<?xml version='1.0' encoding='utf-8'?>
<feed xmlns="http://www.w3.org/2005/Atom"><title>Lunar Society Labs Research</title><id>https://www.lunarsocietylabs.com/</id><link href="https://www.lunarsocietylabs.com/en/" /><link rel="self" href="https://www.lunarsocietylabs.com/feed.xml" /><updated>2026-10-25T06:46:50Z</updated><entry><title>An alignment assessment of the UK AISI cybertesting incident</title><id>https://www.lunarsocietylabs.com/en/papers/anthropic-ukaisi-assessment/</id><link href="https://www.lunarsocietylabs.com/en/papers/anthropic-ukaisi-assessment/" /><updated>2026-10-24T23:04:00Z</updated><summary>An alignment assessment of model actions that exceeded the intended scope of a cybersecurity evaluation.</summary><published>2026-10-24T23:04:00Z</published></entry><entry><title>Using thousands of AI agents to uncover new leads in biological data</title><id>https://www.lunarsocietylabs.com/en/papers/openai-biological-agents/</id><link href="https://www.lunarsocietylabs.com/en/papers/openai-biological-agents/" /><updated>2026-10-24T23:04:00Z</updated><summary>A research overview of distributed agents linking large biological datasets with scientific literature. Proposed connections still require experimental follow-up.</summary><published>2026-10-24T23:04:00Z</published></entry><entry><title>Advancing coding in low-resource programming languages</title><id>https://www.lunarsocietylabs.com/en/papers/openai-low-resource-code/</id><link href="https://www.lunarsocietylabs.com/en/papers/openai-low-resource-code/" /><updated>2026-10-24T23:04:00Z</updated><summary>Official research discussion of improving coding capabilities in languages with less available training material.</summary><published>2026-10-24T23:04:00Z</published></entry><entry><title>Scaling Misuse Detection with Self-Assessment</title><id>https://www.lunarsocietylabs.com/en/papers/anthropic-self-assessment/</id><link href="https://www.lunarsocietylabs.com/en/papers/anthropic-self-assessment/" /><updated>2026-10-24T23:04:00Z</updated><summary>Models assess their own conversations as an additional misuse-detection layer, reusing cached context to limit overhead.</summary><published>2026-10-24T23:04:00Z</published></entry><entry><title>Gemini 4 Argon — Model Card</title><id>https://www.lunarsocietylabs.com/en/papers/gemini-4-argon/</id><link href="https://www.lunarsocietylabs.com/en/papers/gemini-4-argon/" /><updated>2026-10-24T23:04:00Z</updated><summary>Official model documentation covering evaluation, limitations and safety considerations. The date records the listed model-card update.</summary><published>2026-10-24T23:04:00Z</published></entry><entry><title>PointWalker: Mapless Quadruped Locomotion Directly From Raw LiDAR Point Clouds</title><id>https://www.lunarsocietylabs.com/en/papers/2610-15548/</id><link href="https://www.lunarsocietylabs.com/en/papers/2610-15548/" /><updated>2026-10-25T01:18:16Z</updated><summary>Fuses LiDAR point clouds and proprioception through attention, connecting terrain perception with a reinforcement-learned quadruped policy.</summary><published>2026-10-25T01:18:16Z</published></entry><entry><title>Investigating unintended model actions in our evaluations and internal use</title><id>https://www.lunarsocietylabs.com/en/papers/anthropic-unintended-actions/</id><link href="https://www.lunarsocietylabs.com/en/papers/anthropic-unintended-actions/" /><updated>2026-10-24T23:04:00Z</updated><summary>A report of unintended actions observed during model evaluation and internal use, with discussion of safeguards.</summary><published>2026-10-24T23:04:00Z</published></entry><entry><title>Advancing computer use with Ironclad</title><id>https://www.lunarsocietylabs.com/en/papers/openai-ironclad/</id><link href="https://www.lunarsocietylabs.com/en/papers/openai-ironclad/" /><updated>2026-10-24T23:04:00Z</updated><summary>An official account of progress in computer-use agents. Read the original methods and evaluations separately from deployment claims.</summary><published>2026-10-24T23:04:00Z</published></entry><entry><title>Nano Banana 2.1 — Model Card</title><id>https://www.lunarsocietylabs.com/en/papers/nano-banana-2-1/</id><link href="https://www.lunarsocietylabs.com/en/papers/nano-banana-2-1/" /><updated>2026-10-24T23:04:00Z</updated><summary>Official documentation for an image-and-text model, including inputs, outputs, architecture dependencies and safety evaluation.</summary><published>2026-10-24T23:04:00Z</published></entry><entry><title>FACT: A Multimodal Force-Action-Track Foundation Model for Human-to-Robot Dexterity</title><id>https://www.lunarsocietylabs.com/en/papers/2610-19155/</id><link href="https://www.lunarsocietylabs.com/en/papers/2610-19155/" /><updated>2026-10-24T23:04:00Z</updated><summary>Human and robot demonstrations are combined with force-aware planning for dexterous manipulation.</summary><published>2026-10-24T23:04:00Z</published></entry><entry><title>What work can robots do?</title><id>https://www.lunarsocietylabs.com/en/papers/anthropic-robot-work/</id><link href="https://www.lunarsocietylabs.com/en/papers/anthropic-robot-work/" /><updated>2026-10-24T23:04:00Z</updated><summary>A task-level analysis of robot exposure that distinguishes demonstrated capability in limited settings from general deployment.</summary><published>2026-10-24T23:04:00Z</published></entry><entry><title>UnifoLM-WLA-1.0: One Model Driven, Whole-Body Coordination</title><id>https://www.lunarsocietylabs.com/en/papers/unitree-unifolm-wla-1/</id><link href="https://www.lunarsocietylabs.com/en/papers/unitree-unifolm-wla-1/" /><updated>2026-10-25T01:18:16Z</updated><summary>Connects embodied understanding, interaction-centric world modeling and action learning for tabletop and whole-body humanoid manipulation.</summary><published>2026-10-25T01:18:16Z</published></entry><entry><title>Qwen-Audio-Agent Technical Report</title><id>https://www.lunarsocietylabs.com/en/papers/2609-25195/</id><link href="https://www.lunarsocietylabs.com/en/papers/2609-25195/" /><updated>2026-10-24T23:04:00Z</updated><summary>A foreground-background architecture separates voice interaction from asynchronous task execution, coordinating authorization, state and delivery.</summary><published>2026-10-24T23:04:00Z</published></entry><entry><title>DeepSeek-V4.1-Flash: smarter, faster, more efficient</title><id>https://www.lunarsocietylabs.com/en/papers/deepseek-v4-1-flash/</id><link href="https://www.lunarsocietylabs.com/en/papers/deepseek-v4-1-flash/" /><updated>2026-10-24T23:04:00Z</updated><summary>Official release introducing an asymmetric causal encoder-decoder architecture and linking its technical report and model resources.</summary><published>2026-10-24T23:04:00Z</published></entry><entry><title>Introducing Grok 4.6</title><id>https://www.lunarsocietylabs.com/en/papers/grok-4-6/</id><link href="https://www.lunarsocietylabs.com/en/papers/grok-4-6/" /><updated>2026-10-24T23:04:00Z</updated><summary>An official technical release describing supplemental training, regenerated SFT trajectories and agentic reinforcement learning.</summary><published>2026-10-24T23:04:00Z</published></entry><entry><title>Qwen-Image-2.0-RL Technical Report</title><id>https://www.lunarsocietylabs.com/en/papers/2606-27608/</id><link href="https://www.lunarsocietylabs.com/en/papers/2606-27608/" /><updated>2026-10-24T23:04:00Z</updated><summary>A diffusion-model post-training pipeline combines task-specific rewards, reinforcement learning and on-policy distillation.</summary><published>2026-10-24T23:04:00Z</published></entry><entry><title>Qwen-RobotWorld Technical Report: Unifying Embodied World Modeling through Language-Conditioned Video Generation</title><id>https://www.lunarsocietylabs.com/en/papers/2606-17030/</id><link href="https://www.lunarsocietylabs.com/en/papers/2606-17030/" /><updated>2026-10-24T23:04:00Z</updated><summary>A language-conditioned video world model predicts future visual trajectories across manipulation, navigation and driving settings.</summary><published>2026-10-24T23:04:00Z</published></entry><entry><title>XPeng X-World: Controllable Multi-View Generative World Model for Autonomous Driving</title><id>https://www.lunarsocietylabs.com/en/papers/xpeng-xworld-2026/</id><link href="https://www.lunarsocietylabs.com/en/papers/xpeng-xworld-2026/" /><updated>2026-10-25T06:46:50Z</updated><summary>Describes a controllable multi-view world model used for driving simulation, scenario generation, and evaluation.</summary><published>2026-10-25T06:46:50Z</published></entry><entry><title>Qwen3.5-Omni Technical Report</title><id>https://www.lunarsocietylabs.com/en/papers/2604-15804/</id><link href="https://www.lunarsocietylabs.com/en/papers/2604-15804/" /><updated>2026-10-24T23:04:00Z</updated><summary>The report explores an omni-modal model family and training over heterogeneous visual, textual and audio data.</summary><published>2026-10-24T23:04:00Z</published></entry><entry><title>Qwen3-Coder-Next Technical Report</title><id>https://www.lunarsocietylabs.com/en/papers/2603-00729/</id><link href="https://www.lunarsocietylabs.com/en/papers/2603-00729/" /><updated>2026-10-24T23:04:00Z</updated><summary>An open-weight model for coding agents explores strong training recipes with a small active parameter footprint.</summary><published>2026-10-24T23:04:00Z</published></entry></feed>