Knowledge catalogue
arXiv:2507.04277v2 Announce Type: replace Abstract: Real-time low-light image enhancement on mobile and embedded devices requires models that balance visual quality and computational efficiency. Exist
arXiv:2605.01498v1 Announce Type: new Abstract: Visual query localization (VQL) aims to predict the spatio-temporal response of the most recent occurrence in a sequence given a query. Currently, most
arXiv:2605.02615v1 Announce Type: cross Abstract: The lack of analytical models describing diffusion time dependence at intermediate time scales in complex tissue microstructure limits the accurate qu
arXiv:2510.05950v2 Announce Type: replace Abstract: Time series classification (TSC) spans diverse application scenarios, yet labeled data are often scarce, making task-specific training costly and in
arXiv:2605.00907v1 Announce Type: new Abstract: Large language models (LLMs) and multimodal large models (MLLMs) are increasingly used for transportation tasks such as regulation question answering, t
arXiv:2605.02743v1 Announce Type: cross Abstract: The field of sensor-based human activity recognition (HAR) mainly uses posture, motion and context data of Inertial Measurement Units (IMUs) to identi
arXiv:2510.04142v2 Announce Type: replace Abstract: This paper identifies a critical yet underexplored challenge in reasoning alignment from multiple multi-modal large language models (MLLMs): In non-
arXiv:2602.07262v3 Announce Type: replace Abstract: Second-order feature statistics are central to texture recognition, yet existing mechanisms exhibit a structural tension: bilinear pooling and Gram
arXiv:2605.01512v1 Announce Type: new Abstract: Grounding traffic accidents in real CCTV footage is a rare-event problem where training on labeled accident video is often prohibited, yet accurate join
arXiv:2605.00842v1 Announce Type: cross Abstract: Emergent misalignment, where fine-tuning on narrow, non-harmful tasks induces harmful behaviors, poses a key challenge for AI safety in LLMs. Despite
arXiv:2605.00826v1 Announce Type: cross Abstract: Text-to-video retrieval enables users to find relevant video content using natural language queries, a task that has grown increasingly important with
MRC (Multipath Reliable Connection) is OpenAI's networking technology designed to enable efficient large-scale AI training by improving communication reliability and performance across distributed sup
arXiv:2510.09498v3 Announce Type: replace-cross Abstract: Cardiac muscle tissue exhibits highly non-linear hyperelastic and orthotropic material behavior during passive deformation. Traditional consti
arXiv:2605.01517v1 Announce Type: new Abstract: Scalable Vector Graphics (SVG) animation generation is pivotal for professional design due to their structural editability and resolution independence.
arXiv:2605.02280v1 Announce Type: new Abstract: Multiphysics simulation is critical for system-technology co-optimization (STCO) in chiplet-based design, but repeated finite-element solutions of PDE-g
arXiv:2602.22480v2 Announce Type: replace-cross Abstract: An important emerging application of coding agents is agent optimization: the iterative improvement of a target agent through edit-execute-eva
arXiv:2605.01662v1 Announce Type: new Abstract: Large vision-language models (VLMs) have advanced multimodal tasks such as video question answering (QA). However, VLMs face the challenge of selecting
arXiv:2605.02834v1 Announce Type: new Abstract: Videos are unique in their ability to capture actions which transcend multiple frames. Accordingly, for many years action recognition was the quintessen
arXiv:2605.02037v1 Announce Type: new Abstract: We present VILAS, a fully low-cost, modular robotic manipulation platform designed to support end-to-end vision-language-action (VLA) policy learning an
arXiv:2605.01391v1 Announce Type: new Abstract: Existing benchmarks for Vision-Language Models (VLMs) primarily evaluate spatio-temporal understanding on simple single-action videos, closed attribute
arXiv:2605.01220v1 Announce Type: new Abstract: Visual Autoregressive Modeling (VAR) based on next-scale prediction achieves strong generation quality, but their explicit deep stacks fix the amount of
arXiv:2605.02735v1 Announce Type: new Abstract: Continuous latent-space reasoning offers a compact alternative to textual chain-of-thought for multimodal models, enabling high-dimensional visual evide
arXiv:2603.14535v2 Announce Type: replace Abstract: Reinforcement learning has proven its power on various occasions. However, its performance is not always guaranteed when system dynamics change. Ins
arXiv:2605.01194v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models have demonstrated remarkable capabilities and generalization in embodied manipulation. However, their decision-makin
arXiv:2602.18700v2 Announce Type: replace-cross Abstract: LLM agents rely heavily on high-quality trajectory data to guide their problem-solving behaviors, yet producing such data requires substantial
we have very efficient models, especially for their capability level happy codexing yo, i'm actually worried. codex limits are genuinely insane so it's sus af .. i feel this is an intentional move for
we shipped gpt-5.5 instant today to chat; it's rolling out over the next couple days to everyone. for this model, we focused on factuality, crushing hacks, and improving the baseline intelligence. 5.5
We’re also improving memory and personalization. ChatGPT can now better use context from saved memories, past chats, files, and connected Gmail accounts to give more personalized responses. Memory sou
arXiv:2605.02038v1 Announce Type: new Abstract: Single-prompt accuracy is the dominant way to benchmark language models, but it can miss reliability failures that matter. We evaluate a 15-model open-w
SemiAnalysis, a semiconductor industry analysis publication, has a readership that is approximately 90% male according to founder Dylan Patel. This gender skew toward male readers is notably higher th
arXiv:2605.02782v1 Announce Type: cross Abstract: Automatic speech recognition (ASR) systems remain brittle on dysarthric and other atypical speech. Recent audio-language models raise the possibility
arXiv:2605.02363v1 Announce Type: new Abstract: Deployed language models must produce outputs that are both correct and format-compliant. We study this structured-output reliability gap using two math
arXiv:2605.00911v1 Announce Type: new Abstract: Industrial Retrieval-Augmented Generation (RAG) systems depend on optical character recognition (OCR) to transform visual documents into text. Existing
arXiv:2601.19827v3 Announce Type: replace Abstract: Retrieval-Augmented Generation (RAG) extends large language models (LLMs) beyond parametric knowledge, yet it is unclear when iterative retrieval-re
arXiv:2605.00896v1 Announce Type: new Abstract: Operational phase unwrapping is the primary computational bottleneck in InSAR-based volcanic and seismic monitoring. We challenge the industry trend of
arXiv:2602.06932v3 Announce Type: replace Abstract: Speculative decoding can significantly accelerate LLM serving, yet most deployments today disentangle speculator training from serving, treating spe
arXiv:2605.01081v1 Announce Type: new Abstract: The performance of state-of-the-art object detectors degrades significantly under adverse weather, causing a safety-critical domain shift problem for au
arXiv:2605.01018v1 Announce Type: new Abstract: Using multimodal foundation models to analyze table images is a high-value yet challenging application in consumer and enterprise scenarios. Despite its
Open-source technology provider WS02 LLC today announced the launch of WSO2 Agent Manager, an open control plane for artificial intelligence agents that gives enterprises a unified way to identify, go
arXiv:2605.00891v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) have demonstrated strong image-level visual understanding and reasoning, yet their pixel-level perception acros
arXiv:2605.01331v1 Announce Type: new Abstract: Image steganalysis, which aims at detecting secret information concealed within images, has become a critical countermeasure for assessing the security
arXiv:2605.02356v1 Announce Type: new Abstract: We introduce the Z-Domain Neural Operator (ZNO), a causal neural operator whose layers are stable low-rank multiple-input multiple-output (MIMO) rationa
A challenge with AI regulation and vetting is how bad our benchmarks of AI model performance and risks are. There is no benchmark for risks and red-teaming requires experiments from dedicated speciali
arXiv:2605.00284v1 Announce Type: new Abstract: Dirac-Frenkel instantaneous residual minimization evolves nonlinear parametrizations of PDE solutions in time, but ill-conditioning can render the param
arXiv:2605.00551v1 Announce Type: new Abstract: AI agents that interact with graphical user interfaces (GUIs) require effective observation representations for reliable grounding. The accessibility tr
arXiv:2603.22908v3 Announce Type: replace Abstract: Assuming that neither source data nor source model parameters are accessible, black-box domain adaptation (BBDA) represents a highly practical yet c
arXiv:2603.25719v2 Announce Type: replace-cross Abstract: We present an empirical study of how far general-purpose coding agents -- without hardware-specific training -- can optimize hardware designs
arXiv:2605.00334v1 Announce Type: cross Abstract: Production agentic systems make many model calls per user request, and most of those calls are short, structured, and routine. This raises a practical
arXiv:2605.00539v1 Announce Type: new Abstract: Quantization is a key method for reducing the GPU memory requirement of training large language models (LLMs). Yet, current approaches are ineffective f
arXiv:2605.00251v1 Announce Type: cross Abstract: Existing voice deepfake detection and localization models rely heavily on representations extracted from speech foundation models (SFMs). However, dow
arXiv:2605.00369v1 Announce Type: new Abstract: We study how large language models can be used to evolve inventory policies in online, non-stationary environments. Our work is motivated by recent adva
I just sent out the April edition of my sponsors-only monthly newsletter. If you are a sponsor (or if you start a sponsorship now) you can access it here. In this month's newsletter: Opus 4.7 and GPT-
This post discusses Ollama's functionality for resuming work with Anthropic's Claude models, indicating that users can safely restore previous sessions or states using a command-line interface (`ollam
arXiv:2603.15949v3 Announce Type: replace Abstract: Large Language Models have demonstrated strong multilingual fluency, yet fluency alone does not guarantee socially appropriate language use. In high
arXiv:2605.00674v1 Announce Type: new Abstract: Large language models (LLMs) are becoming increasingly capable mathematical collaborators, but static benchmarks are no longer sufficient for evaluating
arXiv:2605.00408v1 Announce Type: new Abstract: While 3D Gaussian Splatting (3DGS) has demonstrated impressive real-time rendering performance, its efficacy remains constrained by a reliance on heuris
arXiv:2605.00310v1 Announce Type: new Abstract: Super-resolution (SR) techniques have made major advances in reconstructing high-resolution images from low-resolution inputs. The increased resolution
arXiv:2605.00333v1 Announce Type: cross Abstract: Frozen Gemma 4 31B weights pretrained exclusively on text tokens, unmodified, transfer across modality boundaries through a thin trainable interface.
arXiv:2407.10853v5 Announce Type: replace Abstract: Bias and fairness risks in Large Language Models (LLMs) vary substantially across deployment contexts, yet existing approaches lack systematic guida
arXiv:2605.00803v1 Announce Type: cross Abstract: Large language models are increasingly deployed as autonomous coding agents and have achieved remarkably strong performance on software engineering be