Knowledge catalogue
arXiv:2608.21398v1 Announce Type: cross Abstract: A trained reinforcement learning policy does not determine the complete behavior that users encounter: deployment code still schedules, admits, suppre
arXiv:2608.22549v1 Announce Type: new Abstract: Batched simulators for autonomous driving have recently enabled training reinforcement learning (RL) agents at scale, encompassing thousands of traffic
arXiv:2606.16661v2 Announce Type: replace-cross Abstract: Fixed-length chunking in Retrieval-Augmented Generation (RAG) often leads to boundary fragmentation, where critical evidence is split across s
arXiv:2608.22802v1 Announce Type: cross Abstract: Medical large language models are often judged by how many clinical questions they answer correctly. That view is useful, but it misses a practical ri
arXiv:2608.21500v1 Announce Type: cross Abstract: Prompt injection is listed as the #1 threat to AI agents. When an agent accesses external data from websites, files, or emails, an attacker may inject
arXiv:2608.22387v1 Announce Type: cross Abstract: Wearable and smartphone-based emotion recognition (WER) remains a challenging setting in affective computing, due to the notorious difficulty and bias
arXiv:2501.06926v5 Announce Type: replace-cross Abstract: Double reinforcement learning (DRL) provides efficient off-policy inference for policy values in nonparametric Markov decision processes (MDPs
arXiv:2608.23100v1 Announce Type: cross Abstract: Robot co-design via bi-level optimization couples within-lifetime controller learning for fitness evaluation with cross-generational morphological evo
arXiv:2608.22965v1 Announce Type: new Abstract: Accurate extrinsic calibration between event-based and frame-based cameras remains a practical bottleneck for heterogeneous stereo systems. Existing app
arXiv:2608.21659v1 Announce Type: new Abstract: Vector sketches remain one of the most concise and immediate mediums for abstract human expression. However, generating high-quality vector strokes that
arXiv:2608.22595v1 Announce Type: cross Abstract: We develop a new framework for flexible, nonlinear, and interpretable off-policy evaluation for infinite-horizon reinforcement learning. To handle lar
arXiv:2608.23493v1 Announce Type: new Abstract: Self-reflection is a powerful mechanism for credit assignment in human learning, converting sparse outcome feedback into actionable guidance. However, i
arXiv:2608.22132v1 Announce Type: cross Abstract: Biomedical multi-hop question answering (QA) requires models to connect evidence across intermediate entities such as diseases, drugs, proteins, and p
arXiv:2312.10376v2 Announce Type: replace Abstract: Typical methods for visual prompt tuning follow the sequential modeling paradigm originating from NLP, learning a sequence of unordered parameterize
arXiv:2608.23499v1 Announce Type: new Abstract: We present a method for analyzing the internal representations of Vision Transformers (ViTs) exploiting the geometry of their learned parameters. Each a
arXiv:2510.01028v2 Announce Type: replace Abstract: Large language models have made revolutionary progress in generating human-like text, yet their outputs often tend to be generic, exhibiting insuffi
arXiv:2608.22788v1 Announce Type: new Abstract: Large-scale rollouts have become a core component of modern LLM systems, spanning reinforcement learning (RL) post-training, on-policy distillation (OPD
arXiv:2608.21713v1 Announce Type: cross Abstract: Reasoning-augmented text-to-image models such as GoT-R1 emit an explicit textual plan - object names, attributes, and bounding boxes - before generati
arXiv:2608.22467v1 Announce Type: new Abstract: Long-horizon language-model tasks --- multi-step reasoning and tool-using agents alike --- are limited by credit assignment. We analyze it through the p
arXiv:2608.23224v1 Announce Type: cross Abstract: Retrieval can efficiently and effectively augment a frozen vision--language--action (VLA) policy without retraining, yet retrieved text becomes a cont
arXiv:2608.22854v1 Announce Type: new Abstract: Practical deployment of large language models (LLMs) requires families of post-trained variants---instruction-tuned, reasoning-tuned, and chat-style mod
arXiv:2608.21898v1 Announce Type: new Abstract: Web agents promise to automate complex digital workflows, but their training remains limited by synthetic environments that look plausible while hiding
arXiv:2608.22869v1 Announce Type: cross Abstract: While Vision-Language-Action (VLA) models have leveraged internet-scale pretraining and task-focused finetuning to achieve strong performance on long-
arXiv:2608.21995v1 Announce Type: cross Abstract: We propose Variance Driven Exploration (VarDE), a principled approach for pure exploration in highly stochastic environments, where the exploration pr
arXiv:2608.21883v1 Announce Type: new Abstract: Multimodal large reasoning models often rely on long Chain-of-Thought (CoT) traces in which a substantial fraction of tokens, such as repeated visual de
arXiv:2511.00391v3 Announce Type: replace Abstract: While recent specialized multimodal code generation models excel in tasks like chart-to-code generation, their reliance on single-task training limi
arXiv:2608.21878v1 Announce Type: new Abstract: Embodied Referring Expression Grounding is the task of enabling an agent to navigate in real environments and to localize a remote object based on natur
arXiv:2608.22521v1 Announce Type: new Abstract: Visual autoregressive (VAR) models have emerged as a fast, high-quality alternative to diffusion for text-to-image generation, but like diffusion models
arXiv:2608.21445v1 Announce Type: new Abstract: Automated seizure detection from electroencephalography (EEG) is essential for continuous neurological monitoring, particularly for subclinical epilepti
arXiv:2608.22584v1 Announce Type: new Abstract: Two-stage neuro-symbolic architectures provide an elegant paradigm for visual problem solving by cleanly separating connectionist perception of predefin
arXiv:2608.22960v1 Announce Type: new Abstract: Coding agents are increasingly evaluated not only by whether they solve a task, but also by how they execute it. However, existing process-level evaluat
arXiv:2608.23075v1 Announce Type: cross Abstract: Learned surrogates for expensive inner solver blocks are a widely pursued route to faster multiphysics simulation. We report a controlled, end-to-end
arXiv:2608.23024v1 Announce Type: new Abstract: Counterfactual medical image generation aims to modify an existing image to reflect a hypothetical scenario in which certain characteristics of the imag
WSJ: 'Anthropic more than doubled its revenue to 11.6 billion in the second quarter. To put its more than 30 trillion vision in context, the 191 technology companies in the S&P 1500 brought in 2.4 tri
arXiv:2608.22852v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used in investment decision-making, yet prior work shows that they exhibit systematic, model-specific inve
arXiv:2603.28200v2 Announce Type: replace-cross Abstract: Guiding collective motion in biological groups is a fundamental challenge in understanding social interaction rules. In this study, we propose
arXiv:2608.20834v1 Announce Type: cross Abstract: Managing divertor heat loads is a central challenge for compact, high-power tokamaks. To increase local flux expansion and decouple the dissipation vo
arXiv:2608.20481v1 Announce Type: cross Abstract: The Model Context Protocol (MCP) is an open source JSON-RPC protocol that standardizes how large language models (LLMs) interact with external systems
arXiv:2106.06060v4 Announce Type: replace-cross Abstract: Traditional competitive markets do not account for negative externalities; indirect costs that some participants impose on others, such as the
arXiv:2508.14313v4 Announce Type: replace-cross Abstract: Test-time scaling strategies for Large Language Models predominantly rely on either reinforcement learning with sparse outcome rewards or sear
arXiv:2608.21229v1 Announce Type: new Abstract: Omnimodal generation is central to a wide range of content creation and editing applications. In-context conditioning is essential to this paradigm. It
arXiv:2608.20390v1 Announce Type: cross Abstract: General-purpose large language models (LLMs) are increasingly used to answer religious questions, but for Islamic content they carry two serious risks
arXiv:2608.21067v1 Announce Type: cross Abstract: Automated plant traits recognition from herbarium images is essential for plant sciences, yet remains challenging because background elements (e.g., t
arXiv:2608.21292v1 Announce Type: new Abstract: Skills play different roles as an agent's policy evolves: they should first provide learnable knowledge, then support capability formation, and finally
arXiv:2511.08142v2 Announce Type: replace Abstract: Federated Learning (FL) is a promising machine learning solution in large-scale IoT systems, guaranteeing load distribution and privacy. However, FL
arXiv:2608.20691v1 Announce Type: new Abstract: Panoramic image generation is increasingly important for immersive applications such as virtual reality, augmented reality, and 3D content creation. Unl
arXiv:2411.08881v3 Announce Type: replace-cross Abstract: AI-based systems, including Large Language Models (LLMs), impact millions by supporting diverse tasks but face issues like misinformation, bia
arXiv:2608.20771v1 Announce Type: new Abstract: Search Agents face a severe reliability crisis during reinforcement learning (RL) fine-tuning. Heuristic Top-K retrieval often causes critical evidence
arXiv:2608.21030v1 Announce Type: cross Abstract: Video multimodal large language models have advanced significantly, yet fine-grained motion-temporal understanding remains fragile. The core bottlenec
arXiv:2608.20738v1 Announce Type: new Abstract: Graph Neural Networks (GNNs) are widely used on graph-structured data, but most suffer from two key weaknesses. First, message passing behaves as a low-
arXiv:2608.21041v1 Announce Type: cross Abstract: Geospatial representation learning from satellite imagery is a fundamental problem for large-scale urban analysis and real-world applications. Despite
arXiv:2608.20909v1 Announce Type: new Abstract: Offline RL methods commonly jointly train the actor and critic, where the critic is used to guide the actor toward higher-value actions. This coupled le
arXiv:2601.21948v2 Announce Type: replace Abstract: Neural visual decoding is a central problem in brain-computer interface research, aiming to reconstruct human visual perception and to elucidate the
arXiv:2608.20852v1 Announce Type: new Abstract: This paper presents a reference-guided reinforcement learning framework to generate stand-up motion for a 29-DOF Unitree G1 humanoid on deformable soft
arXiv:2608.20611v1 Announce Type: new Abstract: Semantic-ID-based generative recommendation casts retrieval and ranking as autoregressive generation over hierarchical item identifiers. A common recipe
arXiv:2505.16222v2 Announce Type: replace Abstract: With the growing use of large language models(LLMs) as evaluators, their application has expanded to code evaluation tasks, where they assess the co
arXiv:2602.06159v3 Announce Type: replace Abstract: Driven by the emergence of Controllable Video Diffusion, existing Sim2Real methods for autonomous driving video generation typically rely on explici
arXiv:2512.24407v2 Announce Type: replace Abstract: In many sequential decision-making problems, researchers observe actions but not the rewards that drive behavior, yet still wish to evaluate and com
arXiv:2608.20905v1 Announce Type: new Abstract: Face-to-face audiovisual interaction is central to human communication, conveying rich emotional and social cues. However, existing multimodal dialogue
arXiv:2608.20478v1 Announce Type: new Abstract: Routine gastrointestinal endoscopy is intrinsically bidirectional: the instrument is advanced to reach target anatomy and later withdrawn or retroflexed