Knowledge catalogue
arXiv:2607.07895v1 Announce Type: new Abstract: Research on stereotypes in large language models (LLMs) has largely focused on English-speaking contexts, due to the lack of datasets in other languages
arXiv:2607.08137v1 Announce Type: cross Abstract: Federated reinforcement learning (FRL) is crucial for enabling collaborative learning across multiple agents without sharing raw data, thereby enhanci
arXiv:2602.12612v2 Announce Type: replace-cross Abstract: Traditional methods for automating recommender system design, such as Neural Architecture Search (NAS), are often constrained by a fixed searc
arXiv:2607.07968v1 Announce Type: new Abstract: Soft robotic exogloves can provide hand rehabilitation and assistance. Fitting these gloves often relies on standardized measurements not tailored to th
arXiv:2606.17482v2 Announce Type: replace Abstract: Generating adversarial driving scenarios is critical for evaluating and improving autonomous vehicle decision-making systems in simulation. Recent a
arXiv:2607.07756v1 Announce Type: new Abstract: Multimodal learning usually requires a dedicated encoder per modality. When a tabular modality is involved, prior work has been mostly using a plain MLP
Try Grok 4.5 in Perplexity Grok 4.5 is now available as an orchestrator model in Computer for Consumer Pro and Max subscribers. We evaluated it against five other orchestrator configurations on WANDR.
We’re Hiring: Software Engineer (R&D, Infrastructure and Platform Reliability) 🐟 https://sakana.ai/careers/infrastructure-platform-reliability-engineer/ Sakana Fugu, our Multi-Agent System as a Model,
arXiv:2607.07976v1 Announce Type: cross Abstract: Reinforcement learning (RL) has achieved remarkable success in enhancing the reasoning capabilities of large language models (LLMs). However, widely u
arXiv:2607.08605v1 Announce Type: cross Abstract: Sparse autoencoders (SAEs) have emerged as a promising technique for mechanistic interpretability by learning a set of sparse latent features in large
arXiv:2607.07989v1 Announce Type: cross Abstract: Large language model (LLM) based multi-agent systems enable complex problem solving through coordinated reasoning and action, but their distributed st
arXiv:2601.02918v3 Announce Type: replace Abstract: Image Quality Assessment (IQA) is a long-standing problem in computer vision. Previous methods typically focus on predicting numerical scores withou
1 week left. This is your last chance to apply for the 72 Hour Video Hackathon. Build alongside the world's best creators and developers, get access to cutting-edge models, and compete for $150K in pr
arXiv:2607.06990v1 Announce Type: new Abstract: Multi-robot systems provide the parallelism and redundancy necessary for long-horizon tasks, while Large Language Models (LLMs) offer the reasoning capa
arXiv:2607.06726v1 Announce Type: new Abstract: Faithful visual attribution identifies which image regions support a model prediction. Search-based perturbation methods lead the insertion--deletion fa
arXiv:2607.07708v1 Announce Type: cross Abstract: Structure-property relationships are foundational to biology, chemistry and materials science, where function, reactivity and physical response emerge
arXiv:2510.04547v5 Announce Type: replace-cross Abstract: Large pretrained vision encoders are central to multimodal intelligence, powering applications from on-device vision processing to vision-lang
arXiv:2211.14966v2 Announce Type: replace Abstract: Deep neural networks (DNNs) are highly vulnerable to adversarial attacks. Ideally, a robust model should perform well on both perturbed training dat
arXiv:2512.11935v2 Announce Type: replace Abstract: Agentic AI systems increasingly connect large language models (LLMs) to external scientific tools, yet whether and when tool access improves predict
arXiv:2607.07538v1 Announce Type: new Abstract: Training a model with noisy gradient descent can be idealized as overdamped Langevin dynamics on the loss landscape, and a natural safety question is to
b9945 is a build-tagged release of llama.cpp, an open-source library that performs inference on large language models and is co-developed alongside the GGML tensor library. The llama.cpp project does
arXiv:2607.07267v1 Announce Type: cross Abstract: Claims about the universality of human concepts have been predominantly assessed through linguistic similarity across languages and cultures. However,
The race to build the fastest AI infrastructure is reshaping the semiconductor industry, with inference speed emerging as the defining competitive dimension of the AI era. As AI model wars intensify a
arXiv:2607.07129v1 Announce Type: new Abstract: Compositionality, by organizing complex behavior as combinations of simpler elements, enables robot learning that is scalable and data efficient. Levera
arXiv:2508.00554v4 Announce Type: replace-cross Abstract: In financial trading, large language model (LLM)-based agents demonstrate significant potential, but their decisions can be sensitive to noisy
arXiv:2607.07209v1 Announce Type: cross Abstract: Modern federated and streaming learning systems often release intermediate models, so privacy must hold for the full trajectory under adaptive interac
arXiv:2603.15685v2 Announce Type: replace-cross Abstract: Omnimodal large language models (OmniLLMs) jointly process audio and visual streams, but the resulting long multimodal token sequences make in
arXiv:2607.06632v1 Announce Type: new Abstract: Adversarial attacks are crafted data manipulations that aim to deteriorate the outcomes of prediction or decision-making algorithms. In the energy syste
arXiv:2607.07178v1 Announce Type: cross Abstract: Recent breakthroughs of Reinforcement Learning (RL) have highlighted its potential for complex agentic Large Language Model (LLM) tasks. However, exis
arXiv:2607.06648v1 Announce Type: cross Abstract: Latent reasoning methods perform multi-step inference entirely in the model's continuous hidden states, promising more compact and efficient reasoning
arXiv:2607.07478v1 Announce Type: cross Abstract: FFT-based spectral preprocessing of learned query-key (Q/K) projections substantially improves transformer attention on character-level language model
arXiv:2607.07321v1 Announce Type: new Abstract: Tool utilization enables Large Language Model (LLM) agents to interact with the real world and resolve complex tasks. However, existing agent frameworks
arXiv:2607.06652v1 Announce Type: new Abstract: Rough path signatures are a universal feature map for continuous paths and, via the expected signature, characterise path distributions. These guarantee
arXiv:2607.07702v1 Announce Type: new Abstract: The optimization of long-horizon agents increasingly relies on reflection-based mechanisms, where a large language model (LLM) acts as an optimizer to d
arXiv:2607.06856v1 Announce Type: new Abstract: Prior work suggests that diffusion representations capture low-level geometry but struggle with high-level semantics. We demonstrate that state-of-the-a
Elon Musk announced progress updates on Grok, his AI assistant developed by xAI, likely highlighting improvements in the model's capabilities, training, or deployment. The post suggests ongoing develo
arXiv:2607.07233v1 Announce Type: new Abstract: Deep generative models offer a promising paradigm for topology optimization, enabling rapid design exploration. However, these approaches lack intrinsic
arXiv:2607.06972v1 Announce Type: new Abstract: Prompt-free adaptation of the Segment Anything Model (SAM) has emerged as a promising paradigm for automatic medical image segmentation. Existing method
Grok 4.5 is looking like a success with help from Cursor data but underneath the surface we expect future Grok/Cursor model training is likely to speed up in the coming months. We've spent several mon
arXiv:2304.03388v2 Announce Type: replace Abstract: Deep Neural Networks (DNNs) have become ubiquitous for their ability to solve problems across various domains, including computer vision, natural la
arXiv:2607.07034v1 Announce Type: cross Abstract: We introduce Intrinsic Green's Learning (IGL), a framework that models a target function on a manifold as the solution to a linear PDE whose source te
It seems like SpaceX/Grok and Meta/Muse have started to keep pace in the near-frontier category while also introducing a new category of cheap, fast & closed specialized coding models. Both were tied
arXiv:2607.06623v1 Announce Type: cross Abstract: Process industries rely on time-series forecasting and soft sensing to estimate quality variables that are hard to measure online. Labeled data are sc
arXiv:2607.06974v1 Announce Type: new Abstract: Large language models (LLMs) increasingly improve their reasoning at test time via additional computation, yet most existing works treat each problem in
arXiv:2607.07383v1 Announce Type: new Abstract: Knowledge-based Visual Question Answering (KB-VQA) requires models to retrieve visual entities matching the query image from large-scale encyclopedic kn
Modal is a cloud computing platform that provides serverless infrastructure for running Python code at scale, enabling developers to execute functions, train machine learning models, and process data
🤗 MOSS-Transcribe-Diarize-0.9B is now open source on @huggingface. Built with an end-to-end audio-to-structured-transcript paradigm: >0.9B open-source ASR model >Apache license 2.0 >128k long-context
arXiv:2607.07368v1 Announce Type: cross Abstract: AI control is a family of techniques to prevent an AI with malicious goals from subverting its operator's intent. AI Control usually studies a single
Muse Spark 1.1 has been released and is now accessible through Vercel's AI Gateway, representing an update to the Muse Spark model or service. This release likely includes performance improvements, ne
arXiv:2607.06678v1 Announce Type: new Abstract: How can pretrained Vision-Language-Action (VLA) models retain long-horizon visual histories with high-frequency updates without sacrificing efficiency?
arXiv:2510.02999v5 Announce Type: replace-cross Abstract: Existing gradient-based jailbreak attacks on Large Language Models (LLMs) typically optimize adversarial suffixes to align the LLM output with
arXiv:2607.07023v1 Announce Type: new Abstract: Supervised fine-tuning (SFT) is often treated as a capability-adaptation step, while alignment is attributed to later preference optimization or reinfor
arXiv:2607.06655v1 Announce Type: cross Abstract: In this report, we present Pelican-VLA 0.5, a unified VLA model that integrates vision-language understanding, future-frame generation, and action pre
Unified identity security platform provider Permiso Security Inc. today launched the Risk Score Engine, a model that assigns continuous, multidimensional risk scores to every human, machine and artifi
arXiv:2503.15770v3 Announce Type: replace-cross Abstract: Depth foundation models (DFMs) offer strong learned priors for 3D perception from single RGB images but lack physical depth cues, leading to a
POV: you're in SF during a once-in-a-generation shift in technology. You just graduated, or you came out for the summer. AI billboards everywhere, models getting smarter every week, equal parts exciti
arXiv:2601.00086v3 Announce Type: replace Abstract: Large language models (LLMs) often struggle to use tools reliably in domain-specific settings, where APIs may be idiosyncratic, under-documented, or
arXiv:2607.06909v1 Announce Type: new Abstract: Despite rapid advances in chest X-ray (CXR) foundation models, most radiology report generation (RRG) systems still rely on heavily downsampled inputs (
arXiv:2603.00127v2 Announce Type: replace Abstract: X-Ray Computed Tomography (XCT) is a compelling tool in experimental mechanics, capable of non-destructively extracting information pertaining to th
arXiv:2607.06949v1 Announce Type: new Abstract: Training robust handwriting recognition (HTR) systems requires massive amounts of annotated data, which is often difficult to acquire. While synthetic h