Claims Browser

No surveyed accountability instrument resolves five identified structural tensions in LLM governance

"five structural tensions that no surveyed instrument resolves"

Image classification is too simplistic as a proxy for evaluating privacy-enhancing technologies across generic vision tasks

"This trade-off is commonly evaluated using image classification, which primarily captures semantic separability and remains robust despite significant geometric, spatial layout or local boundary alterations. As a result, it is too simplistic as a proxy for generic vision tasks."

Existing continual learning methods for LLMs that constrain parameter updates or introduce task-specific modules are limited by their reliance on explicit task boundaries during training, making them inapplicable to realistic task-free scenarios

"existing methods typically constrain parameter updates or introduce task-specific adaptation modules. However, these methods often rely on explicit task boundaries during training, limiting their applicability to realistic task-free scenarios."

Existing video diffusion model methods for image-to-scene generation rely on incomplete conditioning signals, leading to stochastic hallucinations, long-term drifts and suboptimal 3D consistency

"Existing methods based on video diffusion model (VDM) commonly rely on incomplete conditioning signals such as sparse point clouds or 2D panoramas, leading to stochastic hallucinations, long-term drifts and suboptimal 3D consistency."

Advances in I2V models introduce new safety risks that existing studies have largely overlooked, particularly in the temporal dimension

"However, these advances also introduce new safety risks. Existing studies mainly focus on jailbreak attacks involving single frame violations, while largely overlooking the temporal dimension unique to video generation models."

Standard Transformers scale down poorly to limited data settings because embeddings consume too large a fraction of parameters and per-token computation is coupled with representational capacity

"We argue that standard Transformers scale down poorly to this setting, because embeddings consume a large fraction of the parameter budget and per-token computation is tied to representational capacity."

Existing extraction methods do not specifically target LLM judges and provide limited support for multiple evaluation protocols under restricted query budgets

"Existing extraction methods do not specifically target LLM judges and provide limited support for multiple evaluation protocols under restricted query budgets."

Parallel analysis-derived component counts and decisions reflect arbitrary hidden-coordinate choices rather than well-defined model properties

"parallel analysis-derived component counts and decisions can reflect hidden-coordinate choice rather than a well-defined property of the model"

The entanglement of β's roles in DPO obscures its function, increases sensitivity to hyperparameter choices, and complicates learning-rate scheduling

"This entanglement obscures the role of $β$, increases sensitivity to hyperparameter choices, and complicates learning-rate scheduling"

Traditional credit risk assessment methods fail to differentiate between temporal patterns indicative of credit risk and those reflecting general customer behavior, leading to suboptimal risk predictions

"Specifically, they fail to differentiate between temporal patterns indicative of credit risk and those reflecting general customer behavior or preferences, leading to suboptimal risk predictions."

A prior work by Song, Ye, Yin and Zhang claiming to improve row count to O(ε^-2 d log^3 n) has a flawed proof that relies on an independence assumption that does not hold in general

"A subsequent work of Song, Ye, Yin and Zhang claimed to improve the row count to $O(ε^{-2}d\log^3 n)$. Unfortunately, their proof relies on an independence assumption that does not hold in general, and we exhibit an explicit instance on which it fails."

Current guided proposal methods have a weakness in estimating gradients from single noisy samples

"the guided proposal estimates its gradient from a single noisy sample"

Current search methods have a weakness in resampling particles at fixed temperature that ignores reward distribution across denoising steps

"the search then resamples particles at a fixed temperature that ignores how rewards spread across each denoising step"

Most ASR advances remain concentrated on high-resource languages while indigenous languages lack speech resources and language technologies

"most advances remain concentrated on high-resource languages, while indigenous languages continue to suffer from a lack of speech resources and language technologies"

Concept-based explainability methods can mistake correlations between concepts as evidence that the model uses them because they evaluate each concept in isolation

"Because each concept is evaluated in isolation, these methods can mistake correlations between concepts as evidence that the model uses them."

FID's first-two-moment summary can miss distributional differences between generative models and real data

"FID's first-two-moment summary can miss distributional differences"

FID and KID cannot distinguish between under-dispersion (mode collapse) and over-dispersion because they are symmetric scalar discrepancies

"FID and KID are scalar discrepancies that are unchanged when the two samples are exchanged and therefore do not encode the direction of a dispersion change: under-dispersion, as can occur in mode collapse, versus over-dispersion"

There is currently no systematic plan for how society will address AI

"The urgent need for—and lack of—a systematic plan for how society will address AI"