All papers

The authors introduce a training loop where multimodal models use text and visual abilities alongside program execution to generate their own training data.

The paper introduces an interactive web-based tool for analyzing and visualizing execution trajectories of software engineering agents.

The paper introduces a method to reduce redundant external knowledge retrieval by estimating what a language model already knows.

This survey reviews in-band telemetry methods and assesses their practical feasibility for monitoring service performance across broadband networks.

The paper applies the sequential minimal optimization algorithm to detect SQL injection attacks using network traffic data.

The paper evaluates ten models for detecting Indonesian AI-generated text and examines how paraphrasing affects detection accuracy.

This paper introduces an adaptive framework to detect radio jamming attacks within machine learning integrated 5G Open RAN architectures.

This position paper proposes multi-agent reinforcement learning and mechanism design to reform academic peer review processes.

This paper presents an agentic framework to compile large multi-modal requirement documents into runnable software systems using test-driven workflows.

This paper surveys foundation models, agent systems, datasets, and computational tools applied to materials science discovery.

The authors introduce a multi-platform benchmark to measure the performance and tool efficacy of code-optimizing agents on high-performance computing kernels.

The study introduces a per-symbol static memory attribution method to analyze SRAM usage between FreeRTOS-C and Embassy-Rust on microcontrollers.

The paper shows that providing whole selected files to coding agents outperforms prompt compression and retrieval under fixed token budgets.

The paper presents an automated framework that revises open-source contribution guidelines by extracting and integrating evidence from code repositories.

The authors propose a method to generate realistic software engineering benchmarks at scale by inverting historical bug fixes into issues.

This case study explores strategies for using automated coding agents to transform external software dependencies into local reimplementations.

The paper examines how different repository instruction categories in agent files affect token efficiency and automated code generation quality.

This empirical study evaluates how quantum circuit compilation and transpilation differences alter program execution behavior across cloud quantum hardware providers.

The paper presents a knowledge-augmented framework and benchmark to help automated code review account for historical software design decisions.

The authors present a physical robotic framework for non-invasive automated testing to detect data loss in Android mobile applications.

This paper audits quantum software testing tools and benchmarks against 410 real-world defects to identify gaps in fault coverage.

This paper presents a spectral Duhamel-Picard method and physics-informed neural operator for simulating post-exposure bake processes in semiconductor manufacturing.

This paper presents a short, simplified proof establishing a polylogarithmic upper bound for list replicability across binary concept classes.

This paper introduces a latent annealed posterior sampling framework with a variance-preserving bridge for inferring random fields from sparse observations.

This paper proposes an interpretable graph neural network architecture that separates node features, topological motifs, and feature-topology interactions into additive terms.

This paper provides theoretical analysis explaining why stop gradients and adaptive weighting are necessary for stable training of generative flow maps.

This paper theoretically analyzes what spectral measurements and learning curves reveal about internal representations in quadratic neural networks.

This paper examines how training data size affects the functional performance and feature reproducibility of sparse autoencoders on language models.

This paper investigates internal model circuits and prompt properties that drive code language models to default to Python code output.

This paper presents a calibration method to compress multi-head latent attention key-value caches to two bits for efficient long-context language model inference.

This paper introduces an on-policy residual learning framework with an entropy-contraction objective to improve generation speed and quality in diffusion language models.

This paper proposes a quantile-guided residual control framework to improve uncertainty representation and accuracy in diffusion-based time-series forecasting models.

This paper introduces a scalable vertical sliced Wasserstein distance to measure distances between probability distributions whose individual elements are themselves distributions.

This paper uses stochastic normalizing flows with open boundary conditions to mitigate topological freezing in lattice gauge theory simulations.

This paper aligns electroencephalography signals with text representations in a shared embedding space to improve the decoding of imagined speech intent.

This paper develops sliced Wasserstein sketches with computable error certificates for compact representation and fast comparison of probability measures.

This paper evaluates deep learning models and temporal interpretability techniques to predict continuous music enjoyment ratings from electroencephalography signals.

This paper derives closed-form generalization loss expressions for high-dimensional continual linear regression, characterizing optimal step sizes and equivalent regularization strengths.

This paper develops a statistical ensemble method to detect radio-frequency jamming in heterogeneous open radio access networks using standard telemetry.

This paper establishes analytical geometric bounds on coverage probability and timing uncertainty for multi-agent drone meshes intercepting approaching objects.

This paper resolves mathematical conjectures on polynomial compositions, proving the identifiability of neural networks using general polynomial activation functions of sufficient degree.

This paper shows that routing statistics across task phases fail to provide an effective guide for mixed-precision quantization in mixture-of-experts models.

This paper investigates internal geometric representations of task-relevant physical concepts within vision-language-action policies for robotic manipulation.

This paper investigates using analog radio frequency computing over wireless networks to deliver low-power neural network inference to resource-constrained edge devices.

This paper analyzes the effect of rank constraints on generalization and test error in diffusion score matching networks.

This paper proves conditions under which supervised neural networks can uniquely identify and recover true underlying data latents in generative and discriminative settings.

This paper introduces a Bayesian nonparametric Gaussian process mixture model using an adaptive Pitman-Yor prior to detect persistent regimes in time series.

This paper proposes a self-training speech denoising framework that corrects teacher predictions via noise filtering and observation mixing without clean references.

This paper demonstrates that frozen short-context drafting models can effectively serve ultra-long context speculative decoding without fine-tuning on long sequences.

This paper uses CatBoost and an automated SHAP analysis pipeline on clinical data to estimate biological age with interpretable biomarker insights.

This paper introduces a generative surrogate model for neutrino-nucleus scattering calibrated directly to measured experimental cross sections without event-level data.

This paper analyzes why machine-learning-predicted warm starts for the simplex method reduce iterations much more than actual solve time.

This paper presents a flexible doubly robust proximal learning framework to estimate causal effects on right-censored time-to-event outcomes under unmeasured confounding.

The paper analyzes the convergence and asymptotic properties of stochastic gradient descent with constant step size using nonlinear time series theory.

The paper analyzes the theoretical statistical properties and oracle inequalities of sign-betting regression under model misspecification.

The paper presents an offline distillation method combining entropy-difference sampling and answer mixing to transfer reasoning abilities into smaller language models.

The paper presents a GPU-accelerated robust adaptive control framework that accounts for guaranteed future parameter learning during real-time robotic trajectory planning.

The paper demonstrates that ensemble spread in streamflow forecasting models becomes significantly under-dispersed and underestimates error during extreme flood events.

The paper evaluates cross-subject transfer and accuracy limits of decoding imagined speech from scalp electroencephalography signals across three public datasets.

The paper aligns intermediate representations of text-to-music models with symbolic music transcriptions to improve generation quality efficiently.