arXiv:2602.15259v1 Announce Type: new Abstract: Generative AI agents equate understanding with resolving explicit queries, an assumption that confines interaction to what users can articulate. This assumption breaks down when users themselves lack awareness of what is missing, risky, or worth considering. In such conditions, proactivity is not merely an efficiency enhancement, but an epistemic necessity. We refer to this condition as epistemic incompleteness: where progress depends on engaging with unknown unknowns for effective partnership. Existing approaches to proactivity […]

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I started core Reinforcement Learning(offline/online/O2O and policy regularization) during my PhD study and I’m now doing LLM post training RL. Now people use diffusion policy or VLA for robotics. I interviewed two robotics companies but both get rejected. I believe that’s because I don’t have real-world robotics related experience, only MuJoCo. Any advice on how I could get a Robotics RL research job? Do humanoid projects? Be familiar with Isaac? Or others? submitted by /u/Blasphemer666 [link] [comments]

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Learning causal structures typically represented by directed acyclic graphs (DAGs) from observational data is notoriously challenging due to the combinatorial explosion of possible graphs and inherent ambiguities in observations. This paper argues that causal learning is now ready for the emergence of a new paradigm supported by rapidly advancing technologies, fulfilling the long-standing vision of leveraging human causal knowledge. This paradigm integrates scalable crowdsourcing platforms for data collection, interactive knowledge elicitation for expert opinion modeling, robust aggregation techniques […]

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arXiv:2601.16398v1 Announce Type: new Abstract: Algorithmic audits are essential tools for examining systems for properties required by regulators or desired by operators. Current audits of large language models (LLMs) primarily rely on black-box evaluations that assess model behavior only through input-output testing. These methods are limited to tests constructed in the input space, often generated by heuristics. In addition, many socially relevant model properties (e.g., gender bias) are abstract and difficult to measure through text-based inputs alone. To […]

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We present KokborokMT, a high-quality neural machine translation (NMT) system for Kokborok (ISO 639-3; trp), a Tibeto-Burman language spoken primarily in Tripura, India with approximately 1.5 million speakers. Despite its status as an official language of Tripura, Kokborok has remained severely under-resourced in the NLP community, with prior machine translation attempts limited to systems trained on small Bible-derived corpora achieving BLEU scores below 7. We fine-tune the NLLB-200-distilled-600M model on a multi-source parallel corpus comprising 36,052 sentence pairs: […]

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Introduction Disclaimer: This article is a cross post from Pytorch Medium Blog Post. One of the most exciting advancements, that has pushed the frontier of the Artificial Intelligence (AI) in recent years, is Deep Reinforcement Learning (DRL). DRL belongs to the family of machine learning algorithms. It assumes that intelligent machines can learn from their actions similar to the way humans learn from experience. Over the recent years we could witness some impressive real-world applications of DRL. The […]

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In the U.S. digital economy, small and medium-sized businesses (SMBs) and creators in remote regions face structural disadvantages in access to integrated advertising and incentive platforms, largely due to accelerating privacy regulations and the fragmentation of cross channel datasets. This paper proposes a unified federated and differentially private measurement framework that integrates Topics/Protected Audience, Attribution Reporting, and SKAdNetwork, aiming to achieve privacy-preserving incentive optimization and cross-channel effectiveness measurement for web and mobile environments. The framework prioritizes compliant data […]

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Most founders have a version of this story. Personally, I’ve watched this happen (and made this mistake myself): the tool looks great in a demo, then headcount grows and reality hits. You pick a modern, impressive-looking tool. The demo is smooth. The UI feels fast. Everyone’s excited. For a while, it works. Then the company grows. You hire more people. Someone leaves. Finance asks for historical data. An auditor wants proof of approvals. A manager needs limited access. […]

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arXiv:2603.10032v1 Announce Type: new Abstract: Memory constraints in long-running agents require structured management of accumulated facts while preserving essential information under bounded context limits. We introduce HTM-EAR, a hierarchical tiered memory substrate that integrates HNSW-based working memory (L1) with archival storage (L2), combining importance-aware eviction and hybrid routing. When L1 reaches capacity, items are evicted using a weighted score of importance and usage. Queries are first resolved in L1; if similarity or entity coverage is insufficient, retrieval falls […]

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