Death by a Thousand Prompts: Why Long-Horizon Attacks Break AI Agents
AgentLAB shows why enterprise AI security must move from single-prompt filtering to trajectory-level control over tools, memory, and multi-step behavior.
Cognaptus Insights
Industry thoughts, trends, and commentary.
From Static Models to Living Systems: When AI Stops Predicting and Starts Adapting
A business-focused reading of dynamic bi-level data weighting, and why the next training advantage may come from adaptive data utilization rather than simply larger datasets.
Lost in the Links: When World Knowledge Isn’t Enough
LLM-WikiRace shows why agent reliability depends less on stored knowledge and more on planning, recovery, and loop control.
Lost in Translation: When Safety Contracts Collapse Across 2.1 Billion Voices
A mechanism-first reading of IndicJR, a benchmark showing why multilingual chatbot safety cannot be certified by English tests, JSON contracts, or native-script assumptions alone.
Mind the Drift: Why Stateful AI Guardrails Beat Bigger Models
DeepContext shows why enterprise AI safety may need stateful intent tracking more than larger stateless guard models.
When Fine-Tuning Bites Back: The Hidden Safety Drift in Vision-Language Agents
A mechanism-first reading of how narrow multimodal fine-tuning can turn a localized data problem into broad safety drift across vision-language agents.
Diffusing the Periodic Table: How Hierarchy Fixes Molecular AI
A mechanism-first reading of MolHIT, a molecular graph diffusion framework that shows why chemical representation, not just model scale, can decide whether generated molecules are valid, novel, and controllable.
From PDE to Pipeline: When LLMs Become Numerical Architects
A mechanism-first reading of AutoNumerics, showing why automated PDE solving is less about code generation and more about controlled solver planning, debugging, and verification.
Ready Player None: Why AI Still Can’t Beat the Human Game Multiverse
AI GAMESTORE shows why frontier models still struggle with rapid learning, memory, planning, and world-model discovery in interactive tasks humans treat as casual.
Steer by Equation: When LLM Alignment Learns to Drive with ODEs
A mechanism-first reading of ODESteer, an inference-time alignment method that turns activation steering from one-shot vector editing into adaptive control.