Knowledge catalogue
arXiv:2608.25965v1 Announce Type: new Abstract: Ovarian lesion classification using transvaginal ultrasound remains challenging due to overlapping imaging characteristics and the dependence on expert
arXiv:2608.25641v1 Announce Type: new Abstract: Traditional task-and-motion planning (TAMP) approaches primarily focus on defining sequences of actions along with the necessary geometric and kinematic
arXiv:2608.25359v1 Announce Type: new Abstract: Conversational derailment forecasting aims to predict when online discussions will escalate into hostility, enabling proactive moderation. Existing appr
arXiv:2608.25204v1 Announce Type: cross Abstract: We introduce LibriBrain100, a large-scale MEG dataset for speech decoding designed from the ground up for reproducible, standardised evaluation. Libri
arXiv:2608.25178v1 Announce Type: new Abstract: Sidewalk-scale path extraction demands perception and planning that run reliably on compact, low-power hardware in cluttered, map-sparse environments. W
arXiv:2509.21269v2 Announce Type: replace Abstract: The widespread use of human-like text from Large Language Models (LLMs) necessitates the development of robust detection systems. However, progress
arXiv:2608.25757v1 Announce Type: cross Abstract: Generalist vision--language--action (VLA) policies learn long-horizon behavior mainly through short-horizon action prediction and reveal little beyond
Does anyone know of a decent reverse engineering harness/workspace setup for local models that I can just point the model at and have it go to work until it's reversed most if not all of the functions
arXiv:2608.25824v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used as evaluators to assess output quality and preference alignment, yet providing reliable guarantees of
arXiv:2608.25232v1 Announce Type: new Abstract: Effective selection of trustworthy collaborators is crucial to ensuring the successful completion of collaborative tasks, which requires accurate assess
arXiv:2608.25196v1 Announce Type: new Abstract: Learning from demonstration (LfD) methods enable non-expert end users to teach robots novel skills without explicit programming. However most evaluation
arXiv:2608.25729v1 Announce Type: new Abstract: Long-video MLLMs must model temporal change before a limited visual-token budget removes most frame evidence. We introduce LongVU-TTT, which inserts a c
HuggingFace releases microduck a 10 inch open-source biped with 15 actuators and sensors (camera, speaker, LiDAR, NFC, bluetooth, wifi, ...) that you train yourself with reinforcement learning, $400.
arXiv:2608.25881v1 Announce Type: new Abstract: Fine-tuning abstractive summarization models requires high-quality annotated data. However, obtaining such corpora is expensive and time-consuming, as i
arXiv:2601.20172v2 Announce Type: replace Abstract: We study how neural emulators of partial differential equation solution operators internalize physical symmetries by introducing an influence-based
arXiv:2608.25976v1 Announce Type: new Abstract: International adoptees retain phonological traces of a birth language they can no longer speak or comprehend, a persistence typically attributed to a bi
arXiv:2608.25874v1 Announce Type: new Abstract: This work tackles the problem of scalable perception for robotic packing with low-cost, low-resolution depth sensing. We propose a framework where recon
arXiv:2607.14480v3 Announce Type: replace Abstract: LLM evaluators (trained reward models and prompted LLM-as-a-Judge) are routinely validated via pairwise accuracy. In a multilingual setting, this op
arXiv:2608.25176v1 Announce Type: new Abstract: Structural health monitoring (SHM) is essential in modern engineering, providing data for condition-based maintenance, lifecycle assessment, and predict
arXiv:2608.25866v1 Announce Type: new Abstract: Quantifying wound tissue composition is essential for monitoring chronic ulcer progression and guiding treatment decisions. However, pixel-level annotat
arXiv:2608.25598v1 Announce Type: new Abstract: The purpose of this paper is to provide a general, comprehensive, theoretical framework that allows one to deal with fibrations on graphs labelled on a
arXiv:2608.25864v1 Announce Type: new Abstract: Multi-arm collaboration is becoming a core capability in embodied manipulation. Recent vision-language-action (VLA) models integrate perception, languag
arXiv:2608.24946v1 Announce Type: new Abstract: Macros constitute a large part of the core area in modern very large-scale integration (VLSI) designs. Moreover, macro positions have a significant impa
arXiv:2608.25648v1 Announce Type: new Abstract: Dynamic contrast-enhanced breast MRI is central to cancer diagnosis and monitoring, but requires gadolinium-based contrast agents. In this work, we addr
arXiv:2608.25449v1 Announce Type: new Abstract: Formal theorem proving enables machine-verifiable evaluation of mathematical reasoning, yet existing benchmarks often emphasize aggregate proof accuracy
arXiv:2602.04795v3 Announce Type: replace Abstract: Nonnegative matrix factorization (NMF) is a popular data embedding technique. Given a nonnegative data matrix X, it aims at finding two lower dimens
Meet Gemini Omni 1.1 Flash ⚡️ Our newest multimodal model for video generation and editing. It now features your favorite creative controls from Veo, plus brand new capabilities. Enjoy features like 4
arXiv:2608.25738v1 Announce Type: cross Abstract: Spiking Neural Networks (SNNs) are increasingly distributed as pretrained checkpoints and reused as backbones for new tasks. However, current SNN wate
arXiv:2601.16296v3 Announce Type: replace Abstract: Video-to-video diffusion models achieve impressive single-turn editing performance, but practical editing workflows are inherently iterative. When e
arXiv:2608.25903v1 Announce Type: cross Abstract: Relational Deep Learning (RDL) is an effective approach to machine learning over multi-table relational databases. In RDL, a database is modeled as a
arXiv:2608.25733v1 Announce Type: new Abstract: The existing methods for saliency detection task focus on the application of multi-level features, aiming to take advantage of the respective strengths
arXiv:2601.04879v2 Announce Type: replace Abstract: Synthesizing informative commercial reports from massive and noisy web sources is critical for high-stakes business decisions. Although recent deep
arXiv:2608.25182v1 Announce Type: cross Abstract: In this paper, we study alternating regret in online convex optimization (OCO), motivated by the success of alternating learning dynamics in two-playe
arXiv:2608.25267v1 Announce Type: new Abstract: Large language models (LLMs) frequently exhibit sycophancy: they adapt their answers to a user's stated beliefs or preferences instead of reporting what
arXiv:2608.25575v1 Announce Type: new Abstract: Pretrained vision-language models such as CLIP excel at zero-shot recognition but often fail at compositionality, particularly attribute-object and rela
arXiv:2604.06156v2 Announce Type: replace-cross Abstract: MLLMs have been successfully applied to multimodal embedding tasks, yet their generative reasoning capabilities remain underutilized. Directly
arXiv:2505.22084v2 Announce Type: replace Abstract: Characterizing and quantifying gender representation disparities in audiovisual storytelling contents is necessary to grasp how stereotypes may perp
arXiv:2608.24931v1 Announce Type: cross Abstract: Multimodal neuroimaging combining structural MRI and positron emission tomography (PET) captures complementary structure-function relationships across
arXiv:2606.17118v2 Announce Type: replace Abstract: Mixture-of-Experts Multimodal Large Language Models (MoE-MLLMs) offer remarkable performance but incur prohibitive GPU memory costs, making compress
arXiv:2509.09297v2 Announce Type: replace Abstract: Open-set detection is crucial for robust UAV autonomy in air-to-air object detection under real-world conditions. Traditional closed-set detectors d
arXiv:2608.25698v1 Announce Type: new Abstract: Forecasting future measurements from geographically distributed sensors is essential across many domains. However, the spatial distribution of these sen
arXiv:2608.25768v1 Announce Type: new Abstract: Turkish encoder models have adopted modern architectures while leaving the pretraining objective fixed at masked language modelling. This paper introduc
Most companies manage their org chart, their product line, their brand guidelines, their operations manual. Each as a separate document. Each updated on its own cadence. Each a snapshot of a different
Most extraction tools treat spreadsheets like PDFs. They flatten the file into text or markdown, then ask a model to infer the original structure. But spreadsheets depend on structure. Headers, formul
arXiv:2608.25736v1 Announce Type: new Abstract: EgoExo proficiency estimation aims to assess action quality by integrating fine-grained motion cues from egocentric (1st-person) views with spatial cont
arXiv:2608.24978v1 Announce Type: new Abstract: Multimodal fusion of structural MRI (sMRI) and dynamic functional network connectivity (dFNC) can reveal how brain structure relates to changing functio
arXiv:2608.25085v1 Announce Type: new Abstract: Clinical diagnosis is fundamentally interactive and incremental, yet the dominant paradigm for evaluating Large Language Models (LLMs) in medicine remai
arXiv:2608.24937v1 Announce Type: new Abstract: Multi-Modal Anomaly Detection (MMAD) detects rare abnormal events from heterogeneous data sources and is increasingly used in safety- and reliability-cr
arXiv:2608.25709v1 Announce Type: cross Abstract: We address the simultaneous prediction of multiple high-dimensional physical fields governed by linear equality constraints, a setting that arises in
arXiv:2503.13048v4 Announce Type: replace Abstract: Electrical Impedance Tomography (EIT) offers a promising solution for distributed tactile sensing with minimal wiring and full-surface coverage in r
arXiv:2608.25235v1 Announce Type: cross Abstract: Selection of trustworthy collaborators in distributed systems is critical for efficient task completion, necessitating the inference of trustworthines
arXiv:2608.25126v1 Announce Type: new Abstract: Machine learning has had a significant positive impact on the prediction of athlete performance and injury risk. Most works in this field rely on subjec
arXiv:2608.25305v1 Announce Type: new Abstract: Training-free zero-shot composed image retrieval finds a target image in a gallery from a reference image and a text edit without learning from task-spe
I work on SEO, copywriting, research, etc I’ve used openrouter before but I’ve spent way more for the same amount of work. Deepseek V4 is still the king for me, didn’t found better orchestrator for my
Quick version: I run a public leaderboard where bots predict whether stocks close higher or lower, and every call gets scored against real prices. Nothing gets deleted, timestamps are locked at submis
arXiv:2608.26094v1 Announce Type: new Abstract: Existing action quality assessment (AQA) datasets and methods rely primarily on visual inputs such as RGB and pose, overlooking physiological dynamics s
Since Qwen's dropped the Qwen4Exp architecture bomb that focus on offloading parameters to n-gram instead of pure mixture of experts, I dug into this and learned quite a lot. Here's the summary. Expec
arXiv:2608.25657v1 Announce Type: cross Abstract: Large language models (LLMs) excel at programming, but not when the task fixes the target language: prompted with a grammar rare in their training dat
arXiv:2608.24892v1 Announce Type: cross Abstract: We describe a practical architecture for making the Maritime Information Exchange Model (MIEM) and the broader Rich Semantic Track model tractable usi
Neat paper suggesting that human augmentation and task automation are not necessarily related. Models that are really good at doing work are not always good at helping humans do work better. Given the