All entries

arXiv:2605.26933v1 Announce Type: new Abstract: Unsupervised visual object tracking is a challenging task that requires following arbitrary targets in videos without training on ground-truth annotatio

arXiv:2605.27136v1 Announce Type: new Abstract: Uncertainty quantification (UQ) remains a critical challenge in Large Vision Language Models (LVLMs) for reliable predictions and real-world deployment.

arXiv:2506.11253v2 Announce Type: replace Abstract: Machine unlearning removes certain training data points and their influence from AI models (e.g., when a data owner revokes their consent to allow m

arXiv:2512.09700v3 Announce Type: replace Abstract: General-purpose object detectors face fundamental structural limitations when applied to ship detection in satellite imagery, where the ship scale d

arXiv:2605.26554v1 Announce Type: cross Abstract: Contextual dueling bandits form a cornerstone of preference-based decision-making, with critical applications in recommender systems and large languag

Lipdub (by Lightricks) Input a text prompt dialogue to dub/translate a characters speech. https://links.comfy.org/4v8Tr00 Obscura Remova (by WepeNerd) Used as occlusion removal, haze cleanup, foregrou

arXiv:2412.20505v2 Announce Type: replace Abstract: Participatory Urban Planning (PUP) is increasingly supported by LLM-based agents, yet existing methods largely rely on static preference elicitation

LiteParse 🤝 Rust🦀 We refactored the LiteParse library and CLI porting it to Rust, and here's what that means for you: ⚡ Parsing up to 100X faster, with sub-second processing for documents as large as

LiteParse v2.0 is out now, and it is blazing fast + runs everywhere! We rewrote everything from scratch in Rust, and now: - up to 100x faster parsing - install natively in Rust, JS/TS, and Python - a

arXiv:2605.27156v1 Announce Type: cross Abstract: Retrieval-Augmented Generation (RAG) enhances Large Language Models (LLMs) by incorporating external knowledge, particularly for long-tail domains suc

Astronomers directly measured the mass of a black hole in a 'little red dot' observed when the universe was just 700 million years old, finding it is much too massive for its host galaxy, suggesting i

arXiv:2605.26781v1 Announce Type: new Abstract: Advanced Large Multimodal Models (LMMs) have demonstrated impressive performance in K-12 reasoning tasks, exhibiting great promise as intelligent tutors

arXiv:2510.13217v2 Announce Type: replace-cross Abstract: Search systems are increasingly used for reasoning-intensive queries, where what makes a document relevant requires understanding or reasoning

arXiv:2605.27088v1 Announce Type: new Abstract: Aligning LLMs for math tutoring typically requires RL-based training with multi-GPU infrastructure. We investigate whether training-free prompt optimiza

arXiv:2601.18987v5 Announce Type: replace-cross Abstract: Determining whether a program terminates is a central problem in computer science. Turing's Halting Problem established termination as undecid

arXiv:2605.26756v1 Announce Type: new Abstract: Diffusion models can unintentionally memorize training samples, raising concerns about privacy and copyright. While recent methods can detect memorizati

arXiv:2605.27365v1 Announce Type: cross Abstract: Vision-language models (VLMs) commonly formulate visual grounding and detection as a coordinate-token generation problem, serializing each 2D box into

arXiv:2605.26244v1 Announce Type: new Abstract: Audio-visual generation is rapidly advancing from short clips to minute-long content, while existing evaluation protocols remain largely confined to sho

arXiv:2605.26486v1 Announce Type: new Abstract: Despite advances in audio-driven video generation, achieving commercial-grade stability remains challenging. We present LongCat-Video-Avatar 1.5, an upg

Look back at last week’s I/O announcements with @NotebookLM. You can listen to an audio overview, watch the video recap, and even check out our detailed slide deck summarizing all of the biggest news

arXiv:2605.26710v1 Announce Type: new Abstract: The distance at which a mobile robot reacts to a person strongly impacts various qualities of the human-robot interaction. In this paper, we focus on th

arXiv:2605.27268v1 Announce Type: cross Abstract: Modern Large Language Models (LLMs) are often criticized for producing repetitive and homogeneous text, despite possessing vast latent vocabularies. W

arXiv:2605.27254v1 Announce Type: cross Abstract: Selecting which instances to label is a key challenge in low-label tabular learning. For recent Tabular Foundation Models such as TabPFN, context sele

arXiv:2605.26438v1 Announce Type: cross Abstract: Large language models can recognize when they are being evaluated (evaluation awareness) and behave differently because of that, which undermines the

arXiv:2601.15283v2 Announce Type: replace Abstract: We present a novel approach for interactive light editing in indoor scenes from a single multi-view scene capture. Our method leverages a generative

arXiv:2605.27331v1 Announce Type: new Abstract: Competition law experts conducting legal research must review extensive volumes of cases, decisions, and judicial reports to identify precedents and ass

arXiv:2605.27186v1 Announce Type: new Abstract: Large language models often solve tasks from a fully specified prompt but degrade when the same requirements unfold over multiple turns, known as the lo

Managed Deep Agents is built for agents that need to work over long time horizons, use tools, preserve context, and produce artifacts. A few examples of what teams are building: ✅ Support + triage age

Managed deep agents is the easiest way to build and deploy long horizon agents Private preview, dm me if you want access Managed Deep Agents is built for agents that need to work over long time horizo

arXiv:2605.26333v1 Announce Type: new Abstract: Educational virtual laboratories can make experimental training more scala-ble, adaptive, and accessible, especially when students have limited access t

arXiv:2605.26782v1 Announce Type: new Abstract: Robotic haptic devices combined with virtual reality offer novel opportunities to train fine force generation, an essential yet overlooked component of

arXiv:2605.27246v1 Announce Type: cross Abstract: This position statement looks back on two decades of work on shallow embeddings of non-classical logics in classical higher-order logic (HOL), a line

Jonathan Vanian / CNBC: Mark Zuckerberg tells shareholders that a Meta cloud computing business is “definitely on the table” if it overspends on data centers and has excess capacity — Meta CEO Mark Zu

arXiv:2605.27345v1 Announce Type: new Abstract: Reliable evaluation is essential for understanding large language model (LLM) performance, yet today's go-to metrics, namely token-overlap scores (e.g.,

arXiv:2605.26741v1 Announce Type: cross Abstract: Inverse design of materials has significantly advanced target-driven formulation optimization, yet existing materials machine learning benchmarks rema

arXiv:2510.08932v2 Announce Type: replace Abstract: Recently, a growing body of research has focused on either optimizing CTR model architectures to better model feature interactions or refining train

arXiv:2605.26189v1 Announce Type: cross Abstract: Quantization-aware training (QAT) with low-bit floating-point formats enables efficient LLM deployment, yet introduces subtle failure modes invisible

arXiv:2605.26726v1 Announce Type: cross Abstract: Neural cellular automata (NCA) provide a lightweight alternative to encoder-decoder segmentation networks. However, it can be difficult to decide when

arXiv:2605.26343v1 Announce Type: new Abstract: Mechanistic interpretability has identified small sets of attention heads that implement specific behaviours in transformer language models, but recover

arXiv:2601.08267v3 Announce Type: replace Abstract: While reasoning-enhanced large language models perform strongly on English medical tasks, a persistent multilingual gap remains, with substantially

arXiv:2603.01131v2 Announce Type: replace-cross Abstract: Large language models (LLMs) have shown promise in clinical diagnosis but remain limited by unreliable report generation, weak evidence ground

arXiv:2605.26567v1 Announce Type: new Abstract: Clinical practice guidelines (CPGs) encode evidence-based decision logic that clinicians apply by evaluating patient variables, conditional criteria, an

arXiv:2605.26621v1 Announce Type: cross Abstract: Volumetric Reasoning Segmentation (VRS) aims to segment a target region in a 3D medical scan from a free-form clinical query, where the referent is of

arXiv:2502.06567v2 Announce Type: replace-cross Abstract: Quantizing machine learning models has demonstrated its effectiveness in lowering memory and inference costs while maintaining performance lev

arXiv:2605.26667v1 Announce Type: new Abstract: Large language model (LLM) agents increasingly rely on external memory systems to remain consistent across long-horizon interactions, but little empiric

arXiv:2605.26154v1 Announce Type: cross Abstract: LLM-driven agents are capable of selecting external tools to complete users' tasks. However, attackers could compromise such process, steering agents