Line of inquiry
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How reliably can language models perform causal versus temporal reasoning?
A broader line of inquiry — a family of 41 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 41
Specific inquiring lines the field asks around this — ordered from the most general framing down to the most specific angle.
- How does semantic association differ from mechanistic causal reasoning?
- Why do causal reasoning directions succeed while temporal reasoning directions fail?
- Do LLMs show stronger reasoning about causality than about temporal ordering?
- Can external actions provide causal necessity that language models lack?
- What are collider structures and why do they reveal reasoning errors?
- How might human-LLM teams reinforce each other's causal reasoning mistakes?
- Can activation patching reveal which reasoning steps actually matter?
- Why do LLMs inherit causal biases from their training data?
- What makes counterfactual thinking different from behavioral pattern matching?
- What architectural changes would help LLMs distinguish causal relationships from temporal sequences?
- Can LLMs distinguish causal mapping from superficial similarity matching?
- Can a Reflect mechanism detect and revise failed causal predictions?
- Do causal rules enforce robustness that statistical patterns alone cannot maintain?
- Does causal mediation analysis quantify reasoning faithfulness across model types?
- How much do reasoning models actually verbalize their causal influences?
- Can causal models be extended to include non-causal cognition?
- When does prediction without causal mechanism fail to support good decisions?
- How do causal belief networks extracted from interviews enable intervention reasoning?
- Is reward propagation in RL formally dual to cause inference in memory?
- Why do causal graphs alone fail to capture human reasoning processes?
- Why do foundation models rely on surface patterns instead of causal structure?
- What training architecture models the causal structure of partner influence?
- How does causal structure avoid behaviorist limitations in LLM social simulation?
- How do world models create indirect causal grounding without physical environment contact?
- Are traditional cognitive theories missing interaction effects between mechanisms?
- What architectural features enable counterfactual reasoning in world models?
- What makes a causal abstraction more transferable than a generic heuristic?
- Can causal belief networks extracted from interviews predict how people respond to policy changes?
- Can functional semantic grounding substitute for true causal grounding?
- How does this motivational bias connect to LLMs' causal reasoning failures?
- How do humans use associative reasoning without causal connections?
- What makes causal explanations stronger anxiety predictors than counterfactuals or dissonance?
- What makes causal belief networks more auditable than prompted personas?
- How can extracted causal belief networks enable intervention simulation?
- How do perception and generation share timing in a single causal stream?
- Why do attention circuits need causal verification beyond feature visualization?
- Can belief networks from interviews simulate how people change their minds?
- Does the reversal curse stem from the same one-way commitment architecture?
- Why should low-probability severe risks trigger early intervention?
- Can event boundaries be identified from statistical regularities without understanding events?
- Where do collider-type reasoning errors appear in real-world decisions?