All papers

A two-level prioritization framework evaluates semantic novelty and reliability of disease trajectory patterns extracted from electronic health records.

PolyNets embeds continuous polynomial equations into recurrent neural structures to perform fast, training-free physics simulations with known parameters.

The paper demonstrates that simple feature propagation and shallow classifiers yield competitive baselines compared to complex architectures in relational deep learning.

DualGCN uses dual-branch graph knowledge distillation with topology-smoothed semantic priors to address item cold-start collapse in recommender systems.

The paper investigates the emergent dynamics of the Continuous Thought Machine during image classification using non-normal maps and fixed-point curves.

The paper investigates novelty and anomaly detection techniques across rain gauge sensor networks and computer vision scenarios.

The paper benchmarks how detector architectures, model scale, and crowd density influence multi-pedestrian tracking performance in video sequences.

The paper proposes a hierarchical two-loop optimization framework that uses residual errors to trigger active evidence acquisition during inference.

The paper introduces topological regularization to align latent spaces across independently trained autoencoders without inter-model communication.

The paper proposes a deep unfolding network integrating manifold optimization for unsupervised feature selection in image processing.

Proves theoretically that self-conditioning enables genuine multi-step reasoning in flow matching where standard samplers fail.

Adds content-addressable associative memory to latent world models to prevent collapse and improve long-horizon planning.

Extracts generative image priors directly from the loss-gradient geometry of pretrained discriminative models without retraining.

Develops a closed-loop distillation pipeline where student feedback refines teacher models to improve adversarial robustness.

Trains a reinforcement learning controller to manage frame memory dynamically for visual object tracking in foundation models.

Applies low-rank continual pretraining to vision transformers to improve task alignment in prostate magnetic resonance imaging segmentation.

Uses contrastive uncertainty learning during training to improve anatomical boundary segmentation in pelvic magnetic resonance imaging.

Investigates the interaction between learnable image resizing and spectral-domain convolutional filter parameterization in neural networks.

This paper develops a self-supervised representation learning approach based on pointwise mutual information factorisation for scalable generative latent tokenization.

This paper proposes a mixture-of-experts transformer architecture using discrete Hodge-Laplacian operators to solve steady partial differential equations on unstructured meshes.

This paper investigates 36 handcrafted physiological markers from speech production to detect deepfake speech using standard classifiers.

This paper proposes an adaptive Gaussian mixture model framework to detect physical-layer channel state information spoofing attacks in wireless localization.

This paper analyzes the mathematical limits of statistical safety certification for embodied and agentic AI facing adaptive adversaries.

This paper examines the verifiability gap in quantized model supply chains, showing that common GGUF artifacts lack necessary build environment disclosures.

This paper constructs an empirical graph of 62 private transformer inference systems to evaluate comparability, cryptographic progress, and scaling behavior.

This paper analyzes latent backdoor poisoning in flow-based intrusion detection and introduces a perturbation-based diagnostic and mitigation methodology.

The paper introduces a token-level sparse attention method using a learned query-key subspace and an indexed kernel to accelerate long-context decoding.

The paper analyzes gendered reference replacements across science fiction and general fiction corpora using a masked language model.

The paper analyzes intermediate training checkpoints to trace when language models acquire morphological structures versus semantic embeddings during pretraining.

The paper examines using pre-trained language models without task-specific training for social science record linkage and demographic inference from names.

The paper presents a four-step methodological procedure to integrate ontology-guided coding and topic modeling representations of qualitative text data.

The paper proposes a method combining offline execution-verified prompt updates and test-time refinement to improve program generation in language models.

The paper introduces a training-free sampling framework that uses internal model activations to efficiently estimate rare failure probabilities in language models.

The paper proposes a parameter-efficient method leveraging frozen model representations for multi-output text regression tasks across diverse benchmarks.

The paper presents an approach using character-level tokens, larger models, and synthetic data for the multilingual BabyLM data-efficiency challenge.

The paper formulates task routing across language model providers as an auction mechanism to encourage truthful cost reporting while minimizing context switching.

The paper introduces a multi-stage topic modeling pipeline that merges fine-grained document clusters using a language model to balance efficiency and quality.

The paper analyzes prefix replay curricula and handoff rules under reinforcement learning to improve long-horizon task completion for language model agents.

The paper presents a vision-language policy combining pre-aligned representations and multi-stage learning to improve robotic person tracking among distractors.

The paper introduces a framework that decomposes user feedback into atomic rules to retain only reusable preferences for personalizing language models.

The paper introduces a reinforcement learning framework using intermediate-state branching and segment-level credit assignment for diffusion language models.

The paper studies how visual input signals affect scaling behavior and non-visual reasoning during native multimodal pretraining.

The paper proposes an agentic framework enabling multimodal models to identify information gaps and retrieve in-context demonstrations iteratively.

The paper presents adapted small language models designed to directly score agricultural candidate choices in a single forward pass.

The paper develops a streaming end-to-end inverse text normalization architecture using a pretrained text-to-text model with a read-tag-write policy.

The paper proposes a decoding algorithm that denoises consecutive blocks concurrently by promoting sampler states in diffusion language models.

The paper presents an item response theory framework to jointly evaluate student performance on standard questions and tutoring dialogue subtasks.

The paper introduces a threat modeling framework examining adversarial query demotion and graph edit attacks on graph-based RAG systems.

The paper presents a method using an external encoder to induce latent soft-token reasoning on frozen language model backbones.

The paper introduces a multi-agent framework that reframes harmful requests using Moral Foundations Theory to test model jailbreak vulnerabilities.

The paper proposes a prefix-augmented distillation method providing one-step student branch feedback during language model pretraining.

The paper proposes a pair-norm guided mixed-precision quantization method for key caches during long-output transformer inference.

The paper proposes an interface for typed decision models that selectively invokes and interrupts reasoning to balance accuracy and latency.

The paper introduces a soft margin preservation target for supervised fine-tuning to retain non-target token rankings.

The paper proposes a training-free interface for heterogeneous language models to communicate facts using shared symbolic word indices.

The paper introduces a unified routing framework that learns both execution mode selection and model assignment across diverse tasks.

The paper analyzes retrieval failures in Mamba architectures under query-first settings and explores token representations and selection gates.

The paper presents a formal spatiotemporal framework to specify and verify road vehicle safety rules across continuous trajectories.

The paper proposes a relation-semantic reasoning framework to resolve disconnects between answer reachability and valid paths in biomedical graphs.

The paper presents a four-stage training pipeline combining preference optimization and distillation for instruction-following machine translation.