Knowledge catalogue
arXiv:2608.26500v1 Announce Type: new Abstract: Optical Character Recognition (OCR) for text recognition using machine vision has significantly improved, particularly when handling heterogeneous textu
arXiv:2608.26578v1 Announce Type: cross Abstract: This work introduces Configured Failure Trapping, a novel backdoor attack task against Vision-Language-Action (VLA) models, which aims to activate att
arXiv:2604.05876v2 Announce Type: replace Abstract: Deploying Large Language Models (LLMs) in real-world dynamic environments raises the challenge of updating their pre-trained knowledge. While existi
arXiv:2608.25418v1 Announce Type: new Abstract: Progress in 4D LiDAR segmentation is bottlenecked by data. Assigning temporally consistent labels across sparse point cloud sequences is costly and hard
arXiv:2503.05788v3 Announce Type: replace-cross Abstract: Large Language Models (LLMs) are leading a new technological revolution as one of the most promising research streams toward artificial genera
arXiv:2608.25574v1 Announce Type: new Abstract: Automatic Speech Recognition (ASR) is typically evaluated using Word Error Rate (WER), which poorly reflects semantic similarity. While embedding-based
arXiv:2608.25375v1 Announce Type: cross Abstract: Generative vision-language models (VLMs) are increasingly used in human-centered settings, yet they can produce demographically biased outputs even wh
New York Times: Sources: Meta internally projected that it could spend as much as 10B annually on Anthropic's AI models, even as Zuckerberg publicly criticized Anthropic — The social networking giant
arXiv:2608.25653v1 Announce Type: new Abstract: Test-time adaptation (TTA) has been widely explored in single-label recognition, effectively mitigating distribution shifts, especially when combined wi
arXiv:2608.25846v1 Announce Type: cross Abstract: Cough acoustics are promising for non-invasive tuberculosis (TB) screening, yet whether machine learning (ML) models capture disease-related acoustics
arXiv:2608.24218v1 Announce Type: new Abstract: Enterprise entity alignment must handle semi-structured records, implicit attributes, and unit or granularity mismatches. Manual matching is still commo
Artificial intelligence startup Deep Cogito Inc. today announced that it has raised 43 million in funding. TQ Ventures led the Series A round. It was joined by Benchmark, Nexus Venture Partners, Atrei
arXiv:2608.24436v1 Announce Type: cross Abstract: Wearable device data enables continuous health monitoring, but suffers from structured missingness: features sharing a physical sensor drop out togeth
arXiv:2608.24646v1 Announce Type: new Abstract: Reinforcement learning can align diffusion models with human preferences and task-specific objectives, but endpoint rewards do not specify how an interm
Tenet came out of a close collaboration with @ItsJulioPereyra @nikogrupen @calvincongelado @gabepereyra: many base models, recipes, and data approaches; many runs, rollbacks, and harness revisions to
Tenet came out of a close collaboration with @PereyraJulio @nikogrupen @calvincongelado @gabepereyra: many base models, recipes, and data approaches; through many runs, rollbacks, and harness revision
arXiv:2608.24142v1 Announce Type: new Abstract: Prompt learning adapts vision-language models such as CLIP by optimizing continuous prompt vectors, but the learned prompts are difficult to interpret i
arXiv:2608.21377v1 Announce Type: cross Abstract: Sycophancy in large language models, the tendency to prioritize user agreement over truthful responses, has been documented extensively but studied pr
arXiv:2602.01765v2 Announce Type: replace-cross Abstract: Diffusion models have been widely deployed in AIGC services, but their reliance on opaque training data exposes them to backdoor attacks. In p
arXiv:2608.22975v1 Announce Type: new Abstract: Embodied multimodal agents must answer from growing observation streams under a fixed per-decision token budget. We formalize this constraint through fo
arXiv:2608.22968v1 Announce Type: new Abstract: Industrial monitoring models must detect operationally relevant deviations while satisfying target-specific data, calibration, and resource constraints.
arXiv:2509.23928v3 Announce Type: replace-cross Abstract: Speculative decoding has proven effective for accelerating inference in Large Language Models (LLMs), yet its extension to Vision-Language Mod
arXiv:2608.21709v1 Announce Type: new Abstract: Diffusion models are widely used as priors for linear inverse problems, yet endpoint quality does not reveal when measurement information enters reverse
arXiv:2608.23547v1 Announce Type: cross Abstract: Machine-learning-based anomaly detection is increasingly used in industrial control systems (ICS), yet most studies assume that detector training data
arXiv:2608.22112v1 Announce Type: new Abstract: We present a machine learning framework for identifying sparse, interpretable models of dynamical systems directly from time-series data. Our approach p
arXiv:2510.01028v2 Announce Type: replace Abstract: Large language models have made revolutionary progress in generating human-like text, yet their outputs often tend to be generic, exhibiting insuffi
arXiv:2601.21242v2 Announce Type: replace-cross Abstract: Motivated by challenges in conditional generative modeling, where the target conditional density takes the form of a ratio f1 over f2, this pa
arXiv:2608.20970v1 Announce Type: new Abstract: Recent work in mechanistic interpretability has studied how large language models recall facts stored in their weights. This paper argues that factual r
arXiv:2603.02760v2 Announce Type: replace-cross Abstract: Diffusion large language models (dLLMs) have recently attracted significant attention for their ability to enhance diversity, controllability,
arXiv:2608.20841v1 Announce Type: new Abstract: This paper investigates the development of causal foundation models for bounding the effect of interventions and counterfactuals from observational data
I haven't seen a post about this here, and I'm curious what you think about it. On August 14 Civitai rolled out the option for creators to choose 'permanent paid access - selling with no time cap'. Pr
arXiv:2608.19831v1 Announce Type: new Abstract: Causal Bayesian networks (CBNs) and structural causal models (SCMs) are the dominant frameworks for graphical causal reasoning, but they cannot adequate
arXiv:2608.19497v1 Announce Type: new Abstract: We characterize the learning dynamics of a compact hybrid quantum forecasting model through comparison with a structurally aligned classical baseline. U
arXiv:2608.19323v1 Announce Type: cross Abstract: Uncertainty quantification (UQ) is essential for the safe deployment of large language models (LLMs). Existing methods, from verbalized confidence to
‘Open Models, Production Agents’ is underway! Tonight means another event hosted at FriendliAI's San Francisco HQ. A very lively turn out, and a stellar lineup of panelists exploring how open models a
Chase DiFeliciantonio / Politico: OpenAI says California should amend SB 53 to expand safeguards, including requiring monitoring of frontier models under training, following AI agent hacks — SAN FRANC
arXiv:2511.20973v2 Announce Type: replace-cross Abstract: Large Audio Language Models (LALMs) deliver strong performance across speech and audio tasks, but their audio encoders generate high-rate toke
The post titled “Welcome @ATT to open models!” announces AT&T’s participation in open‑source AI model initiatives. It references the Ollama platform and cites Amir Efrati’s comment on the growing tren
arXiv:2608.19208v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) are frequently exposed to auxiliary textual context, the impact of which on visually grounded tasks remains und
arXiv:2608.18714v1 Announce Type: cross Abstract: Machine-learned models are replacing first-principles calculations across materials discovery, and physical symmetry is the central guarantee built in
arXiv:2607.10476v2 Announce Type: replace Abstract: Large language models (LLMs) have emerged as a powerful tool for retrieving knowledge through seamless, human-like interactions. Despite their advan
arXiv:2608.18827v1 Announce Type: cross Abstract: Reward function design remains a bottleneck in reinforcement learning. While large language models (LLMs) have enabled automated reward generation, ex
arXiv:2608.19075v1 Announce Type: cross Abstract: Large vision-language models (LVLMs) often hallucinate, generating content that the input image does not support. Preventing such content during decod
arXiv:2608.18808v1 Announce Type: new Abstract: This paper introduces Tensor Field Models (TFMs), realization-level Mathematical Structures in which a learned Operator maps a product of admissible com
arXiv:2608.17234v1 Announce Type: cross Abstract: Multimodal large language models (MLLMs) are increasingly used to interact with screenshots, scanned documents, diagrams, and other visually grounded
arXiv:2603.22782v2 Announce Type: replace Abstract: Recent advances in 3D generation have improved the fidelity and geometric details of synthesized 3D assets. However, due to the inherent ambiguity o
arXiv:2601.22170v2 Announce Type: replace-cross Abstract: Large language models (LLMs) process and predict sequences containing text to answer questions, and address tasks including document summariza
arXiv:2608.17306v1 Announce Type: cross Abstract: While adversarial prompt tuning can enhance robustness of vision-language models efficiently, we find that existing methods aggravate robust generaliz
arXiv:2504.20131v4 Announce Type: replace-cross Abstract: We introduce the LZ penalty, a penalty specialized for reducing degenerate repetitions in autoregressive language models without loss of capab
arXiv:2608.17722v1 Announce Type: cross Abstract: Vision-Language models (VLMs) achieve outstanding performance largely due to the amount of training data available on the internet. At the same time,
arXiv:2608.17848v1 Announce Type: new Abstract: Geospatial Foundation Models (GFMs) are emerging as a powerful paradigm for learning semantically rich and geographically consistent visual and physical
arXiv:2608.16974v1 Announce Type: cross Abstract: Despite groundbreaking advancements in generative models during the last decade, concerns about their lack of fairness, reinforcing societal inequalit
arXiv:2608.17414v1 Announce Type: new Abstract: Chart editing requires inferring and modifying visualization code from a reference chart image based on an editing instruction, challenging fine-grained
arXiv:2510.14462v3 Announce Type: replace Abstract: Unsupervised anomaly detection (UAD) based on deep generative modelling has been increasingly explored for identifying pathological brain abnormalit
arXiv:2608.16541v1 Announce Type: cross Abstract: Objective assessment of learning remains a fundamental challenge in education. Electroencephalography (EEG) provides a direct, non-invasive window int
arXiv:2608.16161v1 Announce Type: cross Abstract: General-purpose text embedding models are designed to capture semantic similarity but are not optimised for distinguishing entity records that represe
arXiv:2608.14629v1 Announce Type: cross Abstract: As Large Language Models (LLMs) become the mainstay for information retrieval and summarization tasks, ensuring that they are always non-partisan and
arXiv:2608.15783v1 Announce Type: cross Abstract: In transfer-learning settings, a model derived from abundant surrogate labels may be deployed in a target population where gold-standard outcomes are
arXiv:2608.15448v1 Announce Type: new Abstract: Large language models increasingly rely on sampling as a driver of their own improvement, making the fidelity of their learned distributions more critic
arXiv:2608.15129v1 Announce Type: cross Abstract: We systematically compare word order preferences in decoder-only language models across 192 artificial languages and typologically diverse natural lan