Thinking Without Understanding: When AI Learns to Reason Anyway
A practical reading of simulated reasoning: why reasoning models are no longer mere stochastic parrots, but still not grounded human reasoners.
Cognaptus Insights
Industry thoughts, trends, and commentary.
Causality Remembers: Teaching Social Media Defenses to Learn from the Past
A mechanism-first reading of ACCD, a memory-guided framework that makes coordinated behavior detection more adaptive, label-efficient, and operationally useful.
Crossing the Line: Teaching Pedestrian Models to Reason, Not Memorize
A mechanism-first reading of PedX-LLM, a vision-and-knowledge-enhanced local LLM for generalizable pedestrian crossing behavior inference.
Hard Problems Pay Better: Why Difficulty-Aware DPO Fixes Multimodal Hallucinations
A mechanism-first reading of DA-DPO, showing why multimodal preference tuning fails when easy preference pairs dominate the learning signal.
Pressing by Cosine, Defending by Distance: When Football Learns Semantics
A mechanism-first reading of a semantic-distance football DSS: how tactical intuition becomes an auditable recommender, and why feasibility is not yet proof of better match outcomes.
When LLMs Stop Guessing and Start Complying: Agentic Neuro-Symbolic Programming
How AgenticDomiKnowS turns low-resource neuro-symbolic programming from expert-only craft into a staged, reviewable workflow.
When Systems Bleed: Teaching Distributed AI to Heal Itself
A mechanism-first reading of ReCiSt, a bio-inspired agentic framework that turns distributed-system failures into containment, causal diagnosis, adaptive reasoning, and reusable operational memory.
ODEs Without the Drama: How FPGAs Finally Make Physical AI Practical at the Edge
MERINDA shows that practical physical AI begins by redesigning solver-heavy model recovery for parallel hardware—not by placing the same algorithm on a smaller device.
Prompted to Death: When Words Become a Denial-of-Service
A comparison of ordinary prompts, evolutionary search, and reinforcement-learning attackers reveals why an LLM’s willingness to stop is becoming an operational security property.
Safety First, Reward Second — But Not Last
Why hard-constrained reinforcement learning must preserve the zero-violation objective without training agents to become safely useless.