Control the Crowd Before It Arrives: What PedNStream Can—and Cannot—Test
PedNStream offers fast, network-scale testing of crowd interventions, but its operational value depends on disciplined calibration and validation.
Cognaptus Insights
Industry thoughts, trends, and commentary.
Coverage Is Not a Range: RPS for Ordinal Conformal Prediction
RPS conformal prediction turns class probabilities into contiguous ordinal ranges that balance interval width against the severity of uncovered outcomes.
Slow Policy, Fast Power: Where Agentic Control Belongs in Wireless Networks
Agentic-LTPO shows how agents can adapt wireless objectives without placing probabilistic language models inside the real-time beamforming loop.
Agree Once, Remember Later: The Commit Boundary in Personal Agents
A benchmark of stateful personal agents shows that the largest sycophancy risk emerges when user claims are written into durable state and reused later.
Approved One by One, Risky in Combination: Testing Agent Skills Before They Execute
SkillFuzz shows how marketplace operators can screen composition-induced agent risks before committing scarce sandbox and review capacity.
Rotate Before You Update: Deeper Test-Time Adaptation for Graph Models
T3R shows how graph models can adapt deeper layers from unlabeled test data, provided teams qualify the auxiliary signal, bound the update budget, and accept the latency cost.
Average at Your Own Risk: The Metric Setting That Can Reverse the Winner
A practical guide to choosing micro, macro, weighted, and exemplar aggregation according to the operational unit a classifier must serve.
Look Again Before You Answer: Visual RAG Needs a Search Policy
ProMSA shows that visual retrieval improves when models learn to switch search modality, retry failed matches, and stop under explicit budgets.
Reconstructing the Wrong Winner: Choosing VAEs for Sign-Language Generation
Reconstruction error is an incomplete acceptance test for the representation model that a downstream sign-language generator must learn to use.
Measure for Measure: Why AI Evaluation Must Follow the Failure
Two very different AI papers reveal why credible evaluation must trace each intervention from the mechanism it changes to the operational outcome it is meant to improve.