Ontology-Guided KGQA Cuts Noisy Multi-Hop Reasoning Paths
How ontology constraints reduce noisy paths in multi-hop KGQA and improve reasoning for complex queries.
How ontology constraints reduce noisy paths in multi-hop KGQA and improve reasoning for complex queries.
How a speech-based cognitive impairment framework turns SHAP and linguistic features into clinical explanations for usability.
A position paper argues LLM unlearning should mean dataset-defined deletion, not output suppression or behavior editing.
How formalized policies can deterministically govern agent tool calls beyond probabilistic prompt steering and filters.
How single-run LLM benchmarks can miss usable performance, and why model choice, retries, and cost matter.
Why reused coding agent config files can become an unmanaged control layer with security and operational risks.
In financial recommendations, linking anonymous web sessions and logged-in app behavior requires explainability and privacy checks before performance gains.
A study on filtering infeasible motion attempts in cluttered scenes using point-cloud predictors before sampling-based planning.
How prompt-level NVC constraints shift LLM safety from toxicity blocking to de-escalation quality, with key tradeoffs.
OpenFinGym shifts financial AI evaluation from single-task accuracy to workflow-level testing across prediction, trading, and risk.
A comparison of SBI and MCMC in SECIR epidemiological models, focusing on posterior agreement, speed, and repeated use.
A look at AgentX and the shift from model changes to automating hypothesis, code, experiment, and analysis loops.
Enterprise AI value is shifting from single-response quality to long-running workflow execution and review gates.
A curated link roundup from recently collected official updates and tech news.
Examines whether emotion vectors in open-weight LLMs are internal representations or merely correlated signals for behavior.
HiLSVA emphasizes plan-first workflows, human oversight, and provenance over full autonomy in scientific visualization agents.
A look at how generative AI earns revenue, why infrastructure costs loom large, and how investment and cloud deals shape profitability.
KARLA explores retrieving facts during token generation, reframing RAG tradeoffs around noise, latency, cost, and attribution.
Examines whether government early access and company-gated previews are turning AI model launches into a de facto permit system.
Why LLM agent privacy risks arise from data flows, memory, tools, logs, and delegated permissions in operation.
AI investment news should be read through official verbs and numbers, not AGI narratives. Build, explore, and assess matter.
Examines the Blind Trust Problem in video reasoning and a reliability-based strategy for frame and tool selection.
How meaning-preserving text substitutions can mislead classifiers and LLM guardrails, and what teams should measure first.
How RAG mixes past and current facts, causing stale-fact errors, and why temporal validity matters in retrieval.