Validations Queue
111,300 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 40 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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Bulk auto-confirm by similarity threshold
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 | |
|---|---|---|---|---|---|---|
| 0.59 | github_release 2019-06-20 | 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% | ||
| 0.59 | github_release 2021-08-05 | 248_032 First-generation neural uploads will be destructive; 2nd-4th generation will be non-destructive. Alex Wissner-Gross | Biotech/Longevity | 42% | ||
| 0.59 | github_release 2026-06-18 | CMQ_047 Autonomous code agents and AutoResearch systems will close the loop on complex scientific experimentation without human-in-the-loop. Andrej Karpathy | AI | 51% | ||
| 0.59 | github_release 2025-10-10 | 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% | ||
| 0.59 | github_release 2025-03-24 | CMQ_026 NVIDIA silicon roadmap: Blackwell (2025) → Vera Rubin (2026) → Vera Rubin Ultra (2027) → Feynman (2028) — annual architectural cadence. Jensen Huang | Semis | 83% | ||
| 0.59 | github_release 2020-03-09 | 248_032 First-generation neural uploads will be destructive; 2nd-4th generation will be non-destructive. Alex Wissner-Gross | Biotech/Longevity | 42% | ||
| 0.59 | github_release 2021-11-05 | CMQ_026 NVIDIA silicon roadmap: Blackwell (2025) → Vera Rubin (2026) → Vera Rubin Ultra (2027) → Feynman (2028) — annual architectural cadence. Jensen Huang | Semis | 83% | ||
| 0.59 | github_release 2025-09-12 | 229_039 Figure will integrate additional sensors (infrared, ultraviolet, etc.) into future humanoids. Brett Adcock | Robotics | 28% | ||
| 0.59 | github_release 2023-08-31 | 229_028 Figure will NOT license out its neural net or hardware IP to third-party form-factor builders. Brett Adcock | Robotics | 69% | ||
| 0.59 | github_release 2024-03-27 | CMQ_044 Future data-center architectures optimized for agentic workflows may require 1:2 or even 2:1 CPU-to-GPU ratio (vs historical 1:12) to prevent GPU idle-waiting. Morgan Stanley | AI/Compute | 65% | ||
| 0.59 | github_release 2024-02-22 | 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% | ||
| 0.59 | github_release 2022-08-05 | CMQ_030 In the modern AI pipeline, the CPU no longer merely supports the model — it drives the model (agentic workloads invert historical CPU:GPU ratio). Jensen Huang | AI/Compute | 34% | ||
| 0.59 | github_release 2025-12-22 | SEM_021 Largest AI models will see a 100x leap in size by 2026, driven largely by Chinese research efforts using quantization breakthroughs. Dave Blundin | AI/China | 45% | ||
| 0.59 | github_release 2026-04-17 | SEM_021 Largest AI models will see a 100x leap in size by 2026, driven largely by Chinese research efforts using quantization breakthroughs. Dave Blundin | AI/China | 45% | ||
| 0.59 | github_release 2024-10-30 | S_HUMANOID_CONSUMER_2030 Humanoid R3: 1M+ consumer by Nov 2030 | humanoid_deployment | 20% | ||
| 0.59 | github_release 2025-01-31 | CMQ_026 NVIDIA silicon roadmap: Blackwell (2025) → Vera Rubin (2026) → Vera Rubin Ultra (2027) → Feynman (2028) — annual architectural cadence. Jensen Huang | Semis | 83% | ||
| 0.59 | github_release 2026-04-06 | AUT_002 Models excelling at highly structured mathematical benchmarks exhibit a 'unified capability substrate' enabling dominance in seemingly unrelated fields (coding, logical reasoning, scientific discovery) — the 'mathematical skeleton' of the technological... Alex Wissner-Gross | AI | 59% | ||
| 0.59 | github_release 2026-03-17 | AI_010 The 2026 development landscape has entered the 'Slopacolypse' — AI writes the vast majority of new code, developer manual-coding skills atrophy, and engineering transitions from syntax-writing to high-level architectural prompting and 'vibe coding'. Andrej Karpathy | AI | 90% | ||
| 0.59 | github_release 2025-12-14 | 248_022 Deepseek-style hyperdeflation moments from algorithmic innovation will become more frequent but less effective at causing price swings. Alex Wissner-Gross | Markets/Stocks | 42% | ||
| 0.59 | github_release 2023-04-10 | 229_028 Figure will NOT license out its neural net or hardware IP to third-party form-factor builders. Brett Adcock | Robotics | 69% | ||
| 0.59 | github_release 2023-01-19 | 241_031 Scientists don't agree yet on approach for recursive self-improvement Eric Schmidt | AI | 48% | ||
| 0.59 | github_release 2023-12-15 | SEM_022 FP4 / ternary-weight architectures decouple AI capability from raw transistor density — embargoed nations maintain competitive development. Dave Blundin | AI/Architecture | 65% | ||
| 0.59 | github_release 2026-05-11 | 247_057 Parameter scaling race is over; frontier labs plateauing at 10T parameters Alex Wissner-Gross | AI | 28% | ||
| 0.59 | github_release 2026-03-23 | AUT_021 Defining software movement of 2026: startups building autonomous platforms specifically designed to clean/structure/continuously validate multimodal data — unstructured corporate sludge (PDFs, logs, videos, emails) causes autonomous agentic workflows t... Marc Andreessen | AI | 52% | ||
| 0.59 | github_release 2025-02-28 | INF_026 'Software 3.0' LLM infrastructure will operate like public utilities — requiring massive upfront capex (training compute, specialized hardware), specialized networking protocols for synchrony across hundreds of thousands of GPUs, and flawless uninterru... Andrej Karpathy | AI | 64% | ||
| 0.59 | github_release 2025-05-09 | 248_050 Opus 4.7 removes manual dials; prompts become the new hyperparameters. Alex Wissner-Gross | AI | 45% | ||
| 0.59 | github_release 2024-03-21 | 232_011 Jury is still out on whether speech will be the modality of the future for high-bandwidth operation. Alex Wissner-Gross | AI | 38% | ||
| 0.59 | github_release 2024-02-16 | 232_014 Recursive self-improvement is already here, not 12 months away. Alex Wissner-Gross | AI | 70% | ||
| 0.59 | github_release 2020-06-29 | COD_TECH_001 A16/N2-class TSMC process availability materially supports 2027 AI accelerator ramps Codex Research Pack | Semis | 50% | ||
| 0.59 | github_release 2024-03-15 | 240_020 New architecture won't map to current NVIDIA architecture; will create next Anthropic/OpenAI Dave Blundin | AI | 46% | ||
| 0.59 | github_release 2023-03-31 | CMQ_026 NVIDIA silicon roadmap: Blackwell (2025) → Vera Rubin (2026) → Vera Rubin Ultra (2027) → Feynman (2028) — annual architectural cadence. Jensen Huang | Semis | 83% | ||
| 0.59 | github_release 2023-03-31 | S_HUMANOID_CONSUMER_2030 Humanoid R3: 1M+ consumer by Nov 2030 | humanoid_deployment | 20% | ||
| 0.59 | github_release 2023-12-15 | 238_022 From here forward, training data will be synthetic (pre-training era of human internet data is over) Alex Wissner-Gross | AI | 39% | ||
| 0.59 | github_release 2019-07-04 | 235_005 AI capability will grow 100x this year in raw parameter count as lower bound. Dave Blundin | AI | 49% | ||
| 0.59 | github_release 2025-10-23 | 241_031 Scientists don't agree yet on approach for recursive self-improvement Eric Schmidt | AI | 48% | ||
| 0.59 | github_release 2019-08-14 | 234_048 Next major revolutions in foundation models will come from small language models Alex Wissner-Gross | AI | 41% | ||
| 0.59 | github_release 2026-06-15 | 235_037 Auto-regressive transformers and diffusion models will consolidate into one unified architecture. Alex Wissner-Gross | AI | 35% | ||
| 0.59 | github_release 2025-10-10 | S_HUMANOID_ENTERPRISE_2028 Humanoid R2: 100K+ enterprise by Nov 2028 | humanoid_deployment | 50% | ||
| 0.59 | github_release 2025-03-24 | 238_009 Recursive self-improvement is already happening now (no longer three years out) Alex Wissner-Gross | AI | 78% | ||
| 0.59 | github_release 2023-07-20 | 247_057 Parameter scaling race is over; frontier labs plateauing at 10T parameters Alex Wissner-Gross | AI | 28% | ||
| 0.59 | github_release 2023-06-15 | CMQ_026 NVIDIA silicon roadmap: Blackwell (2025) → Vera Rubin (2026) → Vera Rubin Ultra (2027) → Feynman (2028) — annual architectural cadence. Jensen Huang | Semis | 83% | ||
| 0.59 | github_release 2026-04-15 | 235_008 Anthropic/OpenAI will be forced to release first-party OpenClaw competitor in next couple months. Alex Wissner-Gross | AI | 42% | ||
| 0.59 | github_release 2023-01-12 | SEM_021 Largest AI models will see a 100x leap in size by 2026, driven largely by Chinese research efforts using quantization breakthroughs. Dave Blundin | AI/China | 45% | ||
| 0.59 | github_release 2026-04-23 | 240_020 New architecture won't map to current NVIDIA architecture; will create next Anthropic/OpenAI Dave Blundin | AI | 46% | ||
| 0.59 | github_release 2026-03-27 | 240_013 Sam Altman predicts another architecture breakthrough as big as transformers over LSTMs Sam Altman | AI | 41% | ||
| 0.59 | github_release 2024-03-27 | 240_020 New architecture won't map to current NVIDIA architecture; will create next Anthropic/OpenAI Dave Blundin | AI | 46% | ||
| 0.59 | github_release 2023-07-29 | 241_031 Scientists don't agree yet on approach for recursive self-improvement Eric Schmidt | AI | 48% | ||
| 0.59 | github_release 2026-03-06 | INF_001 Reaching AGI by 2027 will require deployment of hundreds of millions of AI GPUs — forcing total mobilization of US industrial capacity for semiconductor fab and data-center shell construction. Leopold Aschenbrenner | AI | 48% | ||
| 0.59 | github_release 2025-04-25 | COD_TECH_001 A16/N2-class TSMC process availability materially supports 2027 AI accelerator ramps Codex Research Pack | Semis | 50% | ||
| 0.59 | github_release 2025-02-10 | CMQ_026 NVIDIA silicon roadmap: Blackwell (2025) → Vera Rubin (2026) → Vera Rubin Ultra (2027) → Feynman (2028) — annual architectural cadence. Jensen Huang | Semis | 83% |