Knowledge catalogue
arXiv:2610.06666v1 Announce Type: new Abstract: Latent reasoning lets a large language model (LLM) think in a continuous space and verbalize only the answer. We argue that an effective latent thought
arXiv:2610.06274v1 Announce Type: new Abstract: Many agent runtimes give the agent a tool for editing its own configuration. Some of that configuration grants abilities, such as enabling a tool. Other
arXiv:2610.05818v1 Announce Type: new Abstract: Vision-language-action models (VLAs) act on instructions without being able to refuse, so screening harmful requests falls to monitors. We test whether
arXiv:2610.04303v1 Announce Type: new Abstract: Recursive computation repeatedly compresses or reuses intermediate states, creating a simple tension: information that must remain useful across longer
arXiv:2610.04978v1 Announce Type: new Abstract: Data selection, curricula and the evaluation of post-training recipes all assume that we can tell, before training, which problems a model will improve
arXiv:2610.05273v1 Announce Type: new Abstract: Visual token pruning is an effective way to accelerate vision-language models and is especially useful for vision-language-action (VLA) inference, where
arXiv:2610.06285v1 Announce Type: new Abstract: Concept bottleneck models (CBMs) are neural classifiers that allow to explain their decisions via high-level concepts, potentially enabling understandin
arXiv:2605.05592v2 Announce Type: replace Abstract: Increasing a voting budget can improve or reduce population accuracy, and an initial gain or loss need not persist. We give a complete structural cl
arXiv:2610.04686v1 Announce Type: cross Abstract: Multi-agent debate can improve reasoning, yet often fails to beat simple majority voting. We argue that successful debate requires two distinct mechan
arXiv:2610.05645v1 Announce Type: new Abstract: A diffusion model starts from pure noise and removes it step by step. Somewhere along the way it stops being able to become 'anything' and becomes commi
arXiv:2610.05322v1 Announce Type: new Abstract: Test-time compute can improve mathematical reasoning, but can short-budget runs predict how mathematical reasoning scales with additional compute? We in
arXiv:2610.05492v1 Announce Type: new Abstract: Vision-language-action (VLA) models have shown strong potential as generalist robot policies, but adapting them to unseen tasks often requires costly pa
arXiv:2610.04756v1 Announce Type: cross Abstract: Self-evolving large language model (LLM) systems repeatedly propose, evaluate, and incorporate updates to prompts, skills, or other persistent artifac
arXiv:2610.05550v1 Announce Type: new Abstract: The component that dominates a latent world model's prediction error need not be the one whose repair most improves action selection. We show this by co
arXiv:2610.05598v1 Announce Type: new Abstract: An organization often holds too little labeled data to train a model that generalizes, and the records that would supply the rest sit with organizations
arXiv:2610.04874v1 Announce Type: cross Abstract: In this note, we prove that the noncommutative AM-GM inequality holds if nge 2lceil m/2 rceil^2. The motivation comes from counterexamples constructed
arXiv:2610.05719v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models serve as unified policies for robotic manipulation, yet their expensive inference forces robots to pause between pol
arXiv:2610.03831v1 Announce Type: new Abstract: Capable systems often show jagged competence: low average error alongside failures on particular inputs. We ask where it comes from in a task built from
arXiv:2610.04104v1 Announce Type: new Abstract: Semantic Knowledge-driven Contrastive Learning (SKCL) uses a language model to decide which classes are related, and pulls each image towards the protot
Across the public sector, the question has shifted from “what’s possible?” to “how can we drive mission impact?” We’ll be answering this question at the Google Public Sector Summit on October 20th at
Hi, I'm on M4 Pro 64GB Mac, I'm using Qwen3.8 27B for coding related stuff, but it's quite slow. I want a fast model that will be good when it comes to reasoning, medical stuff, would be nice if it co
arXiv:2610.04845v1 Announce Type: new Abstract: Aligning large language models (LLMs) with human values is important for safe, efficient, and beneficial AI deployment. However, human values are multif
Over the past several months, we conducted an empirical post-mortem investigation into 109 autonomous AI agent security incidents (cataloged with 193 falsification criteria across tool-use and multi-a
arXiv:2610.04073v1 Announce Type: new Abstract: Recent advances in attention-based deep learning have motivated their adoption for remote sensing image classification; however, their benefits for cryo
arXiv:2610.04907v1 Announce Type: new Abstract: Subliminal learning lets a student inherit a teacher's behavioral trait from semantically unrelated data, yet published demonstrations typically require
Telecom operators are increasingly building their AI strategies on open models — and the reasons go beyond mere cost. Open models give telcos the ability to trust, control and customize AI across thei
will Anthropic’s IPO be based on its best quarter, or reality? Microsoft has cut internal Claude spending by over a third, and Meta's Claude Code users have halved. - per The Information 'Microsoft wa
arXiv:2610.05341v1 Announce Type: new Abstract: Chronic wound management affects over 8.2 million patients in the United States and imposes substantial clinical and economic burden. Clinical wound ass
arXiv:2610.06318v1 Announce Type: new Abstract: Dense affordance supervision is an appealing auxiliary signal for vision-language-action (VLA) policies, yet naively co-training an affordance head can
Without a doubt evals are the biggest blocker in most agent development pipelines. We’re going to be changing that for everyone in LangSmith in a few short weeks… Hot take: evals are the #1 thing keep
arXiv:2610.06509v1 Announce Type: new Abstract: Deep learning has revolutionised weather forecasting in recent years, especially through atmospheric foundation models, which offer competitive skill fo
arXiv:2407.18137v2 Announce Type: replace Abstract: Small object detection and tracking in videos remain critical yet underexplored challenges in computer vision, particularly for applications such as
You can now run Clef via llama.cpp! llama serve -hf ggml-org/Clef-GGUF Ollama vs llama.cpp speed test on my M5 Max MBP: The new v0.6.0 release packs a lot of good stuff: - Clef support (text + vision)
arXiv:2610.04147v1 Announce Type: new Abstract: A decade after its inception, Inception crop has become the standard crop-based data augmentation method for training deep vision models. Not only is it
arXiv:2610.04869v1 Announce Type: new Abstract: An interaction has two parts: someone decides to act, and then chooses whom to act on. Temporal link prediction has concentrated on the second, and we s
arXiv:2610.05601v1 Announce Type: new Abstract: Existing attacks on unlearned diffusion models assume that the erased concepts are known in advance and focus on recovering them. In practice, however,
Zendesk launched a self‑service agent builder powered by Anthropic’s Claude, moving from proof of concept to early access in four months with a five‑person team. Within seven weeks of launch the platf
arXiv:2508.19131v3 Announce Type: replace-cross Abstract: The advancement of robotics and autonomous navigation systems hinges on the ability to accurately predict terrain traversability. Traditional
arXiv:2610.03402v1 Announce Type: new Abstract: To deploy deep neural networks on edge hardware, highly efficient inference schemes are necessary that retain high accuracy. This work presents W16A16,
arXiv:2610.03715v1 Announce Type: cross Abstract: We introduce 4DCodeBench, a benchmark for 4D inverse graphics through code generation, in which agents reconstruct dynamic scenes from video as execut
A while ago, I posted here getting OSS-120B and GLM-4.6 playing full games of Civilization V. Since then, models have moved pretty far, and we wanted a better understanding about models' capabilities
arXiv:2610.03105v1 Announce Type: new Abstract: Communication in shared space interweaves verbal and non-verbal signals, and pointing gestures anchor language to the environment: 'put the cup on that
arXiv:2610.02461v1 Announce Type: new Abstract: Accurate thermal analysis of heterogeneous 2.5D/3D-IC packages is essential yet computationally prohibitive. A single full-package FEM simulation can ta
arXiv:2610.02764v1 Announce Type: new Abstract: In structured rating prediction, does a personal AI use historical item-rating associations, or mainly the user's rating tendencies? We audit this disti
arXiv:2610.03332v1 Announce Type: new Abstract: Accurate three-dimensional (3D) reconstruction of individual dendrites in serial block-face scanning electron microscopy (SBF-SEM) is essential for quan
arXiv:2610.02529v1 Announce Type: new Abstract: Syntactic ambiguity poses a persistent challenge for Arabic NLP, particularly in morphologically rich nominal constructions where multiple structu6ral i
arXiv:2610.02439v1 Announce Type: new Abstract: Generative graph models are central to understanding and simulating complex networks. However, existing approaches have complementary strengths and limi
arXiv:2610.02664v1 Announce Type: new Abstract: Long-horizon agents are now playing an increasingly significant role in assisting humans with complex problem-solving. However, it is exactly their exte
arXiv:2610.02970v1 Announce Type: new Abstract: Large language models (LLMs) have shown promising potential for biomedical named entity recognition (BioNER) through instruction following and in-contex
arXiv:2610.02634v1 Announce Type: cross Abstract: Magnetic resonance elastography (MRE) is a noninvasive imaging modality for quantifying the viscoelastic properties of soft tissues from shear wave pr
arXiv:2603.19617v2 Announce Type: replace Abstract: Federated learning (FL) is a communication-efficient framework for solving distributed optimization problems. Standard FL methods aggregate local up
arXiv:2610.02245v1 Announce Type: cross Abstract: Amortized simulation-based inference (SBI), which is trained on radiative-transfer simulators, recovers exoplanet atmospheres accurately on synthetic
OpenAI initially claimed that GPT‑6 could reach artificial general intelligence (AGI). Within a month, it publicly acknowledged that current AI systems lack sufficient controllability and highlighted
arXiv:2610.02342v1 Announce Type: new Abstract: Natural Visibility Graph (NVG) based analysis characterizes network traffic through topological descriptors reflecting different structural properties.
arXiv:2610.03458v1 Announce Type: new Abstract: Post-training with verifiable rewards can induce reward hacking, motivating the use of monitors within the training objective rather than solely for off
arXiv:2610.02668v1 Announce Type: new Abstract: Automated liquid handlers execute digital protocols, but operators must assemble the deck and confirm that the physical setup matches the intended exper
arXiv:2610.03597v1 Announce Type: new Abstract: Federated learning lets many clients train a shared model together without ever sending their private data to a central server. Each client shares only
arXiv:2504.03432v4 Announce Type: replace-cross Abstract: Solving (Stampacchia) variational inequalities (SVIs) is a foundational problem at the heart of optimization. However, this expressivity comes
arXiv:2610.02451v1 Announce Type: new Abstract: Effective wildfire monitoring requires relating visual evidence to physical fire dynamics, yet real videos with synchronized physical annotations are sc
arXiv:2508.11902v4 Announce Type: replace Abstract: This study examines how much handwritten-character information is retained by a deliberately simple first-order edge representation. Instead of lear