How Sparse is Your Thought? Cracking the Inner Logic of Chain-of-Thought Prompts
A mechanism-first reading of how chain-of-thought prompting reshapes internal model features, when it helps, and why smaller models may not benefit.
A mechanism-first reading of how chain-of-thought prompting reshapes internal model features, when it helps, and why smaller models may not benefit.
A practical reading of H-MEM, a hierarchical memory architecture that makes long-term LLM agents faster and more coherent by changing the shape of memory, not merely its size.
A mechanism-first look at how information bottleneck regularisation may help high-capacity asset pricing models forget noise without throwing away return-relevant structure.
A controlled pilot study shows why selecting an LLM for financial-report analysis requires multiple evaluation lenses, not a single leaderboard score.
A mechanism-first look at SiRA, the agent architecture that uses explicit world-model simulation to plan before acting.
A comparison-based reading of how different LLM agent types conform, polarize, or preserve dissent in synthetic forum discussions.
A mechanism-first reading of how gpt-4o-mini produces culturally decorated but structurally standardised stories, and what that means for organisations using LLMs in global communication.
CliCARE shows why clinical AI needs patient timelines, guideline alignment, and expert-calibrated evaluation—not just larger context windows.
A mechanism-first look at SDeflection, a jailbreak defence that redirects harmful prompts into safe adjacent answers instead of betting everything on refusals.
A practical reading of how LLM research borrows psychology—and where citation turns into conceptual debt.