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.
How EU AI Act Article 14 frames human oversight, intervention authority, and semi-automated operations for high-risk AI.
Shows how latent confounding can skew Bayesian causal discovery posterior toward spurious edges, not just uncertainty.
Why AI infrastructure constraints may shift from GPUs to HBM and server memory, and what investors should watch.
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 look at a paper that redesigns structured pruning scores to reduce inference burden while preserving accuracy in LLM deployment.
How anthropomorphism, emotional framing, and role prompts may shift refusal behavior and safety responses in models.
Public research suggests rising LLM scores reflect tools, memory, and planning systems, not a simple march toward AGI.
A curated link roundup from recently collected official updates and tech news.
Why combining audio with generated multilingual transcripts matters for speech emotion analysis, and where errors and cost tradeoffs remain.
Meta’s planned AI chip production from September highlights tighter control over training and inference infrastructure, not just models.
RAID found six scoring exploits in NHL 26 goalie AI in one run, highlighting automated QA and reusable red-team testing.
Examines whether closed government-company talks are enough to judge frontier AI release safety and accountability gaps.
A study showing that deployment rules, not just models, can causally reshape multi-agent behavior and safety outcomes.
An overview of PCBWorld, a KiCad-based environment for evaluating PCB routing AI with native actions and DRC feedback.
VASP Agent targets reliable scientific automation by combining input consistency, long-run supervision, and output validation.
Examines how conversational AI and games compete for attention, highlighting different user needs and social dynamics.
Examines whether model merging can outperform averaging in DiLoCo aggregation while balancing communication costs and final performance.
AI coding agents may raise productivity while reducing developer understanding, retention, and long-term problem-solving capacity.
How to separate session, RAG, and model parameter paths in generative AI to design confidentiality, deletion, and audit controls.
A concise look at FreqDepthKV, a method targeting KV cache bottlenecks in long-context LLM inference.
Using 141-country employment data, this piece explains why frontier AI exposure varies by job mix, productivity potential, and labor risk.
Applying LLMs to SSH research requires checking multilingual corpora, knowledge graphs, evaluation, bias, and governance together.