Expert-Guided LLMs for Marine Lead Data Extraction
How expert-guided LLM agents structure marine lead and isotope data hidden in scientific literature.
How expert-guided LLM agents structure marine lead and isotope data hidden in scientific literature.
Coding model differences appear not in prose quality but in planning, tool use, and context handling scope.
A look at a proposed metric that approximates neural simplicity bias with data-dependent polynomials and its limits.
Examines limits of RTG-only conditioning and how Q-guided alignment aims to improve controllability and reliability in offline RL.
AI pricing is better understood through usage caps, fallback rules, and inference infrastructure efficiency, not subscription fees alone.
A look at rubric- and concept-based grading that makes open-ended scoring more reviewable, editable, and accountable.
CyberJurors evaluates agent systems on multi-round, multimodal evidence handling and platform rule adaptation in e-commerce disputes.
Why multimodal AI still struggles with charts and scientific figures, and how to verify image-based conclusions in practice.
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.