Clarifies why ANTShapes should be used to test condition-specific weaknesses in SNN object classification through Unity-based procedural generation, rather than as proof of low-power edge AI performance.
A survey argues medical LLMs should be judged by clinical reasoning capacity, not just benchmark accuracy.
Public research suggests rising LLM scores reflect tools, memory, and planning systems, not a simple march toward AGI.
Why LLM agreement can mislead evaluation, with correlated errors, shared wrong answers, and safer judging protocols.
Korean LLMs are better judged by naturalness, pragmatic understanding, and instruction following than by one rank.
An overview of PCBWorld, a KiCad-based environment for evaluating PCB routing AI with native actions and DRC feedback.
Question-based AI speeds research, but answer accuracy and source verification remain critical for reliable work.
Why LLM firms foreground coding as a core benchmark, and how that bias helps developers but raises barriers for nondevelopers.
As multiple-choice medical benchmarks saturate, open-ended clinical reasoning and safety are becoming key measures.
PACE examines whether low-cost non-agent benchmarks can predict expensive agent benchmark performance.
Code model evaluation should weigh real task success, retries, latency, and token cost, not benchmark scores alone.
Strong language performance may not imply a stable world model. Reassessing LLMs through failures in time, space, and physics.
MMG-Pop uses multimodal and temporal graph signals from Bluesky and Reddit to reassess social popularity prediction.
How single-run LLM benchmarks can miss usable performance, and why model choice, retries, and cost matter.
OpenFinGym shifts financial AI evaluation from single-task accuracy to workflow-level testing across prediction, trading, and risk.
How RAG mixes past and current facts, causing stale-fact errors, and why temporal validity matters in retrieval.
Why automated LLM-built benchmarks for relational reasoning need difficulty control, reliable answers, and bias checks.
RAGBench and LegalBench show why enterprise LLM evaluation must separate retrieval quality from domain-specific judgment.
HOLMES probes higher-order logic reasoning beyond final answers, exposing limits in LLM rule, predicate, and constraint handling.
IV-CoT targets structural prompt fidelity in text-to-image generation by separating layout planning from appearance rendering.
Chinese LLM progress is best judged by benchmarks, independent evaluations, and cost efficiency rather than executive claims.
LLM reasoning should be judged not only by accuracy, but also by consistency, constraint tracking, and self-checking.
TadA-Bench shifts protein AI evaluation from static prediction scores to experiment selection and chronology-preserving replay.
CodeGolf Bench measures concise code generation across 60 languages, but its scores should not be read as real-world engineering productivity.
