All papers

This paper presents a self-supervised reinforcement learning method that uses hyperspherical representations and spherical interpolation to infer planning waypoints.

This paper proves a computational separation between information-theoretic and polynomial-time regret when tracking small expert subsets in online convex optimization.

This paper introduces a continual learning replay method to incrementally evaluate and adapt molecular communication models across changing experimental settings.

This paper analyzes non-monotone accuracy dynamics during early neural network training when models are initialized with vanishingly small weights.

This paper evaluates whether a pretrained tabular foundation model can perform power grid steady-state analysis without task-specific retraining.

This paper proposes a decoupled marginal sharpening method to improve inference-time reasoning in language models without weight updates or reward models.

This paper provides matching upper and lower bounds for learning near-optimal policies from a single continuing trajectory in average-reward reinforcement learning.

This paper introduces a graph-enhanced framework to discover disease trajectories and forecast temporal clinical endpoints from electronic health records.

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 study evaluates coding agents generating images via executable code, analyzing their strengths in geometric constraints over photorealistic drawing.

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

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

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.

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 survey provides an eight-dimension taxonomy and comparative analysis of hybrid protocols combining post-quantum cryptography with quantum key distribution.

This paper presents TrackShield, a runtime resource-management layer that protects multi-object tracking pipelines from availability attacks by enforcing deterministic admission control.

The authors propose AutoPoly-HE, a framework that automatically selects dataset-tailored polynomial activations for homomorphic encrypted neural network inference.

This paper establishes sample complexity bounds for differentially private estimation of Markov chain transition matrices under item and user privacy.

The authors present a post-quantum authenticated key agreement protocol for decentralized edge peer-to-peer energy trading systems.

The authors present an approach using domain-informed language models to discover semantic logic flaws in open-source drone autopilot software.

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

The authors study whether adaptive and proximal federated optimizers improve the detection of unseen attack classes in network intrusion detection.

This paper introduces DarKnight, a framework that uses locally hosted vision-language models to solve dark web CAPTCHAs during threat intelligence gathering.

The authors derive statistical entropy tests under IEEE-754 validity constraints to detect non-randomness in encrypted stereolithography CAD files.

This paper investigates finite-sample key-recovery attacks against bilinear compressive security using a covariance-based estimation approach.

This paper proposes ESAU, a lightweight swarm remote attestation framework for internet-of-things devices using untrusted cloud proxies.

The authors propose FreeSBee, a compiler pipeline combining taint analysis and automated control-flow transformations to defend against timing side-channels.

This paper proves that two-out-of-two secret sharing is achievable from non-extractable randomness sources, answering an open cryptographic question.

The authors present CTI-TDS, a data selection method using quality filtering and optimal transport to improve instruction tuning for threat intelligence.

This paper examines evaluation practices for Rust security testing tools and proposes a methodology to support scientifically sound assessments.

The authors propose Phantomware, a data augmentation framework designed to generate realistic malware variants for detecting emerging malicious campaigns.

This paper presents Tulva, a modular asynchronous threshold fully homomorphic encryption scheme evaluated for privacy-preserving smart contract applications.

The authors introduce a cost model and metric for evaluating side-channel key rank against an adversary equipped with a quantum computer.

This paper introduces DiffTry, a stateless retry mechanism that transforms retries into request prioritization to prevent microservice overload storms.

The authors introduce DescribeEvent to show that language models can forecast temporal events when using natural-language descriptions and symbolic labels.

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 shows that the Bradley-Terry model fails on Chatbot Arena for similar models, and introduces a parameter-efficient correction for ties.

The paper introduces a benchmark and similarity metric to measure the algorithmic diversity of solutions produced by code generation models.

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 regression-based meta-evaluation framework and benchmark to assess text evaluation metrics for multilingual style-personalized generation.

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

The paper investigates base language model benchmarking setups to identify evaluation configurations that produce consistent, non-random, and monotonic performance signals.

The paper presents a double-annotated sentiment dataset for Nepali and evaluates several open-source and commercial language models on it.