Theme of inquiry
What enables reasoning capability in transformer language models?
A question within its area, explored through 6 lines of inquiry below — each a family of specific questions the research asks.
69 specific questions
- Can models maintain reasoning-output coupling while improving domain accuracy?
- Does supervised fine-tuning improve accuracy while damaging the quality of reasoning?
- Why do reasoning gains resist clear attribution to specific training changes?
- Does SFT degrade reasoning quality while improving domain accuracy?
- Why do benchmark scores rise while reasoning quality declines?
- How does optimizing for accuracy during training degrade downstream reasoning quality?
- Can fine-tuning ever teach semantic inference instead of amplifying training shortcuts?
51 specific questions
- Does architectural design matter more than model scale for reasoning tasks?
- What makes multi-paradigm chaining a distinct reasoning topology?
- Can small models solve complex tasks using externalized reasoning graphs?
- Why does reasoning graph topology evolve differently across training phases?
- Can a single recursive network replace hierarchical dual-network architectures?
- What makes a causal abstraction more transferable than a generic heuristic?
- Do reasoning systems reuse cognitive structures across unrelated topics?
74 specific questions
- Do base models contain latent reasoning that minimal training can unlock?
- What latent reasoning capability do base models already possess before training?
- Can minimal training signals unlock latent reasoning capability in base models?
- Can models possess latent reasoning capability that training signals fail to unlock?
- What makes reasoning capability a pre-training rather than post-training phenomenon?
- Does the base model already contain latent reasoning capability?
- Can minimal training signals unlock reasoning already latent in pretrained representations?
39 specific questions
- Why do research ideation systems suffer from diversity collapse despite high novelty metrics?
- Does optimizing directly for semantic diversity improve both reasoning quality and exploration?
- Why does diversity collapse occur in multi-agent research ideation despite high novelty?
- Can few-shot examples narrow generative diversity in creative tasks?
- How does cohort diversity prevent label-free reasoning from collapsing into homogenized answers?
- What makes external diversity more effective than sequential revision steps?
- Can diverse critiques on a single problem unlock reasoning without diverse problem sets?
19 specific questions
- Why does training data format shape reasoning strategy more than domain content?
- How does training format shape reasoning strategy more than content?
- How much does training data format influence reasoning strategy versus domain content?
- Why does training data format shape reasoning strategy more than content?
- Does training data format shape reasoning strategy more than domain content?
- Can training format itself shape what reasoning strategy a model learns?
- Does training data format shape model reasoning more than domain content?
40 specific questions
- Why might latent reasoning capture types of thinking that verbalized CoT cannot?
- Can latent reasoning stay readable without explicit token-by-token decoding?
- Can models hide their reasoning in continuous space rather than natural language?
- Can latent reasoning scale test-time compute without verbalized tokens or special training?
- Can latent reasoning in continuous space scale beyond supervised reasoning tasks?
- Can latent reasoning achieve the same substitution without tokens?
- Can latent reasoning scale test-time compute without verbal tokens?