All papers

The paper introduces an evaluation framework to measure the trade-off between task quality and computation cost when replacing iterative neural modules.

The paper presents an enterprise case study showing that portable skills and structured navigation layers simplify retrieval pipelines in customer support.

The paper introduces a benchmark and framework to evaluate how multimodal models track entities and reason about future scene evolution in videos.

The paper proposes a data-adaptive importance sampling method to stabilize variance and prevent sample size collapse in asynchronous RL post-training.

The paper introduces INT4 KV cache quantization with selective INT8 protection to accelerate asynchronous rollouts in reinforcement learning.

The paper presents an instrumented predator-prey simulation framework to evaluate communication, scheduling, and collective dynamics in remote decision-making agents.

The paper presents a framework that jointly learns query rule assignment and rule-constrained evidence retrieval over structured knowledge bases.

The paper proposes a multi-winner banner ad auction mechanism using diffusion models guided by bids and text descriptions to generate ads.

The paper presents two geometric methods to evaluate where and why representational structures align across different large language models.

The paper develops a text-to-speech model that simulates realistic clinical dysfluency in aphasia speech across multiple speakers for training healthcare professionals.

The paper presents a pretraining framework aligning wearable PPG signals with text across both individual pulse morphologies and broader demographic contexts.

The paper introduces an open-source agent framework that improves code-rendered explainer videos through program-level debugging, static rules, and unit testing.

The paper examines positive activation bias in neural networks, showing that reducing this bias slows initial training but improves final model performance.

The paper introduces a flow-matching model that enforces exact linear identity constraints among EEG channels to generate physically valid clinical signals.

The paper introduces a benchmark based on enterprise projects to evaluate agent performance on clarifying underspecified requirements and iterative optimization.

The paper proposes a structured chain-of-thought grounding method to track visual evidence across video frames for goal-oriented embodied navigation.

The paper introduces a diagnosis framework to evaluate whether multimodal benchmark items genuinely require multi-image reasoning rather than unimodal shortcuts.

The paper introduces a finite-window theoretical framework to determine what historical state information dynamical systems and distributed AI workflows must retain.

The paper introduces a large language model-based metric for offline evaluation of user-product relevance in dynamic product advertising retrieval.

The paper presents a certificate-based search method paired with a symbolic checker to resolve complex, distributed dependencies in protocol specifications.

The paper develops a dataset and framework to simulate realistic user social media actions using psychological profiles and retrieved interaction histories.

The paper proposes a reinforcement learning method that assigns distinct credit to initial code generation and subsequent post-execution correction steps.

The paper presents an in-context learning method that extracts compact concept vectors from demonstrations to guide sequential action choices for agents.

The paper proposes prototyping agent workflows with large language models and distilling converged live production interactions into smaller models.

The paper releases a multimodal dataset and dual-encoder model to align natural language descriptions with global Earth system meteorological data.

The paper presents a unified taxonomy and design space organizing recursive self-improvement methods for language model agents.

The paper introduces a reinforcement learning approach for multi-turn agents that identifies recurring decision patterns to improve intermediate step credit assignment.

The paper studies expert width and dimension balance in latent mixture-of-experts architectures to optimize parameter capacity across model layers.

The paper introduces a multi-head prediction method that injects future long-range supervision to improve autoregressive video generation consistency.

The paper introduces a hybrid kinematic graph neural network and elastic transformer framework for efficient Indian sign language recognition from video.

The paper presents a framework to learn dexterous robotic hand control directly from human monocular video demonstrations without teleoperation.

This paper presents a parameter-efficient video diffusion adaptation method combining point tracking with gradient conflict resolution to preserve visual priors.

This paper proposes a dual-alignment framework to keep speech-conditioned audio generation robust and consistent across clean and noisy acoustic environments.

This paper presents a multimodal diffusion framework that jointly designs protein sequences and physical structures to ensure interface complementarity.

This paper introduces a benchmark to evaluate how language agents discover, coordinate, and query across multiple distinct databases to answer questions.

This paper introduces an aligned language model family trained with structured reinforcement learning for clinical reasoning and traditional Chinese medicine prescribing.

This paper introduces a pairwise evaluation benchmark and protocol designed to measure subjective aesthetic taste while controlling for shortcut artifacts.

This paper presents a framework that converts long-term real user behavior records into supervisory tasks to train personalization-focused language models.

This paper proposes a framework using language model attributes to ground and label novel visual classes discovered dynamically at test time.

This paper integrates adversarial inverse reinforcement learning into a fuzzy control framework to estimate reward functions directly from demonstration data.

This paper introduces a hierarchical multi-timescale world model to reduce compounding rollout errors and drift in sequential decision-making systems.

This paper introduces a self-improving synthetic data generation pipeline to train embedding models across diverse retrieval domains without extensive manual annotation.

This paper presents a framework that uses history tracking and problem-specific tools to help language agents solve complex combinatorial optimization tasks.

This paper proposes a critic-free reinforcement learning algorithm using prefix trees to assign step-level credit for interactive multi-step tasks.

This paper introduces energy-based channel grouping and bit allocation to preserve accuracy when quantizing large language models to two bits.

This paper proposes an evaluation framework to test whether credit assignment techniques in modular financial reinforcement learning truly improve economic decisions.

This paper shows recurrent layers can predict future attention importance, enabling memory eviction in hybrid language models before queries arrive.

This paper investigates using frozen, pre-trained Transformer scorers instead of fixed mathematical formulas for multimodal contrastive representation learning.

This paper proposes a two-deadline architecture for visual agents to rapidly capture fleeting visual targets before slower semantic reasoning completes.

This paper presents a training framework that teaches language models to orchestrate tools through executable code instead of sequential prompt accumulation.

This paper introduces a compact text-to-speech model trained with a realism critic to improve prosodic naturalness during zero-shot voice cloning.

This paper presents a multi-agent framework that translates natural language into verified, executable data analytical programs using schema-grounded refinement.

This paper develops a graph edge attention neural solver for dynamic pricing and vehicle routing in same-day delivery operations.

This paper presents a multi-year network traffic time-series dataset to evaluate long-term anomaly detection and forecasting models.

This paper introduces a method to enable composable driving-style control in end-to-end autonomous driving planners using task vectors.

This paper introduces a persistent survival sandbox benchmark to evaluate sustained goal execution and program synthesis in autonomous agents.

This paper proposes a depth-progressive framework using 2D generative diffusion models to build 3D seismic velocity models efficiently.

This paper evaluates an edge-cloud hybrid architecture using AWS IoT Greengrass for real-time machine learning inference in predictive maintenance.

This position paper outlines a conceptual operating model and set of principles for integrating persistent AI agents into product management.

This paper introduces an Indian language text-to-speech benchmark and an inference-time duration estimation method for alignment-free synthesis.