Vertical Integration Matters More Than Model Speed
AI vertical integration is less about chips than controlling the training stack, latency, throughput, utilization, and recovery.
AI vertical integration is less about chips than controlling the training stack, latency, throughput, utilization, and recovery.
A look at structuring table QA with guided cell navigation and staged inference to improve accuracy and verify evidence paths.
MOCHA treats agent skills as multi-field artifacts and argues they must be optimized with platform constraints in mind.
A study on claim verification that proposes ternary decisions and explainable argumentation under incomplete or conflicting evidence.
How prompt-guided image compression for VLMs shifts focus from human visual quality to preserving clues needed for tasks.
Why mathematics must address AI through values, practice, teaching, technology, and ethics to protect autonomy.
A unified view of probabilistic trustworthy AI: performance bottlenecks may lie in memory and random data movement, not just compute.
How infant low-data visual learning links concepts, causality, and prediction to reshape AI vision and robotics design.
How wireless world models combine 3D geometry and wave propagation to improve real-world generalization in AI-native 6G.
View LLM agents as runtime-adaptive computation graphs to optimize accuracy, cost, latency, debugging, and control.
In courts, AI outcomes hinge less on model accuracy than on judge uptake, override patterns, accountability, and TEVV.
In medical AI robotics, governance, validation, and monitoring matter more than performance demos alone.
Examines why structured exploration and verifiable workflows may matter more than longer reasoning in LLM binary analysis.
Models with identical predictions can still produce different feature attributions, challenging XAI reliability, audits, and governance.
How combining LLMs with computational argumentation could shift AI from making decisions for us to reasoning with us.
A paper argues educational AI performance may depend less on model size and more on roles, skills, tools, runtime, and educator expertise.
A minimal theory of multi-agent coordination through environmental memory, incentive fields, and feedback loops.
A new estimator for stable dependence analysis across autoencoder inputs, latents, and reconstructions, beyond mutual information pitfalls.
Why SBOMs miss agentic AI runtime behavior, environment drift, and exploitability context—and how active provenance AIBOMs address it.
ML-based NIDS can be evaded via adversarial examples like FGSM and GAN. Evaluate robustness and compare ensemble defenses.
AI co-writing can shift users from ideation to reactive selection, affecting expressed claims and even post-writing attitudes.
Industrial LLM hallucinations framed as a reproducibility problem, comparing five prompt strategies to reduce output variance across repeated runs.
Reframes RF channels as sensors and jointly learns quantum probes with models under 5 ms/sample and pipeline constraints.
UniPINN targets three bottlenecks in multi-flow PINNs: shared vs specific features, negative transfer, and loss-scale imbalance.