Theme of inquiry
How should agent architectures balance modularity, efficiency, and execution patterns?
A question within its area, explored through 6 lines of inquiry below — each a family of specific questions the research asks.
66 specific questions
- What mechanisms drive test-time compute allocation in reasoning tasks?
- Does test-time compute actually substitute for having larger model parameters?
- Can test-time compute allocation shift from solutions to strategies?
- Can inference budgets be allocated adaptively based on prompt difficulty?
- How should inference budgets adapt based on prompt difficulty?
- Where does inference compute stop substituting for model capacity?
- Does inference-time compute improve pretraining data efficiency in practice?
29 specific questions
- Can multiple small models outperform a single large model with good routing?
- What makes routing a better investment than training larger models?
- Should model routing decisions account for prompt-tier dependencies?
- Can embedding-cluster routing outperform a single frontier model?
- How do routing and test-time compute scaling work together as optimization axes?
- Why might diverse smaller models with routing beat one giant model?
- Can routing policies remain meaningful over behaviorally homogeneous model pools?
32 specific questions
- Why does decoupling planning from execution improve over sequential interleaving?
- How does separating decomposition from execution improve multi-step reasoning accuracy?
- How does decomposing tasks prevent interference between planning and execution?
- Does algorithmic decomposition prevent planning-execution interference in reasoning?
- How does separating decomposition from execution improve multi-step reasoning?
- Does decoupling reasoning from tool use actually improve accuracy?
- How does decoupling reasoning from tool observations improve parallel execution?
44 specific questions
- Can parallel reasoning chains outperform longer sequential chains with the same compute?
- Can parallel thinking outperform sequential thinking under the same token budget?
- Why does parallel thinking outperform sequential thinking under fixed token budgets?
- What makes parallel thinking more efficient than sequential chains?
- When does sequential chain-of-thought dramatically beat parallel voting approaches?
- Can parallel independent reasoning outperform sequential iterative refinement?
- What advantages emerge from running 13 times more parallel reasoning chains with the same budget?
47 specific questions
- Can context compression preserve what matters without introducing bias?
- Why do long-context language models struggle with compositional reasoning tasks?
- Can fast-slow separation improve both memory and generation in language models?
- How does externalized state affect the long-context bottleneck in language models?
- What makes multi-session context tracking harder than single-turn underspecification problems?
- Could superposed decoding algorithms maintain multi-task representation during generation?
- How does accumulated context history degrade iteration quality in long-horizon tasks?
11 specific questions
- Why does memory consolidation degrade agent performance below baseline?
- Why do continuously consolidated agent memories eventually degrade below no-memory baseline?
- Why does uniform memory consolidation sometimes degrade below the no-memory baseline?
- Why does consolidating more state sometimes hurt performance below the no-memory baseline?
- When does memory consolidation help agents instead of hurting performance?
- Why does LLM memory consolidation regress below no-memory baselines?
- What makes memory consolidation fragile compared to raw trajectory storage?