Signals, research, and debates around general intelligence and superintelligence.
Hub content is updated incrementally.
Uses the AVO case to show why teams evaluating long-running autonomous agents should prioritize state persistence, failure recovery, execution loops, and supervision over single-call model performance.
Learn how MLREF shifts LLM-based reward optimization from rewriting whole reward programs to reusing reward modules, and use the article’s criteria to decide whether it is worth A/B testing in robotics RL workflows.
Uses a wastewater treatment simulator comparison to clarify when to choose a live simulator oracle, structured parameter injection, or retrieval-based grounding for industrial LLM systems, focusing on accuracy, latency, and portability.
Practical guidance for teams deploying IR-VLMs with thermal sensor inputs, covering how to interpret QR-STT-style structured thermal trigger risks and stress-test classification, captioning, and VQA output stability.
How model-specific refusal thresholds and context handling shape overrefusal, safety boundaries, and response quality.
ConceptSMILE audits concept-based explanations for stability, faithfulness, and consistency under input perturbations.
Enterprise generative AI success depends less on response quality than on data control, access, auditability, and connector governance.
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.
AI coding quality depends not only on output, but on who made key decisions and how requirements, tests, and traceability were controlled.
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.
