Search: “papers”

arXiv:2608.07038v1 Announce Type: cross Abstract: Human-Oriented Binary Reverse Engineering (HOBRE) aims to transform decompiled pseudocode into a more human-friendly representation, thereby reducing

arXiv:2608.07220v1 Announce Type: new Abstract: Mixed strategy equilibrium predicts i.i.d play: past actions should not help predict future decisions. Human players, however, systematically depart fro

arXiv:2608.06952v1 Announce Type: cross Abstract: Impersonation is a fundamental security threat in face recognition systems (FRSs). While the security of FRSs has been challenged by various attack ve

arXiv:2501.15790v2 Announce Type: replace Abstract: Synthetic minority oversampling is typically designed and evaluated against a predictive objective, generating samples that improve downstream class

arXiv:2504.17584v2 Announce Type: replace-cross Abstract: Attention-FC Disaggregated (AFD) LLM inference systems offload memory-bound Attention operations to memory-rich accelerators (e.g., CPUs, HBM-

arXiv:2608.07424v1 Announce Type: new Abstract: Test-time scaling is often implemented by spending more compute along one axis: sampling more solutions, extending a chain of thought, or applying a str

arXiv:2608.06691v1 Announce Type: new Abstract: Real-time human action recognition on Internet-of-Things (IoT) edge devices requires models that capture rich spatio-temporal cues within strict latency

arXiv:2608.07458v1 Announce Type: cross Abstract: Recent optimization studies on Retrieval-Augmented Generation (RAG) have exploited chunk-level KV cache reuse to avoid processing long retrieved conte

arXiv:2608.06674v1 Announce Type: new Abstract: Adversarial vulnerabilities remain a major concern for the safe deployment of neural networks, particularly in object detection, a core task embedded in

arXiv:2608.07147v1 Announce Type: new Abstract: Reinforcement learning with Verifiable Reward (RLVR) has emerged as a powerful paradigm for training coding agents, where the execution feedback from co

arXiv:2608.06841v1 Announce Type: new Abstract: Object detection is a fundamental task in visual perception, providing structured region representations for recognition, grounding, reasoning, and inte

arXiv:2608.06671v1 Announce Type: new Abstract: Wastewater-based surveillance is an effective tool for disease monitoring and can provide early warning of outbreaks. Although wastewater viral loads (W

arXiv:2509.21945v2 Announce Type: replace-cross Abstract: To efficiently tune configuration for better software system performance (e.g., latency) at the deployment and maintenance stage, many tuners

arXiv:2608.07062v1 Announce Type: new Abstract: Test-time adaptation (TTA) has become a practical way to adapt deployed models to unlabeled target data, a setting that is especially relevant in comput

arXiv:2608.06953v1 Announce Type: cross Abstract: Agent memory systems compress what they store, and compression is built to drop qualifiers, so a claim's epistemic standing tends not to survive being

arXiv:2608.06469v1 Announce Type: cross Abstract: Collaborative machine learning among financial institutions must be both group-fair and robust against deliberate adversarial manipulation. Existing f

arXiv:2608.06434v1 Announce Type: cross Abstract: Embodied intelligence demands both long-horizon reasoning and real-time closed-loop responsiveness. Recent dual-system Vision-Language-Action (VLA) ar

arXiv:2608.06876v1 Announce Type: cross Abstract: In the era of Industrial Internet of Things (IIoT) and Cyber-Physical Systems (CPS), Federated Learning (FL) offers a promising decentralized intellig

arXiv:2608.07282v1 Announce Type: new Abstract: The recent advances in neural language models have also spurred much work in computational psycholinguistics, asking whether neural LMs are also promisi

arXiv:2608.06779v1 Announce Type: cross Abstract: Large Language Models (LLMs) have accelerated drug discovery, particularly in the automated design of antimicrobial peptides (AMPs). However, current

arXiv:2608.07411v1 Announce Type: new Abstract: In the context of geodata, existing Large Language Models have often been studied in a homogeneous setting, which has considerably limited insights into

arXiv:2608.07405v1 Announce Type: cross Abstract: Foundation segmentation models can provide supervision for spacecraft imagery without manual training masks, but their predictions vary with textual p

arXiv:2608.07283v1 Announce Type: new Abstract: Grammar engineering requires expertise in linguistic formalism and computational implementation, especially in parallel grammar projects that balance cr

arXiv:2604.06756v2 Announce Type: replace Abstract: Large language models (LLMs) has been widely adopted as a scalable surrogate for human evaluation, yet such judges remain imperfect and susceptible

arXiv:2608.07285v1 Announce Type: cross Abstract: AI-generated music is increasingly used at the stem level, with producers integrating synthetic drums, basslines, or vocals alongside human-performed

arXiv:2608.07118v1 Announce Type: new Abstract: Credit assignment in multi-turn agent reinforcement learning operates at two levels: assigning trajectory-level credit to actions and distributing each

arXiv:2608.06898v1 Announce Type: cross Abstract: Researchers who seek to build social robot applications on foundation models are faced with a difficult question: how should we pick a model? Public l

arXiv:2608.07018v1 Announce Type: cross Abstract: Horizon detection in images of ice-covered waters is a challenging problem for maritime navigation due to low contrast between water and sky, cluttere

arXiv:2603.27435v2 Announce Type: replace-cross Abstract: Large language models (LLMs) are increasingly being used to generate comprehensive, knowledge-intensive reports. However, while these models a

arXiv:2603.21607v2 Announce Type: replace Abstract: While retrieval-augmented generation (RAG) significantly improves the factual reliability of LLMs, it does not eliminate hallucinations, so robust u

arXiv:2608.06803v1 Announce Type: cross Abstract: Ising machines are emerging as promising hardware for combinatorial optimization. With recent advances in CMOS Ising technology, they are becoming att

arXiv:2608.07074v1 Announce Type: new Abstract: Dense point cloud maps, as a typically used mapping representation, are difficult to deploy on resource-constrained robots because their memory consumpt

arXiv:2608.07208v1 Announce Type: cross Abstract: Existing measures of how much a text is about a concept read the surface of the text: dictionary word shares, topic proportions, embedding similaritie

arXiv:2608.07180v1 Announce Type: cross Abstract: Recent advances in deep reinforcement learning (RL) have shown that improving neural network architectures can yield substantial gains in sample effic

arXiv:2608.06378v1 Announce Type: cross Abstract: Driver emotions can affect risk perception, decision-making, and vehicle control under complex road conditions. Existing studies mainly focus on drive

arXiv:2608.07040v1 Announce Type: new Abstract: Solving combinatorial optimization problems (COPs) requires not only efficient algorithms but also carefully crafted formulations. While recent works ha

arXiv:2509.16664v2 Announce Type: cross Abstract: Retrieval systems rely on representations learned by increasingly powerful models. However, due to the high training cost and inconsistencies in learn

arXiv:2608.06701v1 Announce Type: cross Abstract: Fixing GitHub issues in large-scale projects is a long-horizon task, especially when a fix requires changes across multiple locations or the issue des

arXiv:2608.07444v1 Announce Type: cross Abstract: Accurate user equipment (UE) localization is critical for beam management in reconfigurable intelligent surface (RIS)-assisted millimeter-wave (mmWave

arXiv:2608.06802v1 Announce Type: new Abstract: On-policy distillation (OPD) trains a student on its own rollouts with token-level supervision from teacher models, but its effectiveness can depend str

arXiv:2509.00559v3 Announce Type: replace Abstract: Humans intuitively navigate social interactions by simulating unspoken dynamics and reasoning about others' perspectives, even with limited informat

arXiv:2608.06907v1 Announce Type: new Abstract: Legged robots require robust agility to perceive and interact with complex and dynamic environments within a constrained time. However, most existing qu

arXiv:2510.09904v2 Announce Type: replace-cross Abstract: Despite their widespread use, training deep Transformers can be unstable. Layer normalization, a standard component, improves training stabili

arXiv:2608.06477v1 Announce Type: cross Abstract: Computer-use agents (CUAs) face a growing threat from indirect prompt injection, where adversarial instructions are planted in the environment such as

arXiv:2509.05208v2 Announce Type: replace Abstract: Large language models (LLMs) excel at program synthesis, yet their ability to produce symbolic graphics programs (SGPs) that render into precise vis

arXiv:2608.06587v1 Announce Type: cross Abstract: Robot swarms utilize many independent limited-sensing agents to produce complex emergent behaviors without requiring centralized control. However, lit