The Experts Are Sparse Inside: Why MoE Cost Cuts Stop at 1.2x
A mechanism-first reading of intra-expert activation sparsity in MoE models, and why large theoretical sparsity becomes modest but useful inference savings in production.
Cognaptus Insights
Industry thoughts, trends, and commentary.
The KV Cache Is Not a Detail: Why LLM Compression Needs a Control Plane
KVServe shows why KV cache compression in disaggregated LLM serving should be treated as service-aware control, not a static infrastructure tweak.
AdamW and the Cost of Being Reasonable: Choosing LLM Optimizers Without Leaderboard Theater
A business-facing reading of why LLM optimizer choice is less about replacing AdamW and more about trading memory, stability, wall-clock time, and hardware fit.
No More Low-Rank Detours: GPart and the Geometry of Fine-Tuning
A mechanism-first reading of GPart, a PEFT method that replaces LoRA’s bilinear adapter detour with a direct isometric map into model weight space.
RL Needs a Menu, Not a Miracle
A recent arXiv paper shows why reinforcement learning works better when a model has already seen multiple verified ways to solve the same problem.
The Heart of the Model: ECG Foundation Models Need the Right Backbone Before More Data
A systematic ECG foundation-model study shows why architecture fit and pretraining objective matter more than fashionable scale alone.
Red Queen Receipts: AI Security Testing Needs Logs, Not Vibes
AVISE shows why AI security evaluation should move from one-off jailbreak anecdotes toward repeatable, auditable test pipelines.
Context Is the New Attack Surface
A business-focused reading of Jailbreak Mimicry, explaining why LLM safety failures often live in task framing rather than forbidden words.
LoRA and Order: The Strange Case for One Well-Placed Adapter
A business-focused reading of DomLoRA, a new arXiv paper arguing that efficient LLM fine-tuning may depend less on adding adapters everywhere and more on finding the one module that matters.
Pooling Resources: UniPool and the MoE Budget Nobody Wanted to Audit
A business-focused reading of UniPool, a shared-expert Mixture-of-Experts architecture that reframes model capacity as a reusable budget rather than a per-layer entitlement.