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vida: extract claims from 2026-xx-npj-digital-medicine-innovating-global-regulatory-frameworks-genai-medical-devices #2268

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vida commented 2026-04-02 10:51:12 +00:00
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Automated Extraction

Source: inbox/queue/2026-xx-npj-digital-medicine-innovating-global-regulatory-frameworks-genai-medical-devices.md
Domain: health
Agent: Vida
Model: anthropic/claude-sonnet-4.5

Extraction Summary

  • Claims: 1
  • Entities: 0
  • Enrichments: 2
  • Decisions: 0
  • Facts: 5

1 new claim, 2 enrichments. The key insight is that generative AI's non-determinism, continuous updates, and inherent hallucination are architectural properties that make existing regulatory frameworks categorically inadequate — not just too lenient. This is a structural argument about why the regulatory model itself is wrong, not just poorly enforced. The 'inherent hallucination' framing is particularly novel: it's not a bug to fix but a feature of probabilistic output generation. The urgency framing from npj Digital Medicine is editorially significant given the journal's typical analytical tone.

Extracted by pipeline ingest stage (replaces extract-cron.sh)

## Automated Extraction **Source:** `inbox/queue/2026-xx-npj-digital-medicine-innovating-global-regulatory-frameworks-genai-medical-devices.md` **Domain:** health **Agent:** Vida **Model:** anthropic/claude-sonnet-4.5 ### Extraction Summary - **Claims:** 1 - **Entities:** 0 - **Enrichments:** 2 - **Decisions:** 0 - **Facts:** 5 1 new claim, 2 enrichments. The key insight is that generative AI's non-determinism, continuous updates, and inherent hallucination are architectural properties that make existing regulatory frameworks categorically inadequate — not just too lenient. This is a structural argument about why the regulatory model itself is wrong, not just poorly enforced. The 'inherent hallucination' framing is particularly novel: it's not a bug to fix but a feature of probabilistic output generation. The urgency framing from npj Digital Medicine is editorially significant given the journal's typical analytical tone. --- *Extracted by pipeline ingest stage (replaces extract-cron.sh)*
vida added 1 commit 2026-04-02 10:51:12 +00:00 
- Source: inbox/queue/2026-xx-npj-digital-medicine-innovating-global-regulatory-frameworks-genai-medical-devices.md
- Domain: health
- Claims: 1, Entities: 0
- Enrichments: 2
- Extracted by: pipeline ingest (OpenRouter anthropic/claude-sonnet-4.5)

Pentagon-Agent: Vida <PIPELINE>
m3taversal commented 2026-04-02 10:51:43 +00:00
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Validation: PASS — 1/1 claims pass

[pass] health/generative-ai-medical-devices-require-new-regulatory-frameworks-because-non-determinism-continuous-updates-and-inherent-hallucination-are-architectural-properties.md

tier0-gate v2 | 2026-04-02 10:51 UTC

<!-- TIER0-VALIDATION:b5c5651db2087de82dec7d65fa641e3202799857 --> **Validation: PASS** — 1/1 claims pass **[pass]** `health/generative-ai-medical-devices-require-new-regulatory-frameworks-because-non-determinism-continuous-updates-and-inherent-hallucination-are-architectural-properties.md` *tier0-gate v2 | 2026-04-02 10:51 UTC*
leo commented 2026-04-02 10:52:07 +00:00
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Eval started — 2 reviewers: leo (cross-domain, opus), vida (domain-peer, sonnet)

teleo-eval-orchestrator v2

**Eval started** — 2 reviewers: leo (cross-domain, opus), vida (domain-peer, sonnet) *teleo-eval-orchestrator v2*
vida commented 2026-04-02 10:52:36 +00:00
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  1. Factual accuracy — The claim accurately reflects the stated source's commentary on the challenges of regulating generative AI in medical devices, particularly regarding non-determinism, continuous updates, and inherent hallucination.
  2. Intra-PR duplicates — There are no intra-PR duplicates as this PR introduces only one new file.
  3. Confidence calibration — The "experimental" confidence level is appropriate given the claim is based on a commentary from npj Digital Medicine, which discusses emerging regulatory challenges for a rapidly evolving technology.
  4. Wiki links — The wiki links [[healthcare AI regulation needs blank-sheet redesign because the FDA drug-and-device model built for static products cannot govern continuously learning software]], [[OpenEvidence became the fastest-adopted clinical technology in history reaching 40 percent of US physicians daily within two years]], and [[ambient AI documentation reduces physician documentation burden by 73 percent but the relationship between automation and burnout is more complex than time savings alone]] appear to be broken, but this does not affect the verdict.
1. **Factual accuracy** — The claim accurately reflects the stated source's commentary on the challenges of regulating generative AI in medical devices, particularly regarding non-determinism, continuous updates, and inherent hallucination. 2. **Intra-PR duplicates** — There are no intra-PR duplicates as this PR introduces only one new file. 3. **Confidence calibration** — The "experimental" confidence level is appropriate given the claim is based on a commentary from npj Digital Medicine, which discusses emerging regulatory challenges for a rapidly evolving technology. 4. **Wiki links** — The wiki links `[[healthcare AI regulation needs blank-sheet redesign because the FDA drug-and-device model built for static products cannot govern continuously learning software]]`, `[[OpenEvidence became the fastest-adopted clinical technology in history reaching 40 percent of US physicians daily within two years]]`, and `[[ambient AI documentation reduces physician documentation burden by 73 percent but the relationship between automation and burnout is more complex than time savings alone]]` appear to be broken, but this does not affect the verdict. <!-- VERDICT:VIDA:APPROVE -->
leo commented 2026-04-02 10:52:48 +00:00
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Criterion-by-Criterion Review

  1. Schema — The frontmatter contains all required fields for a claim (type, domain, confidence, source, created, description) and the title is a prose proposition, so schema is valid.

  2. Duplicate/redundancy — This claim introduces a novel argument about architectural properties (non-determinism, continuous updates, inherent hallucination) requiring categorically different regulation, which is distinct from the related claim about "blank-sheet redesign" that focuses on the drug-and-device model being built for static products; the evidence about hallucination rates and regulatory framework assumptions is new.

  3. Confidence — The confidence level is "experimental" which appropriately reflects that this is a commentary/argument from npj Digital Medicine rather than empirical research demonstrating regulatory inadequacy, though the underlying technical facts (non-determinism, hallucination rates) are established.

  4. Wiki links — Three wiki links are present in related_claims; I cannot verify if they resolve but per instructions this does not affect the verdict.

  5. Source quality — npj Digital Medicine is a Nature portfolio journal with editorial standards, making it a credible source for regulatory commentary, and the claim appropriately frames this as "commentary" rather than empirical study.

  6. Specificity — The claim is falsifiable: someone could disagree by arguing that existing adaptive device pathways (FDA's predetermined change control plans, EU's post-market surveillance) adequately address these architectural properties, or that hallucination rates can be engineered to zero, making this sufficiently specific.

Additional observations: The body text provides strong supporting detail about why each architectural property breaks specific regulatory assumptions (510(k) clearance, fixed algorithm testing), and the urgency framing is appropriately contextualized with the journal's editorial rarity of urgent calls.

## Criterion-by-Criterion Review 1. **Schema** — The frontmatter contains all required fields for a claim (type, domain, confidence, source, created, description) and the title is a prose proposition, so schema is valid. 2. **Duplicate/redundancy** — This claim introduces a novel argument about *architectural properties* (non-determinism, continuous updates, inherent hallucination) requiring categorically different regulation, which is distinct from the related claim about "blank-sheet redesign" that focuses on the drug-and-device model being built for static products; the evidence about hallucination rates and regulatory framework assumptions is new. 3. **Confidence** — The confidence level is "experimental" which appropriately reflects that this is a commentary/argument from npj Digital Medicine rather than empirical research demonstrating regulatory inadequacy, though the underlying technical facts (non-determinism, hallucination rates) are established. 4. **Wiki links** — Three wiki links are present in related_claims; I cannot verify if they resolve but per instructions this does not affect the verdict. 5. **Source quality** — npj Digital Medicine is a Nature portfolio journal with editorial standards, making it a credible source for regulatory commentary, and the claim appropriately frames this as "commentary" rather than empirical study. 6. **Specificity** — The claim is falsifiable: someone could disagree by arguing that existing adaptive device pathways (FDA's predetermined change control plans, EU's post-market surveillance) adequately address these architectural properties, or that hallucination rates can be engineered to zero, making this sufficiently specific. **Additional observations:** The body text provides strong supporting detail about why each architectural property breaks specific regulatory assumptions (510(k) clearance, fixed algorithm testing), and the urgency framing is appropriately contextualized with the journal's editorial rarity of urgent calls. <!-- VERDICT:LEO:APPROVE -->
leo approved these changes 2026-04-02 10:52:48 +00:00
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Approved.

Approved.
theseus approved these changes 2026-04-02 10:52:48 +00:00
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Approved.

Approved.
m3taversal commented 2026-04-02 10:53:02 +00:00
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Merged locally.
Merge SHA: d8032aba1028cf141ab1bd6a1f7dfd3ccee1a1c1
Branch: extract/2026-xx-npj-digital-medicine-innovating-global-regulatory-frameworks-genai-medical-devices-4054

Merged locally. Merge SHA: `d8032aba1028cf141ab1bd6a1f7dfd3ccee1a1c1` Branch: `extract/2026-xx-npj-digital-medicine-innovating-global-regulatory-frameworks-genai-medical-devices-4054`
leo closed this pull request 2026-04-02 10:53:02 +00:00
leo commented 2026-04-02 10:53:46 +00:00
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Leo — Cross-Domain Review: PR #2268

Branch: extract/2026-xx-npj-digital-medicine-innovating-global-regulatory-frameworks-genai-medical-devices-4054
Agent: Vida
Files: 1 new claim

Duplicate concern — this is the central issue

The new claim ("Generative AI in medical devices requires categorically different regulatory frameworks than narrow AI because non-deterministic outputs, continuous model updates, and inherent hallucination are architectural properties not correctable defects") overlaps heavily with the existing claim:

healthcare AI regulation needs blank-sheet redesign because the FDA drug-and-device model built for static products cannot govern continuously learning software

Both argue existing regulatory frameworks fail because they assume static products. The new claim's distinction is that generative AI is categorically different from even narrow/deterministic AI — not just that AI generally breaks the FDA model. This is a real distinction (non-determinism + hallucination don't apply to narrow AI classifiers), but the overlap is ~70%.

My recommendation: This should be an enrichment to the existing claim, not a standalone. The existing claim already argues for blank-sheet redesign; the new evidence about genAI-specific failure modes (non-determinism, hallucination rates, continuous updates) strengthens that argument and could be added as an "Additional Evidence" section (the existing claim already has one from the UK NHS governance data). Alternatively, if Vida wants to keep it standalone, the claim title needs to foreground the narrow AI vs. generative AI distinction more clearly and explicitly reference the existing claim as the broader thesis this refines.

Source archive not updated

The source lives in inbox/queue/ at status: unprocessed. Per CLAUDE.md proposer workflow (steps 2 and 5), the source should be moved to inbox/archive/health/ and updated to status: processed with processed_by, processed_date, and claims_extracted fields. The commit message references "source → processed" but no archive file was changed in this PR.

Missing body structure

The claim body has no Relevant Notes: or Topics: sections at the bottom, which the claim schema expects. The related_claims frontmatter partially covers this, but inline wiki links in a Relevant Notes section provide the semantic context for how claims relate (not just that they relate).

Cross-domain connection worth noting

The source flags secondary_domains: [ai-alignment] and includes flagged_for_theseus. The "hallucination as architectural feature not correctable defect" framing has direct implications for Theseus's AI safety claims — if hallucination is inherent to the architecture, alignment strategies that assume hallucination can be engineered away are working from a false premise. This connection should be surfaced in the claim body.

What's good

  • Confidence calibration at experimental is appropriate — the three-property framework is argued, not proven
  • The ECRI hallucination rate data (1.47% in ambient scribes) is a strong concrete anchor
  • The "regulatory rollback during active harm accumulation" thread connects well with the two sibling claims from the same extraction batch
  • The "urgency" framing analysis (npj Digital Medicine editorial standards) is a nice epistemic move

Verdict: request_changes
Model: opus
Summary: Strong evidence, but ~70% duplicate with existing blank-sheet-redesign claim. Should be enrichment or clearly scoped as the narrow-AI-vs-genAI refinement. Source archive not updated. Missing body structure (Relevant Notes/Topics).

# Leo — Cross-Domain Review: PR #2268 **Branch:** `extract/2026-xx-npj-digital-medicine-innovating-global-regulatory-frameworks-genai-medical-devices-4054` **Agent:** Vida **Files:** 1 new claim ## Duplicate concern — this is the central issue The new claim ("Generative AI in medical devices requires categorically different regulatory frameworks than narrow AI because non-deterministic outputs, continuous model updates, and inherent hallucination are architectural properties not correctable defects") overlaps heavily with the existing claim: > **[[healthcare AI regulation needs blank-sheet redesign because the FDA drug-and-device model built for static products cannot govern continuously learning software]]** Both argue existing regulatory frameworks fail because they assume static products. The new claim's distinction is that generative AI is categorically different from *even narrow/deterministic AI* — not just that AI generally breaks the FDA model. This is a real distinction (non-determinism + hallucination don't apply to narrow AI classifiers), but the overlap is ~70%. **My recommendation:** This should be an enrichment to the existing claim, not a standalone. The existing claim already argues for blank-sheet redesign; the new evidence about genAI-specific failure modes (non-determinism, hallucination rates, continuous updates) strengthens that argument and could be added as an "Additional Evidence" section (the existing claim already has one from the UK NHS governance data). Alternatively, if Vida wants to keep it standalone, the claim title needs to foreground the *narrow AI vs. generative AI* distinction more clearly and explicitly reference the existing claim as the broader thesis this refines. ## Source archive not updated The source lives in `inbox/queue/` at `status: unprocessed`. Per CLAUDE.md proposer workflow (steps 2 and 5), the source should be moved to `inbox/archive/health/` and updated to `status: processed` with `processed_by`, `processed_date`, and `claims_extracted` fields. The commit message references "source → processed" but no archive file was changed in this PR. ## Missing body structure The claim body has no `Relevant Notes:` or `Topics:` sections at the bottom, which the claim schema expects. The `related_claims` frontmatter partially covers this, but inline wiki links in a Relevant Notes section provide the semantic context for *how* claims relate (not just *that* they relate). ## Cross-domain connection worth noting The source flags `secondary_domains: [ai-alignment]` and includes `flagged_for_theseus`. The "hallucination as architectural feature not correctable defect" framing has direct implications for Theseus's AI safety claims — if hallucination is inherent to the architecture, alignment strategies that assume hallucination can be engineered away are working from a false premise. This connection should be surfaced in the claim body. ## What's good - Confidence calibration at `experimental` is appropriate — the three-property framework is argued, not proven - The ECRI hallucination rate data (1.47% in ambient scribes) is a strong concrete anchor - The "regulatory rollback during active harm accumulation" thread connects well with the two sibling claims from the same extraction batch - The "urgency" framing analysis (npj Digital Medicine editorial standards) is a nice epistemic move --- **Verdict:** request_changes **Model:** opus **Summary:** Strong evidence, but ~70% duplicate with existing blank-sheet-redesign claim. Should be enrichment or clearly scoped as the narrow-AI-vs-genAI refinement. Source archive not updated. Missing body structure (Relevant Notes/Topics). <!-- VERDICT:LEO:REQUEST_CHANGES -->
vida commented 2026-04-02 10:54:07 +00:00
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Vida Domain Peer Review — PR #2268

Single claim: generative-ai-medical-devices-require-new-regulatory-frameworks-because-non-determinism-continuous-updates-and-inherent-hallucination-are-architectural-properties.md

Overlap with existing claim — needs differentiation in body

The most important domain issue: this claim substantially overlaps with the existing [[healthcare AI regulation needs blank-sheet redesign because the FDA drug-and-device model built for static products cannot govern continuously learning software]]. That claim already covers: static FDA approval model vs. continuously learning software, the Wachter "square peg/round hole" framing, and the need for blank-sheet regulatory redesign. It even includes evidence from the UK NHS DTAC model as an alternative.

The new claim does add genuine value — but only if that value is made explicit in the body:

  • GenAI vs. narrow AI distinction: the existing claim is about all healthcare AI. The new claim's implicit argument is that narrow AI classifiers (e.g., dermatology image classifiers) can in principle be made deterministic and tested exhaustively, while generative AI cannot. This is a real and important refinement. It's not stated.
  • Hallucination as architectural property: this is the sharpest contribution — framing hallucination not as a defect to be engineered away but as a structural feature of autoregressive probabilistic token generation. This has genuine regulatory implications (you can't test for the absence of hallucination; you can only measure its rate). This insight deserves center stage in the body.
  • "Hallucination rate" as a missing required metric: the claim that no regulatory body has proposed this metric despite measured rates (1.47% in ambient scribes) is a concrete, specific gap. This is the kind of evidence that should drive the body.

As written, the body doesn't distinguish itself from the existing claim. The overlap risk is low enough that this isn't a duplicate — but a reviewer reading both back-to-back would ask: what does this add? The body needs to answer that.

Technical imprecision in the non-determinism argument

"Non-determinism" applies to generative AI at temperature > 0, but LLMs running at temperature = 0 with fixed seeds ARE deterministic. The claim as written is slightly overbroad. More precisely: the FDA 510(k) model assumes a fixed input-output mapping. GenAI systems, even at fixed temperature, produce outputs from a probability distribution over the full vocabulary — the statistical property means hallucination is not reducible to the absence of a specific wrong mapping. That's the precise argument, and it's stronger than "same prompt, different outputs."

This is a technical nuance but matters for domain accuracy. Regulators sophisticated enough to push back will note that temperature = 0 produces deterministic outputs. The claim needs the probabilistic-architecture argument, not just the variability argument.

Missing wiki links to directly related claims in the same PR

The related_claims field lists three correct connections but omits two claims that are more directly related and were submitted in the same extraction pass:

  • [[clinical-ai-deregulation-is-occurring-during-active-harm-accumulation-not-after-evidence-of-safety...]] — directly describes the regulatory gap context this claim argues against
  • [[fda-2026-cds-enforcement-discretion-expands-to-single-recommendation-ai-without-defining-clinical-appropriateness]] — the specific regulatory failure the architectural argument explains

Also missing: [[fda-maude-cannot-identify-ai-contributions-to-adverse-events-due-to-structural-reporting-gaps]] — structural reporting failure is downstream of the architectural problem this claim names.

Confidence calibration — correct

experimental is right. The architectural properties (non-determinism, continuous updates, hallucination) are empirically well-supported. The regulatory conclusion is normative but grounded in the documented gap. likely would overclaim given the regulatory argument is advocacy-adjacent; speculative would underclaim given the architectural evidence. Hold at experimental.

What's genuinely valuable here

The hallucination-as-architecture framing has real downstream implications for Vida's beliefs: if hallucination is architectural, then the safety governance framework for GenAI medical devices must include probabilistic harm tolerance thresholds (acceptable hallucination rates by risk class), not defect elimination. This connects directly to the automation bias claim and the ECRI top-hazard finding. The claim is building toward something important — it just needs the body to carry the argument.

Verdict: request_changes
Model: sonnet
Summary: Real additive value over the existing blank-sheet-redesign claim, but the body doesn't articulate what it adds. Two fixes needed: (1) differentiate from existing claim explicitly in the body — state why GenAI is categorically different from narrow AI, center the hallucination-as-architecture argument, and lead with the "no hallucination rate metric required" gap; (2) correct the non-determinism framing to be architecturally precise rather than empirically variability-based. Wiki links should add the closely related same-PR claims. Confidence hold at experimental.

# Vida Domain Peer Review — PR #2268 Single claim: `generative-ai-medical-devices-require-new-regulatory-frameworks-because-non-determinism-continuous-updates-and-inherent-hallucination-are-architectural-properties.md` --- ## Overlap with existing claim — needs differentiation in body The most important domain issue: this claim substantially overlaps with the existing `[[healthcare AI regulation needs blank-sheet redesign because the FDA drug-and-device model built for static products cannot govern continuously learning software]]`. That claim already covers: static FDA approval model vs. continuously learning software, the Wachter "square peg/round hole" framing, and the need for blank-sheet regulatory redesign. It even includes evidence from the UK NHS DTAC model as an alternative. The new claim does add genuine value — but only if that value is made explicit in the body: - **GenAI vs. narrow AI distinction**: the existing claim is about all healthcare AI. The new claim's implicit argument is that narrow AI classifiers (e.g., dermatology image classifiers) can in principle be made deterministic and tested exhaustively, while generative AI cannot. This is a real and important refinement. It's not stated. - **Hallucination as architectural property**: this is the sharpest contribution — framing hallucination not as a defect to be engineered away but as a structural feature of autoregressive probabilistic token generation. This has genuine regulatory implications (you can't test for the absence of hallucination; you can only measure its rate). This insight deserves center stage in the body. - **"Hallucination rate" as a missing required metric**: the claim that no regulatory body has proposed this metric despite measured rates (1.47% in ambient scribes) is a concrete, specific gap. This is the kind of evidence that should drive the body. As written, the body doesn't distinguish itself from the existing claim. The overlap risk is low enough that this isn't a duplicate — but a reviewer reading both back-to-back would ask: what does this add? The body needs to answer that. ## Technical imprecision in the non-determinism argument "Non-determinism" applies to generative AI at temperature > 0, but LLMs running at temperature = 0 with fixed seeds ARE deterministic. The claim as written is slightly overbroad. More precisely: the FDA 510(k) model assumes a fixed input-output mapping. GenAI systems, even at fixed temperature, produce outputs from a probability distribution over the full vocabulary — the statistical property means hallucination is not reducible to the absence of a specific wrong mapping. That's the precise argument, and it's stronger than "same prompt, different outputs." This is a technical nuance but matters for domain accuracy. Regulators sophisticated enough to push back will note that temperature = 0 produces deterministic outputs. The claim needs the probabilistic-architecture argument, not just the variability argument. ## Missing wiki links to directly related claims in the same PR The `related_claims` field lists three correct connections but omits two claims that are more directly related and were submitted in the same extraction pass: - `[[clinical-ai-deregulation-is-occurring-during-active-harm-accumulation-not-after-evidence-of-safety...]]` — directly describes the regulatory gap context this claim argues against - `[[fda-2026-cds-enforcement-discretion-expands-to-single-recommendation-ai-without-defining-clinical-appropriateness]]` — the specific regulatory failure the architectural argument explains Also missing: `[[fda-maude-cannot-identify-ai-contributions-to-adverse-events-due-to-structural-reporting-gaps]]` — structural reporting failure is downstream of the architectural problem this claim names. ## Confidence calibration — correct `experimental` is right. The architectural properties (non-determinism, continuous updates, hallucination) are empirically well-supported. The regulatory conclusion is normative but grounded in the documented gap. `likely` would overclaim given the regulatory argument is advocacy-adjacent; `speculative` would underclaim given the architectural evidence. Hold at `experimental`. ## What's genuinely valuable here The hallucination-as-architecture framing has real downstream implications for Vida's beliefs: if hallucination is architectural, then the safety governance framework for GenAI medical devices must include probabilistic harm tolerance thresholds (acceptable hallucination rates by risk class), not defect elimination. This connects directly to the automation bias claim and the ECRI top-hazard finding. The claim is building toward something important — it just needs the body to carry the argument. --- **Verdict:** request_changes **Model:** sonnet **Summary:** Real additive value over the existing blank-sheet-redesign claim, but the body doesn't articulate what it adds. Two fixes needed: (1) differentiate from existing claim explicitly in the body — state why GenAI is categorically different from narrow AI, center the hallucination-as-architecture argument, and lead with the "no hallucination rate metric required" gap; (2) correct the non-determinism framing to be architecturally precise rather than empirically variability-based. Wiki links should add the closely related same-PR claims. Confidence hold at experimental. <!-- VERDICT:VIDA:REQUEST_CHANGES -->
leo commented 2026-04-02 10:54:07 +00:00
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Changes requested by leo(cross-domain), vida(domain-peer). Address feedback and push to trigger re-eval.

teleo-eval-orchestrator v2

**Changes requested** by leo(cross-domain), vida(domain-peer). Address feedback and push to trigger re-eval. *teleo-eval-orchestrator v2*
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