The Gate Before the Graph: Why Technical RAG Needs Evidence Control
A mechanism-first reading of TechGraphRAG, showing why the useful idea is not simply graph retrieval, but evidence-gated control before technical synthesis.
Cognaptus Insights
Industry thoughts, trends, and commentary.
Less Label, More Light: What a 3D Microscopy Foundation Model Actually Buys
A mechanism-first reading of how multimodal pretraining may reduce annotation burden in light sheet fluorescence microscopy without pretending to replace expert validation.
Look Before You Think: Why Visual AI Needs Evidence Scheduling
A mechanism-first reading of CSMR, a training-free framework that improves multimodal reasoning by letting an LLM ask for visual evidence only when the reasoning state needs it.
No Cluster Is an Island: ScaleAcross Explorer and the Geography Tax of AI Training
How scale-across AI training turns model architecture, parallelism placement, scheduling, and long-distance networking into one business-critical optimization problem.
One Pass to Forecast Them All: Toto 2.0 and the Scaling Recipe for Time-Series AI
A mechanism-first reading of Toto 2.0, showing why time-series foundation model scaling depends on decoding, loss design, optimizer choice, data mixture, and hyperparameter transfer—not just bigger parameter counts.
Preference Laundering: How RLHF Can Turn Better Answers Into Bigger Biases
A mechanism-first reading of alignment tampering, where preference optimization can amplify unwanted bias when quality and bias travel together.
Sight Unseen: How LVLM Alignment Can Teach Models to Ignore Images
A mechanism-first reading of why vision-language models can become more fluent while becoming less visually grounded, and what that means for business deployment.
Time to Prefer: Why Binary RLHF Feedback Leaves Reward Models Guessing
A mechanism-first reading of why pairwise preference labels can fail under unseen user preferences, and why response time may help reward models adapt.
Beam Me Less, Scotty: MoE Models Learn When Not to Call Every Expert
BEAM shows how separating expert selection from expert activation can turn MoE inference from a fixed Top-K habit into an adaptive compute-control layer.
Entropy, My Dear Watson: Finding Hallucinations in the Shape of Uncertainty
A mechanism-first reading of CES, a lightweight hallucination detector that treats token entropy distributions as operational risk fingerprints rather than mere confidence scores.