ConceptSMILE Audits Concept Explanations Under Input Perturbations
ConceptSMILE audits concept-based explanations for stability, faithfulness, and consistency under input perturbations.
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.
A study examines how LLMs' emotion interpretation consistency can weaken under semantic stress in affective dialogue.
Question-based AI speeds research, but answer accuracy and source verification remain critical for reliable work.
Drawing on OECD and ILO reports, this explains how AI reshapes tasks before jobs and shifts learning toward understanding and verification.
AI-assisted reading can lower comprehension barriers, but heavy reliance on summaries may weaken deep thinking.
A look at MultAttnAttrib for long-document multimodal QA, covering attribution benefits, limits, and evaluation criteria.
How CoAx exposes backup circuits that single ablation can miss due to self-repair in transformers.
How ContextNest frames context governance with a verifiable knowledge vault layer for auditable AI agents beyond retrieval quality.
A look at RL research using latent space to generate counterfactual feedback in StarCraft II and its coaching potential.
Reviewing where AI and quantum information already deliver practical gains, and why quantum ML advantage still needs caution.
AI can boost productivity but also amplify errors, making foundational learning essential for problem framing, verification, and judgment.