1205 articles available
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.
Clarifies what MoganBert-TR’s CLM-to-MLM pretraining result means for Turkish search and embedding products, and where the evidence should not be overextended.
Clarifies why ICVD should be treated as a benchmark for testing recognition of 12 simulated NICU care actions, not as evidence that automated bedside documentation is ready for deployment.
A practical guide for deciding whether Agent-G² is a better fit than scheduled guidance when tuning expert trajectory prefix depth in long-horizon, sparse-reward agent RL.
Learn how to assess repository-level code agents by separating functional success, exploit-based validation, and newly introduced vulnerabilities before using them in security-sensitive workflows.
Uses the AVO case to show why teams evaluating long-running autonomous agents should prioritize state persistence, failure recovery, execution loops, and supervision over single-call model performance.
Using the Panasonic Avionics AWS-based IFEC diagnostic case, this article helps readers distinguish agentic AI for narrowing failure causes from AI that replaces aviation safety or maintenance decisions.
Learn how MLREF shifts LLM-based reward optimization from rewriting whole reward programs to reusing reward modules, and use the article’s criteria to decide whether it is worth A/B testing in robotics RL workflows.
A practical guide to why English-only safety evaluation is insufficient for multilingual deployment, and how to assess LSR Anchoring as a no-retraining mitigation by checking harmful refusal gains, benign over-refusal, model-language fit, and validation limits.
Clarifies when CAS is appropriate as a causal attribution and audit tool, and helps readers check the graph, data, and identification assumptions needed before using its scores.
Clarifies where HyperANFIS showed performance gains over ANFIS and why preserving IF-THEN rules should not be treated as proof of better human understandability.
Shows why model alignment alone is not enough for agents that change external state, and gives product teams concrete criteria such as tool allowlists, permission separation, pre-execution checks, human approval, and audit logs.
A practical reading of the AMIE video consultation study for healthcare leaders: why real-time medical AI should be assessed for audio, video, latency, nonverbal cues, clinical reasoning, and supervised use before deployment.
Clarifies when multi-time fusion may be useful in EEG emotion recognition, how mixed-emotion labels can be assessed, and why current evidence does not support direct clinical decision use.
Practical criteria for treating AI agent safety evaluations as controlled operational deployments when agents can access real systems, accounts, tools, or credentials.
Uses a wastewater treatment simulator comparison to clarify when to choose a live simulator oracle, structured parameter injection, or retrieval-based grounding for industrial LLM systems, focusing on accuracy, latency, and portability.
Helps teams decide when to prefer context-based personalization over per-user adapters or reward models, with practical criteria around data sparsity, cold starts, and generalization.
Shows how IRT can reduce benchmark redundancy, correlated scores, and evaluation-awareness issues, while clarifying why it should support audits and model selection rather than serve as a final safety verdict.
A practical checklist for AI product, security, and governance leaders on controlling isolation, internet access, sandboxing, logging, and incident response in external high-risk AI evaluations.
A practical guide for deciding when KC-Agent-style memory reuse and incremental validation can help production ML teams respond to data drift beyond periodic retraining.
Practical guidance for teams deploying IR-VLMs with thermal sensor inputs, covering how to interpret QR-STT-style structured thermal trigger risks and stress-test classification, captioning, and VQA output stability.
Shows how to judge LLM price cuts by realized workload cost rather than user-growth claims, and outlines decision criteria for closed APIs, open source models, and self-hosting.
Shows why blocking isolated prompts is insufficient and gives practical criteria for detecting fraud abuse through multilingual comment generation, repeated translation, account clusters, and external platform behavior.
Shows why UrbanDS should be read as a signal for data discovery and graph-based planning in LLM data science agents, not as proven evidence for immediate product adoption.