Knowledge catalogue
arXiv:2606.04906v1 Announce Type: cross Abstract: Although it is generally agreed that AI-generated text poses a broad societal risk, there is no common understanding in the AI-generated text detectio
This benchmark demonstrates how integrating Alluxio with Ray Data achieves 20x faster training data read speeds for cross-region machine learning workloads on Anyscale's platform. The study shows perf
95% token reduction. 30x faster execution. 90%+ task completion. Today at #MSBuild, we announced a major shift to move reasoning upstream: Pinecone Nexus now integrates directly with @Microsoft OneLak
arXiv:2606.03609v1 Announce Type: cross Abstract: Embodied agents that navigate cities rely on world models that predict how their surroundings will change as they move. But for navigation, what matte
arXiv:2606.03646v1 Announce Type: new Abstract: This paper constructs the first benchmark on semi-supervised multi-modal crowd counting. To lay the foundation for this unexplored task, we first formul
arXiv:2606.03675v1 Announce Type: new Abstract: Methane is a potent greenhouse gas, and detecting leaks early via hyperspectral satellite imagery can help climate change mitigation efforts. Meanwhile,
arXiv:2606.02644v1 Announce Type: cross Abstract: Agentic scaffolds have dramatically improved LLM performance on complex, long-horizon tasks, yielding both broad benefits and amplified risks in domai
arXiv:2601.04120v2 Announce Type: replace-cross Abstract: Optimal control of obstacle problems arises in a wide range of applications and is computationally challenging due to its nonsmoothness, nonli
A workflow audit is no longer the best way to figure out how to use AI in your job. Despite the advice from AI labs, I'm more convinced, because of AI's reasoning capabilities and long context horizon
arXiv:2606.02755v1 Announce Type: cross Abstract: Large language model (LLM) applications are increasingly expected to satisfy deterministic institutional requirements while relying on probabilistic g
arXiv:2602.12430v4 Announce Type: replace-cross Abstract: The transition from monolithic language models to modular, skill-equipped agents marks a defining shift in how large language models (LLMs) ar
Carl Franzen / VentureBeat: Alibaba releases Qwen3.7-Plus, a multimodal proprietary model with a 1M-token context window, costing $2 per 1M tokens, 60% less than text-only Qwen3.7-Max — However, like
arXiv:2606.03967v1 Announce Type: cross Abstract: We describe AlignAtt4LLM, an IWSLT 2026 simultaneous speech translation system for English to German, Italian, and Chinese. The system is a synchronou
arXiv:2606.03774v1 Announce Type: new Abstract: Eye tracking is essential for smart glasses, as it provides insight into user attention for ambient intelligence applications. However, most existing ey
arXiv:2606.03217v1 Announce Type: cross Abstract: Chain-of-thought (CoT) reasoning has become a widely used mechanism for eliciting multi-step reasoning in large language models by generating intermed
arXiv:2606.03559v1 Announce Type: new Abstract: For nonconvex optimization problems whose objective is the prediction function of a trained Support Vector Regression (SVR) model with the Gaussian radi
Another banger open-source release. Miso One is an 8B text-to-speech model with real emotional range, so voiceovers carry warmth, hesitation, and excitement instead of sounding flat. It's purpose-buil
arXiv:2606.02915v1 Announce Type: new Abstract: Visual posters are a compact medium for communicating dense information, yet progress on automatic poster generation remains difficult to measure becaus
arXiv:2606.03116v1 Announce Type: cross Abstract: The rapid advancement of instruction-guided audio generation has highlighted the critical need for robust alignment evaluation. Current automated eval
arXiv:2606.03355v1 Announce Type: new Abstract: Physics models are inherently imperfect due to misspecified or missing mechanisms, resulting in systematic discrepancies between model predictions and r
arXiv:2604.04087v2 Announce Type: replace Abstract: We introduce ArrowFlow, a machine learning architecture that operates entirely in the space of permutations. Its computational units are ranking fil
This week we've got tandem hands-ons with Google's new Gemini AI agent - Spark - from my colleagues David Pierce and Jay Peters. Their takeaways are similar: It's so effective that it's scary. Spark k
arXiv:2606.03175v1 Announce Type: new Abstract: Instance Goal Navigation (IGN) requires an embodied agent to find a specific object instance among distractors from an underspecified natural-language d
arXiv:2605.11954v2 Announce Type: replace Abstract: Large language models (LLMs) are increasingly used in social science as scalable measurement tools for converting unstructured text into variables t
arXiv:2507.21638v2 Announce Type: replace Abstract: The development of reinforcement learning (RL) algorithms has been largely driven by ambitious challenge tasks and benchmarks. Games have dominated
arXiv:2606.02924v1 Announce Type: new Abstract: Autonomous driving perception is typically evaluated on clean benchmark data, yet real-world deployment requires robustness to rare, structured, and pot
arXiv:2606.02604v1 Announce Type: cross Abstract: ESG and climate risk data remain fragmented across heterogeneous Scope 1, Scope 2, and Scope 3 reporting environments, while conventional validation p
arXiv:2606.03031v1 Announce Type: new Abstract: Structured financial audit verification is difficult for language-model agents because correctness depends on structured evidence rather than text alone
arXiv:2606.03173v1 Announce Type: cross Abstract: The rise of `kidfluencers' on YouTube has raised ethical concerns about child digital labor and exploitation. While emerging legislation attempts to r
arXiv:2606.02775v1 Announce Type: new Abstract: The KV-cache is the right memory for datacenters but the wrong memory for robots. Datacenter inference batches many short requests and resets them, amor
arXiv:2606.02809v1 Announce Type: new Abstract: Evaluating vision-language models (VLMs) on medical images requires benchmarks that are clinically grounded, scalable, and controlled for evaluation con
arXiv:2606.02724v1 Announce Type: cross Abstract: Audio-visual speaker tracking aims to localize and track active speakers by leveraging auditory and visual cues, enabling fine-grained, human-centric
arXiv:2606.02798v1 Announce Type: new Abstract: Many decision-support settings require systems that adapt to individual users, but evaluation data for this problem remain limited. Existing benchmarks
arXiv:2606.03920v1 Announce Type: new Abstract: Understanding a video requires more than recognizing isolated moments, as humans continuously track entities, states, and events over time. This capacit
arXiv:2509.14636v2 Announce Type: replace Abstract: Scale-consistent ego-motion estimation is fundamental for autonomous ground robots. Bird's-Eye-View (BEV) representation naturally addresses the sca
arXiv:2606.03879v1 Announce Type: cross Abstract: As foundation models scale toward fusing more heterogeneous visual streams, understanding how diverse encoders interact under joint training becomes a
arXiv:2606.03318v1 Announce Type: new Abstract: Despite great advances in tool-use capabilities of large language models (LLMs), existing evaluation benchmarks struggle to fully align with real-world
arXiv:2606.03829v1 Announce Type: new Abstract: Financial-research answers are decision-relevant only when another analyst can audit how they were produced: which source was chosen, which period and a
arXiv:2606.03328v1 Announce Type: cross Abstract: Post-training pruning compresses large language models to high sparsity using a small unlabelled calibration set, and recent work has concluded that t
arXiv:2606.03096v1 Announce Type: new Abstract: Large Language Models (LLMs) are increasingly integrated into various domains, making knowledge editing techniques crucial yet potentially hazardous. Cu
arXiv:2603.19250v2 Announce Type: replace Abstract: Evaluating language models in streaming environments is critical, yet underexplored. Existing benchmarks either focus on single complex events or pr
arXiv:2606.03590v1 Announce Type: new Abstract: Kalman filter (KF)-based multi-object tracking (MOT) remains a strong baseline for autonomous driving due to its strong performance, computational effic
arXiv:2606.03327v1 Announce Type: cross Abstract: Text-to-SQL systems are typically evaluated by query-level execution correctness, but this terminal signal provides little guidance about which interm
arXiv:2603.01372v2 Announce Type: replace-cross Abstract: Concept Bottleneck Models (CBMs) enhance the interpretability of end-to-end neural networks by introducing a layer of concepts and predicting
arXiv:2602.01483v2 Announce Type: replace-cross Abstract: We propose causal preference elicitation, a Bayesian framework for expert-in-the-loop causal discovery that actively queries local edge relati
arXiv:2606.03499v1 Announce Type: new Abstract: 3D Gaussian Splatting (3DGS) has rapidly emerged as a leading representation for real-time novel view synthesis, but recent work shows it is vulnerable
arXiv:2606.02973v1 Announce Type: new Abstract: Are utterances by AI chatbots meaningful? Concretely, if a user asks, say, Anthropic's agent Claude, 'What is the capital of Spain?' and Claude answers,
arXiv:2606.02802v1 Announce Type: new Abstract: Large language models (LLMs) exhibit strong natural-language reasoning abilities for clinical decision support, but struggle to effectively model struct
arXiv:2606.03157v1 Announce Type: new Abstract: Large language models (LLMs) have been widely adopted in healthcare, yet they still encounter significant challenges in complex clinical decision-making
arXiv:2606.02603v1 Announce Type: new Abstract: Camouflaged object detection has improved substantially, but most standard benchmarks evaluate models only on clean images. This is not realistic becaus
arXiv:2606.03650v1 Announce Type: cross Abstract: Choosing or ranking language models for a specific application is hardest when no task-specific labeled data exists, and standard public benchmarks ca
arXiv:2606.02655v1 Announce Type: cross Abstract: External regret certifies stability only against replacing one's behavior by a fixed alternative. In a quantum game, this misses a natural physical mo
arXiv:2606.03292v1 Announce Type: cross Abstract: Many systems used in real-world environments require adding new categories and incorporating new information without forgetting what was previously le
arXiv:2507.19684v2 Announce Type: replace-cross Abstract: Socially interactive humanoid robots must engage with humans through their bodies, adapting in real time to a partner's movement, intent, and
arXiv:2606.03723v1 Announce Type: new Abstract: Low-rank adaptation (LoRA) enables parameter-efficient specialization of foundation models, but the proliferation of task-specific adapters fragments ca
arXiv:2606.02742v1 Announce Type: new Abstract: Spatial reasoning is fundamental to robotics, autonomy, and embodied AI, yet modern vision-language models (VLMs) remain unreliable on metric distance q
arXiv:2606.03888v1 Announce Type: new Abstract: Self-supervised learning has enabled large-scale pre-training on 2D natural images, producing general-purpose visual representations that transfer effec
arXiv:2606.02800v1 Announce Type: cross Abstract: We introduce Cosmos 3, a family of omnimodal world models designed to jointly process and generate language, image, video, audio, and action sequences
arXiv:2606.02581v1 Announce Type: cross Abstract: Retrieval-augmented generation (RAG) faces a fundamental three-way tension: deeper retrieval improves factual grounding but inflates token costs and e
arXiv:2512.00360v2 Announce Type: replace Abstract: We study timestamped question answering over educational lecture videos under a single-GPU latency/memory budget. Given a natural-language query, th