Machine-generated text is becoming a routine part of the web. Most detectors, however, are still evaluated on curated datasets rather than real web content. We introduce PAWN++, a compact supervised detector that learns which likelihood signals from two frozen language models are most informative. On MAGE, our method reaches 98.36 AUROC in distribution and 98.42 under a joint domain and generator shift. It also transfers well to external benchmarks without target-specific training.

To test whether this robustness extends to the web, we construct WATTLE, a benchmark of 41,300 texts in which each machine output is tied to its original human web page. The resulting data capture realistic transformations of web content as well as variation across languages and time. We use WATTLE to compare our method with seven other detectors. Performance deteriorates sharply on realistic rewrites: our method reaches 70.1 AUROC overall and 58.1 on text produced by a frontier model. Likelihood-based detectors fall below chance, and no evaluated system identifies more than 12% of frontier rewrites at a 1% false-positive rate. These results expose a substantial gap between standard evaluation scores and reliable detection on the web.

Embodied Vision–Language–Action (VLA) models are typically obtained by fine-tuning powerful pretrained VLMs on robotics data. Yet we do not know whether these agents still "know the basics" about the world after adaptation, nor how much commonsense and factual knowledge is preserved versus catastrophically forgotten. When a VLA fails on a knowledge-sensitive task, it is unclear whether the failure reflects degraded world knowledge or merely poor generalization of low-level control. We introduce Act2Answer, a lightweight protocol that adapts VLM knowledge benchmarks to VLA evaluation by requiring agents to act to answer. We map each benchmark question to a discrete embodied answer-selection decision in a controlled tabletop environment, where the agent answers via a single object-placement action into the region corresponding to its selected option, producing an action-grounded success rate with reduced control confounds. We introduce layerwise intent probing–linear classifiers on per-layer representations predicting the agent’s option selection–to localize answer-relevant information across model depth. Evaluating 9 state-of-the-art VLA models, we rank them across basic commonsense and world-knowledge categories and find that answer-relevant signals peak in middle VLM-backbone layers but attenuate in upper backbone layers and the action head.

Latent context compression replaces a document with a short sequence of learned vectors, but whether this beats simply keeping raw tokens under the same decoder-context budget is rarely tested. We run that test on held-out document QA, matching budgets per example against raw-token truncation and selector-specific readers trained on first-N, LLMLingua-2, and question-conditioned BM25 contexts. Our minimal compressor consists of a text encoder, learned pooling, and direct projection. It beats every tested common-cap retention baseline at nominal pooling factors cr=8 and cr=16, reaching answer perplexity 3.464 versus 5.349 for the strongest baseline at cr=8. The advantage persists in first-sentence free generation, and a manual check of answers is directionally consistent. Adding a post-pooling transformer hurts with twelve layers and with one layer. The twelve-layer result holds in the discovery run, two replications, and a second decoder family. Removing the post-pooling transformer also makes the compressor smaller and faster. Learned pooling followed by direct projection provides the strongest quality and efficiency among the tested compressor designs.