All papers

The paper proposes a policy optimization method that treats reasoning transitions as learned coordinates in an energy space to internalize self-correction.

The paper retargets object motion rather than human hands from video demonstrations to generate post-training data for various robot embodiments.

The paper proposes a progressive validation framework for zero-shot tool selection that evaluates functional compatibility and contextual relevance.

The paper presents a method to consolidate context gains into parametric model weights using prospective queries and low-rank feed-forward updates.

The paper analyzes when first-moment momentum accelerates reinforcement learning with verifiable rewards under exact and sampled policy gradients.

The paper introduces an adaptive perturbation method for sharpness-aware minimization that modulates perturbation radii using local loss curvature.

The paper introduces a compact tabular foundation model combining distribution-aware representations with dataset-conditioned sparse mixture-of-experts computation.

The paper analyzes the performance of retrieval-augmented generation architectures on open-domain question answering tasks involving culturally grounded South Asian narratives.

The paper presents a recursive self-improvement framework using scientific agent swarms that mutate hypotheses and recombine code based on experimental feedback.

Proposes a severity-weighted character error rate that charges text transcription errors based on their practical impact on reader understanding.

Implements adaptive action grids in value-based reinforcement learning that selectively refine promising bins while pruning suboptimal actions.

Introduces a counterfactual risk management method to calibrate adaptive decision-making against shifting opponent behaviors within fixed deviation budgets.

Derives token-level credit from single rollouts using hindsight future context rather than sampling multiple attempts per prompt.

Uses explicit three-dimensional vascular guidance in an iterative diffusion pipeline to improve anatomical accuracy in computed tomography super-resolution.

Combines privileged teacher signals with value estimation in policy optimization to provide denser token-level supervision for reasoning tasks.

Studies multi-developer agentic systems where independently built specialist agents collaborate to outperform larger systems created by single developers.

Presents a graph-based framework with decision certificates to determine when multimodal agents should acquire visual evidence under cost constraints.

Introduces a decomposed rubric synthesis method to create training data for open multimodal models that generate visual code artifacts.

Develops and tests a lightweight convolutional neural network to classify potato leaf diseases on field-collected farm images.

Introduces a benchmark based on real online discussions to evaluate both cognitive and behavioral persuasion skills in language models.

Proposes an interactive agent framework that utilizes physics simulation and spatial exploration for complex 3D object assembly and composition.

Shows that periodically clearing agent search history and restarting from the top candidate prevents automated research agents from plateauing.

Uses teacher critiques on incorrect student rollouts to improve on-policy distillation and recover supervision during mathematical reasoning tasks.

Develops a post-training framework that trains computer-use agents for video games using synthetic interactive experiences and an expanded action space.

Replaces discrete attention lookup in transformers with integration over a continuous memory curve to improve validation likelihood.

Analyzes the price of anarchy and economic efficiency of first-price auctions where AI agents bid to serve user prompts.

Presents a retraining-free method to adapt transformer sizes dynamically across diverse computational budgets using joint soft pruning of components.

Introduces multi-step action forecasting targets alongside actor rollouts to train language agents more effectively from intermediate trajectory steps.

Proposes a hierarchical residual policy learning framework to fine-tune mobile robot manipulation policies with decoupled base and arm adjustments.

Generatively models humanoid robot control policy weights using a latent diffusion framework combined with a linear autoencoder and Diffusion Transformer.

This paper reports the fabrication and continuous-wave optical performance of laterally coupled distributed feedback semiconductor lasers.

This paper evaluates a buffer-free transistor structure designed to suppress current drift and lag in high-power electronics.

This paper surveys capacitance-voltage measurement techniques to evaluate defect states and reliability in gallium nitride transistor devices.

This paper presents an empirical study evaluating open-source language models for automated unit test generation in software development.

This paper evaluates the ability of language models to generate non-functional requirements from functional software specifications.

This paper introduces an evaluation benchmark using multimodal models to assess generated videos across human preference dimensions.

This paper combines evolutionary computation with language models to optimize register-transfer level hardware code for correctness and efficiency.

This case study evaluates a non-programmer using an AI development tool to build a database application in a non-English language.

The paper examines how prompting conditions and automated agent reviews affect code complexity placement across two software architectures.

The authors use coding agents and execution validation to add new database dialect features into reusable database test generators.

The paper models payment authorization hold lifecycles using formal state machines to generate conformance tests for provider application interfaces.

The authors present a framework that uses localized Taylor approximations to repair high floating-point calculation errors in numerical C routines.

The paper studies whether flexible search strategies used by coding agents improve the identification of commits that introduce software bugs.

The authors provide a formal correctness contract to identify genuine breaks in asynchronous streaming financial reconciliation systems.

The paper introduces a benchmark to evaluate how accurately language models and coding agents generate mock objects, stubs, and assertions.

The paper presents an interface that lets coding agents navigate and edit multiple abstraction levels when generating accelerator kernels.

The authors combine zero-shot test suite generation with agentic refinement to improve test coverage and mutation scores.

The paper combines formal dynamic logic models and runtime monitoring to verify trajectory-following control in the Autoware autonomous driving platform.

The paper proposes a code summarization method that encodes abstract syntax tree paths using self-supervised learning to reduce training overhead.

The paper evaluates pre-trained code models under a standardized experimental setup to compare the impacts of their pre-training tasks fairly.

The paper studies how often human and agent pull request reviews reference existing repository code outside the submitted change.

The authors present a software architecture that separates incremental statistical calculations from execution runtimes and data delivery pipelines.

The paper proposes a framework that identifies missing information in bug reports and generates targeted follow-up questions only when needed.

The authors analyze statistical literature to turn visual judgment guidelines into testable correctness specifications for probabilistic programs.

The paper presents a method using domain constraints to tune Kubernetes workload configurations without disrupting live production performance.

This paper presents a three-stage generation and repair framework to resolve API constraints and tensor shape errors in deep learning programs.

The authors introduce tool-grounded requirement profiles to audit and stabilize how register-transfer-level code generation is evaluated.

The paper compares three cross-project bug localization model architectures across 13 open-source Python projects against limited within-project baselines.

The paper introduces a graph framework to maintain consistency across visual elements, workflows, and code when generating websites from prototypes.

This paper reviews challenges in mobile app analysis across code artifacts, runtime systems, and application ecosystems.