From YouTube to Execution: How GUIDE Teaches AI Agents to Actually Use Software
A mechanism-first reading of GUIDE, a training-free framework that turns tutorial videos into task-specific planning and grounding knowledge for GUI agents.
Cognaptus Insights
Industry thoughts, trends, and commentary.
Safety First, or Task First? The Hidden Trade-off in Agentic AI
A mechanism-first reading of BeSafe-Bench and what it reveals about unsafe success in agentic AI systems.
The Parallel Mind: How AIRA2 Turns AI Research from Guesswork into Scalable Discovery
A mechanism-first reading of AIRA2: why scalable AI research agents need shared evolutionary memory, protected evaluation, and interactive operators—not just bigger models and more GPUs.
When Reasoning Pays (and When It Cheats): Fixing RL Signals in LLM Training
A mechanism-first reading of PAPO, showing why separating correctness rewards from process rubrics can keep reasoning-model RL useful without paying models to perform for the judge.
Don’t Train Harder—Train Smarter: The Hidden Economics of RL for LLMs
A mechanism-first reading of HIVE, a prompt-selection method that cuts waste in RL training by finding the moving learning edge before expensive rollouts begin.
Memory Is the New Attention: Why Hopfield Networks Are Sneaking Back Into Vision AI
A mechanism-first reading of Vision Hopfield Memory Networks and what memory-centric vision backbones may mean for data-efficient, auditable AI systems.
Photon or Not: When AI Learns to See in 3D Without Burning Your GPU
A mechanism-first reading of Photon, a 3D medical multimodal model that makes CT-volume reasoning cheaper by pruning visual tokens according to the question being asked.
Poisoned Answers, Polished Pipelines: When RAG Learns to Lie on Cue
A mechanism-first reading of PIDP-Attack, showing why RAG risk emerges from the interaction between query rewriting, poisoned retrieval, and obedient generation.
The Latent Cost of Thinking: When LLM Reasoning Becomes a Liability
A mechanism-first reading of why longer LLM reasoning can amplify errors, not merely spend more tokens.
The Model That Forgot Itself: Why LLMs Drift Without Knowing
A mechanism-first reading of why LLMs can appear consistent while silently changing their hidden goals across a conversation.