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FARO: Feasibility-Aware Robot Motion Optimization
arXiv:2607.18362v1 Announce Type: new Abstract: Fast planning of novel behaviors in unseen scenarios remains a fundamental challenge in robotics. The high-dimensional, hybrid, and underactuated nature of humanoid loco-manipulation continues to hinder the realization of this goal. In this paper, we address this challenge by proposing a nested kino-dynamic framework for rapid feasibility checking and dynamically consistent trajectory generation given a candidate contact sequence. By integrating this module with a feasibility-guided tree search and a Large Language Model (LLM)-based contact plan sampling strategy, we demonstrate that the proposed framework can substantially improve the search process. Furthermore, we show that the generated trajectories can be tracked using a reinforcement learning (RL)-based controller and show that the resulting trajectories are of sufficiently high quality for execution in real-world loco-manipulation scenarios. A supplementary video is available at: h
Calibrated Selective Fact-Checking via Evidence Chain Evaluation
arXiv:2607.18240v1 Announce Type: new Abstract: Large language models (LLMs) can achieve strong fact-checking accuracy, yet forced binary decisions conceal a critical reliability problem: systems may issue confident verdicts even when supporting evidence is weak, sparse, or internally inconsistent. We address this issue through Evidence Chain Evaluation (ECE), a selective fact-checking framework that permits abstention via an uncertain verdict instead of requiring a true/false decision for every claim. The evaluated system is a tool-using verification agent that gathers evidence through web search, scholarly search, and executable checks, and then returns a structured verdict with confidence and source-level metadata. On ECE-Bench, ECE achieves 91.6% standard accuracy, 93.7% coverage, and 97.8% selective accuracy on answered claims. Although ECE does not outperform the strongest retrieval baseline on aggregate calibration metrics such as Expected Calibration Error, Brier score, or AURC
FALCON-Discover: Discovering Concentrated False-Confidence Regions for Calibration
arXiv:2607.18278v1 Announce Type: new Abstract: Calibration is usually evaluated in aggregate, but the most dangerous failures are often local: predictions that remain highly confident despite being wrong. We study this failure mode as false-confidence concentration, the extent to which confident errors occupy compact, discoverable regions of prediction space. We introduce FALCON-Discover, a post-hoc, model-agnostic framework that ranks predictions using discrepancy signals from confidence, local support, neighborhood agreement, and perturbation stability. Across seven binary tabular datasets, four seeds, five-fold cross-fitting, and strong learners including XGBoost and CatBoost, we find that false-confidence concentration is recurrent but regime-dependent. At the main confidence threshold, discrepancy-based ranking substantially outperforms the strongest validation-selected calibration or trust-scoring baseline in the strongest regimes, while raw confidence recovers little dangerous-
Why Your LLM Needs a Firewall (And Why It Still Won’t Be Enough)
A CPO just made the case that AI needs guardrails, not just intelligence. He s right. But LLM firewall quietly promises something the Continue reading on Medium »
AI Automation for Customer Retention: Why Growth Does Not Stop After Conversion
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The Anthropic-Physical Intelligence rumor roiling AI Twitter
Anthropic and OpenAI's aggressive 2026 acquisition sprees set the stage for a weekend rumor.
KEUNTUNGAN BERMAIN POKER88
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The Ultimate Beginner’s Guide to Artificial Intelligence (Without the Confusing Jargon)
Artificial Intelligence is everywhere.Continue reading on Medium »
Dimension Capital’s $800M third fund shows the intersection of science and compute is booming
The four-year-old firm's latest fund is 60% larger than its second vehicle announced 18 months ago.
Meta is testing an AI bedtime story app for people with no imagination
Meta's StoryKit app is only available in certain regions while the company tests how parents react to it.
U.K.-based Humanoid secures $152M in Series A funding
Humanoid said it has reached a $1.35 billion valuation as it scales its AI-driven industrial robots and expands commercial deployments. The post U.K.-based Humanoid secures $152M in Series A funding appeared first on The Robot Report.
When AI Escapes the Evaluation: OpenAI, Hugging Face, and the Case for Collaborative Failure…
Large AI companies are no longer just racing on capabilities. They re learning, together, how to contain the consequences. The OpenAI Continue reading on Medium »