All papers

Quantifies SMT solver runtime instability across program verification queries using the coefficient of variation across multiple domains.

Evaluates LLM judges assessing AI-generated database specifications against trivial baselines using a leakage-resistant benchmark.

Empirically analyzes how refactoring commits correlate with software quality metrics like complexity, code smells, and vulnerabilities.

Introduces an execution-grounded benchmark to evaluate code generation for real-world web service and API calls across multiple models.

Evaluates coding agents on generating full application test suites from specifications using a benchmark of open-source projects and mutants.

Uses agentic reasoning over program concurrency semantics to steer thread scheduling and reproduce elusive concurrency bugs.

Proposes an agentic workflow that drafts and maintains Architecture-as-Code documentation with mandatory human review gates in the IDE.

Introduces an automated environment generation pipeline to synthesize diverse runnable desktop GUI software applications for training computer-use agents.

Introduces a configuration-driven platform for reproducible multi-agent reinforcement learning experiments within the ARGoS swarm robotics simulator.

Argues conceptually that unsupervised machine learning serves four distinct goals rather than a single unifying objective.

Introduces a spectral weight decay update inducing low-rank weight matrices to improve SVD-based post-training model compression.

Presents a correlation-based multiplicative weight update method for training morphological neural network layers without standard gradient backpropagation.

Introduces an attention-aligned recurrent architecture that maintains persistent state representations across sequences using rolling low-dimensional traces.

Derives mathematical dimension thresholds required to compute Boolean gates using neural representations stored in superposition.

Proposes label-free test-time adaptation using spatial redundancy to improve respiration sensing on massive MIMO base stations against interference.

Reviews literature on adaptive combinatorial optimization combining machine learning policies with quantum computing workflows.

Presents a retrieval-augmented genomic language model scaling pretraining context length to capture long-range biological sequence interactions.

Analyzes optimal random quantisers for high-dimensional spherically symmetric distributions to reduce distortion with moderate sample sizes.

Develops an onboard inertial and throttle sensor detector with false-alarm controls to identify alcohol-impaired e-scooter riders during trips.

Compresses tabular in-context learning key-value caches into query-calibrated prototypes to improve throughput without sacrificing accuracy.

Analyzes the infinite-prompt limit of softmax attention on Gaussian mixtures to explain when attention behaves linearly or selectively.

Models self-improving systems with symbolic archives as dynamical processes, establishing theoretical optimization bounds and empirical stability.

Evaluates a pipeline combining speaker diarization and self-supervised speech representations to detect dysarthria during awake craniotomy procedures.

Establishes theoretical sample complexity bounds for posterior sampling in noisy one-bit compressed sensing under prior distribution mismatch.

Examines the disparity between reachability and self-reported parameters in an LLM-driven audio interface for timbre generation.

Proposes a dynamic structural pruning method using biologically inspired plasticity to reduce neural network parameters during training.

Develops a kernel Stein discrepancy framework using operator-valued kernels to assess conditional goodness-of-fit for unnormalized models.

Analyzes how latent-dimension scaling impacts dynamical-system learning and rollout error using Koopman operator spectral reliability metrics.

Establishes non-asymptotic statistical error bounds and optimization convergence for learning structured linear dynamical systems from highly subsampled observations.

Proposes a market-level mixture-of-experts coordination framework using cost-aware gating and revenue allocation across heterogeneous model services.

Presents a probabilistic framework to disaggregate coarse socioeconomic observations into fine spatial scales using satellite embeddings.

Introduces a continuous-time causal prior-data fitted network simulator to estimate market impact without confounding biases.

Provides theoretical guarantees for efficiently learning Cox survival analysis models using first-order optimization under outcome-dependent truncation and censoring.

Develops a low-cost piezoresistive glove system that aligns human touch demonstrations with robot sensors via contact events.

Presents a score-based method to learn cluster causal directed acyclic graphs from interventional data under linear Gaussian assumptions.

Presents scalable methods using distance sketches and random feature embeddings to efficiently approximate quadratic entropy across large distributions.

Learns linear shape parameterisations from past designs using PCA to improve sample efficiency in Bayesian shape optimisation.

Analyzes test-time compute strategies, including parameter adaptation and prediction aggregation, to improve predictions in tabular foundation models.

The paper proposes ghost tasking, adding auxiliary tasks to enable parametrized Gaussian processes for non-parametrizable linear differential equations.

The paper introduces CausalDreamer, disentangling world model latent representations along controllability and reward relevance dimensions for control.

The paper develops an active-set likelihood framework for archetypal analysis on discrete binary, count, and categorical data.

The paper analyzes Gaussian diffusion sampling to show how time-dependent trajectories eliminate condition number dependence seen in Langevin dynamics.

The paper combines holomorphic neural networks with singular basis functions to solve 2D and 3D Laplace boundary-value problems.

The paper proposes using simulator output gradients within Bayesian active learning to infer input parameters of expensive differential equation simulators.

The paper introduces Differentiable Systematic Resampling, a smooth relaxation enabling gradient-based optimization in variational sequential Monte Carlo.

The paper introduces SCORE, an efficient spectral framework for approximating and estimating high-dimensional structured Gaussian covariance matrices.

The paper develops a neural density functional preserving physical symmetries to model ion correlations in inhomogeneous polymer electrolytes.

The paper evaluates 1,368 quantum neural network configurations on financial time series forecasting using spot exchange rate data.

The paper proposes a Bayesian optimization approach that uses linear response approximations to maintain state-preservation constraints in PDE systems.

The paper introduces MA-OPS to achieve optimal regret in adversarial Markov decision processes subject to stochastic hard constraints.

The paper investigates synthetic graph generation and introduces TS-GGAD to train generalist graph anomaly detection models without real-world training graphs.

The paper introduces DataSense-Bench to evaluate whether AI agents can select and rank effective training data subsets for fine-tuning.

The paper proposes DI-SCUSUM, a training-free quickest change detection method using diffusion-smoothed empirical score differences.

The paper proposes ISBO, combining log Gaussian Cox processes with the INLA-SPDE approach for scalable spatio-temporal Bayesian optimization.

The paper audits test-time adaptation methods for time-series forecasting under realistic, delay-aware conditions to evaluate actual streaming performance.

The paper introduces RIFT, a deterministic, parameter-free anomaly detection algorithm based on minimum spanning trees inspired by Isolation Forests.

The paper presents AdaptLSTM, an adaptive online learning method that selectively updates cloud workload forecasts upon detecting distribution drift.

The paper examines how multi-agent Q-learning movement policies generate spatial clustering and traveling bands in spatial public goods games.

The paper develops an online-to-nonconvex conversion framework for stochastic nonsmooth nonconvex composite optimization achieving optimal oracle complexity bounds.

The paper presents asdex, an automatic sparse differentiation library in JAX that uses graph coloring to compute sparse Jacobians efficiently.