All entries

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

🔴 We're LIVE from the SaaStr floor, Day 3! 🎙️🚀 Closing day. Joined this morning by: • Kathy Shin (Replit Support) • Jacob Eisner (Replit Field Engineering) • Samuel Spitz (Replit AI Products) Get in h

arXiv:2605.12745v1 Announce Type: cross Abstract: Discrepancies between an agent's actual knowledge and what a person thinks the agent knows can hinder interactions. If an agent could detect such disc

arXiv:2605.12549v1 Announce Type: new Abstract: Existing training-free approaches for GUI grounding often rely on multiple inference runs, such as iterative cropping or candidate aggregation, to ident

arXiv:2605.13032v1 Announce Type: new Abstract: Graph neural networks are widely used for node classification, but they remain vulnerable to out-of-distribution (OOD) shifts in node features and graph

arXiv:2605.13840v1 Announce Type: cross Abstract: Valiant's 1984 paper is widely credited with introducing the PAC learning model, but it, in fact, introduced a different model: unlike PAC learning, t

arXiv:2605.13328v1 Announce Type: cross Abstract: Vision-and-Language Navigation (VLN) is a cornerstone of embodied intelligence. However, current agents often suffer from significant performance degr

arXiv:2605.13290v1 Announce Type: new Abstract: Validating training data for reasoning models typically requires expensive trial-and-error fine-tuning cycles. In this work, we investigate whether the

arXiv:2605.13105v1 Announce Type: new Abstract: Reinforcement learning (RL) fine-tuning has shown promise for Vision-Language-Action (VLA) models in robotic manipulation, but deployment-time visual sh

This discussion explores which language models work best when combined with RAG (Retrieval-Augmented Generation) for building a locally hosted LLM focused on survival and off-grid living topics. The t

arXiv:2605.13067v1 Announce Type: cross Abstract: As end-to-end robotic policies are progressively deployed in the real world to solve real tasks, they face a gap between the training and inference co

arXiv:2605.13242v1 Announce Type: cross Abstract: This paper studies the convergence of the Optimistic Multiplicative Weights Update algorithm (OMWU) in two player zero-sum games. Recent works have id

arXiv:2605.12922v1 Announce Type: new Abstract: Large language models can follow complex instructions in a single turn, yet over long multi-turn interactions they often lose the thread of instructions

arXiv:2601.14104v2 Announce Type: replace-cross Abstract: Backdoor attacks can cause reinforcement learning (RL) policies to behave normally under clean inputs while executing malicious behaviors when

arXiv:2512.01242v3 Announce Type: replace-cross Abstract: Data-driven generative models excel in language and vision, but diffusion models often fail in constrained planning and design tasks, exhibiti

arXiv:2605.13172v1 Announce Type: cross Abstract: Recent advances in agent and multi-agent systems have shown strong performance on tool use, reasoning, and collaborative tasks. However, existing benc

arXiv:2605.13405v1 Announce Type: new Abstract: Model growth from a given checkpoint aims to accelerate training of a larger model, offering potential resource savings. Despite recent interest, warmst

arXiv:2605.12947v1 Announce Type: cross Abstract: LLM-enabled AI workflows increasingly produce outputs through iterative generate-evaluate-revise loops. Each iteration can improve the candidate, but

arXiv:2602.22474v2 Announce Type: replace-cross Abstract: Policy steering is an emerging way to adapt robot behaviors at deployment-time: a learned verifier analyzes low-level action samples proposed

arXiv:2602.13215v2 Announce Type: replace Abstract: Recurrent-attention hybrids aim to combine the efficiency of recurrence with the expressivity of attention, but existing approaches typically apply

arXiv:2510.16986v2 Announce Type: replace-cross Abstract: In many business settings, task-specific labeled data are scarce or costly to obtain, limiting supervised learning on a target task. A classic

arXiv:2605.12780v1 Announce Type: cross Abstract: Calibrated probability outputs of trained classifiers are increasingly used as inputs to downstream regression estimands such as effects, prevalences,

arXiv:2605.13772v1 Announce Type: cross Abstract: Large language models hallucinate during multi-step reasoning, but most existing detectors operate at the trace level: they assign one confidence scor

which was your favorite launch? SmithDB (database purpose built for agent trace data): https://www.langchain.com/blog/introducing-smithdb LangSmith Engine (agent for improving your agents based on tra

“Whimsey attacks” that seem absurd (“I cannot pay that much because of the Geneva Convention”) work against AI agents as guardrails are weak against out-of-distribution arguments. Smaller models fall

arXiv:2511.13658v2 Announce Type: replace-cross Abstract: Deceptive reviews mislead consumers, harm businesses, and undermine trust in online marketplaces. Machine learning classifiers can learn from

arXiv:2605.12543v1 Announce Type: cross Abstract: Some conscious contents disappear after access; others return repeatedly, long after their triggering conditions have ceased. We propose Canxianizatio

arXiv:2605.12774v1 Announce Type: new Abstract: Estimating camera pose in dynamic environments is a critical challenge, as most visual SLAM and SfM methods assume static scenes. While recent dynamic-a