Pretty Text, Ugly Logic: When Image Models Learn to Write but Not to Reason
A comparison-based reading of why visually clear AI-generated text can still hide broken reasoning, and what that means for document, slide, and dashboard automation.
Cognaptus Insights
Industry thoughts, trends, and commentary.
Right Answer, Wrong Audit: When Reasoning Models Grade the Destination, Not the Route
A mechanism-first reading of VAIR, a benchmark showing why correct answers can make large reasoning models unreliable auditors of flawed reasoning.
Safe Hands, Unsafe Audit: Why Robot Success Does Not Prove Robot Safety
A cross-layer reading of robotic manipulation safety, showing why task completion is not enough evidence for safe deployment.
Talk Is Cheap, Until It Trains ASR
A comparison-driven reading of how LLM-generated synthetic conversations can improve conversational ASR, and why the useful question is not more data, but better-matched data.
Curved Space, Straighter Retrieval: Why Graph RAG Needs Geometry
HyRAG shows that graph RAG failures may come less from weak retrieval and more from the wrong geometry for hierarchical knowledge.
Memory Lane, With Garbage Collection: What eMoT Gets Right About Reasoning Agents
A mechanism-first reading of eMoT, a reasoning framework that treats successful reasoning patterns as reusable procedural memory rather than disposable chain-of-thought text.
Mind the Slot: Jailbreak Prompts Have Weak Points, Not Just Bad Words
SlotGCG shows that LLM jailbreak risk is shaped not only by adversarial token content, but by where those tokens touch the prompt.
Pocket Experts: MobileMoE and the Memory Math of On-Device AI
MobileMoE shows that capable on-device AI is not just a smaller-model problem, but a routing, memory, quantization, and runtime-engineering problem.
State of Delay: KVBuffer and the Memory Tax of Linear Attention
A mechanism-first reading of KVBuffer, showing why constant-time linear attention still needs IO-aware serving design before it becomes operationally cheap.
Step Right Up: Why Multi-Agent AI Needs Process Control, Not Just More Agents
A practical reading of two new multi-agent reasoning papers: reliable agentic AI depends on when reasoning is shared, checked, and repaired.