Mind the Gap: Why AI Still Struggles to Build Common Ground
A case-first reading of DPIP, a multimodal benchmark showing why AI agents still confuse visible task progress with genuinely shared belief.
A case-first reading of DPIP, a multimodal benchmark showing why AI agents still confuse visible task progress with genuinely shared belief.
A timeline-style reading of how AI moved from encoding legal interpretations, to modeling interpretive disputes, to generating legal arguments that still need human judgment.
A mechanism-first reading of Judge Reliability Harness and why LLM judges need reliability audits before they become business-critical evaluators.
A mechanism-first reading of how massive activations, normalization, and attention-sink geometry interact inside modern Transformer language models.
BeamPERL shows that exact physics rewards can specialize compact LLMs, but they do not automatically produce transferable scientific reasoning.
A WebGIS case study shows why reliable agentic AI depends less on bigger prompts and more on persistent memory, enforceable rules, and auditable workflow structure.
Agentics 2.0 argues that reliable enterprise AI workflows need typed, composable, evidence-preserving transformations—not just better prompts or louder agents.
RealPref shows why longer chat history alone does not make an AI assistant genuinely personal, and what businesses should build instead.
A mechanism-first reading of Microsoft’s Phi-4-reasoning-vision-15B report, and why smaller multimodal models may win practical AI deployments through sharper perception, cleaner data, and selective reasoning.
A mechanism-first reading of how managerial ambiguity makes LLM advice look useful before it is actually grounded.