Knowledge catalogue
arXiv:2607.26314v1 Announce Type: cross Abstract: Stealth, the discipline of achieving an objective without revealing your presence, capabilities, or collected intelligence, is what separates sophisti
arXiv:2607.27303v1 Announce Type: cross Abstract: Temporal heterogeneous graphs offer a natural abstraction for dynamic relational systems in which diverse node and relation types co-exist and evolve
arXiv:2607.26307v1 Announce Type: new Abstract: Contemporary LLM-based coding agents produce code as black-box outputs: the rationale behind each line is hidden, the evolution of the code through benc
Try Grok 4.5 http://X.ai/cli BREAKING: Grok 4.5 outperforms GPT-5.6 Terra across almost all shared benchmarks on AskClash, leading in ACB, GPQA, SWE-P, and Atlas while also achieving a higher overall
I've been benchmarking a two-card box for a few weeks and I still can't quite get over some of these numbers, so I'm dumping them here. Box: RTX 4090 (24GB) + RTX 5060 Ti (16GB), i9-13900K, 64GB DDR5.
And Grok 4.6 comes out in a week BREAKING: Grok 4.5 ranked #1 on LaurenBench with a score of 56.9%, ahead of Claude Sonnet 5, GLM 5.2, Claude Opus 5, Kimi K3 and GPT-5.6. The benchmark tests real-worl
arXiv:2607.26298v1 Announce Type: new Abstract: We built and evaluated a self-serve entity resolution (ER) system on six benchmarks spanning 864 to 5M records, and three lessons emerged that are absen
Gemini Robotics ER 2 helps robots reason, collaborate, and solve real-world tasks. It represents a step change in video understanding, tool orchestration, and multi-robot collaboration for robotic app
arXiv:2607.26582v1 Announce Type: new Abstract: Selecting a zero-shot out-of-distribution (OOD) detector for a new deployment is typically based on benchmark rankings, implicitly assuming that the hig
arXiv:2607.26094v1 Announce Type: cross Abstract: Reinforcement Learning from Human Feedback (RLHF) is the standard approach for aligning large language models with human preferences, but its quality
arXiv:2607.26751v1 Announce Type: new Abstract: State-of-the-art intracortical brain-to-text systems pair a neural-sequence phone decoder with an external language model. Two design axes remain undere
arXiv:2607.26326v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) achieve strong performance by integrating visual inputs with the rich priors of pretrained language models. How
arXiv:2607.27027v1 Announce Type: new Abstract: Gradient-boosted trees dominate tabular machine learning, yet canonical correlation analysis has always relied on linear or neural encoders. We propose
arXiv:2607.26922v1 Announce Type: new Abstract: Multi-agent LLM pipeline systems break down the task among multiple roles for better reasoning, but are benchmarked mainly with large-scale commercial m
BREAKING: Grok 4.5 ranked #1 on LaurenBench with a score of 56.9%, ahead of Claude Sonnet 5, GLM 5.2, Claude Opus 5, Kimi K3 and GPT-5.6. The benchmark tests real-world AI agents across conversation,
SpaceXAI has released its new Grok Voice Think Fast 2.0, which on the Artificial Analysis Speech‑to‑Speech benchmark outperformed leading models from OpenAI, Google, Alibaba and DeepSlate. The claim w
arXiv:2607.25675v1 Announce Type: new Abstract: Text-space optimization adapts large language models (LLMs) by editing external natural-language artifacts rather than model weights, so the optimized a
arXiv:2607.24889v1 Announce Type: cross Abstract: Financial models combine public disclosures with analyst assumptions to produce forecasts and valuations. While some components can be checked mechani
arXiv:2607.24780v1 Announce Type: new Abstract: Evaluating frontier LLMs is challenging: static benchmarks suffer from contamination and saturation -- leaving users unable to distinguish top models an
arXiv:2607.25524v1 Announce Type: cross Abstract: Unmanned aerial vehicle (UAV)-satellite cross-view geo-localization matches UAV images against satellite imagery and has achieved impressive accuracy
arXiv:2607.24809v1 Announce Type: cross Abstract: Remaining useful life prediction for aircraft centrifugal air compressors in real commercial operations poses challenges that controlled benchmark dat
arXiv:2607.22980v1 Announce Type: new Abstract: Brain-computer interfaces (BCIs) have long sought calibration-free operation, but classifiers are typically benchmarked by discrimination alone, blind t
arXiv:2607.23044v1 Announce Type: cross Abstract: Quantum devices are open systems whose dynamics interleave coherent evolution with dissipation, and benchmarking, error mitigation, and error correcti
arXiv:2607.24743v1 Announce Type: cross Abstract: Multimodal large language models (MLLMs) hold immense potential to revolutionize clinical practice, yet deploying them in the medical domain is fundam
arXiv:2607.22962v1 Announce Type: new Abstract: LLM agents that operate over many turns accumulate facts in an external memory store and reuse them as premises for downstream reasoning. A hallucinated
arXiv:2607.23448v1 Announce Type: cross Abstract: Expensive constrained optimization problems in real-world industry design often involve constraint thresholds that are difficult to determine in advan
arXiv:2607.24586v1 Announce Type: cross Abstract: Large Language Models can produce fluent text that is false, unsupported by the available evidence, or inconsistent with information that appears to b
arXiv:2607.22555v1 Announce Type: new Abstract: Medical diagnosis is a multi-stage process: extract facts, consult knowledge, generate a differential analysis, and select the best diagnosis with expla
arXiv:2607.23197v1 Announce Type: new Abstract: Anomaly detection on multivariate sensor time series is critical for industrial monitoring of cyber-physical systems (CPS), where even subtle deviations
arXiv:2607.24651v1 Announce Type: cross Abstract: Reliable visual document understanding requires a model to attribute each answer to the evidence regions that support it. Recent benchmarks and system
arXiv:2607.23542v1 Announce Type: new Abstract: Efficient border control is becoming a significant global challenge, mainly due to severe congestion and extended passenger waiting times. To mitigate t
arXiv:2601.17039v2 Announce Type: replace-cross Abstract: Mangroves are critical for climate-change mitigation, requiring reliable monitoring for effective conservation. While deep learning has emerge
arXiv:2607.22738v1 Announce Type: cross Abstract: Current 3D generative models mostly produce a final surface: a visually strong but largely opaque mesh. Interactive 3D worlds need more than a surface
arXiv:2510.24411v3 Announce Type: replace Abstract: Computer-using agents powered by Vision-Language Models (VLMs) have demonstrated human-like capabilities in operating digital environments like mobi
arXiv:2607.22695v1 Announce Type: new Abstract: Large Language Models (LLMs) are capable of generalizing human language for the completion of never-before-seen tasks, leading to widespread deployment.
arXiv:2607.23794v1 Announce Type: cross Abstract: Pathological diagnosis is inherently multi-scale, requiring the integration of global tissue architecture at low magnification with cellular morpholog
arXiv:2607.22861v1 Announce Type: cross Abstract: Pathology foundation models (FMs) produce powerful tile-level representations which remain sensitive to scanner and staining variability, undermining
arXiv:2603.03536v2 Announce Type: replace-cross Abstract: Current LLM-based conversational recommender systems (CRS) primarily optimize recommendation accuracy and user satisfaction. We identify an un
arXiv:2607.22712v1 Announce Type: cross Abstract: Single-cell light microscopy images have become an important data source for characterizing cell phenotypes, but their complexity and heterogeneity po
arXiv:2607.24588v1 Announce Type: new Abstract: Early warning of extreme weather is essential for mitigating the societal, economic, and environmental risks posed by hazardous weather events. However,
arXiv:2607.18080v2 Announce Type: replace-cross Abstract: Multimodal video misinformation detection is commonly formulated as a holistic video-understanding task, where the entire video and its associ
arXiv:2607.24519v1 Announce Type: cross Abstract: Pretrained EEG foundation models are increasingly proposed for clinical decoding, but their transfer across populations and robustness to negative con
arXiv:2607.18088v2 Announce Type: replace-cross Abstract: Conformal prediction certifies that a classifier's prediction sets cover the truth, and that certificate is marginal. Many recognition benchma
arXiv:2607.24276v1 Announce Type: cross Abstract: Large language models (LLMs) process text through subword tokenizers rather than directly reading characters or words. Because these tokenizers are tr
arXiv:2607.23951v1 Announce Type: new Abstract: Video temporal grounding (VTG) aims to localize the continuous video interval described by a natural-language query. However, current VLM-based methods
arXiv:2607.22727v1 Announce Type: new Abstract: Medical image segmentation models often report high benchmark accuracy under ideal imaging conditions, yet their failures under clinical degradation can
arXiv:2607.21632v1 Announce Type: new Abstract: Traditional benchmarks for LLMs primarily rely on static datasets and objective scoring metrics, which often fail to capture differences in response qua
arXiv:2607.16077v2 Announce Type: replace Abstract: Palm-vein recognition is a highly secure biometric modality due to the uniqueness and subcutaneous nature of vein patterns. However, low contrast in
arXiv:2512.03424v4 Announce Type: replace Abstract: State Space Models (SSMs) model long token sequences of point cloud with linear complexity, but require an unordered point cloud to be serialized. E
arXiv:2607.22145v1 Announce Type: new Abstract: Open-source LiDAR-inertial odometry (LIO) systems have achieved remarkable benchmark accuracy, yet current state-of-the-art implementations are primaril
arXiv:2607.22182v1 Announce Type: new Abstract: Large language model (LLM) evaluation spans diverse tasks and benchmarks, yet evidence remains organized around tasks rather than the capabilities they
arXiv:2607.22135v1 Announce Type: new Abstract: Existing BraTS-GLI datasets provide a widely used benchmark for adult glioma MRI segmentation, but their task definition focuses on tumor subregions and
arXiv:2507.18311v3 Announce Type: replace Abstract: Large Vision-Language Models (LVLMs) have shown impressive capabilities across a range of tasks that integrate visual and textual understanding, suc
arXiv:2607.16811v2 Announce Type: replace Abstract: We revisit Gaussian Mixture Models (GMMs) as a lightweight, interpretable tool for anomaly detection and, in particular, for detecting distributiona
arXiv:2607.21820v1 Announce Type: cross Abstract: Audio deepfake detectors are trained to distinguish genuine speech from synthetic speech and often perform well on standard benchmarks. Yet the same d
arXiv:2607.21655v1 Announce Type: cross Abstract: Robotic learning takes place in dynamic environments with large behavior spaces. A terminal success signal only tells the robot whether the task is co
arXiv:2607.21637v1 Announce Type: new Abstract: This paper explores the efficacy of quasi-Monte Carlo (QMC) weight initialization for meta-reinforcement learning within modern benchmark environments.
I finished the speed leg of my spec-decode benchmarking for Qwen3.6-27B, main algorithms across quants. Overall: the heavier the quant, the more spec-decode buys you (10 of 10 speculative configs rank
arXiv:2607.22529v1 Announce Type: new Abstract: LLM training is shifting from manual design and annotation to interaction-driven self-evolution. However, existing self-evolutionary methods face a fund
arXiv:2607.22014v1 Announce Type: cross Abstract: Multimodal Large Language Models (MLLMs) are emerging as core reasoning modules for embodied agents, yet it remains unclear how well general-purpose m