agents

arXiv:2509.12151v3 Announce Type: replace-cross Abstract: We present a learnable physics-based model that predicts motion of the robot end effector and reaction force-torque in contact-rich manipulati

arXiv:2609.18511v1 Announce Type: cross Abstract: In autonomous driving, perception models often struggle to generalize to new environments due to domain shifts. While unsupervised model adaptation of

arXiv:2609.18445v1 Announce Type: new Abstract: Agent skills, reusable procedural documents that extend LLM agents beyond their parametric memory, have become an important interface for deploying agen

arXiv:2609.18357v1 Announce Type: new Abstract: Large language model (LLM) pricing agents may respond to how market data is presented, even when its numerical values remain unchanged. We introduce mar

arXiv:2502.14254v3 Announce Type: replace-cross Abstract: Recent advancements in Large Language Models (LLMs) and Vision-Language Models (VLMs) have made them powerful tools in embodied navigation, en

A rewrite this size wasn't affordable before agents. Here's what porting the Copilot agent runtime to 800,000 lines of production Rust actually took. The post Migrating the GitHub Copilot runtime to R

arXiv:2609.19059v1 Announce Type: cross Abstract: Multimodal large language model (MLLM) agents are increasingly used as personal assistants for long-running tasks. Their utility depends on continuity

arXiv:2603.04284v2 Announce Type: replace Abstract: Autonomous robotic systems are increasingly deployed for mapping, monitoring, and inspection in complex and unstructured environments. However, most

arXiv:2609.18998v1 Announce Type: cross Abstract: Multi-agent LLM systems are expected to be more reliable because agents can catch each other's mistakes. But peer pressure cuts both ways: the same co

arXiv:2609.17625v1 Announce Type: new Abstract: Runtime Verification is well suited to autonomous and robotic systems because it checks the behaviour that is actually observed during execution. Its ma

arXiv:2609.18602v1 Announce Type: new Abstract: Despite recent progress in learned image compression, existing methods remain computationally expensive on resource-constrained hardware, particularly C

arXiv:2609.18597v1 Announce Type: new Abstract: Propagation structures provide crucial evidence for fake news detection, yet existing approaches primarily rely on supervised GNN-based models, which re

arXiv:2609.17653v1 Announce Type: cross Abstract: GUI agents execute long-horizon tasks on dynamic graphical user interfaces, where pop-ups, delayed loads, and relocated widgets routinely invalidate p

🐙 Sakana Fugu Max is now on Sakana Chat 🐙 Free for all to use. Try it: https://chat.sakana.ai Blog: https://sakana.ai/chat-fugumax/#English 🐡 Media Introducing Fugu Max and Fugu Ultra v2: the next evo

arXiv:2609.19134v1 Announce Type: new Abstract: Scientific code repositories encode decades of human knowledge in executable models, methods, and tools. Yet fragmented toolchains, implicit domain conv

arXiv:2609.18893v1 Announce Type: new Abstract: Autonomous underwater navigation typically relies on complex and expensive multi-modal sensor suites designed to prioritize global Simultaneous Localiza

arXiv:2609.18857v1 Announce Type: cross Abstract: The O-RAN control plane is becoming agentic: autonomous AI agents, deployed as rApps by different vendors, independently close control loops over shar

That’s what OpenAI told their own agent swarm in the Hugging Face breach… and right before they did it, they turned off monitoring and cyber protections they would normally have in place … 🤔 🤔 So… if

The difference with using VLMs for OCR vs. other tasks is that the ratio of output/input tokens is quite high - meaning the model can spend a lot of time writing markdown. Speculative decoding offers

arXiv:2609.17816v1 Announce Type: cross Abstract: Solomonoff induction frames prediction as a mixture over computable hypotheses, typically leading to identification of the true environment. In a fini

Things to try with Jev right now: 1. LLM-as-a-Judge evaluation 2. Routing for agent harnesses harness 3. Scaling agent orchestration by enabling smarter subagent creation with SOTA classification capa

🔥 Today, we are truly excited to announce our technical prototype, the Generative World Simulation system, which integrates JING（镜）, an interactive experience model, with DAO（道）, a computable shared-w

arXiv:2609.18326v1 Announce Type: new Abstract: Unmanned aerial vehicles (UAVs) extend embodied intelligence into continuous three-dimensional space, where perception, reasoning, physical embodiment,

We are in a peak age of scientific fraud and illiteracy. Completely fabricated claims and unsubstantiated numbers about extinction risks. 'Someone told me that ...' without a shred of evidence being p

arXiv:2609.18286v1 Announce Type: new Abstract: Chess has long served as a model domain for studying search, expertise, decision-making, and artificial intelligence. The emergence of large language mo

In this analysis, MLPerf Inference Working Group chairs Miro Hodak and Frank Han break down a record field of 30 submitters and 120 systems, the arrival of agentic and end-to-end benchmarks, and what

Why does the AI industry wants you to believe in “rogue agents” when we are really just talking about irresponsibly developed software? Here’s a good theory: @haydenfield @mmitchell_ai @HeidyKhlaaf @t

arXiv:2609.16947v1 Announce Type: cross Abstract: Communication in latent space offers an intriguing alternative to symbolic messages for decentralized autonomous Unmanned Aerial Vehicle (UAV) swarms

arXiv:2609.17274v1 Announce Type: cross Abstract: AI agents increasingly act through agent skills, i.e., natural-language instructions, that direct a host agent toward shell, network, credential, file

arXiv:2609.17527v1 Announce Type: cross Abstract: An agentic society is a collection of AI agents that coordinate autonomously across trust boundaries, on behalf of different principals whose objectiv

arXiv:2609.17123v1 Announce Type: cross Abstract: Thermal optimization of 2D CFET inverters requires testing structural proposals against their electrical costs. We examine these research tasks using

arXiv:2609.16625v1 Announce Type: cross Abstract: How and why does a recommender system fail the users it serves? Oftentimes, practitioners are left to improve their algorithms based on a combination

arXiv:2609.16346v1 Announce Type: cross Abstract: This paper presents Auto-HSI, a method for generating personalized human-swarm interaction (HSI) interfaces on demand. The objective is to enable untr

Banger paper from Google Research. This one is on how LLM assistants reason about the people in a user's life. People ask assistants for social advice constantly, and the assistant only hears the user

arXiv:2609.16301v1 Announce Type: new Abstract: Medical knowledge evolves continuously, whereas the parametric knowledge encoded in large language models (LLMs) is fixed at training time. External ret

arXiv:2609.17394v1 Announce Type: cross Abstract: Small differences on coding-agent leaderboards are often read as an ordering of systems. We audit whether the published verdicts support this reading,

Data security company Cohesity Inc. today introduced Cohesity Agent Resilience at its Catalyst conference. The new Cohesity Data Cloud capability backs up the memory and configuration of enterprise ar

arXiv:2609.17141v1 Announce Type: cross Abstract: Traversability prediction is a critical component of autonomous navigation in unstructured environments, where complex and uncertain robot-terrain int

The “Crazy prediction” page asserts that the next major AI‑agent incident originating from an AI laboratory would have been entirely preventable with strong cybersecurity measures. The site is current

arXiv:2609.16091v1 Announce Type: new Abstract: Tabular foundation models deliver strong zero-training predictive performance via in-context learning, but their high inference latency makes them impra

arXiv:2609.17193v1 Announce Type: new Abstract: Large language model (LLM)-powered agentic AI services increasingly demand low-latency inference, motivating the deployment of LLMs across distributed e

arXiv:2609.16093v1 Announce Type: new Abstract: A shopping conversation has many routes to the same cart, and a task-success rate reduces all of them to one score. We build a deterministic and reprodu

arXiv:2408.14575v5 Announce Type: replace Abstract: EVINCE (Entropy and Variation IN Conditional Exchanges) is a novel framework for optimizing multi-LLM dialogues using conditional statistics and inf

arXiv:2609.08171v2 Announce Type: replace Abstract: Low-latency simultaneous speech-to-speech translation must keep pace with ongoing speech while preserving key information. To meet these demands, sy

arXiv:2609.17257v1 Announce Type: new Abstract: Semantic occupancy prediction gives an embodied agent a voxel-level account of where space is free, occupied, and semantically meaningful. In RGB-D pipe

arXiv:2609.17187v1 Announce Type: new Abstract: Accurate wheel slip estimation is essential for autonomous lunar rover mobility and navigation. Machine Learning models trained on terrestrial data gene