Agent Seer: Synthesizing Scenarios from Specification Understanding
Updated 2 h ago · first seen 11 Sept 2026
paper_01M294AHMB6WSVA3ESPN7N9074
- Published
- 28 Aug 2026
- T1 · 2 h ago
- arXiv
- 2608.26133
- T1 · 2 h ago
Abstract
Evaluating AI agents that use external tools requires realistic test scenarios that capture how practitioners compose tools and iterate across conversation turns. Constructing such scenarios by hand demands deep domain expertise, does not scale across tool ecosystems, and produces static benchmarks that cannot track evolving APIs. We observe that tool specifications—function names, natural-language descriptions, and typed parameter schemas—already encode sufficient semantic information to synthesize realistic evaluation scenarios without manual curation or live tool execution. Agent Seer…
Authors 3
Harish Karumuri, Mahesh Vemula, David Lopes Pegna
Specification
- Paper
Source:Apple Machine Learning ResearchT1observed 2 h agohigh
- arXiv id
- 2608.26133
Source:Apple Machine Learning ResearchT1observed 2 h agohigh
Source:Apple Machine Learning ResearchT1observed 2 h agohigh
- Published
- 28 Aug 2026
Source:Apple Machine Learning ResearchT1observed 2 h agohigh
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Attributed facts
6
Source tiers
T16
Freshest observation
2 h ago
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- Published by
- Apple
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Claim history · Abstract
Abstractabstract1
| Value | Valid from → to | Status | Source | Confidence | Extractor |
|---|---|---|---|---|---|
| Evaluating AI agents that use external tools requires realistic test scenarios that capture how practitioners compose tools and iterate across conversation turns. Constructing such scenarios by hand demands deep domain expertise, does not scale across tool ecosystems, and produces static benchmarks that cannot track evolving APIs. We observe that tool specifications—function names, natural-language descriptions, and typed parameter schemas—already encode sufficient semantic information to synthesize realistic evaluation scenarios without manual curation or live tool execution. Agent Seer… | → current | current | Apple Machine Learning ResearchT1 | high | deterministic |
Claims are temporal and append-only: a new observation closes the previous claim (valid_to) instead of overwriting it. Conflicting claims from different sources are kept side by side and flagged — never averaged. Methodology →
New paper: Agent Seer: Synthesizing Scenarios from Specification Understanding (Apple)
apple_ml
| Source | Document | Type | Tier | Last observed | Snapshots |
|---|---|---|---|---|---|
| Apple Machine Learning Research | machinelearning.apple.com/rss.xml | feed | T1· Official | 2 h ago | 1 |
| Apple Machine Learning Research | machinelearning.apple.com/research/agent-seer-synthesizing-scenarios | paper_page | T1· Official | 2 h ago | 1 |
Tier 1 = official/primary, 2 = quality secondary, 3 = community, 4 = unverified. Every snapshot is archived; see all sources and the methodology.