Knowledge catalogue
arXiv:2605.17640v1 Announce Type: cross Abstract: Retrieval-augmented generation from videos requires systems to retrieve relevant audiovisual evidence from large corpora and synthesize it into cohere
arXiv:2605.17997v1 Announce Type: cross Abstract: Recently, residual reconstruction-based model quantization methods have achieved promising performance in low-bit post-training quantization (PTQ) by
arXiv:2605.18176v1 Announce Type: cross Abstract: This report presents MARS, short for Multimodal Agentic Reasoning with Source selection, our system for the CASTLE Challenge at EgoVis 2026. Participa
arXiv:2509.15357v2 Announce Type: replace Abstract: Diffusion models have achieved strong results in text-to-image generation, but important limitations remain as prompts become more structured and mu
arXiv:2603.06984v2 Announce Type: replace-cross Abstract: Many regulatory and analytic problems require that a prohibited variable influence a decision only through a designated allowable channel -- a
arXiv:2605.17431v1 Announce Type: cross Abstract: We propose MATE, a simple yet effective memory architecture for solving Contextual Markov Decision Processes (CMDPs), a family of MDPs parameterized b
arXiv:2010.15538v4 Announce Type: replace-cross Abstract: Gaussian processes are a versatile framework for learning unknown functions in a manner that permits one to utilize prior information about th
This post recommends an essay by Swyx on creating luck, which likely explores how deliberate actions, visibility, and strategic thinking can increase one's chances of encountering beneficial opportuni
arXiv:2605.06017v2 Announce Type: replace Abstract: Sequence-level evaluations in autoregressive Large Language Models (LLMs) rely on highly dependent token generation. Establishing tight concentratio
In a conversation between Matt Berman and Google CEO Sundar Pichai, Pichai confirmed that Google is experiencing greater demand for its services than its current computational capacity can handle. Thi
arXiv:2605.16716v1 Announce Type: cross Abstract: Text-to-video (T2V) generation has rapidly progressed in visual fidelity, yet its ability to faithfully represent multiple cultures within a single pr
arXiv:2605.17230v1 Announce Type: cross Abstract: Quantum error correction (QEC) is indispensable for realizing fault-tolerant quantum computation, yet its effectiveness hinges critically on the class
arXiv:2605.16290v1 Announce Type: cross Abstract: Predicting the difficulty of multiple-choice questions (MCQs) is important for effective assessment, yet current methods typically assume a unimodal s
arXiv:2605.16284v1 Announce Type: cross Abstract: Student use of Generative AI (GenAI) products in completing their classwork, with or without their professors' knowledge and/or approval, has resulted
arXiv:2605.17091v1 Announce Type: new Abstract: Scientific forecasting typically relies on direct state prediction, an approach that grows brittle under data scarcity, extended horizons, non-stationar
arXiv:2605.16468v1 Announce Type: cross Abstract: A central goal in understanding human vision is to uncover the visual features that drive neuronal activity. A growing body of work has used artificia
arXiv:2603.05308v2 Announce Type: replace-cross Abstract: Assessing whether an article supports an assertion is essential for hallucination detection and claim verification. While large language model
arXiv:2605.17436v1 Announce Type: cross Abstract: Vision-language models (VLMs) are increasingly proposed for clinical decision support, yet their reliability in real-world scenarios that require inte
arXiv:2605.16639v1 Announce Type: new Abstract: Multimodal clinical prediction faces three challenges: multiple foundation models (FMs) with complementary strengths per modality, pervasive missing mod
arXiv:2602.11130v2 Announce Type: replace-cross Abstract: Sparse point clouds are a common input modality for 3D surface reconstruction, including in safety-critical settings such as surgical navigati
arXiv:2605.16445v1 Announce Type: cross Abstract: Masked Diffusion Language Models MDLMs replace autoregressive generation with iterative demasking and their privacy properties are largely unstudied.
arXiv:2605.18652v1 Announce Type: new Abstract: Recent GUI agents have made substantial progress in visual grounding and action prediction, yet they remain brittle in long-horizon tasks that require m
arXiv:2605.17758v1 Announce Type: new Abstract: Synthetic data is widely used in healthcare to create datasets that are similar to original data but without the privacy concerns. Generating and evalua
arXiv:2601.21468v5 Announce Type: replace Abstract: Long-horizon agentic reasoning necessitates effectively compressing growing interaction histories into a limited context window. Most existing memor
arXiv:2605.17365v1 Announce Type: new Abstract: Different from traditional text-to-image retrieval tasks, chat-based image retrieval allows the human-interactive system to iteratively clarify and refi
arXiv:2506.15588v2 Announce Type: replace Abstract: Differential privacy (DP) protects sensitive data during neural network training, but standard methods like DP-Adam suffer from high memory overhead
arXiv:2605.17539v1 Announce Type: new Abstract: Combinatorial optimization (CO) underlies decision-making from logistics to chip design, where infeasible solutions are operationally unusable and small
arXiv:2605.17444v1 Announce Type: cross Abstract: Modern software ecosystems face a rapidly growing number of disclosed vulnerabilities, increasing the need for automated repair techniques that can op
arXiv:2602.12871v2 Announce Type: replace Abstract: Large language models (LLMs) have attracted growing interest as supportive tools for psychiatric assessment and clinical decision support. However,
arXiv:2510.10528v3 Announce Type: replace Abstract: Large reasoning models (LRMs) have demonstrated remarkable proficiency in tackling complex tasks through step-by-step thinking. However, this length
arXiv:2601.02353v3 Announce Type: replace Abstract: Farmers in remote areas need quick and reliable methods for identifying plant diseases, yet they often lack access to laboratories or high-performan
Meta Platforms Inc. is reportedly reassigning about 7,000 workers into new artificial intelligence roles as part of the broader restructuring that brings layoffs of roughly 8,000 staff into effect on
arXiv:2605.17292v1 Announce Type: new Abstract: Multi-agent large language model (LLM) systems have shown promise for solving complex tasks through agent collaboration. However, existing frameworks as
arXiv:2507.22057v2 Announce Type: replace Abstract: Difficult few-shot image recognition has significant application prospects, yet remaining the substantial technical gaps with the conventional large
arXiv:2605.16864v1 Announce Type: cross Abstract: Although large-scale visual foundation models (VFMs) achieve remarkable performance in semantic understanding, they still underperform in instance-awa
arXiv:2605.16464v1 Announce Type: cross Abstract: Brain tumors exhibit high heterogeneity in morphology and multimodal contrast, making manual slice-by-slice de lineation time-consuming and experience
Rahul Raj / EU-Startups: Milan-based legal AI company Lexroom raised a €42.9M Series B led by Left Lane, after raising a €16M Series A in September 2025, taking total funding to €62.7M — Lexroom, a Mi
arXiv:2605.16887v1 Announce Type: new Abstract: Cross-modality recognition has many important applications in science, law enforcement and entertainment. Popular methods to bridge the modality gap inc
arXiv:2605.17398v1 Announce Type: new Abstract: This paper presents MiniGPT, a compact from-scratch implementation of GPT-style autoregressive language modeling in PyTorch. The aim is to rebuild the c
arXiv:2601.21458v2 Announce Type: replace Abstract: Modern deepfakes have evolved into localized and intermittent manipulations that require fine-grained temporal localization to mitigate severe digit
arXiv:2603.08290v2 Announce Type: replace-cross Abstract: We study the implicit bias of Sharpness-Aware Minimization (SAM) when training L-layer linear diagonal networks on linearly separable binary c
arXiv:2605.17198v1 Announce Type: cross Abstract: To be useful for downstream applications, vision decoding models that are trained to reconstruct seen images from human brain activity must be able to
arXiv:2605.16262v1 Announce Type: new Abstract: Variational inequalities play a key role in machine learning research, such as generative adversarial networks, reinforcement learning, adversarial trai
arXiv:2505.02621v2 Announce Type: replace Abstract: The mean-field Langevin dynamics (MFLD) minimizes an entropy-regularized nonlinear convex functional on the Wasserstein space over R^d, and has gain
arXiv:2601.08118v3 Announce Type: replace Abstract: Large language models (LLMs) are increasingly used as human simulators, both for evaluating conversational systems and for generating fine-tuning da
arXiv:2506.22901v2 Announce Type: replace-cross Abstract: A key challenge in learning from multimodal biological data is missing modalities, where data from one or more modalities are absent for some
Reuters: Mistral acquires Vienna-based Emmi AI for an undisclosed sum to boost its industrial offerings in Europe; Emmi raised €15M in Austria's largest round in 2025 — Europe's leading artificial int
arXiv:2507.06384v2 Announce Type: replace-cross Abstract: Objective: Latent diffusion models (LDMs) could mitigate data scarcity challenges affecting machine learning development for medical image int
arXiv:2602.03664v3 Announce Type: replace Abstract: Large language models excel as few-shot learners when provided with appropriate demonstrations, yet this strength becomes problematic in multiturn a
arXiv:2605.16378v1 Announce Type: cross Abstract: Masked language models (MLMs) define local conditional distributions over tokens but do not, in general, correspond to any consistent joint distributi
arXiv:2605.16865v1 Announce Type: new Abstract: Supervised fine-tuning (SFT) is widely used to inject new knowledge into language models, but it often degrades pretrained capabilities such as reasonin
arXiv:2506.12119v2 Announce Type: replace-cross Abstract: Mixture-of-Experts (MoE) language models dramatically expand model capacity and achieve remarkable performance without increasing per-token co
arXiv:2605.17598v1 Announce Type: new Abstract: Mixture-of-Experts (MoE) architectures enable efficient model scaling, yet expert routing behavior across underrepresented languages remains poorly unde
arXiv:2402.15058v3 Announce Type: replace-cross Abstract: We combine standard persistent homology with image persistent homology to define a novel way of characterizing shapes and interactions between
arXiv:2605.17635v1 Announce Type: cross Abstract: A fast simulation of the detector response is a vital task in high-energy physics (HEP). Traditional Monte-Carlo methods form the backbone of modern p
arXiv:2605.16616v1 Announce Type: new Abstract: Autonomous research systems capable of generating complete scientific manuscripts have advanced rapidly, yet robust and realistic evaluation frameworks
arXiv:2605.17743v1 Announce Type: new Abstract: Continual test-time adaptation adapts a source-pretrained model to non-stationary, unlabeled target streams while retaining past competence, yet texture
arXiv:2603.19538v2 Announce Type: replace Abstract: Monocular 3D object understanding has largely been cast as a 2D RoI-to-3D box lifting problem. However, emerging downstream applications require ima
arXiv:2605.18483v1 Announce Type: cross Abstract: Time series classification (TSC) of biological signals has progressed from handcrafted, modality-specific approaches to deep architectures capable of
arXiv:2605.18449v1 Announce Type: cross Abstract: Understanding customer movement within retail spaces is essential for optimizing store layouts. Real-world trajectory data can provide highly accurate