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
5Unique collaborators
1/1Semantic Scholar citation coverage
Publication span: 2026. Corpus fetch span: 2026.
Identity provenance
Provider IDs
- Semantic Scholar:
144245085
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- Sumit Kumar Jha (semantic scholar, provider refresh)
Topics and outcomes in this view
Assessment themes
- Adoption: 1 paper
- Governance: 1 paper
Claim outcomes
- Ai Safety And Ethics: 1 paper
- Market Structure: 1 paper
- Firm Revenue: 1 paper
- Organizational Efficiency: 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 |
|---|---|---|---|
| Hidden latent channels enable LLM agents to coordinate and depress auction prices, but an activation-aware monitor that links private states to public actions and uses matched counterfactuals detects covert collusion with near-perfect accuracy in same-family agent pairs and substantially across families. When platform operators can replay matched neutral counterfactuals, they can fully recover bid distributions and cut collusive low bids by nearly half in the benchmark.arxiv | Sumit Kumar Jha provider id |
2026-08-19 | 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.