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
7Unique collaborators
1/1Semantic Scholar citation coverage
Publication span: 2026. Corpus fetch span: 2026.
Identity provenance
Provider IDs
- Semantic Scholar:
51251519
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- Ritiz Tambi (semantic scholar, provider refresh)
Topics and outcomes in this view
Assessment themes
- Adoption: 1 paper
- Human Ai Collab: 1 paper
- Productivity: 1 paper
Claim outcomes
- Organizational Efficiency: 1 paper
- Ai Safety And Ethics: 1 paper
- Error Rate: 1 paper
- Other: 1 paper
- Regulatory Compliance: 1 paper
- Task Completion Time: 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 |
|---|---|---|---|
| A new audio benchmark finds wide differences in voice-agent performance: some systems autonomously resolve 71% of simulated credit-card calls while others resolve just 43%, with most failures occurring during verification or execution despite high connect rates; the benchmark uniquely cross-checks spoken claims against actual database actions.arxiv | Ritiz Tambi provider id |
2026-07-29 | 1 |
Citation observation summary
Semantic Scholar supplied counts for 1 of 1 papers in this view; 0 are missing. The observed paper counts sum to 1 cumulative citations. This is a coverage summary, not an author score or h-index.