A shift from IDE plugins to terminal-native CLI coding agents, highlighting AGENTS.md and context pipelines that shape reliability and verification loops.
AgentSelect defines narrative-query to end-to-end agent configuration recommendation, proposing a benchmark with queries, agents, and interactions.
CoT perturbations can sharply reduce accuracy. Unit conversion remains hard at scale; isolate checks and use self-consistency.
Examines multi-rater 3D lesion segmentation, limits of vanilla diffusion, and VDD anchored to consensus priors improving GED/CI.
GIPO targets scarce, stale interaction data by replacing hard importance-ratio clipping with log-ratio Gaussian trust weights for stable reuse.
Reframes agentic AI failures as governance issues, proposing dual-helix governance with a Knowledge/Behavior/Skills architecture.
LLM-based conversational recommenders may infer sensitive triggers from dialogue, risking personalized safety violations unless constraints are enforced.
PlugMem externalizes long-term memory as a plug-in to reduce retrieval bloat and relevance loss, while highlighting persistent injection risks.
As AI enters battlefield planning, HITL, TEVV validation, auditability, and accountability design matter more than raw performance.
Why AI performance gains don’t instantly raise productivity, and how to close the lag using task scores and NIST AI RMF.
Resizing, tiling, and tokenization can shift what models see, turning map/geography misreads into repeatable product risk.
A Pentagon contract dispute highlights how AI safety guardrails become enforceable via contract terms and deployment controls.
How whitespace, Unicode normalization, and token boundaries can look like reasoning failures, and how to control evaluation setups.
Examines how LLM-generated target queues and prioritization can steer human selection, shaping autonomy boundaries, auditability, and control.
A decision memo separating reasoning, long-term memory, and continual learning into testable metrics to reduce AGI narrative confusion.
Use Roofline (I ≤ π/β) to classify LLM inference kernels as memory- or compute-bound, and guide bandwidth, cache, and interconnect decisions.
How hidden sampling controls and unreliable web search can raise hallucination risk and verification costs in paid AI chat.
Remote sensing lead time drops by narrowing candidate areas, prioritizing HITL review, and measuring preprocessing, co-registration, and QA.
Reporting exists, but unclear SLA, ownership, and evidence requirements for imminent threats make operational protocols central to AI safety.
How AI integration speeds weapon decision cycles and raises escalation risk, with safeguards in DoDD 3000.09 and NIST AI RMF.
In high-risk deployments, prioritize uncertainty, false positives/negatives, and closed-loop failure propagation over single-model scores.
CleaveNet predicts and generates peptides from cleavage efficiency across 18 MMPs, linking designs to nanoparticle urine sensors.
Domain shift, post-processing, and adversarial attacks weaken detection. Treat scores as evidence and add provenance and stress tests.
A Korean word-chain mini-benchmark using “checkmate” words to separate rule-following, admitting impossibility, and fake-word evasion across reasoning_effort settings.