Efficient Unlearning with Privacy Guarantees
arXiv:2507.04771v2 Announce Type: replace-cross Abstract: Privacy protection laws, such as the GDPR, grant individuals the right to request the forgetting of their personal data not only from database
Knowledge catalogue
arXiv:2507.04771v2 Announce Type: replace-cross Abstract: Privacy protection laws, such as the GDPR, grant individuals the right to request the forgetting of their personal data not only from database
arXiv:2606.29850v1 Announce Type: new Abstract: Visual pointing maps a language instruction to pixel co ordinates, a core skill for embodied AI. We describe our PointArena 2026 solution, which achieve
arXiv:2512.10310v2 Announce Type: replace Abstract: While Multimodal Large Language Models (MLLMs) have demonstrated significant promise in Vision-Language Navigation (VLN), existing agents remain hea
arXiv:2509.12159v2 Announce Type: replace-cross Abstract: Multimodal Large Language Models have demonstrated exceptional performance in UI2Code tasks, significantly enhancing website development effic
arXiv:2606.29540v1 Announce Type: cross Abstract: Large language models (LLMs) can leave subtle stylistic traces in assisted text; one of the most cited is the em-dash (Unicode U+2014). Yet no one has
arXiv:2603.19054v2 Announce Type: replace-cross Abstract: Recent advances in Streaming Video Understanding has enabled a new interaction paradigm where models respond proactively to user queries. Curr
arXiv:2606.28592v1 Announce Type: new Abstract: Physical caregiving robots need to assist different users with different tasks in diverse environments, and they come in many embodiments. While substan
arXiv:2606.30058v1 Announce Type: new Abstract: Comparing object orientations and positions across different instances requires their poses to be expressed in a shared canonical frame. Establishing su
arXiv:2510.25013v2 Announce Type: replace-cross Abstract: Mechanistic interpretability aims to reverse-engineer large language models (LLMs) into human-understandable computational circuits. However,
arXiv:2606.30256v1 Announce Type: new Abstract: Safety benchmarks often buy scalability by fixing the prompt, the language, and the turn structure. For emotional-support chatbots, that bargain hides p
arXiv:2606.29504v1 Announce Type: new Abstract: Video Intelligence Surveillance (VIDINT) on over-the-shoulder footage is a proposed vector for monitoring human-computer interaction patterns without di
arXiv:2606.29192v1 Announce Type: new Abstract: Global Navigation Satellite System (GNSS) navigation is widely used to provide absolute, outdoor positioning in field robotics. Advances in Real-Time Ki
arXiv:2606.30398v1 Announce Type: new Abstract: Accurately predicting the temporal evolution of clinical biomarkers is crucial for the early diagnosis and management of neurodegenerative diseases such
arXiv:2504.07660v2 Announce Type: replace Abstract: Facial expression detection requires spotting when expressions occur and recognizing which emotional category they belong to. Despite their close re
arXiv:2602.16634v2 Announce Type: replace-cross Abstract: The rare-event sampling problem has long been the central limiting factor in molecular dynamics (MD), especially in biomolecular simulation. R
arXiv:2602.19778v4 Announce Type: replace-cross Abstract: Automatic Chord Recognition (ACR) is constrained by the scarcity of aligned chord labels, as well-aligned annotations are costly to acquire. A
arXiv:2606.28405v1 Announce Type: new Abstract: Recent advances in network architecture design have introduced layer attention to enhance inter-layer interactions. In such frameworks, each layer queri
arXiv:2606.29267v1 Announce Type: new Abstract: Visual grounding aims to associate free-form textual queries with specific regions in an image. While recent Multimodal Large Language Models (MLLMs) ha
arXiv:2101.05993v2 Announce Type: replace-cross Abstract: Selecting an appropriate classification algorithm for a given data set remains a challenging problem in data mining and machine learning. Exis
arXiv:2606.30531v1 Announce Type: new Abstract: Tool-augmented language-model agents are often evaluated by whether they select the correct tool, produce valid API arguments, and complete the requeste
arXiv:2606.16620v2 Announce Type: replace-cross Abstract: Inference-time scaling has become the dominant lever for improving language-model reasoning, but existing methods derive rollout diversity fro
arXiv:2606.28671v1 Announce Type: new Abstract: Stackelberg differential games (SDGs) provide a powerful framework for hierarchical decision-making in stochastic and continuous-time environments, yet
arXiv:2606.28669v1 Announce Type: new Abstract: To address parameter misspecification and sudden structural environmental changes in conventional stochastic differential game (SDG) frameworks, this pa
arXiv:2606.29424v1 Announce Type: new Abstract: Model routing balances solution accuracy and computational cost by selecting among models of varying capabilities. While recent multi-round frameworks i
arXiv:2606.28628v1 Announce Type: cross Abstract: Localized generative editing needs localized evaluation: full-image identity metrics are structurally confounded under hard-composited edits. We prese
arXiv:2603.09785v3 Announce Type: replace Abstract: This paper introduces an updated and combined version of the bidirectional English-German EPIC-UdS (spoken) and EuroParl-UdS (written) corpora conta
arXiv:2606.28859v1 Announce Type: new Abstract: Stone inscriptions are invaluable sources of historical and linguistic knowledge, yet their automated analysis remains a major challenge due to surface
arXiv:2512.21545v2 Announce Type: replace Abstract: Object removal must prevent the masked target from reappearing and reconstruct the occluded background with structural and contextual fidelity, rath
arXiv:2606.28449v1 Announce Type: cross Abstract: Background: Digital health technologies allow for frequent, remote gait monitoring in people with multiple sclerosis (MS). However, to differentiate d
arXiv:2606.30152v1 Announce Type: cross Abstract: Contextual language models conflate grammatical gender and social semantic bias in gendered languages such as Spanish. Existing gender debiasing appro
arXiv:2606.29916v1 Announce Type: cross Abstract: Long-running language agents need mechanisms for deciding which experiences should persist after the working context is gone. Retrieval systems can re
arXiv:2601.18723v2 Announce Type: replace Abstract: Although Vision--Action (VA) and Vision--Language--Action (VLA) policies have advanced robotic manipulation, their evaluation remains dominated by b
arXiv:2606.30219v1 Announce Type: new Abstract: LLM evaluation and AI safety face a shared measurement problem: benchmark scores, reward-model signals, and reported safety metrics can improve while th
arXiv:2504.14969v2 Announce Type: replace Abstract: This paper proposes a framework for evaluating large language models (LLMs) on Chinese topic constructions, focusing on their sensitivity to island
arXiv:2507.05257v4 Announce Type: replace-cross Abstract: Recent benchmarks for Large Language Model (LLM) agents primarily focus on evaluating reasoning, planning, and execution capabilities, while a
arXiv:2504.02918v3 Announce Type: replace Abstract: Recent advances in image and video generation raise hopes that these models possess world modeling capabilities-the ability to generate realistic, p
arXiv:2606.28455v1 Announce Type: cross Abstract: World models can predict future physical states, but prediction accuracy alone does not explain how physical information is organized and used inside
arXiv:2606.29384v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models have become an important paradigm of embodied AI. However, existing VLA models typically assume well-lit and stable
arXiv:2606.20092v2 Announce Type: replace Abstract: Memory remains a critical bottleneck for long-horizon robotic manipulation, as standard Vision-Language-Action (VLA) policies often fail when task-r
arXiv:2606.28980v1 Announce Type: cross Abstract: Ovarian cancer is frequently diagnosed at an advanced stage, making preoperative contrast-enhanced computed tomography (CT) central to staging and sur
arXiv:2606.28409v1 Announce Type: cross Abstract: Software-compilable C programs routinely fail to complete the four-stage pipeline of a high-level synthesis (HLS) toolchain -- compilation, C simulati
arXiv:2606.29182v1 Announce Type: new Abstract: Open-ended scientific discovery with large language models (LLMs) increasingly operates as a long-horizon loop of hypothesis search and verification, wh
arXiv:2606.28938v1 Announce Type: new Abstract: Modern vision-language models (VLMs) for driving assistants typically treat vehicle dynamics as a black box, resulting in decisions that lack awareness
arXiv:2606.29430v1 Announce Type: new Abstract: Low-light image enhancement is severely ill-posed when the input frame contains missing structure, saturated noise, and weak local contrast. Event camer
arXiv:2606.29082v1 Announce Type: new Abstract: Would experience designing faster GPU kernels also help close in on a long-standing open mathematical conjecture? Large Language Models (LLMs) integrate
arXiv:2606.28381v1 Announce Type: cross Abstract: Symbolic regression via genetic programming routinely fails on small, wide datasets - a regime common in clinical-trial monitoring, biostatistics, and
arXiv:2606.29684v1 Announce Type: cross Abstract: This research investigates the optimization of Convolutional and Dense Neural Networks (CNNs and DNNs) for autonomous steering using the (N+M) Evoluti
arXiv:2606.28920v1 Announce Type: new Abstract: Remote sensing visual grounding (RSVG) aims to locate specific objects in high-resolution RS imagery using free-form natural language descriptions. Whil
arXiv:2606.28824v1 Announce Type: cross Abstract: This paper develops a continuum theory of exit-and-join coalition dynamics in nonatomic cooperative games. We extend the Aumann-Shapley value and the
arXiv:2606.30420v1 Announce Type: new Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) is a powerful paradigm for improving the reasoning capabilities of large language models (LLMs). H
arXiv:2512.07287v3 Announce Type: replace-cross Abstract: As intents unfold and environments change, multi-turn agents face continuously shifting decision contexts. Although reusing past experience is
arXiv:2606.29823v1 Announce Type: cross Abstract: The database community has repeatedly advanced the state of the art by recognizing that new workloads demand new system architectures. We argue that l
arXiv:2606.28960v1 Announce Type: new Abstract: Physicians now pose millions of clinical questions to AI tools each week, yet these tools are evaluated largely on hypothetical or exam-style questions,
arXiv:2509.21530v2 Announce Type: replace Abstract: Data augmentation is a widely used strategy to improve model robustness and generalization by enriching training datasets with synthetic examples. W
arXiv:2606.29963v1 Announce Type: new Abstract: The structural vulnerabilities of point cloud-based 3D object detectors remain poorly understood. Prior work has studied adversarial robustness primaril
arXiv:2606.19317v2 Announce Type: replace-cross Abstract: A longstanding goal of research on interpretable deep learning is to replace opaque neural computations with human-meaningful symbolic descrip
arXiv:2606.29952v1 Announce Type: cross Abstract: Detecting out-of-distribution (OOD) data is crucial for reliable machine learning deployment. Among detection strategies, post-hoc methods are particu
arXiv:2412.08108v3 Announce Type: replace-cross Abstract: Large Vision-Language Models (LVLMs) have achieved remarkable performance on multimodal tasks but remain highly vulnerable to small adversaria
arXiv:2606.29980v1 Announce Type: new Abstract: Zero-shot Transfer in Reinforcement Learning (RL) aims to train an agent that can generate optimal policies for any reward function, without additional
arXiv:2604.02714v2 Announce Type: replace Abstract: End-to-end autonomous driving models based on Vision-Language-Action (VLA) architectures have shown promising results by learning driving policies t