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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 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 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 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 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 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.
Does reusing Transformer layers improve language-model quality when parameter, compute, and KV-cache budgets are matched?
Layer looping increases effective computation by revisiting shared parameters, but comparisons can mistake extra computation or memory for an architectural improvement. The central difficulty is isolating the effect of reuse while holding major training and inference budgets constant.
For a known tensor-network graph, which structural parameters control MPS/TTN overhead and tomography complexity?
The same tensor-network state can require different resources when represented as an MPS or TTN, depending on the structure of its underlying graph. That structure also affects how much data and computation are needed to learn the state, including when the input is not exactly representable by the chosen network.
How can 3D class-incremental models learn new categories while remaining robust across heterogeneous point-cloud domains?
As 3D models learn new object categories over time, point clouds from CAD models, scans, reconstructions, and corrupted observations can respond differently to continual updates. Standard catastrophic-forgetting measures may miss this domain-specific performance discrepancy.
How can 3D Gaussian splatting reconstruct large aerial surfaces without cross-region seams or local geometric inconsistencies?
Aerial scenes may be divided into independently optimized regions, breaking continuous surfaces and creating stitching artifacts. Constraints centered on individual Gaussians and fixed regularization can also fail when local geometry varies between structured and unstructured areas.
How can 3D Gaussian Splatting training stay efficient as high-resolution scenes require more Gaussian primitives?
During 3D Gaussian Splatting optimization, the number of Gaussian primitives can keep increasing, raising the cost of each training run. Higher-resolution scenes intensify this burden and can slow convergence.
How can 3D occupancy models learn from noisy 2D pseudo-labels without 3D annotations?
2D pseudo-labels may contain both depth errors and semantic mistakes. Projecting these imperfect targets into 3D can propagate inaccuracies through the predicted occupancy field.
How can 3D tokenizers preserve reconstruction fidelity with extremely short token sequences?
Existing 3D tokenizers can lose substantial reconstruction quality when their latent representations are compressed to extremely low token budgets. Spatial representations and fixed-size global-token sets may both struggle to preserve complete object geometry under this constraint.
How can 3D UV unwrapping produce semantically coherent seams while keeping parameterization distortion low?
Geometric UV-unwrapping methods can reduce parameterization distortion without producing visually meaningful seam layouts. Generative methods may improve semantic coherence but can misread local mesh topology, leading to inaccurate cuts.
How can 70B language models fit on one GPU while preserving long-context speed and accuracy?
A 70B model must fit its weights and growing KV cache within one GPU’s limited memory. Long prompts make compression choices affect both decoding speed and model accuracy.
How can a navigation map encode heterogeneous route costs compactly while answering new goals without retraining?
A reusable map must preserve nonuniform additive edge costs without storage that grows too quickly as the environment expands. It must also support new goal queries from one learned representation rather than rebuilding or retraining for each goal.
How can a single graph-learning model handle text-, image-, and multimodal-attributed graphs?
Attributed graphs may contain textual node features, visual node features, or both, while many graph-learning models assume one fixed modality schema. Supporting these settings separately makes reuse across graphs and modality configurations difficult.
How can advertising enter token-by-token generated responses while preserving incentive compatibility and response quality?
Generated responses do not offer the fixed advertising slots assumed by conventional mechanisms, so advertising may need to influence the generation process itself. That influence must not compromise truthful advertiser participation or the usefulness of the response.
How can aerodynamic surrogate models predict vehicle pressure and wall shear stress without losing global flow or local geometric effects?
High-fidelity CFD can make early-stage vehicle design exploration too costly. A useful surface-field predictor must represent both long-range aerodynamic coupling and geometry-induced local variation.
How can aerodynamic surrogates trained on CFD be grounded in scarce wind-tunnel measurements without retraining?
CFD-trained aerodynamic surrogates can reproduce numerical predictions while retaining systematic discrepancies from wind-tunnel observations. The practical difficulty is using limited experimental measurements to correct those discrepancies without discarding the surrogate’s existing capabilities.
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