A comparison-based reading of ChemoTimelines 2025 shows why clinical LLM extraction is less about bigger models and more about choosing the right tradeoff between fine-tuning, reasoning, dictionaries, and aggregation.
A mechanism-first look at AdmTree, a semantic-tree compressor that shows why long-context efficiency is really a memory-structure problem.
PhyVLLM shows why video models need explicit motion modeling, not just more frames, when business decisions depend on physical dynamics.
A rapid review of biomedical AI benchmarks shows why high task scores do not yet prove that AI systems can function as durable research collaborators.
A case-first analysis of how ontology-derived context and justification loops can make enterprise agentic AI more accurate, auditable, and operationally governable.
A healthcare AI study shows why strong headline accuracy can hide weak clinical extraction, especially when multilingual LLMs meet non-English EHR text without task-specific validation.
A measured interpretation of evidence that presentation order has limited impact on explanation-based human-AI debugging, with practical safeguards for XIL workflows.
A mechanism-first reading of how Bayesian optimisation and MCTS turned sphere-packing SDP design into a sample-efficient search problem.
ASTRIDE extends classical threat modeling for agentic AI by adding AI-agent-specific attacks and automating diagram-driven security review with fine-tuned VLMs and a reasoning LLM.
A mechanism-first reading of SIMA 2 and what it shows about training embodied agents in virtual worlds before asking them to survive the real one.