Knowledge catalogue
arXiv:2605.22109v1 Announce Type: cross Abstract: Multimodal Large Language Models (MLLMs) are increasingly deployed in human-facing roles where personality perception is critical, yet existing benchm
arXiv:2605.21523v1 Announce Type: cross Abstract: Steganalysis models excel on benchmark datasets but struggle in the wild when analyzed images are produced by a processing pipeline unseen during trai
arXiv:2605.20328v1 Announce Type: cross Abstract: Optimization problems such as the NP-complete 3-SAT provide an important benchmark for the difficult task of finding ground-states in strongly correla
arXiv:2605.22414v1 Announce Type: new Abstract: Visual Question Answering (VQA) holds great promise for clinical support, particularly in ophthalmology, where retinal fundus photography is essential f
arXiv:2605.20278v1 Announce Type: cross Abstract: Long-form image captioning exposes a reward granularity problem in RL: captions are judged as whole sequences, while the important errors occur at the
arXiv:2411.01141v2 Announce Type: replace Abstract: There are two shortages in the current Large Language Models (LLMs) era. The first is short of multilingual models, where most LLMs are English-cent
arXiv:2605.21213v1 Announce Type: cross Abstract: In this work, we present quantum reinforcement learning (RL) as a solution strategy for process synthesis problems. Building on our prior work, we dev
arXiv:2605.20241v1 Announce Type: cross Abstract: Prompt-level safety probes for large language models use hidden-state representations to separate safe from unsafe prompts, but strong average detecti
arXiv:2508.08636v2 Announce Type: replace Abstract: Large language models (LLMs) have revolutionized artificial intelligence by enabling complex reasoning capabilities. While recent advancements in re
arXiv:2605.20824v1 Announce Type: new Abstract: Many sequence computations are easier to study as movement through internal states than as isolated local circuits. We introduce Markovian Circuit Traci
arXiv:2605.20410v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly deployed in socially sensitive settings despite substantial documentation that they encode gender biases.
arXiv:2605.20873v1 Announce Type: cross Abstract: Planning is a fundamental capability for large language models (LLMs) because such complex tasks require models to coordinate goals, constraints, reso
arXiv:2605.21256v1 Announce Type: new Abstract: Standard clinical Natural Language Processing (NLP) benchmarks often yield inflated metrics by forcing deterministic classification on ambiguous instanc
arXiv:2605.20558v1 Announce Type: new Abstract: Neural morphological generation systems often achieve high aggregate accuracy on benchmark datasets, yet such performance can conceal systematic errors
arXiv:2605.19503v1 Announce Type: cross Abstract: Reinforcement learning for legged locomotion has matured into a stack of multi-component reward functions and physics-engine benchmarks whose morpholo
brain dump of how/why we use Evals to measure agents before & after shipping to prod 1. Good Evals simulate what our real users will do and encounter. They’re not really random benchmark tasks, they r
arXiv:2605.20165v1 Announce Type: new Abstract: Vision-Language Models (VLMs) achieve strong performance on spatial question answering benchmarks, yet it remains unclear whether such gains reflect gen
arXiv:2605.18827v1 Announce Type: cross Abstract: Multiple-choice QA benchmarks usually evaluate small language models (SLMs) as direct answerers, but deployed language-model systems increasingly rely
arXiv:2605.19688v1 Announce Type: new Abstract: Document manipulation localization models achieve strong performance on public benchmarks yet fail to generalize to operational document workflows. We i
IBM researchers have achieved a record-breaking quantum circuit by advancing quantum computing benchmarking and performance capabilities. The work likely demonstrates progress in quantum algorithm imp
arXiv:2605.19752v1 Announce Type: new Abstract: Accurately identifying metabolites i.e. small molecules from mass spectrometry data remains a core challenge in metabolomics, with broad applications in
arXiv:2605.19242v1 Announce Type: cross Abstract: World simulators can provide safe and scalable environments for training Physical AI systems before real-world deployment. Large video generation mode
arXiv:2605.19233v1 Announce Type: cross Abstract: Unmanned aerial vehicles (UAVs) are cyber-physical systems whose attack surface spans networked avionics and on-board sensor fusion: a compromised GPS
Figure AI livestreamed three F.03 humanoid robots sorting packages continuously starting May 14, 2026 , generating significant viral attention and becoming a benchmark demonstration for autonomous hum
arXiv:2605.18882v1 Announce Type: cross Abstract: LLM agents exhibit a consistent tendency to over-call, invoking tools even in situations where none is needed. On the When2Call benchmark, six models
Model swaps look like configuration changes, but they behave more like product migrations. A new model may be cheaper, faster, easier to get capacity for, or stronger on public benchmarks.... The post
arXiv:2512.24365v2 Announce Type: replace-cross Abstract: Scientific machine learning (SciML) offers neural-network alternatives to numerical workflows in geotechnical engineering. This paper benchmar
arXiv:2511.11934v3 Announce Type: replace-cross Abstract: We present a systematic benchmark of out-of-distribution (OOD) detection CSFs through a representation-centric lens. Our study spans CNN and V
arXiv:2605.17270v1 Announce Type: new Abstract: Modern visual object trackers show impressive results on general targets, yet their performance drops substantially when dealing with scene text. Althou
arXiv:2605.18727v1 Announce Type: cross Abstract: Evaluating embodied systems on real dexterous hardware requires more than isolated primitive skills: an agent must perceive a changing tabletop scene,
arXiv:2605.17478v1 Announce Type: new Abstract: Visual Geometry Grounded Transformers (VGGT) have set new benchmarks in high-fidelity 3D scene reconstruction. However, as the sequence length increases
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:2605.17152v1 Announce Type: new Abstract: Multimodal LLMs are evolving from vision-language to tri-modality that see, hear, and read, yet pipelines and benchmarks remain English-centric and comp
arXiv:2605.17360v1 Announce Type: new Abstract: Real-time duplex interaction is essential for multimodal AI systems operating in real-world scenarios, where models must continuously process streaming
arXiv:2605.17860v1 Announce Type: cross Abstract: While modern Automatic Speech Recognition (ASR) systems achieve high accuracy on benchmark corpora, their performance often degrades when there is rea
arXiv:2605.18128v1 Announce Type: new Abstract: Existing Multivariate Time Series Anomaly Detection (MTSAD) frameworks increasingly rely on integrating Graph Neural Networks (GNNs) with sequence model
Read more on what we've learned while developing long-horizon agent evals: Curious finding while creating evals and benchmarks for long-horizon (100+ turn) agents While it’s generally thought that a d
arXiv:2605.16628v1 Announce Type: new Abstract: The SCARED dataset is a widely used benchmark for endoscopic depth estimation, offering ground-truth 3D reconstructions captured with a structured light
arXiv:2512.17843v4 Announce Type: replace-cross Abstract: By evaluating Large Language Models (LLMs) through uniform, text-only interfaces, current academic benchmarks obscure how the unique designs a
arXiv:2605.17789v1 Announce Type: cross Abstract: Memory systems for AI assistants were built for single-user dialogue and fail characteristically when applied to multi-party social group settings. Th
arXiv:2605.16381v1 Announce Type: cross Abstract: Proactive streaming video understanding requires models to continuously process video streams and decide when to respond, rather than merely what to r
arXiv:2605.17153v1 Announce Type: new Abstract: Neural network verifiers aim to provide formal guarantees on model behavior, but existing verification benchmarks are fundamentally limited by their lac
arXiv:2605.16410v1 Announce Type: new Abstract: Test-time scaling (TTS) methods have proven highly effective for LLMs, yet their application to vision-language models (VLMs) remains relatively underex
arXiv:2605.18063v1 Announce Type: new Abstract: Object counting is a foundational vision task with over a decade of dedicated research, yet state-of-the-art models still fail systematically in the mix
arXiv:2304.10726v3 Announce Type: replace-cross Abstract: We introduce the Deep Learning Vulnerability Analyzer (DLVA) for Ethereum smart contracts based on neural networks. We train DLVA to judge byt
arXiv:2605.18580v1 Announce Type: new Abstract: Outcome-only evaluation can certify economically unsafe agents: a policy can hit a business KPI while violating deployable behavioral discipline. In hot
arXiv:2605.18132v1 Announce Type: cross Abstract: Generative 3D models are deployed in gaming, robotics, and immersive creation, making source attribution critical: given a 3D asset, can we identify w
arXiv:2605.15708v1 Announce Type: new Abstract: Recent advances in 3D datasets and multimodal models have greatly improved natural language 3D scene understanding. However, most 3D referring segmentat
arXiv:2605.15906v1 Announce Type: new Abstract: Image degradations can occur during acquisition, processing, and transmission, altering visual appearance and affecting downstream vision tasks. They ar
arXiv:2605.15761v1 Announce Type: new Abstract: Evaluation leaderboards such as LMArena play a central role in benchmarking large language models by aggregating pairwise human preferences into model r
arXiv:2605.15518v1 Announce Type: new Abstract: The effective detection and governance of Large Language Model (LLM) generated content has become increasingly critical due to the growing risk of misus
arXiv:2603.03066v2 Announce Type: replace Abstract: Existing AI-generated video quality assessment (AIGVQA) methods mainly focus on global perceptual realism and coarse text-video alignment, while ove
arXiv:2508.20810v3 Announce Type: replace Abstract: Rigorous evaluation of domain-specific language models requires benchmarks that are comprehensive, contamination-resistant, and maintainable. Static
GLM-5.1 from @Zai_org is now live on OrcaRouter • #1 open-source model on SWE-Bench Pro • Beats closed source models on real-world repo repair benchmarks • MIT licensed • 200K context • Built for long
arXiv:2605.15226v1 Announce Type: cross Abstract: We ask whether agentic AI systems built for software engineering transfer to realistic hardware engineering. Existing hardware LLM benchmarks isolate
arXiv:2605.15235v1 Announce Type: new Abstract: Multimodal physiological data powers clinical AI systems from intensive care units to wearable devices, but sensors routinely fail in practice. Two fail
arXiv:2605.15509v1 Announce Type: new Abstract: While Isaac Lab provides massive parallel UAV simulation, OmniSafe and safe-control-gym provide constrained-RL benchmarks, and CBFKit provides control-b
arXiv:2605.15846v1 Announce Type: cross Abstract: Coding agents are increasingly deployed in real software development, where a single version iteration requires months of coordinated work across many
arXiv:2605.15868v1 Announce Type: new Abstract: In this work, we address the critical yet underexplored challenge of symmetric multimodal-to-multimodal (MM2MM) retrieval, where queries and contexts ar
arXiv:2605.15607v1 Announce Type: new Abstract: Large language models (LLMs) achieve high pass rates on code generation benchmarks, yet whether they can transfer this ability to languages absent from