-2026 Elon Musk | AI | 40% | ||
| 0.64 | github_release 2026-01-27 | 247_011 OpenAI user count will soon reach 1 billion Alex Wissner-Gross | AI | 41% | ||
| 0.64 | github_release 2026-05-05 | 246_047 Anthropic ARR reached $30B, surpassing OpenAI's $24-25B. Peter Diamandis | AI | 76% | ||
| 0.64 | github_release 2026-01-08 | INF_072 There is approximately a 50/50 chance that simply scaling existing methodologies (transformer architecture + more data + more compute) will be enough to reach AGI — though "nowhere near" human-level AGI currently. Demis Hassabis | AI | 40% | ||
| 0.64 | github_release 2025-08-06 | CMQ_026 NVIDIA silicon roadmap: Blackwell (2025) → Vera Rubin (2026) → Vera Rubin Ultra (2027) → Feynman (2028) — annual architectural cadence. Jensen Huang | Semis | 83% | ||
| 0.64 | github_release 2023-10-04 | COD_TECH_001 A16/N2-class TSMC process availability materially supports 2027 AI accelerator ramps Codex Research Pack | Semis | 50% | ||
| 0.64 | github_release 2019-10-14 | TK01 AGI Capability Plateau (2026-27 Training Stall) | — | 15% | ||
| 0.64 | github_release 2026-01-23 | 229_047 Figure's 3,000 B200 GPU cluster is coming online for Helix pre-training with much larger GPUs planned. Brett Adcock | AI | 77% | ||
| 0.64 | github_release 2025-06-25 | COD_TECH_001 A16/N2-class TSMC process availability materially supports 2027 AI accelerator ramps Codex Research Pack | Semis | 50% | ||
| 0.64 | github_release 2026-05-22 | INF_039 Consumer-facing AI hardware shortages will worsen: high-Unified-Memory Apple Mac Studio lead times now range 6 days to 6 weeks (up to ~54 days) because data centers are absorbing the world's HBM and memory supply — DRAM market transitioning to hourly v... Alex Finn | Consumer | 77% |