Knowledge catalogue
arXiv:2606.11687v1 Announce Type: new Abstract: Unmanned Aerial Vehicle (UAV) threats have emerged as a defining security challenge of the 21st century. This paper presents DroneShield-AI, a unified o
arXiv:2606.11205v1 Announce Type: cross Abstract: Activation steering can shift LLM behaviour, but standard evaluations do not typically test whether a sycophancy-reduction direction also suppresses a
arXiv:2606.11901v1 Announce Type: cross Abstract: Bimanual robot systems substantially expand manipulation capabilities, but coordinating two arms introduces additional control complexity and failure
arXiv:2606.11968v1 Announce Type: new Abstract: This paper studies efficient online algorithms for multinomial logistic bandits (MLogB), where the feedback distribution over K+1 outcomes follows a mul
arXiv:2606.12077v1 Announce Type: new Abstract: Time-series clustering remains challenging due to the inherent trade-off between clustering effectiveness and computational efficiency. Similarity-based
arXiv:2606.11909v1 Announce Type: new Abstract: Benchmarks are essential for evaluating embodied spatial intelligence, yet their construction is labor-intensive, hard to reuse, and difficult to mainta
arXiv:2606.11324v1 Announce Type: cross Abstract: We introduce Embodied-R1.5, a unified Embodied Foundation Model (EFM) that integrates comprehensive embodied reasoning capabilities, spanning embodied
arXiv:2606.11257v1 Announce Type: new Abstract: Retrieval-Augmented Generation (RAG) pipelines are compute-intensive, combining embedding, retrieval, reranking, and large language model (LLM) generati
arXiv:2606.11232v1 Announce Type: cross Abstract: Existing LLM moral benchmarks usually ask which isolated moral act, value, or foundation a model prefers. This is useful but incomplete. Realistic jud
arXiv:2606.11212v1 Announce Type: new Abstract: Standard Retrieval-Augmented Generation (RAG) pipelines route every query through retrieval and generation unconditionally, incurring unnecessary comput
arXiv:2606.11976v1 Announce Type: cross Abstract: Software engineering tools increasingly rely on LLM based agents to localize files to change to resolve a software issue. Most AI agents explore repos
arXiv:2606.11708v1 Announce Type: new Abstract: Autonomous exploration with UAVs in large-scale, topologically complex environments often suffers from low efficiency due to suboptimal scheduling and d
arXiv:2606.11854v1 Announce Type: cross Abstract: There are two main Parameter-Efficient Fine-Tuning (PEFT) techniques for Large Language Models (LLMs). While Low-Rank Adaptation (LoRA) introduces add
arXiv:2606.11283v1 Announce Type: cross Abstract: We study the problem of generating synthetic data under differential privacy. We establish fixed-parameter tractability (FPT) for this problem where t
arXiv:2606.11445v1 Announce Type: new Abstract: Trust in an AI system is often anchored by explanations of how it works, which one then uses to forecast its behavior on new inputs. For large reasoning
arXiv:2606.11913v1 Announce Type: new Abstract: We propose a new paradigm for long video understanding by treating a long video as a Neural Knowledge Representation (NKR). NKR represents video content
arXiv:2601.04203v2 Announce Type: replace Abstract: We present FronTalk, a benchmark for front-end code generation that pioneers the study of a unique interaction dynamic: conversational code generati
Google LLC today released DiffusionGemma, a large language model based on an emerging machine learning approach known as text diffusion. The company says the algorithm can generate text four times fas
arXiv:2606.11562v1 Announce Type: cross Abstract: Graph analysis underlies many applications whose answers cannot be looked up in a single record or retrieved along a path: laundering rings, drug repu
arXiv:2606.11898v1 Announce Type: new Abstract: Research on Text-Attributed Graphs (TAGs) has gained significant attention recently due to its broad applications across various real-world data scenari
arXiv:2511.07332v2 Announce Type: replace-cross Abstract: Building reliable computer-use agents requires grounding: accurately connecting natural language instructions to the correct on-screen element
This post appears to be from Boris Cherny announcing or sharing content related to 'Code with Claude Tokyo,' likely a coding event, workshop, or community initiative involving Claude AI in Tokyo. The
arXiv:2606.11673v1 Announce Type: cross Abstract: Standard dot-product self-attention computes, in a single layer, only pairwise (order-2) interactions between tokens; representing a generic order-k i
arXiv:2606.12280v1 Announce Type: new Abstract: Post-training quantization lets large text-to-image diffusion transformers run on consumer GPUs, yet the hardware-specific trade-offs are seldom measure
An astrophysicist leverages OpenAI's Codex AI model to accelerate the development of code for simulating black hole physics and behavior. Codex assists in generating complex scientific code more effic
arXiv:2509.11548v2 Announce Type: replace Abstract: Graphical user interface (GUI) grounding is a fundamental task for building GUI agents. However, general vision-language models (VLMs) struggle with
arXiv:2606.12407v1 Announce Type: new Abstract: General-purpose large language models (LLMs) are routinely used as baselines when evaluating specialized pathology models on whole-slide images (WSIs).
arXiv:2606.11499v1 Announce Type: cross Abstract: The performance of modern language models depends critically on pretraining data composition. Yet existing data selection methods rely on auxiliary cl
arXiv:2602.19502v2 Announce Type: replace Abstract: Agentic AI systems are increasingly capable of autonomous data science workflows, yet clinical prediction tasks demand domain expertise that purely
arXiv:2606.11875v1 Announce Type: new Abstract: Emotional validation - explicitly acknowledging that a user's feelings make sense - has proven therapeutic value but has received little computational a
arXiv:2606.11289v1 Announce Type: new Abstract: Diffusion models have consistently driven progress in text-to-image generation. However, it is challenging to attribute recent progress to specific mode
arXiv:2606.11722v1 Announce Type: cross Abstract: Finding interpretable directions in language-model representations is critical for understanding and controlling model behavior. Sparse autoencoders (
arXiv:2606.11643v1 Announce Type: new Abstract: Large language models often remain sensitive to answer format: a question solved correctly in one form may fail in another semantically equivalent form.
arXiv:2602.08986v2 Announce Type: replace-cross Abstract: In hierarchical multi-label classification, a persistent challenge is enabling model predictions to reach deeper levels of the hierarchy for m
arXiv:2606.12207v1 Announce Type: cross Abstract: Embodied intelligence now spans navigation, household assistance, manipulation, autonomous driving, aerial agents, and multimodal large-model control.
arXiv:2606.11295v1 Announce Type: cross Abstract: Recovering cosmological information beyond the power spectrum is a central goal for upcoming cosmological surveys, since late-time non-Gaussian signal
arXiv:2606.11203v1 Announce Type: new Abstract: Structured sequence generation often requires a model to satisfy several input-derived constraints in a single output. Standard decoding methods may ass
arXiv:2510.24515v2 Announce Type: replace Abstract: The Team Orienteering Problem (TOP) generalizes many real-world multi-agent scheduling and routing tasks that occur in autonomous mobility, aerial l
arXiv:2606.11277v1 Announce Type: new Abstract: Reliable extrapolation remains a central challenge for generative models in computational physics, because models trained over finite ranges of time, pa
arXiv:2510.23320v2 Announce Type: replace-cross Abstract: We introduce LibriConvo, a synthetic conversational speech corpus for speaker diarization and automatic speech recognition (ASR), built by ins
arXiv:2606.11220v1 Announce Type: new Abstract: Forecasting human life outcomes is important to gain insights into how individuals attain long and healthy lives. Conventional statistical approaches yi
arXiv:2603.24080v2 Announce Type: replace Abstract: Benchmarks like MMLU suggest flagship language models approach factuality saturation above 90%. LLMpedia shows this picture is incomplete. We materi
arXiv:2606.11258v1 Announce Type: new Abstract: Gradient-based inversion of reaction-diffusion systems is typically approached via surrogate models or physics-informed neural networks (PINNs), while t
arXiv:2606.11922v1 Announce Type: cross Abstract: Recent respiratory sound classification (RSC) studies largely rely on CLS-token driven self-attention architectures such as the Audio Spectrogram Tran
arXiv:2606.11737v1 Announce Type: cross Abstract: Close-in exoplanets exhibit a wide range of orbital architectures and physical properties shaped by both formation conditions and migration processes.
arXiv:2601.21898v2 Announce Type: replace Abstract: The rise of model hubs has made it easier to access reusable model components, making model merging a practical tool for combining capabilities. Yet
arXiv:2509.14860v2 Announce Type: replace-cross Abstract: Image classification has traditionally relied on parameter-intensive model training, requiring large-scale annotated datasets and extensive fi
arXiv:2606.11702v1 Announce Type: cross Abstract: To make clinically grounded decisions, medical AI agents are expected to go beyond simple recognition and be capable of tool retrieval, evidence acqui
arXiv:2606.11868v1 Announce Type: new Abstract: De novo peptide sequencing from tandem mass spectrometry is pivotal in proteomics, enabling identification of novel peptides without reference databases
arXiv:2606.12047v1 Announce Type: cross Abstract: In this paper, we address the problem of zero-shot understanding of accidents from surveillance videos by identifying when an impact event occurs, wha
arXiv:2606.11724v1 Announce Type: new Abstract: Theory of Mind (ToM) reasoning requires inferring agents' beliefs from partial and asymmetric observations, which remains an open challenge for LLMs. Ex
arXiv:2512.08211v2 Announce Type: replace Abstract: Large language models (LLMs) are moving from cloud-centric services toward on-device embedded AI, where models interact with private, longitudinal s
arXiv:2602.22638v2 Announce Type: replace Abstract: Route-planning agents powered by large language models (LLMs) have emerged as a promising paradigm for supporting everyday human mobility through na
arXiv:2606.11870v1 Announce Type: cross Abstract: Machine learning is increasingly applied to accelerate the discovery of novel materials by exploring large compositional and structural design spaces.
arXiv:2606.11826v1 Announce Type: new Abstract: Designing anthropomorphic dexterous robotic hands remains challenging as the design space straddles morphology, actuation, and sensing properties, and p
arXiv:2606.11416v1 Announce Type: cross Abstract: Repository-level benchmarks for evaluating Large Language Model (LLM) code repair on Secure Multi-Party Computation (MPC) software do not yet exist, a
arXiv:2606.12106v1 Announce Type: cross Abstract: This paper presents our solution to the 2026 SoccerNet VQA Challenge. We first develop a cost-effective data synthesis pipeline driven by a Vision-Lan
arXiv:2606.11609v1 Announce Type: new Abstract: Stance detection requires identifying an author's position toward a target, often from short-form texts where stance is implicit, indirect, or rhetorica
arXiv:2606.11739v1 Announce Type: cross Abstract: We introduce a multi-view in-cabin monitoring dataset for public transportation with synchronized RGB and depth images from four inward-facing cameras
arXiv:2606.12300v1 Announce Type: cross Abstract: Temporal grounding--returning the interval [t_s, t_e] for a natural-language query over a video--is the language interface to long-form video, yet has