arXiv:2604.07548v1 Announce Type: new Abstract: Artificial Intelligence (AI) chatbots are increasingly used for emotional, creative, and social support, leading to sustained and routine user interaction with these systems. As these applications evolve through frequent version updates, changes in functionality or behavior may influence how users evaluate them. However, work on how publicly expressed user feedback varies across app versions in real-world deployment contexts is limited. This study analyzes 210,840 Google Play reviews of the chatbot application Character AI, […]

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Dario Amodei envisions a “compressed 21st century” where powerful AI achieves one hundred years of progress in one decade. This radical upside includes curing most diseases, doubling human lifespans, and eliminating global poverty. By accelerating science and supporting democracy, AI offers an inspiring, humanitarian future worth fighting for.

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Autonomous AI agents operating in high-stakes domains—financial trading, medical diagnostics, autonomous code execution—lack formal safety guarantees for their core operational loops, including memory management, tool invocations, and human interactions. Current verification approaches either fail to scale to neural components or ignore the structured control flow of agentic systems entirely. We introduce AgentVerify (Compositional Formal Verification of AI Agent Safety Properties via LTL Model Checking), a model checking framework that specifies and verifies safety properties for agent architectures using […]

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When feedback is absorbed faster than task structure can be evaluated, the learner will favor feedback over truth. A two-timescale model shows this feedback-truth gap is inevitable whenever the two rates differ and vanishes only when they match. We test this prediction across neural networks trained with noisy labels (30 datasets, 2,700 runs), human probabilistic reversal learning (N = 292), and human reward/punishment learning with concurrent EEG (N = 25). In each system, truth is defined operationally: held-out […]

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arXiv:2602.04015v1 Announce Type: new Abstract: Virtual collaboration has transformed how people in mixed-ability teams, composed of disabled and non-disabled people, work together by offering greater flexibility. In these settings, accessibility practices, such as accommodations and inclusive norms, are essential for providing access to disabled people. However, we do not yet know how these practices shape broader facets of teamwork, such as productivity, participation, and camaraderie. To address this gap, we interviewed 18 participants (12 disabled, 6 non-disabled) who […]

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Google Antigravity is a powerhouse. As an agentic development platform, it’s remarkably capable of remembering context and executing complex instructions. But after using it to build a turn-based algorithmic simulation of Genesis, I realized something critical: The platform is only as smart as the architect directing it. If you aren’t a developer, you’ll find yourself staring at a “perfect” piece of software that doesn’t actually work. Here is how I used my engineering intuition to bridge the gap […]

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arXiv:2601.10775v1 Announce Type: new Abstract: We propose a novel LLM-based framework for reasoning in discrete, game-theoretic tasks, illustrated with emph{Tic-Tac-Toe}. The method integrates in-context learning with entropy-guided chain-of-thought (CoT) reasoning and adaptive context retrieval. The model dynamically adjusts both the number of retrieved examples and reasoning paths according to token-level uncertainty: concise reasoning with minimal context is used when uncertainty is low, whereas higher uncertainty triggers expanded multi-path CoT exploration. Experimental evaluation against a sub-optimal algorithmic opponent shows […]

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arXiv:2603.28769v1 Announce Type: new Abstract: Evaluating large language models at scale remains a practical bottleneck for many organizations. While existing evaluation frameworks work well for thousands of examples, they struggle when datasets grow to hundreds of thousands or millions of samples. This scale is common when assessing model behavior across diverse domains or conducting comprehensive regression testing. We present Spark-LLM-Eval, a distributed evaluation framework built natively on Apache Spark. The system treats evaluation as a data-parallel problem, partitioningexamplesacrossexecutorsandaggregatingresultswithproperstatistical […]

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