Line of inquiry
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How should test-time compute scaling work in agentic systems?
A broader line of inquiry — a family of 18 specific questions the research asks around this. Follow one into its inquiring-line page, or move sideways to a related line below.
Questions in this line of inquiry 18
Specific inquiring lines the field asks around this — ordered from the most general framing down to the most specific angle.
- Does test-time compute scaling work for agentic deep research tasks?
- Should agents use parallel or sequential scaling during test time?
- Can the scaling law for discovery extend beyond architectures to agentic systems?
- How does test-time scaling relate to token budget in agentic deep research?
- What scaling laws govern autonomous architecture discovery in AI systems?
- Are cheap testbeds and skewed task distributions linked by design necessity?
- Can task decomposition into microagents with voting scale to million-step problems?
- How should proportionality constraints be implemented in agentic systems?
- Why do long-horizon reasoning tasks need per-turn step limits rather than just compute budgets?
- Do autonomous architecture discoveries follow predictable scaling laws like human research?
- How does task structure determine optimal test-time compute allocation?
- How does iteration cycle time constrain autonomous research budgets?
- Does computational scaling alone explain research breakthroughs without human bottleneck removal?
- How should experiment budgets be allocated across parallel hypothesis-testing teams?
- When should agents stop recursing to optimize success versus cost?
- Can phase-aware static taint analysis scale across different benchmark task types?
- Why should bandit algorithms condition exploration on time-of-period as well as user state?
- How does the three-component definition apply to test-time scaling laws?