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 24 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 | |
|---|---|---|---|---|---|---|
| 0.62 | github_release 2026-03-09 | 247_057 Parameter scaling race is over; frontier labs plateauing at 10T parameters Alex Wissner-Gross | AI | 28% | ||
| 0.62 | github_release 2026-03-09 | CMQ_010 True AGI requires genuine scientific-discovery capabilities (AlphaFold-class breakthroughs) — brute-force LLM scaling alone is insufficient. Demis Hassabis | AI | 49% | ||
| 0.62 | github_release 2025-12-11 | COD_TECH_001 A16/N2-class TSMC process availability materially supports 2027 AI accelerator ramps Codex Research Pack | Semis | 50% | ||
| 0.62 | github_release 2022-12-19 | CMQ_026 NVIDIA silicon roadmap: Blackwell (2025) → Vera Rubin (2026) → Vera Rubin Ultra (2027) → Feynman (2028) — annual architectural cadence. Jensen Huang | Semis | 83% | ||
| 0.62 | github_release 2026-05-15 | CMQ_026 NVIDIA silicon roadmap: Blackwell (2025) → Vera Rubin (2026) → Vera Rubin Ultra (2027) → Feynman (2028) — annual architectural cadence. Jensen Huang | Semis | 83% | ||
| 0.61 | github_release 2026-04-15 | 234_017 OpenAI codex lead predicts current coding agents will seem primitive in 10 weeks OpenAI Codex Lead | AI | 33% | ||
| 0.61 | github_release 2026-02-23 | 247_011 OpenAI user count will soon reach 1 billion Alex Wissner-Gross | AI | 41% | ||
| 0.61 | github_release 2026-02-05 | 235_008 Anthropic/OpenAI will be forced to release first-party OpenClaw competitor in next couple months. Alex Wissner-Gross | AI | 42% | ||
| 0.61 | github_release 2025-12-15 | 247_011 OpenAI user count will soon reach 1 billion Alex Wissner-Gross | AI | 41% | ||
| 0.61 | github_release 2025-11-13 | 240_020 New architecture won't map to current NVIDIA architecture; will create next Anthropic/OpenAI Dave Blundin | AI | 46% | ||
| 0.61 | github_release 2025-11-10 | 232_012 US-China AI situation about to get more complicated regarding open source models and secrets. Ben Horowitz | Geopolitics | 44% | ||
| 0.61 | github_release 2026-03-04 | 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% | ||
| 0.61 | github_release 2026-01-13 | 248_038 We will see humanoid robot threats alongside the current Sam Altman backlash. Salim Ismail | Robotics | 34% | ||
| 0.61 | github_release 2023-11-14 | 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% | ||
| 0.61 | github_release 2023-07-05 | SEM_047 At 200,000-GPU scale, orchestration becomes a literal 'battle against entropy' — single cosmic-ray-flipped transistor can derail 100k-GPU training run. Jimmy Ba | AI/Hardware | 71% | ||
| 0.61 | github_release 2026-03-23 | 240_021 Post-transformer architecture will be even more specialized than GPUs Alex Wissner-Gross | AI | 35% | ||
| 0.61 | github_release 2023-10-04 | 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% | ||
| 0.61 | github_release 2026-05-05 | 247_012 The bar for AI startups will rise to require being recursively self-improving Alex Wissner-Gross | AI | 32% | ||
| 0.61 | github_release 2019-08-15 | AUT_017 Defining 2023-2026 shift: maturation of 'Agentic AI' via heavy fine-tuning of pre-trained LLMs using advanced reinforcement learning techniques — single foundational models seamlessly handling diverse complex tasks (market analysis, software engineerin... Jimmy Ba | AI | 65% | ||
| 0.61 | github_release 2019-05-30 | CMQ_015 Algorithmic efficiencies will deliver ~0.5 OOMs per year of additional effective compute through 2027 — pure multiplier on raw FLOPs. Leopold Aschenbrenner | AI | 50% | ||
| 0.61 | github_release 2018-07-12 | COD_TECH_001 A16/N2-class TSMC process availability materially supports 2027 AI accelerator ramps Codex Research Pack | Semis | 50% | ||
| 0.61 | github_release 2018-07-12 | SEM_022 FP4 / ternary-weight architectures decouple AI capability from raw transistor density — embargoed nations maintain competitive development. Dave Blundin | AI/Architecture | 65% | ||
| 0.61 | github_release 2025-10-15 | 241_037 Chinese AI strategy will stay open source / open weights Eric Schmidt | AI | 49% | ||
| 0.61 | github_release 2023-01-31 | TK11 Autonomous Regulatory Block (Level 4 Halt) | — | 10% | ||
| 0.61 | github_release 2025-11-12 | 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% | ||
| 0.61 | github_release 2026-05-11 | 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% | ||
| 0.61 | github_release 2025-05-12 | CMQ_042 As AI evolves from Generative to Agentic, structural computing bottlenecks shift away from GPU and heavily toward CPU and system memory. Morgan Stanley | AI/Compute | 35% | ||
| 0.61 | github_release 2025-02-28 | 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% | ||
| 0.61 | github_release 2021-02-22 | 243_015 Software space for autonomous may consolidate to fewer than 10 providers Dara Khosrowshahi | Auto/Transport | 46% | ||
| 0.61 | github_release 2023-10-19 | CMQ_026 NVIDIA silicon roadmap: Blackwell (2025) → Vera Rubin (2026) → Vera Rubin Ultra (2027) → Feynman (2028) — annual architectural cadence. Jensen Huang | Semis | 83% | ||
| 0.61 | github_release 2021-07-30 | TK11 Autonomous Regulatory Block (Level 4 Halt) | — | 10% | ||
| 0.61 | github_release 2026-06-03 | 247_011 OpenAI user count will soon reach 1 billion Alex Wissner-Gross | AI | 41% | ||
| 0.61 | github_release 2021-03-13 | 237_018 We are seeing a Cambrian explosion of OpenClaw variants (PicoClaw, IronClaw, NanoClaw, Nanobot, etc.) with many more to come. Alex Wissner-Gross | AI | 40% | ||
| 0.61 | github_release 2020-05-17 | COD_TECH_001 A16/N2-class TSMC process availability materially supports 2027 AI accelerator ramps Codex Research Pack | Semis | 50% | ||
| 0.61 | github_release 2025-11-21 | 247_057 Parameter scaling race is over; frontier labs plateauing at 10T parameters Alex Wissner-Gross | AI | 28% | ||
| 0.61 | github_release 2025-10-07 | 247_057 Parameter scaling race is over; frontier labs plateauing at 10T parameters Alex Wissner-Gross | AI | 28% | ||
| 0.61 | github_release 2025-09-24 | 247_057 Parameter scaling race is over; frontier labs plateauing at 10T parameters Alex Wissner-Gross | AI | 28% | ||
| 0.61 | github_release 2024-09-13 | 240_020 New architecture won't map to current NVIDIA architecture; will create next Anthropic/OpenAI Dave Blundin | AI | 46% | ||
| 0.61 | github_release 2024-09-13 | COD_TECH_001 A16/N2-class TSMC process availability materially supports 2027 AI accelerator ramps Codex Research Pack | Semis | 50% | ||
| 0.61 | github_release 2022-04-28 | 240_020 New architecture won't map to current NVIDIA architecture; will create next Anthropic/OpenAI Dave Blundin | AI | 46% | ||
| 0.61 | github_release 2021-02-09 | 248_032 First-generation neural uploads will be destructive; 2nd-4th generation will be non-destructive. Alex Wissner-Gross | Biotech/Longevity | 42% | ||
| 0.61 | github_release 2026-05-15 | 229_028 Figure will NOT license out its neural net or hardware IP to third-party form-factor builders. Brett Adcock | Robotics | 69% | ||
| 0.61 | github_release 2023-07-25 | 240_016 Everyone is underestimating the next year of AI improvements (more than 2x gain) Dave Blundin | AI | 36% | ||
| 0.61 | github_release 2023-08-25 | AI_025 Most current generative AI wrappers are transient — they will fade into the background as infrastructure layers, analogous to Radio Shack fading in the Windows/PC era; winners will be infrastructure and verticalized depth plays, not thin API-wrapper apps. Mark Cuban | AI | 60% | ||
| 0.61 | github_release 2025-12-11 | 239_004 xAI/Grok will catch up and exceed competitors on coding by mid-2026 Elon Musk | AI | 40% | ||
| 0.61 | github_release 2025-11-13 | 235_008 Anthropic/OpenAI will be forced to release first-party OpenClaw competitor in next couple months. Alex Wissner-Gross | AI | 42% | ||
| 0.61 | github_release 2023-01-12 | 230_040 AI capability/accuracy will improve recursively; output-checking issues will be eliminated quickly. Peter Diamandis | AI | 27% | ||
| 0.61 | github_release 2022-08-25 | CMQ_010 True AGI requires genuine scientific-discovery capabilities (AlphaFold-class breakthroughs) — brute-force LLM scaling alone is insufficient. Demis Hassabis | AI | 49% | ||
| 0.61 | github_release 2022-06-13 | CMQ_026 NVIDIA silicon roadmap: Blackwell (2025) → Vera Rubin (2026) → Vera Rubin Ultra (2027) → Feynman (2028) — annual architectural cadence. Jensen Huang | Semis | 83% | ||
| 0.61 | github_release 2022-01-28 | 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% |