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.
Cognaptus Insights
Industry thoughts, trends, and commentary.
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.
Expert Witness: How MoE Translation Models Can Lose Weight Without Losing the Plot
A mechanism-first reading of how routing statistics can turn a general-purpose MoE LLM into a smaller translation specialist, and where the compression claim stops short of cheaper inference.
Filter Bubble Bursts: When Common Crawl Beats Clean Data
A business-focused reading of why data filtering may be a compute-dependent strategy rather than a universal pretraining rule.