All entries

arXiv:2605.12756v1 Announce Type: cross Abstract: Large language models (LLMs) are pretrained by minimizing the cross-entropy loss for next-token prediction. In this paper, we study whether this optim

arXiv:2605.13026v1 Announce Type: cross Abstract: Masked diffusion models (MDMs) have emerged as a promising alternative to autoregressive models (ARMs) for language modeling. However, MDMs are known

arXiv:2402.15415v2 Announce Type: replace Abstract: Low-Rank Adaptation (LoRA) is the dominant parameter-efficient fine-tuning method due to its favorable compute-performance trade-off, yet it suffers

arXiv:2605.13148v1 Announce Type: cross Abstract: Understanding why deep neural networks (DNNs) fail to generalize to unseen samples remains a long-standing challenge. Existing studies mainly examine

arXiv:2605.13260v1 Announce Type: new Abstract: Physics-Informed Neural Networks (PINNs) and their variational counterparts (VPINNs) are neural networks that incorporate physical laws, making them use

arXiv:2511.17001v2 Announce Type: replace Abstract: Cross-embodiment robot learning requires a unified action representation with consistent semantics across robot platforms. Existing representations

arXiv:2512.06109v3 Announce Type: replace-cross Abstract: This paper develops a unified perspective on several optimal control formulations through the lens of Kullback-Leibler (KL) regularization. We

arXiv:2605.13158v1 Announce Type: new Abstract: Image restoration under multiple adverse weather conditions aims to develop a single model to recover the underlying scene with high visibility. Weather

arXiv:2510.10642v3 Announce Type: replace-cross Abstract: Building generalist robot policies that can handle diverse tasks in open-ended environments is a central challenge in robotics. To leverage kn

arXiv:2509.15642v3 Announce Type: replace Abstract: Joint RGB-infrared perception is essential for achieving robustness under diverse weather and illumination conditions. Although foundation models ex

arXiv:2509.04477v3 Announce Type: replace-cross Abstract: A wide range of optimization problems can often be written in terms of generalized convex functions (GCFs). When this structure is present, it

This article explains how asynchronous processing can be integrated with continuous batching techniques to improve inference efficiency in machine learning models. Continuous batching allows requests

arXiv:2605.13835v1 Announce Type: new Abstract: Class-Incremental Learning (CIL) enables models to continuously integrate new knowledge while mitigating catastrophic forgetting. Driven by the remarkab

The dual-threat landscape of enterprise AI security is coming into focus. The same autonomous agents transforming workforce productivity are also expanding the attack surface — and most organizations

arXiv:2605.13601v1 Announce Type: new Abstract: As intelligent systems are increasingly implemented in our society to make autonomous decisions, their commitment to human values raises serious concern

When Oregon resident Isabelle Reksopuro heard Google was gobbling up public land to fuel its data centers in her home state, she didn't initially know what to believe. 'There's a lot of misinformation

arXiv:2605.12978v1 Announce Type: new Abstract: Learning from past experience benefits from two complementary forms of memory: episodic traces -- raw trajectories of what happened -- and consolidated

arXiv:2605.13277v1 Announce Type: cross Abstract: Visual evidence selection is a critical component of multimodal retrieval-augmented generation (RAG), yet existing methods typically rely on semantic

Ollama v0.24.0-rc0 is the latest release candidate version, published on May 14, 2026. Based on recent Ollama releases, this version likely includes features such as built-in web search (OpenClaw) and

v0.24.0-rc1 is a pre-release version focusing on improvements to Ollama server caching and the desktop launch experience, including plan-aware model gating and disabling Claude Desktop launch. The rel

v0.30.0-rc16 is a pre-release version of Ollama that changes the architecture to directly support llama.cpp instead of building on top of GGML, and allows for compatibility with GGUF file format. MLX

v0.30.0-rc17 is a pre-release version of Ollama that changes the architecture to directly support llama.cpp instead of building on top of GGML and allows compatibility with GGUF file format, with MLX

Aptera Motors announced that five validation vehicles have been driven off its newly established low-volume validation assembly line in Carlsbad, California in May 2026. Running multiple vehicles thro

arXiv:2605.13407v1 Announce Type: new Abstract: Predicting cross-sectional stock returns is challenging due to low signal-to-noise ratios and evolving market regimes. Classical factor models offer int

arXiv:2605.13764v1 Announce Type: cross Abstract: Modern retrieval-augmented generation (RAG) systems convert sensitive content into high-dimensional embeddings and store them in vector databases that

arXiv:2510.15297v4 Announce Type: replace-cross Abstract: We introduce VERA-MH (Validation of Ethical and Responsible AI in Mental Health), an automated evaluation of the safety of AI chatbots used in

arXiv:2605.13318v1 Announce Type: new Abstract: Chatbot usage has increased, including in fields for which they were never developed for--notably mental health support. To that end, we introduce Valid

arXiv:2605.12571v1 Announce Type: cross Abstract: Long video question answering requires locating sparse, time-scattered visual evidence within highly redundant content. Although current MLLMs perform

arXiv:2605.13034v1 Announce Type: new Abstract: Recent deep research systems have improved the ability of large language models to produce long, grounded reports through iterative retrieval and reason

Violin is an open-source video translation tool developed by Together AI that automatically translates video content to break down language barriers for viewers. The skill likely leverages AI models t

arXiv:2605.12904v1 Announce Type: new Abstract: Tabular foundation models (TFMs) have emerged as a powerful paradigm for in-context learning on structured data, enabling direct prediction on new tabul

arXiv:2504.11944v3 Announce Type: replace-cross Abstract: Offline reinforcement learning (RL) learns effective policies from pre-collected datasets, offering a practical solution for applications wher

arXiv:2412.06341v2 Announce Type: replace-cross Abstract: We propose Ciliary-DETR (previous name: Elastic-DETR), a framework for test-time resolution adjustment analogous to biological accommodation.

arXiv:2605.12684v1 Announce Type: cross Abstract: Multimodal large language models (MLLMs) are now routinely deployed for visual understanding, generation, and curation. A substantial fraction of thes

arXiv:2506.15953v2 Announce Type: replace Abstract: Dexterous manipulation is a cornerstone capability for robotic systems aiming to interact with the physical world in a human-like manner. Although v

arXiv:2605.13087v1 Announce Type: cross Abstract: Fine-tuning multilingual ASR models like Whisper for low-resource languages often improves read speech but degrades spontaneous audio performance, a p

arXiv:2605.13798v1 Announce Type: new Abstract: Cross-modal 3D medical image analysis requires voxelwise representations that remain anatomically consistent across imaging contrasts, scanners, and acq

Gary Marcus expresses frustration about the increasing prevalence of bot-generated and LLM-written responses on social media platforms, noting that filtering through such content has become increasing

Ethan Mollick discusses the challenges of helping people adapt to super-exponential technological change, referencing a Wait But Why analysis and acknowledging the difficulty of providing practical so

arXiv:2605.13846v1 Announce Type: cross Abstract: This paper introduces WARDEN, an early language model system capable of transcribing and translating Wardaman, an endangered Australian indigenous lan

arXiv:2605.13095v1 Announce Type: cross Abstract: Watermarking is widely proposed for provenance, attribution, and safety monitoring in generative models, yet is typically evaluated only under adversa

arXiv:2605.13621v1 Announce Type: new Abstract: Infrared-visible object detection improves detection performance by combining complementary features from multispectral images. Existing backbone-specif

We are excited to be partnering with @LangChain for deploying self-improving agents. Continual learning in your production environment unlocks compounding capability gains for model-product optimizati

“we are working harder to manage our tools than we are to solve the actual problems they were meant to fix.” Harvard Business Review research reveals that excessive interaction with AI is causing a sp

arXiv:2605.13674v1 Announce Type: cross Abstract: Weakly supervised semantic segmentation (WSSS) trains dense pixel-level segmentation models from partial or coarse annotations such as bounding boxes,

arXiv:2605.13746v1 Announce Type: cross Abstract: In this paper, we explore a weakly supervised method for anomaly detection. Since annotating videos is time-consuming, we only look at weak video-leve