Knowledge catalogue
arXiv:2610.15422v1 Announce Type: new Abstract: Haptic shared control in surgical robotics combines human-side and robot-side forces at a shared end effector while preserving operator involvement. How
arXiv:2610.16275v1 Announce Type: new Abstract: Flow Matching (FM) has recently emerged as a principled and efficient generative modeling framework for reinforcement learning (RL), enabling expressive
arXiv:2610.14553v1 Announce Type: new Abstract: Autoregressive video generation models generate videos sequentially, with each new block conditioned on previously generated blocks. By training on the
arXiv:2610.16280v1 Announce Type: cross Abstract: Before changing a deployed AI agent (a stricter safety gate, a new model), organizations must approve the change, starting from existing logs. Extendi
arXiv:2610.16330v1 Announce Type: new Abstract: Feed-forward transformer models have driven remarkable progress in 3D vision,yet their quadratic complexity renders them impractical for long video sequ
arXiv:2610.14261v1 Announce Type: new Abstract: Whole-slide images (WSIs) are gigapixel-scale digital scans of histopathology tissue and serve as the primary basis for cancer diagnosis. Existing histo
arXiv:2610.16016v1 Announce Type: new Abstract: Certified robustness is critical for deploying neural networks in safety-sensitive settings, but computing exact certificates typically requires solving
Secure software supply chain solution provider Chainguard Inc. today unveiled a vulnerability-hunting service that uses frontier artificial intelligence models such as Anthropic PBC’s Claude Mythos 5.
arXiv:2610.15813v1 Announce Type: new Abstract: Bitemporal remote sensing change detection requires representations that preserve spatial detail while capturing contextual information relevant to chan
arXiv:2610.16587v1 Announce Type: new Abstract: Financial candlestick understanding requires locating visual elements, aligning them across trading dates and panels, and combining observations through
arXiv:2610.17993v1 Announce Type: new Abstract: Certified machine unlearning ensures the proximity between the unlearned model and a model retrained without the data to be forgotten. Extending this fr
Performance on Model Prompt tokens master tok/s pr tok/s Change RTX PRO 6000 Blackwell Qwen3.5-35B-A3B-Q8_0 2048 13,653.58 16,459.56 +20.6% RTX PRO 6000 Blackwell Qwen3.5-35B-A3B-Q8_0 4096 14,090.26 1
arXiv:2610.16273v1 Announce Type: cross Abstract: Speech timing is fundamental for speech models to recognize words accurately, but how word-timing information is represented in speech models remains
arXiv:2610.17453v1 Announce Type: new Abstract: We propose a continuous learning formulation for T non-forgetting tasks with the spherical parameter space S^{d-1}(sqrt{d}) in asymptotic regime drighta
arXiv:2610.18499v1 Announce Type: new Abstract: Egocentric video understanding spans diverse capabilities, modalities, and evaluation formats, yet these are often studied in isolation despite their ti
arXiv:2610.14214v1 Announce Type: new Abstract: Hallucination detection is critical for ensuring the reliability of Large Language Models (LLMs) in real-world deployments. While recent works, such as
arXiv:2610.16238v1 Announce Type: new Abstract: LLM agents increasingly control robots using VLA policies, perception models, and procedural knowledge. These modules are difficult to reuse because the
arXiv:2610.17326v1 Announce Type: new Abstract: Household robots must successfully navigate im-perfect circumstances, both in their internal models and the external world. We introduce a benchmark of
arXiv:2610.14609v1 Announce Type: new Abstract: Determining whether an image is AI-generated does not establish whether the situation it depicts is factually supported. We formulate Image Factuality A
arXiv:2610.15425v1 Announce Type: new Abstract: In embodied manipulation, Vision Language Action (VLA) models can execute atomic skills, but long-horizon manipulation requires recombining these skills
arXiv:2610.18260v1 Announce Type: new Abstract: Wearable fall detection is hampered by rare fall events, scarce labeled IMU data, and the cost of training deep models on large, imbalanced corpora. Exi
arXiv:2610.17841v1 Announce Type: cross Abstract: Evaluating fashion retrieval systems remains challenging due to the limited availability of benchmarks that capture the fine-grained, open-ended queri
arXiv:2610.15921v1 Announce Type: new Abstract: With ever increasing personalized large language model (LLM) services, the next question to answer is how to improve the quality of services/users' expe
arXiv:2602.00628v3 Announce Type: replace Abstract: We investigate the extent to which an LLM's hidden-state geometry can be recovered from its behavior in psycholinguistic experiments. Across eight i
arXiv:2610.14622v1 Announce Type: new Abstract: Extreme rainfall can rapidly degrade road-network functionality (RNF), disrupting emergency mobility, accessibility, evacuation, logistics, and infrastr
arXiv:2610.15331v1 Announce Type: new Abstract: Full-waveform inversion (FWI) is a powerful approach for reconstructing subsurface velocity models, but its practical performance is limited by ill-cond
arXiv:2610.18587v1 Announce Type: cross Abstract: Multimodal Emotion Recognition (MER) aims to understand human emotions by integrating information from multiple modalities such as speech and text. Ps
arXiv:2607.18360v2 Announce Type: replace-cross Abstract: GPTZero found 53 NeurIPS 2025 accepted papers with hallucinated citations, and venues now screen submissions with automated reference checkers
arXiv:2610.17370v1 Announce Type: new Abstract: Scientific discovery with language models requires finding the best valid solution within a finite computational budget. We study how feedback from this
arXiv:2610.16545v1 Announce Type: new Abstract: Feature caching has become a mainstream training-free strategy for accelerating video diffusion models by reusing intermediate outputs across denoising
arXiv:2610.14023v1 Announce Type: new Abstract: Predicting which scientific connections will form next can point researchers toward the most promising leads. One successful approach learns representat
arXiv:2610.16720v1 Announce Type: new Abstract: Existing benchmarks for GUI agents primarily focus on increasing task difficulty by extending task horizons. Besides, enabling agents to explore unfamil
arXiv:2510.27114v2 Announce Type: replace-cross Abstract: Generative BC policies model expert action distributions well, but they do not explicitly reason about what scene a candidate action would pro
arXiv:2610.16313v1 Announce Type: new Abstract: Vision-language-action (VLA) models have advanced robotic manipulation by connecting visual perception and language instructions to action generation. H
arXiv:2610.18289v1 Announce Type: new Abstract: Autoregressive video diffusion models have emerged as a foundation for long-horizon world rollout. However, when extended far beyond their training wind
Went down the rabbit hole to map the different Post-Transformer architectures. Various directions include Dragon Hatchling (BDH), Mamba, RWKV, xLSTM, Titans, and a bunch of hybrid models coming from d
arXiv:2610.14976v1 Announce Type: new Abstract: Large Vision-Language Models (LVLMs) have excelled in joint visual and language understanding, particularly in image captioning. However, LVLMs remain p
arXiv:2610.17715v1 Announce Type: new Abstract: Flow matching has emerged as a powerful alternative to diffusion models for generative modeling, but standard conditional flow matching is fundamentally
arXiv:2508.21044v2 Announce Type: replace Abstract: Video Large Language Models (VLLMs) excel in video understanding, but their excessive visual tokens pose a significant computational challenge for r
arXiv:2601.14628v2 Announce Type: replace Abstract: Vision-language-action (VLA) models inherit powerful semantic representations from large language models, but carrying over their design priorities
arXiv:2610.15246v1 Announce Type: new Abstract: Tool-using agents alternate between model generation and tool execution, waiting for each tool call to complete before starting the next generation. Thi
arXiv:2610.17143v1 Announce Type: new Abstract: Large language model (LLM)-based multi-agent systems are increasingly applied across diverse domains, yet different applications require different forms
arXiv:2610.15020v1 Announce Type: new Abstract: PATTERN, a synthetic node-classification benchmark still used to rank graph networks, plants a random subgraph in a random host graph and asks which nod
arXiv:2606.12487v2 Announce Type: replace Abstract: Post-training quantization (PTQ) is essential for efficient large language model (LLM) inference, but reliably quantizing activations remains challe
arXiv:2610.17260v1 Announce Type: new Abstract: To reconstruct a predictive 4D representation of the physical world from monocular video captured by a moving camera, we jointly reconstruct dynamic Gau
arXiv:2610.15947v1 Announce Type: cross Abstract: Heap-leaching hydraulics vary between regions of an ore bed, motivating fast prediction models that accommodate different material properties. This st
arXiv:2610.18597v1 Announce Type: cross Abstract: Physical properties provide intuitive controls for sounds made by interacting objects. The challenge is to make a generative model respond predictably
arXiv:2610.18280v1 Announce Type: new Abstract: Advection-diffusion plays an essential role in transport phenomena such as atmospheric and cloud dynamics. Improved forecasting of cloud motion, in turn
arXiv:2610.17991v1 Announce Type: new Abstract: Recovering high-quality images from degraded underwater observations is crucial for underwater exploration. Polarization observations show promise as an
arXiv:2610.14894v1 Announce Type: new Abstract: We present a robust deep generative framework for unsupervised image prototype discovery, built on a heavy-tailed Student's t mixture prior, that bridge
Sampling: temp 1.0, top_p 0.95, top_k 20 for both. Run with evalscope over the OpenAI-compatible API: Qwen3.8-Next on Strata (MI50 32GB), Qwen3.8-27B on llama.cpp (RX 7900 XTX 24GB). Caveat: These are
arXiv:2610.18607v1 Announce Type: new Abstract: Pretrained diffusion models provide powerful generative priors for extreme image compression, but their stochastic, iterative generation processes are n
Recursive self-improvement is one of the most discussed ideas in AI right now. RSI-Anything measures it in code repair, GUI agents, lab planning, and chemistry. They find that the lab-planning score m
arXiv:2610.18388v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) have shown strong potential for remote sensing understanding, yet reliable reasoning remains challenging when t
The post explains that high GPU utilisation does not guarantee fast training of large language models. It identifies two common bottlenecks—missing GPU kernels and excessive waste in the data‑input pi
arXiv:2610.15742v1 Announce Type: new Abstract: Scene classification of remote sensing imagery with multimodal large language models (MLLMs) is formulated as a single-forward-pass decision problem: ca
arXiv:2610.15514v1 Announce Type: new Abstract: Structured pruning reduces the inference cost of large language models (LLMs) by permanently removing neurons and weight rows, yielding dense, hardware-
arXiv:2610.16559v1 Announce Type: cross Abstract: We present a scalable method for identifying nonlinear dynamics from partial measurements, supporting physics-based, neural-network, and gray-box mode
arXiv:2610.17661v1 Announce Type: new Abstract: The rapid development of mobile imaging and social platforms has led to explosive growth in user-generated content images, making their systematic and c
arXiv:2610.14094v1 Announce Type: new Abstract: Existing jailbreak attacks have exposed serious safety weaknesses in multimodal large language models (MLLMs). Most prior attacks rely on specially cons