A mechanism-first analysis of how a GPT-2-style transformer partially learns arithmetic structure from rooted-tree Dyck words—and why that is a benchmark lesson, not a factoring breakthrough.
A mechanism-first reading of learned-rule-augmented LLM evaluators, and why the next AI judge may need better rubrics before bigger brains.
A mechanism-first reading of how graph distance can act as a surprise signal for knowledge-graph reasoning, and why the idea is useful before it is proven.
OctoMed shows that medical reasoning gains may come less from bigger architectures and more from carefully mixed, trace-rich supervised fine-tuning data.
A mechanism-first reading of Hierarchical AI-Meteorologist, an LLM-agent system that turns forecast tables into multi-scale, explainable weather reports.
SHRIKE shows why audio-visual reasoning improves when models first build explicit scene relations, then use question-guided temporal experts to decide where to look and listen.
A mechanism-first reading of MCTR, a metacognitive test-time reasoning framework that separates memory formation, action reasoning, and online policy adaptation.
A mechanism-first reading of an agentic AI inventory framework, separating its operational blueprint from its still-preliminary evidence.
WMAct shows how multi-turn interaction can train LLM agents to compress feedback into reusable world-model reasoning, but only when exploration is disciplined.
A mechanism-first reading of SuperIntelliAgent, explaining how verifier-guided No-to-Yes trajectories turn ordinary generation failures into lightweight continual-learning signals.