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.
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.
A study on filtering infeasible motion attempts in cluttered scenes using point-cloud predictors before sampling-based planning.
OpenFinGym shifts financial AI evaluation from single-task accuracy to workflow-level testing across prediction, trading, and risk.
Physical AI commercialization depends less on demos than on chip supply, CoWoS packaging, and deployment infrastructure.
A comparison of SBI and MCMC in SECIR epidemiological models, focusing on posterior agreement, speed, and repeated use.
TGHE proposes private graph inference around reusable local structures instead of global graph-dependent costs.
Enterprise AI value is shifting from single-response quality to long-running workflow execution and review gates.
HiLSVA emphasizes plan-first workflows, human oversight, and provenance over full autonomy in scientific visualization agents.
Examines the Blind Trust Problem in video reasoning and a reliability-based strategy for frame and tool selection.
How trustworthy is AI-run psychology automation? Focus on theory coding, data quality control, and replication limits.
Examines whether fixing 3D layout and pose before AI stylization improves animation stability, despite flicker and edit costs.
Why AI's growth benefits and existential risks should be compared within one economic framework, not separate debates.
A framework for evaluating VLM visual search with classic human tasks, using token length and search cost beyond accuracy.
FlowR2A reframes autonomous driving planning from scoring actions to learning reward-conditioned action distributions.
DeepBD highlights grounded LLM workflows for inherited disease diagnosis, emphasizing traceable evidence and recall gains.
Why agent safety must shift from internal prompts and filters to external runtime permission enforcement.
Why treating molecular property scores as deterministic rewards can mislead RL, and how uncertainty-aware design may help.
A look at collision handling, view consistency, and editability in compositional 3D scene generation.
Analysis of whether RL alignment generalizes and persists across 53 OOD evaluations and post-training perturbations.
IV-CoT targets structural prompt fidelity in text-to-image generation by separating layout planning from appearance rendering.
OpenAI and Broadcom's 10GW rollout highlights a shift toward inference-first AI infrastructure and system-level optimization.