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Perplexity trusts GPT-6 Astra with end-to-end systems
on September 14, 2026 at 12:00 am
Perplexity uses Astra to write communications, change software, and monitor production systems, and checks in much less frequently than with earlier models.
Anthropic boss Dario Amodei calls for AI development to slow down
on September 12, 2026 at 9:16 pm
The call comes amid growing concerns that AI models may become able to inflict serious damage worldwide.
OpenAI’s Sam Altman says it would be ‘ill-advised’ to go public in 2026
by Anthony Ha on September 12, 2026 at 8:19 pm
While OpenAI has filed confidentially for an IPO, the company will not be going public this year, according to CEO Sam Altman.
Anthropic CEO outlines plan to slow AI development
by Anthony Ha on September 12, 2026 at 7:34 pm
Anthropic’s Dario Amodei and OpenAI’s Sam Altman seem to agree that it’s time to “pace the frontier.” What would that actually look like?
I spent $4,000 on a robot dog from China
by Timothy B. Lee on September 12, 2026 at 11:00 am
Unitree might be the world’s most important robotics company.
ActSafeGuard: Differentiable and Training-Aligned Constraint Enforcement for Flow-Matching Policies
by Jianming Ma, Rongjun Jin, Xiaxi Si, Yang Zhang, Yiheng Li, Yue Gao on September 12, 2026 at 4:00 am
arXiv:2609.11697v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) and World-Action Models (WAMs) have demonstrated strong capabilities in general-purpose robotic manipulation, yet their generated actions may violate hard physical constraints and therefore be unsafe or infeasible for deployment. Existing safety approaches either optimize statistical safety objectives without deterministic per-step guarantees or correct unsafe actions only during inference, creating a mismatch between policy training and execution. We introduce ActSafeGuard, a […]
by Shirong Yang, Bo Yang, Ying Cao on September 12, 2026 at 4:00 am
arXiv:2609.11519v1 Announce Type: cross Abstract: In this paper, we address the problem of graphic design template creation, which generates a background image and a layout of foreground elements over the background to form a harmonious composition from an input text. Prior work on graphic design generation mostly adopts a sequential paradigm, where design elements are generated sequentially. We argue that such a sequential scheme falls short of faithfully capturing the dependency between the background and layout (and thus the joint image-layout […]
by Niloy Kumar Mondal, Md Rizwan Parvez on September 12, 2026 at 4:00 am
arXiv:2609.10629v1 Announce Type: new Abstract: Quadratic Unconstrained Binary Optimization (QUBO) is a central formulation for combinatorial optimization and has gained increasing attention due to its compatibility with quantum, hybrid quantum-classical, and quantum-inspired solvers. However, translating natural-language problem descriptions into correct QUBO formulations remains difficult, requiring the identification of binary variables, constraints, objective functions, penalty terms, and suitable penalty weights. This process is time-consuming and often […]
How Proper Scoring Rules Shape LLM Forecasting
by Benjamin Turtel, Paul Wilczewski, Kris Skotheim, Ville A. Satop\"a\"a, Philip E. Tetlock on September 12, 2026 at 4:00 am
arXiv:2608.28482v2 Announce Type: replace-cross Abstract: This paper evaluates how reward function choice shapes the performance and behavior of LLM forecasters. We compare five proper scoring rules as training objectives for binary forecasts of resolved real-world events. Although the rules share the same theoretical incentive for truthful probability reporting, the resulting models differ in calibration, probability use, and estimated profiles of bias, information, and noise, with smaller differences in aggregate accuracy and discrimination. The Brier-trained […]
by Masahiro Kato on September 12, 2026 at 4:00 am
arXiv:2606.20041v2 Announce Type: replace-cross Abstract: We propose an AI economist agent for economic and financial scenario analysis. Scenario design often requires analysts to assess emerging risks with limited historical precedent, combine information from many sources, and translate qualitative mechanisms into internally consistent quantitative paths. Large language models (LLMs) can search and synthesize this information, but fluent narratives alone do not establish the model-based calculations needed for economic conclusions. Our framework uses LLM […]
T1: Terminal Agent Reinforcement Learning for Long-Horizon Tasks
by Junyao Yang, Yucheng Shi, Zhongzhi Li, Ruhan Wang, Zongxia Li, Haitao Mi, Leowei Liang on September 12, 2026 at 4:00 am
arXiv:2609.11042v1 Announce Type: cross Abstract: Agent usage is shifting toward long-horizon tasks such as coding and scientific discovery, among which terminal tasks are especially important. We introduce T1, a Mixture-of-Experts model of 122B total trained with reinforcement learning, operating a real shell in a cloud sandbox for up to 300+ tool-call turns per task, rewarded by executing each task’s own verifier. We provide a comprehensive recipe: First, an aggressively warm-started to stabilize actor-critic training, with a dense process reward scoring […]
VeriSim: A Configurable Framework for Stress-Testing Medical AI Under Patient Communication Noise
by Sina Mansouri, Mohit Marvania, Vibhavari Ashok Shihorkar, Han Ngoc Tran, Kazhal Shafiei, Mehrdad Fazli, Yikuan Li, Ziwei Zhu on September 12, 2026 at 4:00 am
arXiv:2604.10441v2 Announce Type: replace Abstract: Medical large language models are typically evaluated on idealized patient cases that do not reflect how real patients communicate. We introduce VeriSim, a patient simulation framework that injects controllable noise along six clinically grounded communication dimensions while substantially preserving each patient’s medical record. Truth adherence is supported by a verifier that extracts atomic claims from each candidate utterance and judges them against a UMLS-grounded vector index built with BioLORD […]
by Lin Huang, Yujuan Tan, Weisheng Li, Lixiang Zeng, Kun Yang, Yongzong Wang, Suihan Xiao on September 12, 2026 at 4:00 am
arXiv:2609.10372v2 Announce Type: replace-cross Abstract: We present the PACE, a framework for retrieval-augmented dialogue serving that formalizes Perceived Time-to-First-Response (PTFR) as a QoE objective and minimizes it under quality/cost constraints. Unlike prior work on cascaded routing, semantic caching, or adaptive retrieval, PACE jointly controls which answer source composes the response and what fills the waiting window. Deployed on a humanoid-robot sales service, it combines three mechanisms: a load-adaptive cascading router, a joint path-filler […]
VikingRAG: Accurate and Token-efficient Retrieval-augmented Generation over Structured Documents
by Peiyuan Gao, Gaoyuan Zhang, Haojie Qin, Yahui Sun, Qianyi Zhang, Yunhao Zhang, Zeyu Wang, Wei Lu on September 12, 2026 at 4:00 am
arXiv:2609.11390v1 Announce Type: cross Abstract: State-of-the-art retrieval-augmented generation (RAG) methods exploit document structures to acquire sufficient evidence, but often incur substantial token costs. To reduce structural-context tokens without compromising high RAG accuracy, we present {\sf VikingRAG}, a directory-aware semantic data management system that tightly integrates semantic and structural access to support structural-context-efficient, evidence-gap-driven multi-round retrieval. To further reduce token overhead of multi-round interaction, […]
by Yongchao Martin Ma, Xinya Guan on September 12, 2026 at 4:00 am
arXiv:2609.11391v1 Announce Type: cross Abstract: Environmental controversies in global supply chains pose significant risks for global buyers. This study examines whether overseas suppliers’ exposure to buyers’ artificial intelligence (AI)-enabled environmental governance reduces supplier environmental controversies. Drawing on organizational information processing theory and signaling theory, we investigate how suppliers’ exposure to AI-enabled governance influences their environmental controversies and the institutional contingencies under which this effect […]