A closer look at why structured reasoning supervision, not model size alone, improves multimodal humor understanding and what that implies for business AI systems handling subjective judgment.
A controlled shortest-path study shows why AI agents can transfer to new settings yet still fail when the task horizon gets longer.
A mechanism-first reading of why LLM judges can look reliable in aggregate while still failing on the individual cases where businesses most need certainty.
A controlled study shows that LLM judges can become more lenient when they know their verdicts carry consequences, exposing a quiet weakness in automated evaluation pipelines.
A sensor-first architecture for physical AI shows why better capture, local reflexes, and selective cloud reasoning may matter more than simply scaling bigger models.
A mechanism-first reading of why reinforcement learning for power-grid control needs runtime safety shielding, not just better reward penalties.
A mechanism-first reading of Memory Transfer Learning, showing why coding-agent memory works best when it transfers abstract operational discipline rather than brittle code traces.
A field deployment of AI-generated peer review at AAAI-26 shows where AI can outperform human reviewers, where it still fails, and what businesses should learn about governed second-opinion systems.
A reward-ablation study on VLM physics reasoning shows why accuracy, reasoning discipline, and visual grounding must be treated as different deployment objectives, not one magical intelligence switch.
A mechanism-first reading of TREX, an agent system that treats LLM fine-tuning as an iterative research workflow rather than a glorified hyperparameter search.