Direction, evidence grade, and study type are AI-generated labels (gpt-5-mini), not human-verified. Syntheses are LLM-written. "Tensions" are machine-detected candidates, not confirmed contradictions. A research-acceleration tool, not peer review.
How this is built →
1Distinct papers
6Unique collaborators
1/1Semantic Scholar citation coverage
Publication span: 2026. Corpus fetch span: 2026.
Identity provenance
Provider IDs
- Semantic Scholar:
1861410581
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- Iraklis Varlamis (semantic scholar, provider refresh)
Topics and outcomes in this view
Assessment themes
- Governance: 1 paper
- Org Design: 1 paper
Claim outcomes
- Governance And Regulation: 1 paper
- Ai Safety And Ethics: 1 paper
- Decision Quality: 1 paper
Papers in the Semantic Scholar view
Latest stored Semantic Scholar author observations only. Citation counts below are from the same provider and are not combined with other services.
Scroll the table horizontally to see every column.
| Paper | Author evidence | Date | Provider citations |
|---|---|---|---|
| HANSARD proposes a lifecycle architecture to make autonomous multi-agent AI systems forensically auditable: sealed readiness profiles, out-of-band witnesses at five choke points, and replayable traces produce bounded causal claims rather than trusting self-reports. Its synergy residual metric exposes 'attribution laundering'—when harm arises from the composition rather than any single agent—while evidentiary tiers limit overclaiming.arxiv | Iraklis Varlamis provider id |
2026-08-23 | 0 |
Citation observation summary
Semantic Scholar supplied counts for 1 of 1 papers in this view; 0 are missing. The observed paper counts sum to 0 cumulative citations. This is a coverage summary, not an author score or h-index.