How does test-time scaling work at the agent level? · Gravity7

Sub-Maps

2 notes

How does test-time scaling work for individual research agents?

Can search budget follow the same scaling curves as reasoning tokens in agentic systems? This explores whether deep research exhibits test-time scaling laws similar to reasoning, with implications for inference-compute tradeoffs.

What makes multi-agent teams actually perform better?

Explores what drives performance gains when multiple AI agents collaborate—whether intelligent coordination, team composition, or other factors explain why multi-agent systems work.

Routing and Model Selection

4 notes

Can routers select the right model before generation happens?

Explores whether LLMs can be matched to queries by estimating difficulty upfront, before any generation begins. This matters because routing could cut costs significantly while preserving response quality.

Can routing beat building one better model?

Does directing queries to specialized models via semantic clustering outperform investing in a single frontier model? This challenges whether model improvement or model selection drives performance gains.

What decisions must multi-agent routing systems optimize simultaneously?

Standard LLM routing only picks which model to use. But multi-agent systems involve four interdependent choices: topology, agent count, role assignment, and per-agent model selection. Does optimizing all four together actually improve performance?

Can routing queries to task-matched structures improve RAG reasoning?

Does matching retrieval structure type to task demands—tables for analysis, graphs for inference, algorithms for planning—improve reasoning accuracy over uniform chunk retrieval? This explores whether cognitive fit principles from human learning transfer to AI systems.

Writing Angle

1 note

Are multi-agent systems actually intelligent coordination or just token spending?

Does multi-agent performance come from better coordination strategies, or primarily from distributing tokens across parallel contexts? Understanding this distinction matters for deciding when to build multi-agent systems versus scaling single agents.

Pass 3 Additions (2026-05-03)

2 notes

Does agent interaction time scale separately from reasoning depth?

Can agents improve by taking more environment steps rather than thinking harder per step? This matters because partially observable tasks like web navigation may need exploration and backtracking that deeper reasoning alone cannot provide.

Will agents compete for attention just like users do?

As autonomous agents take over user tasks, will the Web's economic competition shift from human clicks to agent invocations? This explores whether existing ad-market mechanisms could scale to agent decision-making.

Related Areas

5 notes

How does reinforcement learning reshape what models can reason about?

RL training modifies model parameters and exploration strategies, but what capabilities does it actually unlock versus degrade? This map explores RL mechanics, reward dynamics, and the hidden costs of optimization.

How do reasoning models actually fail under pressure?

This explores where reasoning models break down—whether through adversarial attacks, social reasoning gaps, or unfaithful traces that resist monitoring. Understanding failure modes reveals what these systems genuinely can and cannot do.

How should we allocate compute budget at inference time?

Test-time scaling asks how to spend computational budget during inference to make models smarter. The key puzzle: should all prompts get equal compute, or should difficult queries get more?

How should we allocate compute budget at inference time?

Test-time scaling asks how to spend computational budget during inference to make models smarter. The key puzzle: should all prompts get equal compute, or should difficult queries get more?

How should researchers navigate LLM reasoning research?

This note explores how to systematically explore interconnected insights about test-time scaling, reasoning architectures, and language model cognition. It matters because LLM research spans multiple domains—from inference compute to philosophy—and understanding the map helps identify novel connections.