HiL-Bench shows that production AI agents often fail not from weak capability, but from poor judgment about when to ask humans for missing context.
A mechanism-first reading of why LLM agents coordinate brilliantly when sameness is useful, yet struggle when valuable systems need them to stay different.
A mechanism-first reading of how frozen language models can be composed through latent-space communication, what the benchmark gains actually support, and where the idea is still fragile.
A mechanism-first reading of Phantasia, a context-adaptive backdoor attack showing why plausible multimodal outputs can be more dangerous than obvious failures.
A mechanism-first reading of SinaSarc: why Chinese sarcasm detection improves when models learn not only the sentence, but the user behind it.
A mechanism-first reading of MOSAIC, showing how scaling-aware data selection turns AI training data from a volume problem into a marginal-utility allocation problem.
PokeGym shows why embodied VLMs fail less from abstract reasoning limits than from brittle visual-control loops, deadlock recovery, and weak spatial execution.
InstAP shows why fine-grained video AI needs instance-aware training objectives, not just larger caption datasets.
A mechanism-first reading of Cascade shows why fault-tolerant quantum resource estimates may depend as much on decoder capacity as on qubit count.
CivBench shows why serious agent evaluation needs progress signals, not just final scoreboards.