The Tool Response Is Not Your Boss
AgentRedBench shows that enterprise AI-agent risk is less about naughty chat prompts and more about untrusted SaaS content quietly steering authorized tool actions.
AgentRedBench shows that enterprise AI-agent risk is less about naughty chat prompts and more about untrusted SaaS content quietly steering authorized tool actions.
A mechanism-first reading of why EEG-to-music reconstruction improves when models preserve electrode-level structure before alignment and generation.
IterCAD shows why reliable CAD automation depends less on one-shot generation and more on closed-loop execution, visual feedback, and survivor-bias-free evaluation.
A mechanism-first reading of an LLM-orchestrated stroke-care conformance pipeline, and what it teaches operators about turning unstructured policy into auditable process checks.
Two recent papers show why serious claims about AI cognition require evidence of internal organization, not just fluent behavior or attractive labels.
Two very different AI papers show the same operational lesson: reliable systems work when each stage uses only the signal it can actually trust.
A practical framework for diagnosing whether AI performance failures come from data, structure, context, architecture, or adaptation calibration.
A mechanism-first reading of LyraV: why real-time video assistants need synchrony control, not just stronger video QA.
A compute-allocation reading of audio-model scaling: when to buy model capacity, when to buy context, and when to stop pretending LoRA fixes everything.
GLAM shows how heterogeneous robot demonstrations become useful only when their effects are grounded into a target-executable latent action space.