Knowledge catalogue
arXiv:2603.02175v4 Announce Type: replace-cross Abstract: Instruction-based video editing has witnessed rapid progress, yet current methods often struggle with precise visual control, as natural langu
arXiv:2605.13734v1 Announce Type: cross Abstract: LLMs are widely adopted in production, pushing inference systems to their limits. Disaggregated LLM serving (e.g., PD separation and KV state disaggre
LangChain 在 Interrupt 大会上发布了底层数据库 SmithDB 和自动化排障引擎 LangSmith Engine。 Agent 运行会产生海量 trace（执行轨迹），把旧数据库撑到了瓶颈。新底座 SmithDB 放弃了本地磁盘，全面转向对象存储，将核心查询速度拉高了 15 倍。 底座换新后，LangSmith Engine 顺势接管了查 Bug 的体力活。它在后台持续监控生
LangChain Labs has launched as a new initiative from the LangChain team. The announcement was made by Harrison Chase, the creator and lead developer of LangChain, on X (formerly Twitter). LangChain La
LangSmith Engine, 6 months later imo, reading and analyzing long agent traces is a huge bottleneck and obstacle for progress (especially as these systems grow in time horizon). Imagine reading a langs
LangSmith Engine feels like the missing piece for reliable agents. Clustering failures and suggesting targeted fixes turns raw traces into actionable improvements. This is how we scale agent developme
LangSmith Engine is a phase shift because traces are no longer just records to be manually inspected, they’re now the catalyst for recursive agent self-improvement Engine looks at your traces, finds w
LangSmith Fleet has introduced a free model option powered by Fireworks AI for users on Developer and Plus plans, lowering the barrier to entry for getting started with the platform. This update makes
arXiv:2605.12863v1 Announce Type: cross Abstract: This paper introduces language-based agent control (LBAC), a new programming model for agentic applications that brings techniques from programming la
arXiv:2603.03295v2 Announce Type: replace-cross Abstract: Whether in agentic workflows, social studies, or chat settings, large language models (LLMs) are increasingly being asked to replace humans in
arXiv:2505.12741v2 Announce Type: replace Abstract: Language models are increasingly used not only as standalone predictors but also as components in larger inference systems, from test-time reasoning
arXiv:2605.12729v1 Announce Type: cross Abstract: Large language models are increasingly being used to support network operations (NetOps) and artificial intelligence for IT operations (AIOps), includ
arXiv:2605.13045v1 Announce Type: new Abstract: The existing methods for evaluating the medical knowledge of Large Language Models (LLMs) are largely based on atemporal examination-style benchmarks, w
arXiv:2510.18114v3 Announce Type: replace-cross Abstract: Discrete diffusion models have emerged as a powerful class of models and a promising route to fast language generation, but practical implemen
arXiv:2605.12714v1 Announce Type: new Abstract: Hidden states change substantially across the layers of modern language models, but most layer-wise analyses focus on one aspect of that change. We prop
arXiv:2605.13137v1 Announce Type: cross Abstract: Proving theorems in Lean 4 often requires identifying a scattered set of library lemmas whose joint use enables a concise proof -- a task we call glob
arXiv:2506.11274v2 Announce Type: replace-cross Abstract: Test-time scaling has emerged as an effective approach for improving language model performance by utilizing additional compute at inference t
arXiv:2605.13570v1 Announce Type: new Abstract: Constraint-based game content generators that learn local constraints from existing content, such as Wave Function Collapse (WFC), can generate visually
arXiv:2605.13284v1 Announce Type: cross Abstract: Recent advancements in large language models demonstrate that injecting perturbations can substantially enhance extrapolation performance. However, cu
arXiv:2605.13740v1 Announce Type: new Abstract: Whether navigating a building, operating a robot, or playing a game, an agent that acts effectively in an environment must first learn an internal model
arXiv:2605.13751v1 Announce Type: new Abstract: Establishing trustworthy safety assurance for autonomous driving systems (ADSs) requires evidence that failures arise from avoidable system deficiencies
arXiv:2602.13155v2 Announce Type: replace Abstract: Neural networks, particularly message-passing neural networks (MPNNs), are increasingly used as heuristics for hard combinatorial optimization probl
arXiv:2605.12646v1 Announce Type: cross Abstract: It is widely agreed that when AI models assist decision-makers in high-stakes domains by predicting an outcome of interest, they should communicate th
arXiv:2605.13713v1 Announce Type: new Abstract: Volumetric Modulated Arc Therapy (VMAT) is a cornerstone of modern radiation therapy, enabling highly conformal tumor irradiation and healthy-tissue spa
arXiv:2605.13080v1 Announce Type: new Abstract: When humans describe a visual scene, they do not process the entire image uniformly; instead, they selectively fixate on regions relevant to their inten
arXiv:2605.12682v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used as reasoning modules in many applications. While they are efficient in certain tasks, LLMs often stru
arXiv:2605.12561v1 Announce Type: new Abstract: Safe reinforcement learning (RL) typically asks extit{what} an agent should do. We ask extit{when} it needs to act, and show that a single policy can jo
arXiv:2605.12741v1 Announce Type: new Abstract: Enabling Large Language Models (LLMs) to continuously improve from environmental interactions is a central challenge in post-training. While on-policy s
arXiv:2505.15616v2 Announce Type: replace Abstract: Multimodal Large Language Models (MLLMs) have achieved significant advances in integrating visual and linguistic information, yet their ability to r
arXiv:2605.13741v1 Announce Type: cross Abstract: Scene graphs are becoming a standard representation for robot navigation, providing hierarchical geometric and semantic scene understanding. However,
arXiv:2605.12789v1 Announce Type: new Abstract: Large language-vision models (LVLMs) such as CLIP, Flamingo, and BLIP have revolutionized AI by enabling understanding across textual and visual modalit
arXiv:2605.13424v1 Announce Type: new Abstract: We propose last-mile fine-tuning, or Lift, a pipeline in which a pre-trained large language model extracts an initial table from unstructured clipboard
arXiv:2605.13265v1 Announce Type: new Abstract: Split learning (SL) enables collaborative training by partitioning a neural network across clients and a central server, but the cut-layer interface int
Like any AI dev not employed by a closed lab, we share the ambition that at least 10x more should be capable of training frontier models in 2026. And like we all know, some 10x leaps can't survive a c
arXiv:2511.10709v2 Announce Type: replace-cross Abstract: Machine learning models are used for pattern recognition analysis of big data, without direct human intervention. The task of unsupervised lea
arXiv:2605.13503v1 Announce Type: cross Abstract: A key technical difficulty in differential privacy is selecting a privacy budget that satisfies privacy requirements while maximizing utility. A natur
arXiv:2601.06147v2 Announce Type: replace Abstract: Recent work has demonstrated surprisingly good performance of pre-trained LLMs on regression tasks (for example, time-series prediction), with the a
arXiv:2605.13412v1 Announce Type: cross Abstract: Off-the-shelf large language models (LLMs) are increasingly used to automate text annotation, yet their effectiveness remains underexplored for underr
arXiv:2605.13188v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly deployed in settings where the available context is incomplete or degraded. We argue that an LLM generat
arXiv:2605.13782v1 Announce Type: cross Abstract: Traditional autonomous UAV search missions rely on geometric coverage patterns that ignore the semantic context of the target, leading to significant
arXiv:2603.02245v3 Announce Type: replace-cross Abstract: Decoding infant cry causes remains challenging for healthcare monitoring due to short nonstationary signals, limited annotations, and strong d
arXiv:2605.13028v1 Announce Type: new Abstract: We introduce Observation-aware Conformal Uncertainty Local-Calibration (OCULAR), a conformal prediction-based algorithm that uses perception information
arXiv:2605.13068v1 Announce Type: new Abstract: Nonlinear inverse problems often trade inexpensive but fragile first-order updates against curvature-aware methods such as Gauss-Newton and Levenberg-Ma
arXiv:2605.13538v1 Announce Type: cross Abstract: Personally Identifiable Information (PII) redaction usually replaces detected entities with placeholder tokens such as [PERSON], destroying the downst
arXiv:2605.13822v1 Announce Type: new Abstract: This paper considers fixed-wing unmanned aerial vehicle (UAV) corridors comprising a main lane, a circular loiter lane for managing traffic congestion,
Forge Neo allows users to upload reference images to guide AI image generation, supporting JPG, PNG, and WebP formats. The platform supports various image generation and editing models including Flux,
NVIDIA AI has released an official getting started guide for Hermes on DGX Spark, providing new users with foundational resources and instructions for deploying or working with Hermes on NVIDIA's DGX
arXiv:2510.19304v3 Announce Type: replace Abstract: Discrete diffusion models offer a promising alternative to autoregressive generation through parallel decoding, but they suffer from a sampling wall
arXiv:2507.00029v2 Announce Type: replace-cross Abstract: Recent attempts to combine low-rank adaptation (LoRA) with mixture-of-experts (MoE) for multi-task adaptation of Large Language Models (LLMs)
arXiv:2605.13163v1 Announce Type: cross Abstract: Foundation models and low-rank adapters enable efficient on-device generative AI but raise risks such as intellectual property leakage and model recov
arXiv:2605.12752v1 Announce Type: new Abstract: LoRA is widely adopted for continual fine-tuning of Large Language Models due to its parameter efficiency, modularity across tasks, and compatibility wi
Nikkei Asia: LY Corp launches a bid with Bain Capital to buy Kakaku.com, which operates restaurant review and booking site Tabelog, for 4B, challenging EQT's 3.75B offer — TOKYO — The operator of the
arXiv:2605.12556v1 Announce Type: new Abstract: Low-light image enhancement is challenging due to complex degradations, including amplified noise, artifacts, and color distortion. While Retinex-based
arXiv:2605.12570v1 Announce Type: new Abstract: The accurate classification of benign and malignant pulmonary nodules in CT scans is critical for early lung cancer screening, yet remains challenging d
arXiv:2605.13218v1 Announce Type: new Abstract: Cancer is one of the leading causes of death worldwide, making the development of rapid, minimally invasive, label-free and scalable diagnostic strategi
arXiv:2605.12655v1 Announce Type: new Abstract: Multi-agent reinforcement learning (MARL) in real-world use cases may need to adapt to external natural language instructions that interrupt ongoing beh
A utility tool was created to streamline sharing GPU profile traces by uploading them to Hugging Face buckets using the command `uvx trace-tuil -b `. This simplifies the process of distributing perfor
arXiv:2512.16767v2 Announce Type: replace Abstract: Posing 3D characters is a fundamental task in computer graphics. However, existing paradigms, ranging from traditional auto-rigging to recent pose-c
Making humans responsible for their AI use seems like an incredibly reasonable way to address problems & opportunities in the use of AI for academic research, at least in the short term (autonomous sc
arXiv:2605.12640v1 Announce Type: new Abstract: Panoptic segmentation requires the simultaneous recognition of countable thing instances and amorphous stuff regions, placing joint demands on long-rang