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.
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 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.
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 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.
How legal-structure chunking in an EU AI Act RAG corpus affects retrieval quality, citation traceability, and auditability.
How model-specific refusal thresholds and context handling shape overrefusal, safety boundaries, and response quality.
ConceptSMILE audits concept-based explanations for stability, faithfulness, and consistency under input perturbations.
Shows how latent confounding can skew Bayesian causal discovery posterior toward spurious edges, not just uncertainty.
HCC-STAR reads EMR narratives to rank HCC risk, treatment priorities, and evidence-backed explanations.
MetaNCA explores self-organizing neural weights with local rules and tests generalization to unseen architectures.
A survey argues medical LLMs should be judged by clinical reasoning capacity, not just benchmark accuracy.
A look at interpreting LLM jailbreaks as internal path rerouting, with key findings, limits, and safety implications.
Under the EU AI Act, XAI appears closer to supporting evidence for high-risk AI assurance than a substitute for certification.
Korean LLMs are better judged by naturalness, pragmatic understanding, and instruction following than by one rank.
A look at interpreting transformer-based VLM adversarial vulnerability through intermediate spectral subspaces.
Why agentic AI governance must cover autonomy, tool use, external actions, audit logs, and human oversight.
A look at transformer circuit analysis for composite modular multiplication, extending interpretation beyond reversible operations.
VASP Agent targets reliable scientific automation by combining input consistency, long-run supervision, and output validation.
Examines whether individual parameters in sparse transformers carry stable meanings amid polysemantic behavior.
Examines how attention-limited pairwise labels in RLHF can distort reward learning and be mistaken for true preference.
