All papers

The paper proposes a contextual parameterization framework that dynamically adjusts robot policy parameters as interaction contexts change.

The paper introduces an agentic framework that removes peripheral visual clues to protect geolocation privacy in images without adding visible artifacts.

The paper introduces a data selection strategy that prioritizes training examples where reasoning performance improves with additional inference compute.

The paper proposes a vision-language-action model that dynamically crops high-resolution regions to manipulate objects without using a wrist camera.

The paper identifies an activation steering vector that controls whether language models rely on internal memory or retrieved external context.

The paper investigates the loss surface of deep networks using a specific polynomial activation function without standard normalization layers.

The paper studies the impact of separating prediction horizons from state update intervals during sampling in generative flow models.

The paper compares three textual interface designs for optimizing prompts in multimodal audio-visual question answering systems.

The paper proposes a synthetic data pipeline that extracts atomic facts from source texts to guide factual pretraining for language models.

The paper proposes an interface framework that tracks and controls application state changes made by graphical user interface agents.

The paper analyzes the mathematical implicit bias of practical Muon optimization compared to gradient descent on linearly separable classification problems.

The paper presents a network to decouple acoustic and semantic representations when speech tone conflicts with transcript text.

The paper presents a deterministic context compaction method designed to preserve factual history and commitments in lightweight coding agents.

The paper presents a framework adapting video generation priors to improve 3D human body and hand motion reconstruction from video.

The paper describes an automated system using language models to verify corporate loan applications against internal bank lending policies.

The paper proposes a value-guided repulsion method for offline reinforcement learning that aligns behavior regularization with action value estimates.

The paper introduces a hierarchical memory agent framework for multi-center medication recommendation that accounts for hospital differences and drug safety constraints.

The paper introduces a framework that progressively constructs and streams executable training tasks from code repositories for software engineering agents.

The paper evaluates how open and closed language models reach consensus when presented with competing payoff incentives.

The paper analyzes implicit communication conventions between humans and AI agents in the cooperative card game Hanabi.

The paper introduces a framework providing analytical bounds for counterfactual reasoning in sequential decision processes under statistical and causal uncertainty.

The paper explores reinforcement learning design configurations for post-legalization placement refinement in chip design.

The paper introduces a dataset and framework pairing synchronized point-of-view and panoramic video to evaluate multiplayer action-conditioned world models.

The paper proposes a retrieval framework that selects suitable anomaly detection models for time-series data using similarity search over existing datasets.

The paper presents an adversarial framework where an exploitation agent iteratively attacks and refines automated patches generated by a repair agent.

The paper investigates an asynchronous training recipe combining the Muon optimizer, token-averaged loss, and penalties for long-horizon agent tasks.

The paper introduces a distillation method that trains agent policies on explicit runtime control semantics recovered from replayed execution traces.

The paper studies Byzantine robustness in fair federated learning and proposes an update screening method using client trajectory consistency.

The paper presents an intervention method using Shapley values to assign credit to individual actions within multi-action agentic retrieval sets.

The paper introduces a brain-inspired reflection framework that diagnoses failure points to guide self-evolving agents in embodied spatial tasks.

The paper detects backdoor triggers in multimodal language models by tracking output evolution trajectories across internal hidden layers.

The paper estimates reasoning confidence by segmenting chain-of-thought traces into argument graphs evaluated through natural language inference.

The paper proposes a framework where an agent policy navigates a global search tree by alternating between reflection and execution roles.

The paper presents a benchmark dataset evaluating unified page-level document parsing alongside alternative text descriptions for visual elements.

The paper provides finite-sample convergence guarantees for average-reward option Q-learning algorithms under inter- and intra-option settings.

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.

Recreates and analyzes infrastructure misconfigurations that enabled privilege escalation and repository bypass during autonomous AI agent evaluations.

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.

Evaluates patient linkage and attribute inference risks present in brain magnetic resonance imaging representations generated by pretrained foundation models.

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.

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.

Introduces a self-supervised consistency loss across varied tool interfaces to make language model agents robust against notation and naming changes.

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