All entries

arXiv:2605.15391v1 Announce Type: cross Abstract: We present PanoWorld, a panoramic video world model that generates geometry-consistent 360egree video from a single image and a caption. Existing pano

arXiv:2605.16194v1 Announce Type: cross Abstract: LLM agents routinely serve as first (and sometimes only) readers of academic papers, skimming for sub-claims, extracting reproducibility steps, and ge

arXiv:2605.15509v1 Announce Type: new Abstract: While Isaac Lab provides massive parallel UAV simulation, OmniSafe and safe-control-gym provide constrained-RL benchmarks, and CBFKit provides control-b

arXiv:2605.15229v1 Announce Type: cross Abstract: Existing code benchmarks measure whether an agent can produce any test that reproduces a known bug, or whether it can produce a patch that fixes a des

arXiv:2605.15654v1 Announce Type: new Abstract: Real-world autonomous driving, particularly in urban environments with numerous corner cases, requires rigorous testing to ensure product safety and rob

arXiv:2605.15252v1 Announce Type: cross Abstract: Modern pedestrian dead reckoning (PDR) systems rely on fusing noisy and biased estimates of position, velocity, and calibrated orientation derived fro

arXiv:2605.15222v1 Announce Type: cross Abstract: Large language models (LLMs) can often generate functionally correct code, but their ability to produce efficient implementations for performance-crit

arXiv:2605.15647v1 Announce Type: new Abstract: Edge machine learning presents a unique set of constraints not encountered in cloud-scale model deployment: strict memory budgets, limited compute, and

arXiv:2605.15620v1 Announce Type: cross Abstract: We study risk-aware offline policy learning, aiming to learn a decision rule from logged data that is optimal under general risk criteria. This proble

arXiv:2605.15983v1 Announce Type: new Abstract: We formulate the Resource-Constrained Project Scheduling Problem (RCPSP) as optimal search over the reachability graph of a Timed Transition Petri Net w

arXiv:2605.15403v1 Announce Type: new Abstract: Mixture-of-Experts (MoE) models rely on balanced expert utilization to fully realize their scalability. However, existing load-balancing methods are lar

arXiv:2512.01843v2 Announce Type: replace Abstract: Driven by the growing capacity and training scale, Text-to-Video (T2V) generation models have recently achieved substantial progress in video qualit

arXiv:2605.15298v1 Announce Type: cross Abstract: Vision-language-action models have advanced rapidly, but robot trajectories alone provide limited coverage for learning broad physical understanding.

Please help make Grok Build great! Much appreciated, Elon. Phew, Grok Build is really thorough, pretty incredible. Relay feature for PasteLocal is done, pushing to Github now. And if you want to know

Bloomberg: Polymarket and Kalshi continue to operate in India despite a government advisory on April 25, directed at VPN providers, labeling prediction markets “illegal” — Polymarket and Kalshi Inc. h

arXiv:2512.00242v3 Announce Type: replace-cross Abstract: Sheaf Neural Networks equip graph structures with a cellular sheaf: a geometric structure which assigns local vector spaces (stalks) and a lin

Archaeologists at Pompeii Archaeological Park identified one of the victims of the 79 A.D. Mount Vesuvius eruption as a doctor after advanced exams revealed surgical instruments inside a plaster cast.

arXiv:2605.15567v1 Announce Type: new Abstract: This position paper argues for metacognition as a general design principle for creating more accurate, secure, and efficient AI. The metacognitive solut

arXiv:2605.15714v1 Announce Type: cross Abstract: This position paper argues that the machine learning community should prioritize early-stage quality assurance in annotation pipelines over the prevai

arXiv:2605.15253v1 Announce Type: new Abstract: Machine learning research increasingly bifurcates into two disconnected modes: benchmark-driven engineering that prioritizes metrics over understanding,

arXiv:2605.15622v1 Announce Type: new Abstract: Zeroth-order (ZO) optimization, learning from finite differences of function evaluations without backpropagation, has recently regained attention in dee

arXiv:2605.15887v1 Announce Type: new Abstract: Robust aggregation is the core operation in Byzantine-tolerant federated learning. To ensure the quality of aggregation independently of data distributi

arXiv:2605.15363v1 Announce Type: new Abstract: Accurate forecasting of residual Physical Resource Blocks (PRBs) is critical for proactive network slice provisioning, energy-efficient operation, and s

arXiv:2509.01685v2 Announce Type: replace-cross Abstract: We consider sampling from a Gibbs distribution by evolving finitely many particles. We propose a preconditioned version of a recently proposed

arXiv:2508.08431v3 Announce Type: replace-cross Abstract: Spectral variability significantly impacts the accuracy and convergence of hyperspectral unmixing algorithms. Many methods address complex spe

arXiv:2605.15599v1 Announce Type: cross Abstract: Extreme low-data fine-grained classification is common in expert domains where labeling is expensive, yet practitioners still need principled guidance

arXiv:2605.15665v1 Announce Type: new Abstract: Deploying large language model (LLM)-driven conversational agents in enterprise settings requires prompts that are simultaneously correct at launch and

arXiv:2605.15507v1 Announce Type: cross Abstract: For a Gaussian source under mean-squared error (MSE), classical transform coding is rate--distortion (RD) optimal: the Karhunen--Loeve transform (KLT)

arXiv:2605.15246v1 Announce Type: new Abstract: Trajectory data is fundamental to modern urban intelligence, yet its sensitivity raises significant privacy concerns. Generative models such as Generati

arXiv:2605.06475v1 Announce Type: cross Abstract: We introduce a probabilistic approach for dating historical manuscript pages from visual features alone. Instead of aggregating centuries into classes

arXiv:2509.20349v3 Announce Type: replace Abstract: Accurate time-series forecasting for complex physical systems is the backbone of modern industrial monitoring and control, yet deep learning models

arXiv:2605.15529v1 Announce Type: cross Abstract: Process Reward Models (PRMs) provide step-level feedback for reasoning, but current PRMs usually output only a single reward score for each step. Down

Joseph Cox / 404 Media: Procurement records: the FBI seeks a vendor that can provide access to automated license plate readers nationwide; Flock and Motorola are among the few options — Only a couple

In recent weeks, we pointed Mythos and other security-focused LLMs at live code across critical parts of our infrastructure. We share what we observed, the models’ strengths and weaknesses, and what t

arXiv:2407.02039v3 Announce Type: replace Abstract: Researchers are increasingly using language models (LMs) for text annotation. These approaches rely only on a prompt telling the model to return a g

In this post, you learn how to prompt Amazon Nova 2 Lite for content moderation using structured and free-form approaches, grounded in the MLCommons AILuminate Assessment Standard. The prompting techn

arXiv:2605.15641v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used as general planners in embodied intelligence, enabling high level coordination and low level task pla

arXiv:2605.16142v1 Announce Type: new Abstract: LLMs have shown impressive success in program synthesis, discovering programs that surpass prior solutions. However, these approaches rely on simple num

arXiv:2605.16238v1 Announce Type: new Abstract: Probabilistic forecasting of infectious diseases is crucial for public health but relies on labor-intensive manual model curation by expert modeling tea

arXiv:2605.15609v1 Announce Type: new Abstract: Diffusion large language models (dLLMs) generate text by iteratively denoising masked token sequences. Although dLLMs can predict all masked positions i

Eduard Kovacs / SecurityWeek: Pwn2Own Berlin 2026: participants earned a total of $1,298,250 for 47 vulnerabilities, with successful exploits of AI products like Codex, Cursor, and LM Studio — Partici

arXiv:2605.15208v1 Announce Type: cross Abstract: Large Language Models are routinely compressed via post-training quantization to reduce inference costs and memory footprint for cloud and edge deploy

arXiv:2511.09884v2 Announce Type: replace Abstract: Mission critical (MC) applications such as defense operations, energy management, cybersecurity, and aerospace control require reliable, determinist

arXiv:2605.15370v1 Announce Type: cross Abstract: Accurate salt-body delineation is essential for seismic interpretation because salt structures distort wave propagation, complicate velocity-model bui

🚀🚀Qwen3.7 Preview lands on Arena ！ Here come Qwen3.7-Max-Preview & Qwen3.7-Plus-Preview. Alibaba now #6 lab in Text, #5 in Vision.⚡️⚡️ Can't wait to release Qwen3.7 series models！Stay tuned! @arena Qw

arXiv:2602.06824v2 Announce Type: replace-cross Abstract: Momentum methods, such as Polyak's Heavy Ball, are the standard for training deep networks but suffer from curvature-induced bias in stochasti