LLM role-playing, i.e., using LLMs to simulate specific personas, has emerged as a key capability in various applications, such as companionship, content creation, and digital games. While current models effectively capture character tones and knowledge, simulating the inner thoughts behind their behaviors remains a challenge. Towards cognitive simulation in LLM role-play, previous efforts mainly suffer from two deficiencies: data with high-quality reasoning traces, and reliable reward signals aligned with human preferences. In this paper, we propose HER, a […]

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Immersive applications such as virtual and augmented reality impose stringent requirements on frame rate, latency, and synchronization between physical and virtual environments. To meet these requirements, an edge server must render panoramic content, predict user head motion, and transmit a portion of the scene that is large enough to cover the user viewport while remaining within wireless bandwidth constraints. Each portion produces two feedback signals: prediction feedback, indicating whether the selected portion covers the actual viewport, and transmission […]

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Accurate localization of the seizure onset zone (SOZ) from intracranial EEG (iEEG) is essential for epilepsy surgery but is challenged by complex spatiotemporal seizure dynamics. We propose SpaTeoGL, a spatiotemporal graph learning framework for interpretable seizure network analysis. SpaTeoGL jointly learns window-level spatial graphs capturing interactions among iEEG electrodes and a temporal graph linking time windows based on similarity of their spatial structure. The method is formulated within a smooth graph signal processing framework and solved via an […]

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I’ve seen configuration managed on paper, with change notices stacked on drawings like a to-do pile. I’ve seen teams lose the ability to rebuild the same product in another country because they couldn’t produce a complete BOM. The problem wasn’t “conservatism.” The problem was a broken evidence supply chain. Certification-ready digital threads fix that. Here’s what changed when we rebuilt that supply chain: Audit pack assembly: 2 months → 2 weeks Evidence control: Google Drive (no traceability, low access control) […]

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We’re releasing AIRS-Bench, a new benchmark from FAIR at Meta to track whether an AI agent can perform ML research starting from scratch. Our goal was to evaluate the full research lifecycle beyond just coding. The 20 tasks in AIRS-Bench require agents to handle everything from ideation and experiment design to iterative refinement, with no baseline code provided. The tasks are sourced from recent ML papers, so agent performance is measured against the reality of SOTA research. Key […]

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arXiv:2601.03335v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly being used to evolve solutions to problems in many domains, in a process inspired by biological evolution. However, unlike biological evolution, most LLM-evolution frameworks are formulated as static optimization problems, overlooking the open-ended adversarial dynamics that characterize real-world evolutionary processes. Here, we study Digital Red Queen (DRQ), a simple self-play algorithm that embraces these so-called “Red Queen” dynamics via continual adaptation to a changing objective. DRQ uses […]

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In 2023, AI tools are incredibly versatile, encouraging content creators, designers, programmers, and even business owners to use them to achieve their professional goals. Artificial intelligence tools help businesses and individuals to automate processes, make more informed decisions, and improve efficiency. They can analyze large amounts of data quickly and accurately, identify patterns and insights that may not be immediately apparent to humans, and provide recommendations based on that analysis. In this article, we will share some of […]

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arXiv:2601.19929v1 Announce Type: new Abstract: We introduce Stingy Context, a hierarchical tree-based compression scheme achieving 18:1 reduction in LLM context for auto-coding tasks. Using our TREEFRAG exploit decomposition, we reduce a real source code base of 239k tokens to 11k tokens while preserving task fidelity. Empirical results across 12 Frontier models show 94 to 97% success on 40 real-world issues at low cost, outperforming flat methods and mitigating lost-in-the-middle effects.

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arXiv:2602.09110v1 Announce Type: new Abstract: We examine the complexity of computing welfare- and revenue-maximizing equilibria in autobidding second-price auctions subject to return-on-spend (RoS) constraints. We show that computing an autobidding equilibrium that approximates the welfare-optimal one within a factor of $2 – epsilon$ is NP-hard for any constant $epsilon > 0$. Moreover, deciding whether there exists an autobidding equilibrium that attains a $1/2 + epsilon$ fraction of the optimal welfare — unfettered by equilibrium constraints — is NP-hard […]

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