3462 papers
This paper reports on the synthesis and seeks feedback for the future evaluation of an Artefact Model for Regulatory Requirements Engineering (AM4RRE), aiming to bridge the gap between organizational regulatory processes and ad-hoc software development practices to achieve systematic, integrated compliance by design.
The paper proposes SurgFed, a language-guided multi-task federated learning framework that utilizes Language-guided Channel Selection and Language-guided Hyper Aggregation to overcome tissue and task diversity challenges, thereby improving surgical video segmentation and depth estimation across heterogeneous clinical environments.
This paper introduces EmbC-Test, a Retrieval-Augmented Generation (RAG) pipeline that leverages project-specific artifacts to ground large language models, enabling the automated generation of syntactically correct and runtime-valid embedded C tests that reduce testing time by up to 66% while producing 270 tests per hour.
This paper introduces a hybrid algebraic verification technique implemented in TalisMan2.0 that utilizes parallel multimodular reasoning to efficiently verify arithmetic circuits with large operands while avoiding the computational overhead of big integer arithmetic.
The paper presents Context-Nav, a training-free framework for text-goal instance navigation that combines caption-driven frontier ranking for global exploration with viewpoint-aware 3D spatial verification to accurately disambiguate target objects in cluttered environments, achieving state-of-the-art performance on InstanceNav and CoIN-Bench.
This paper investigates the reliability of Vision-Language Models (VLMs) in autonomous driving by exposing their tendencies toward response inconsistency and weak temporal reasoning, and subsequently proposes the FutureVQA benchmark and a self-supervised chain-of-thought tuning method to enhance grounded future scene reasoning without requiring temporal labels.
This paper introduces RuleSafe, a new long-horizon articulated manipulation benchmark featuring non-Markovian safe-unlocking tasks, and proposes VQ-Memory, a vector-quantized temporal representation that significantly enhances the planning, generalization, and efficiency of Vision-Language-Action models in complex robotic simulations.
The paper introduces RESBev, a plug-and-play framework that enhances the robustness of Bird's-eye-view (BEV) perception systems against sensor degradation and adversarial attacks by reframing the problem as latent semantic prediction to reconstruct clean features from corrupted observations.
This paper proposes DCAU-Net, a novel medical image segmentation framework that combines Differential Cross Attention to efficiently model long-range dependencies while reducing computational complexity, and a Channel-Spatial Feature Fusion strategy to adaptively integrate semantic and spatial details, thereby achieving enhanced segmentation accuracy and robustness.
This paper proposes a large language model-driven method for generating dynamic, semantically aligned speech and gestures for pedagogical agents in virtual reality, demonstrating through user experience experiments that such multimodal expressions significantly enhance learning effectiveness, engagement, and social presence while reducing fatigue and boredom.
This paper proposes a reinforcement learning-based post-training strategy that extends Group Relative Policy Optimization (GRPO) with hybrid and process-level rewards to enable existing unified vision-language models to generate high-quality multimodal interleaved outputs without relying on large-scale interleaved datasets.
This paper introduces DynHiL-EQA, a new dataset for dynamic human-in-the-loop Embodied Question Answering, and proposes DIVRR, a training-free framework that enhances robustness and inference efficiency by refining ambiguous views and selectively managing memory in dynamic environments.
This paper introduces NS-VLA, a novel Neuro-Symbolic Vision-Language-Action framework that integrates symbolic encoding, solving, and online reinforcement learning to achieve superior data efficiency, zero-shot generalizability, and expanded exploration in robotic manipulation compared to existing methods.
This paper presents a specialized, LLM-based Automated Program Repair framework designed to automatically secure cross-compartment interfaces in compartmentalized software by integrating a fuzzer, compartment-aware analysis techniques, and a validation component to address the limitations of existing general-purpose repair tools.
This paper presents a comprehensive study of Time-of-Flight non-line-of-sight imaging methods by unifying their theoretical formulations and hardware implementations to establish a common framework for analysis and demonstrate that, under equal constraints, existing techniques share similar performance limitations despite method-specific differences.
The paper introduces MASSE, a dynamic multi-client Searchable Symmetric Encryption (SSE) scheme that extends the OXT framework to enable fine-grained, attribute-based access control and efficient updates without re-encryption, while maintaining forward and backward privacy and demonstrating practical scalability.
The paper introduces GeoSolver, a framework that enhances remote sensing reasoning by leveraging a large-scale process supervision dataset (Geo-PRM-2M) and a novel Process-Aware Tree-GRPO algorithm to train a token-level reward model (GeoPRM), thereby enabling verifiable, step-by-step reasoning and robust test-time scaling for both specialized and general-purpose Vision-Language Models.
This study demonstrates that in multi-robot foraging scenarios with limited optimization budgets, evolving task-specialized controllers fails to improve efficiency and often underperforms compared to successfully optimized generalist behaviors.
This paper formulates cable-towed planar object manipulation as a routing-aware, tensioning-implicit trajectory optimization problem that leverages self-wrapping to dynamically redirect torque, proposing a relaxation hierarchy where the Implicit-Mode Relaxation (IMR) effectively exploits self-wrap for turning maneuvers without the conservatism of explicit routing decisions.
This paper advances the resolution of the bounded derivation depth implies finite controllability (BDD FC) conjecture by proving that universal models generated by BDD rule sets cannot contain arbitrarily large tournaments without entailing a loop query, thereby significantly narrowing the space of potential counterexamples.