All papers

The authors propose a training-free method constructing ontology-grounded semantic identifiers to preserve hierarchy in medical concept embeddings.

The authors introduce a decision-making framework that samples candidate action sequences from a diffusion model and refines them using learned dynamics.

This paper proposes a progress-aware credit assignment framework for multi-turn reinforcement learning in language model agents without using critic models.

This paper proposes a framework and benchmark for 3D semantic occupancy prediction in autonomous driving using four-dimensional radar tensors.

The authors present an image-token compression technique using variable-resolution merging for semantic regions and learned relevance pruning inside the model.

This paper introduces a framework that trains lightweight readers to convert latent 3D geometric representations into tokens readable by vision-language models.

The authors propose a mixture-of-experts diffusion framework that unifies world prediction and action generation models across shared physical dynamics.

This paper presents a training-free, gradient-based method to extract token-level reward signals directly from process reward models.

The authors introduce a system that enables robots to learn new tasks from five human video demonstrations via fast policy finetuning.

This paper presents a theoretical rate-transfer method using stochastic calculus to establish finite-time convergence bounds for stochastic gradient algorithms.

The paper introduces a debugging training algorithm that rewards localized semantic code edits alongside unit test execution.

The paper introduces a benchmark and model for evaluating program correctness and generating diagnostic counterexamples for competitive programming code.

The paper proposes an alternative architecture for fast decision-making that handles missing choices, option permutation, and candidate scaling natively.

The paper presents a runtime system that structures long-horizon agent memory around reusable progress objects rather than raw interaction history.

The paper analyzes how language model aggregation gains divide into candidate additions and context effects under different baseline comparison setups.

The paper presents a framework and benchmark to evaluate whether self-evolving robotic agents genuinely learn reusable skills across tasks.

The paper introduces an adaptive diversity control framework that optimizes cooperative multi-agent reinforcement learning teams using episodic return feedback.

The paper examines back-translation using monolingual data as an alternative to continued pretraining for low-resource translation with language models.

The paper introduces a decoding framework that dynamically selects sampling temperature based on geometric representations of conversational scenarios.

The paper presents an interactive simulation toolkit that enables humans and code agents to iteratively calibrate robot manipulation physics.

The paper analyzes sample complexity rates for offline Q-learning algorithms using Kullback-Leibler regularization.

The paper presents a reinforcement learning framework that trains language models to optimize molecules across multiple turns using iterative feedback.

The paper proposes an agent architecture that dynamically switches between latent reasoning and text interaction while maintaining external tool access.

The paper presents a method for generating visual artifact code by combining text prompts with generated image guidance and cross-modal rewards.

The paper introduces a training-free contrastive decoding method where a small model guides a larger model toward shorter reasoning chains.

The paper presents a dataset and framework with dense progressive rewards to train language agents on long-horizon tasks.

The paper studies how demonstration quality weighting, filtering, and conditioning affect vision-language-action model pretraining and downstream performance.

The paper introduces action chunking for agentic robotics to execute pre-planned primitive routines without redundant model reasoning at each step.

The paper presents circuit and electromagnetic models to simulate bulk current injection testing across complex wiring harnesses.

The paper implements high-frequency injection sensorless control on an FPGA-driven silicon carbide inverter for permanent magnet synchronous machines.

The paper examines security risks and protocol vulnerabilities when deploying commercial IT networking technologies in industrial communication systems.

The paper demonstrates reducing laser amplitude and phase noise by injecting femtosecond squeezed vacuum into ultrafast light sources.

The paper evaluates the storage reliability of an engine control circuit module.

The paper presents a zero-failure reliability demonstration method using degradation data for systems.

Summary pending (next check).

This paper presents AVNet, an optimized convolutional network designed for efficient FPGA deployment to classify circuit board defects.

This paper presents a mid-training method, corpus, and benchmark to help language models understand and reason about time series data.

This paper compares the quality and coverage of unit test cases generated by ChatGPT against an automated generation tool.

This paper introduces an open-source infrastructure and benchmark for multi-fidelity AI-assisted photonic simulation and inverse design.

Summary pending (next check).

The paper reviews retrieval-augmented generation paradigms and examines frameworks across retrieval, generation, and modular components.

The paper presents an 8-bit weight and activation post-training quantization technique that migrates activation outliers to weights.

The paper introduces a post-training mixed-precision quantization method using GPU-compatible 2D weight tiles for efficient inference.

The paper presents an open-source multimodal language model combining a small language backbone with vision encoders for efficient inference.

The paper provides a conceptual taxonomy and review distinguishing task-specific AI agents from collaborative agentic AI systems.

Examines scanning infrared depolarization measurements for defect monitoring and quality control across semiconductor wafer processing stages.

Reviews forty years of automated driving system research, focusing on safety assurance and verification challenges.

Summary pending (next check).

Summary contains IEEE Xplore webpage boilerplate rather than paper content on command and control for unmanned vehicles.

Presents an empirical comparison evaluating usability, reliability, and CI/CD integration challenges of AI-based unit testing tools.

Proposes an agent memory architecture that uses biologically inspired adaptive decay functions to selectively retain and forget context.

Introduces a mixture-of-prompts distillation method to transfer knowledge from hard prompts to soft prompts, improving generalization on unseen classes.

Uses coarse CTC decoding to filter relevant hotwords into LLM prompts for improved contextual speech recognition on rare words.

The paper proposes an edge-cloud collaborative inspection framework using multimodal LLMs dynamically scheduled based on scene complexity.

The paper presents a multi-agent framework that dynamically generates execution context to enable partial code execution.

The paper cleans code smells from training datasets to evaluate and improve the quality of LLM-generated code.

Presents ObliVul, a framework combining safety obligation modeling and counterfactual validation to filter false static analysis vulnerability alerts.

Introduces NosRacer, a dynamic analysis framework to detect race conditions in on-device network operating systems.

Proposes error coverage and certainty metrics to evaluate the reliability of multi-model N-version object detection architectures.

Proposes evaluating generated test suites across pools of valid and invalid implementations rather than against a single reference implementation.