Humanoids, autonomy, and embodied AI.
Hub content is updated incrementally.
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.
Key issues in the MiniMax report: a rumored 2.7 trillion-parameter LLM, possible open weights, licensing, and inference costs.
RAID found six scoring exploits in NHL 26 goalie AI in one run, highlighting automated QA and reusable red-team testing.
SPEAR links Unreal Engine with Python, targeting 73 fps rendering and 14K+ exposed functions for research workflows.
Korean LLMs are better judged by naturalness, pragmatic understanding, and instruction following than by one rank.
How contextual inputs and shared recurrence aim to control diverse robot morphologies with one policy across zero-shot and sim-to-real tests.
A look at interpreting transformer-based VLM adversarial vulnerability through intermediate spectral subspaces.
Examines whether closed government-company talks are enough to judge frontier AI release safety and accountability gaps.
A curated link roundup from recently collected official updates and tech news.
A look at an arXiv paper proposing continual learning for adaptive control of modular soft robots under morphology changes.
Gimitest is an open-source framework for testing RL policies under changing conditions to uncover failures and vulnerabilities.
Why agentic AI governance must cover autonomy, tool use, external actions, audit logs, and human oversight.
HIVE evaluates how vision-language hallucinations propagate into later reasoning and distort downstream predictions.
An overview of PCBWorld, a KiCad-based environment for evaluating PCB routing AI with native actions and DRC feedback.
Examines whether reusable skill files improve quality, auditability, and operations in repetitive AI data science tasks.
VASP Agent targets reliable scientific automation by combining input consistency, long-run supervision, and output validation.
Why backend evaluation should prioritize SSOT consistency and catching critical PR-stage defects over raw code generation.
Examines how conversational AI and games compete for attention, highlighting different user needs and social dynamics.
A curated link roundup from recently collected official updates and tech news.
Examines whether individual parameters in sparse transformers carry stable meanings amid polysemantic behavior.
Applying LLMs to SSH research requires checking multilingual corpora, knowledge graphs, evaluation, bias, and governance together.
Why next-generation LLM competition is defined by base-model transition, disclosure scope, and product rollout speed.
Examines how limits on models and features in free vs paid AI can shape practice, feedback speed, and project scope.
