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Can any finite formal system autonomously derive every theorem within its expressive scope?
A system may be able to express a theorem without having an autonomous procedure that produces it. The central issue is whether finite formal systems can be complete with respect to the theorems they can express.
Can chain-of-thought monitoring detect consequential computation hidden in semantically irrelevant filler tokens?
Language models may gain task performance from semantically irrelevant filler tokens without making the relevant computation interpretable in their visible reasoning. This complicates the use of chain-of-thought as evidence of what a model has computed.
Can compact pretrained brain MRI models transfer across Alzheimer’s tasks and cohorts without task-specific retraining?
Limited labeled neuroimaging data makes task-specific deep learning difficult. It remains uncertain whether features learned for one brain MRI task generalize to different Alzheimer’s-related tasks and cohorts.
Can deterministic artificial affective processing produce hedonic place preference without conscious feelings?
Hedonic place preference is often treated as evidence of feeling because attraction to non-nutritive rewards seems difficult to explain as mere instinct. The problem is determining whether an artificial system can reproduce this behavior through affective information processing without subjective experience.
Can Gaussian-width restricted-eigenvalue guarantees survive heavy-tailed measurements under only a uniform small-ball condition?
Restricted eigenvalue bounds support stable recovery, but heavy-tailed measurements can make empirical control depend on simultaneous threshold occupancy rather than geometric width alone. This creates a gap between Gaussian-design behavior and what uniform small-ball assumptions can guarantee.
Can language-based models replace specialized architectures for structured data without sacrificing structural representation and computation?
Task-level accuracy can look competitive even when a model does not preserve or compute the structure that makes structured-data problems tractable. This makes architectural replacement difficult to judge from predictive performance alone.
Can multimodal chest-radiograph triage trained on NLP-derived labels reliably match expert severity judgments?
Chest-radiograph triage must distinguish urgent examinations from routine ones, but labels extracted from reports may not capture radiologists’ severity judgments. Strong benchmark performance can also coexist with visual explanations that do not localize clinically relevant findings.
Can non-smooth or quantized activations support stable echo-state dynamics beyond conventional spectral-radius expectations?
Echo state network stability is often analyzed using smooth activations and conservative spectral-radius conditions. Irregular or quantized activations may change how reservoir states contract, remain distinct, or converge, but their stability behavior is not fully understood.
Can one squared-loss estimator achieve both minimax and universal exponential rates for finite versus countably infinite hypothesis classes?
Model-selection aggregation seeks minimax excess-risk guarantees, while universal learning seeks exponential rates. Whether one estimator can provide both depends on whether the hypothesis class is finite or countably infinite.
Can post-training ternarization make language models smaller without unacceptable capability loss or slower inference?
Ultra-low-bit weights can shrink model storage, but nominal bit counts may not reflect the stored representation, uneven task degradation, or actual inference speed. Compression may therefore improve footprint without improving end-to-end deployment performance.
Can pre-generalization interventions reveal when training constrains which equally fitting neural-network solutions will later generalize?
Overparameterized networks can fit the same training data while differing substantially on unseen examples. During grokking, generalization emerges after a plateau, making it difficult to determine when training has begun constraining the eventual solution.
Can preprocessing defenses detect adversarial attacks in depthwise-separable edge vision CNNs when they cannot restore predictions?
Preprocessing defenses are often assumed to transfer across model architectures, but depthwise-separable CNNs may respond differently to adversarial perturbations than residual or Inception-style networks. Their failure to recover predictions may still produce measurable differences between clean and adversarial inputs, while image-quality scores may not reflect defensive value.
Can prompt phrasing reliably improve LLM-derived chemical features for drug-toxicity prediction?
Minor changes in prompt phrasing can alter LLM outputs, making it unclear whether prompt optimization produces stable chemical features for toxicity models. This variability complicates the use of LLM-generated features in a costly drug-development process.
Can quantitative attribution metrics show where facial-video rPPG models read pulse signals without indicating heart-rate accuracy?
Facial-video remote photoplethysmography models estimate pulse from short clips, while attribution maps are often interpreted as evidence of what the model uses. Localization of skin regions may not correspond to accurate heart-rate estimation.
Can reducing the complexity of a linear generative prior improve expected reconstruction error in noiseless Gaussian compressed sensing?
Compressed sensing can use a family of linear generative priors with different effective complexities, but restricting that prior may affect reconstruction accuracy in ways that differ from ordinary denoising. The key issue is whether lower-complexity priors reduce expected error in the noiseless setting.
Can scaling vision-language models overcome their limitations in neurosurgical tool detection?
Neurosurgical tool detection requires specialized data and expert labeling, while larger models and longer training demand substantial computational resources. It remains unclear whether adding these resources produces meaningful gains or leaves important limitations unchanged.
Can stochastic weight averaging improve equivariance in augmented classification without repeated ensemble training?
Data augmentation incorporates task symmetries into neural networks, while deep ensembles can require many separate training runs. The practical difficulty is determining whether weight averaging can provide stronger symmetry handling without that repeated cost.
Can tabular foundation models learn transferable physical laws with units and noiseless mechanisms, not just interpolate data?
High predictive accuracy on equation-generated tables may reflect interpolation rather than representation of governing physics. Physical modeling also requires handling units and noiseless mechanisms, which table completion may not capture.
Can text-to-image models match web-scale performance using smaller, reproducible datasets and models?
Billion-scale web-scraped datasets can make text-to-image results difficult to reproduce because their contents and availability change. The central difficulty is determining whether a much smaller, standardized image collection can retain the capabilities associated with those models.
Do gains from fine-tuned genomic variant-effect prediction transfer to zero-shot functional sequence inpainting?
Genomic models can score variants after supervised fine-tuning yet fail to reconstruct biologically functional sequence in zero-shot settings. This leaves unclear whether a masking prior that helps one objective captures the constraints needed for the other.
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