Knowledge catalogue
arXiv:2608.16156v1 Announce Type: new Abstract: Long-horizon large language model (LLM) agents are typically optimized with sparse terminal outcomes, making fine-grained credit assignment across multi
arXiv:2608.16661v1 Announce Type: new Abstract: Hyperspectral reflectance spectroscopy enables non-destructive estimation of plant functional traits, yet current deep learning approaches process spect
arXiv:2608.15304v1 Announce Type: new Abstract: Frontier agentic systems powered by large language models (LLMs) exhibit human-like patterns of cognition. As these systems become deeply integrated acr
arXiv:2608.15799v1 Announce Type: new Abstract: In this paper, we focus on the dictionary of 2048 tokens used in Mimi semantic token codebook, the neural codec of the Moshi language model. We show tha
arXiv:2608.15708v1 Announce Type: new Abstract: We study a critical yet overlooked failure mode in Grounded Video Question Answering: question-invariant grounding, where models predict nearly identica
arXiv:2608.16515v1 Announce Type: cross Abstract: Retrieval-augmented generation (RAG) improves large language models by grounding generation in external evidence, but it also introduces a source trus
arXiv:2608.14940v1 Announce Type: new Abstract: Current agent evaluations score models on the state visible at the end of a stopped run which they count as one trial. However, interpreting the score a
arXiv:2608.14659v1 Announce Type: new Abstract: Large language models for code generation often produce incorrect solutions without reliable indicators of failure. We study whether uncertainty estimat
arXiv:2608.14854v1 Announce Type: new Abstract: While Multimodal Large Language Models (MLLMs) excel in general video understanding, their capability in fine-grained and motion-centric tasks remains l
arXiv:2608.14135v1 Announce Type: cross Abstract: Autonomous pursuit-evasion is a fundamental challenge for Unmanned Aerial Vehicles (UAVs), requiring rapid decision-making under tightly coupled dynam
arXiv:2608.14235v1 Announce Type: new Abstract: The development of reliable plant disease monitoring systems is constrained by limited longitudinal datasets capturing disease progression under natural
arXiv:2602.09430v2 Announce Type: replace-cross Abstract: Robotic laboratories play a critical role in autonomous scientific discovery by enabling scalable, continuous experimental execution. Recent v
arXiv:2608.14228v1 Announce Type: new Abstract: Life science knowledge graphs make large collections of structured data available through SPARQL, but each resource uses its own schema, identifiers, an
arXiv:2608.14146v1 Announce Type: new Abstract: Accurate polyp segmentation is critical for computer-aided colonoscopy, yet endoscopic images often contain low-contrast boundaries, mucosal texture int
arXiv:2608.14539v1 Announce Type: cross Abstract: Determining the biological sex of the individuals who created Upper Paleolithic hand stencils remains a challenging problem due to the absence of grou
arXiv:2608.14359v1 Announce Type: new Abstract: The sustainability constraints of FLaaS consumers pose significant challenges to maintaining carbon-feasible federated training in FLaaS environments. T
arXiv:2608.14011v1 Announce Type: cross Abstract: Generative recommendation autoregressively generates the semantic IDs of the target item, unifying preference modeling and index retrieval within the
arXiv:2608.14428v1 Announce Type: new Abstract: 3D object detection from LiDAR point clouds is a core problem in autonomous driving. Recent advances in self-supervised learning (SSL) enable scalable p
arXiv:2608.13698v1 Announce Type: new Abstract: Reinforcement Learning with Verifiable Rewards (RLVR), often optimized with Group Relative Policy Optimization (GRPO), has become a central recipe for i
arXiv:2608.13914v1 Announce Type: cross Abstract: Electrocardiogram (ECG) recordings are sensitive biomedical data, limiting the ability of hospitals and wearable devices to share raw signals for cent
Join us for a fun night this Thursday (August 20th) in San Francisco! RSVP link below! 👇 This Thursday, August 20th, we’re bringing together four leaders at the forefront of open models, coding agents
arXiv:2608.13769v1 Announce Type: new Abstract: Land classification from satellite imagery is important for land management, environmental monitoring, and urban planning. Machine learning methods such
arXiv:2608.13807v1 Announce Type: cross Abstract: Peripheral nerves buried beneath intact tissue are difficult to visualize during surgery and remain inaccessible to white light wide-field imaging and
arXiv:2602.12314v2 Announce Type: replace-cross Abstract: We present LatentAM, an online 3D Gaussian Splatting (3DGS) mapping framework that builds scalable latent feature maps from streaming RGB-D ob
arXiv:2608.13629v1 Announce Type: cross Abstract: Clinically actionable, patient-specific hemodynamic assessment, specifically wall shear stress, vortex structure and pressure distributions, is critic
arXiv:2608.14198v1 Announce Type: cross Abstract: Banks analyse sequential financial transaction data to perform many tasks, including fraud prevention, credit risk assessment and offer personalizatio
arXiv:2606.05327v2 Announce Type: replace Abstract: Flow matching (FM) has emerged as a powerful framework for learning dynamic transport maps between two empirical distributions. However, less explor
arXiv:2410.07299v3 Announce Type: replace-cross Abstract: We introduce OTIS, an open time series encoder that yields high-quality time series features for downstream deployment on any system, includin
arXiv:2608.14522v1 Announce Type: new Abstract: As AI systems make more morally loaded decisions across society, one response has been moral preference elicitation. In this approach, researchers poll
arXiv:2608.14319v1 Announce Type: new Abstract: We study quantum multi-armed bandits (QMAB) and quantum linear bandits (QLB) in the model of Wan et al. [2023], where the learner queries each arm or ac
arXiv:2608.14148v1 Announce Type: new Abstract: We introduce editable state-conditioned visual instance binding, a multi-turn localization setting in which several support-defined instances are introd
arXiv:2601.11629v2 Announce Type: replace-cross Abstract: We demonstrate that while the current approaches for language model watermarking are effective for open-ended generation, they are inadequate
arXiv:2608.14456v1 Announce Type: new Abstract: Short-horizon forecasting of fine particulate matter (PM2.5) remains difficult when observations from the target domain are limited and the statistical
arXiv:2608.14138v1 Announce Type: cross Abstract: Spatial perception and reasoning from visual observations require recovering geometric structure, establishing correspondences, and understanding spat
arXiv:2608.14229v1 Announce Type: new Abstract: Popular facts are memorised more deeply during pretraining and resist removal longer than rare ones, yet existing LLM unlearning methods apply uniform g
one of my llm hobbies is re-creating graphics demos i used to write in BASIC in the late 1980s. i slopped together an agentic harness and a basic-to-js transpiler in a web page i've been playing with
Wrong! If an AI leverages symbolic operations (conditionals, operations over variables, code interpreters, etc) and neural networks it is neurosymbolic. If it doesn’t, it’s not. ( I laid all of this o
arXiv:2608.12869v1 Announce Type: cross Abstract: Probabilistic Circuits (PCs) are generative models that support exact inference and, unlike deep neural networks, admit an exact and tractable measure
arXiv:2608.13343v1 Announce Type: new Abstract: Computer vision (CV) and machine learning (ML) offer new tools for cultural heritage (CH) artifact analysis, but the CV/ML pipeline remains largely inac
arXiv:2608.12570v1 Announce Type: new Abstract: Fashion retrieval often requires satisfying multiple attributes at once, such as category, color, pattern, and demographic. Monolithic embeddings mix th
arXiv:2608.13160v1 Announce Type: cross Abstract: Multilingual retrieval-augmented generation (mRAG) equips large language models with access to globally distributed external knowledge for complex mul
arXiv:2608.12977v1 Announce Type: cross Abstract: The expanding operational capabilities of large language model (LLM) agents introduce sophisticated security threats. Runtime defenses have emerged as
arXiv:2608.13197v1 Announce Type: new Abstract: Falls are a major health concern for older adults, and wearable sensors have been widely explored for detecting falls and enabling timely intervention.
arXiv:2608.13459v1 Announce Type: cross Abstract: We address the use of large language models (LLMs) to help discover Isabelle proofs. An Isabelle build establishes that the submitted theory is accept
arXiv:2501.05844v4 Announce Type: replace Abstract: Causal Learning has emerged as a major theme of research in statistics and machine learning in recent years, promising computational techniques to r
arXiv:2608.12698v1 Announce Type: new Abstract: Open-world object detection requires models to recognize known categories, reject unfamiliar objects, and incorporate new classes over time. This is esp
arXiv:2606.21202v2 Announce Type: replace-cross Abstract: Reaching global agreement from purely local interactions is a defining problem of collective intelligence, and most models of it assume that a
arXiv:2407.13911v5 Announce Type: replace Abstract: Prompt-based continual learning has shown strong performance in rehearsal-free class-incremental learning by adapting learnable prompts while freezi
arXiv:2608.01735v2 Announce Type: replace Abstract: On-policy (self) distillation (OPSD) is increasingly adopted for language-model post-training. It strengthens the teacher with privileged informatio
Don't classify. Hallucinate! I still have quite a bit of older content on my blog that I never got round to tagging. My blog has 1,856 tags - likely too many to feed to an LLM in one go and say 'which
arXiv:2608.13014v1 Announce Type: new Abstract: Understanding hand-object interaction from egocentric vision is essential for modeling how people physically engage with the surrounding world. Yet reas
arXiv:2608.13420v1 Announce Type: new Abstract: Embodied intelligent virtual agents are expected to operate as persistent, adaptive, and context-aware entities within complex virtual and Metaverse wor
arXiv:2608.12350v1 Announce Type: cross Abstract: The energy demand growth and environmental impacts of artificial intelligence (AI) have generated substantial interest in supplying sufficient low-cos
arXiv:2608.12337v1 Announce Type: new Abstract: Rewards that penalize unsupported claims can improve grounding in long-form generation, but they can also teach models to answer less. We study this ref
arXiv:2608.12845v1 Announce Type: cross Abstract: Semantic ID (SID)-based generative recommendation has recently achieved remarkable success. However, existing methods suffer from a previously overloo
arXiv:2506.06331v2 Announce Type: replace-cross Abstract: By retrieving contexts from knowledge graphs, graph-based retrieval-augmented generation (GraphRAG) enhances large language models (LLMs) to g
arXiv:2505.15274v4 Announce Type: replace Abstract: Probabilities of causation (PoCs) are fundamental quantities for counterfactual analysis and personalized decision making. However, existing analyti
arXiv:2506.13452v3 Announce Type: replace-cross Abstract: Background and Objective: As Deep Brain Stimulation (DBS) advances toward directional leads and optimization-based current steering, selecting
arXiv:2608.12358v1 Announce Type: cross Abstract: Product and engineering teams building role-bearing AI agents face an evaluation gap: an agent can produce accurate, safe, and fluent content while st
arXiv:2608.13040v1 Announce Type: cross Abstract: Enabling agents to learn from experience and internalize it into their policy has become a central problem in self-evolving AI. On-policy self-distill