Replacing gaze annotations with language-driven attention masking makes robot perception annotation-free and up to 5x faster at inference. Picture a robot arm sitting across a table from you. You say: “Put the black bowl in the drawer.” The arm moves. But not toward the bowl. It hovers. It hesitates. Then it grabs the wrong thing. From the outside this looks like a minor coordination failure. From the inside, it is a fundamental problem with how the robot perceives […]

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Hello everyone, I’m preparing to submit a research paper to arXiv (cs.AI) focused on: – Multi-agent systems – Backend code synthesis Since my arXiv account is new, I require an endorsement from an existing arXiv author to proceed (as per the updated policy). What I’m looking for: An arXiv author in Computer Science (cs.AI or related) With a few recent publications What’s involved: A quick endorsement via arXiv (~1–2 minutes) Not peer review — just a verification step […]

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arXiv:2604.02444v1 Announce Type: new Abstract: While recent advances in large language models have significantly improved Text-to-SQL and table question answering systems, most existing approaches assume that all query-relevant information is explicitly represented in structured schemas. In practice, many enterprise databases contain hybrid schemas where structured attributes coexist with free-form textual fields, requiring systems to reason over both types of information. To address this challenge, we introduce OmniTQA, a cost-aware hybrid query processing framework that operates over both structured […]

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arXiv:2602.18520v1 Announce Type: new Abstract: Providing timely, rubric-aligned feedback on student-drawn diagrams is a persistent challenge in STEM education. While large multimodal models (LMMs) can jointly parse images and generate explanations, their tendency to hallucinate undermines trust in classroom deployments. We present Sketch2Feedback, a grammar-in-the-loop framework that decomposes the problem into four stages — hybrid perception, symbolic graph construction, constraint checking, and constrained VLM feedback — so that the language model verbalizes only violations verified by an upstream […]

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arXiv:2602.07224v1 Announce Type: new Abstract: In this work, we review and analyze both the theoretical and numerical aspects of strongly and weakly coupled thermoelastic systems. By employing spectral analysis techniques and establishing uniform resolvent estimates, we derive uniform polynomial decay rates for the associated semigroups under a suitable class of boundary conditions. Particular attention is paid to the role of modal approximations in energy analysis. The theoretical results are complemented by numerical experiments that illustrate how the regularity […]

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arXiv:2601.08005v1 Announce Type: new Abstract: Frontier AI regulations primarily focus on systems deployed to external users, where deployment is more visible and subject to outside scrutiny. However, high-stakes applications can occur internally when companies deploy highly capable systems within their own organizations, such as for automating R&D, accelerating critical business processes, and handling sensitive proprietary data. This paper examines how frontier AI regulations in the United States and European Union in 2025 handle internal deployment. We identify three […]

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arXiv:2602.17834v1 Announce Type: new Abstract: In the last decade, subgraph detection and enumeration have emerged as a central problem in distributed graph algorithms. This is largely due to the theoretical challenges and practical applications of these problems. In this paper, we initiate the systematic study of distributed sub-hypergraph enumeration in hypergraphs. To this end, we (1)~introduce several computational models for hypergraphs that generalize the CONGEST model for graphs and evaluate their relative computational power, (2)~devise algorithms for distributed […]

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Multiple operator learning concerns learning operator families ${G[α]:Uto V}_{αin W}$ indexed by an operator descriptor $α$. Training data are collected hierarchically by sampling operator instances $α$, then input functions $u$ per instance, and finally evaluation points $x$ per input, yielding noisy observations of $G[α][u](x)$. While recent work has developed expressive multi-task and multiple operator learning architectures and approximation-theoretic scaling laws, quantitative statistical generalization guarantees remain limited. We provide a covering-number-based generalization analysis for separable models, focusing on the […]

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Non-Intrusive Load Monitoring (NILM) aims to estimate appliance-level consumption from aggregate electrical signals recorded at a single measurement point. In recent years, the field has increasingly adopted deep learning approaches; however, cross-domain generalization remains a persistent challenge due to variations in appliance characteristics, usage patterns, and background loads across homes. Transfer learning provides a practical paradigm to adapt models with limited target data. However, existing methods often assume a fixed appliance set, lack flexibility for evolving real-world deployments, […]

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arXiv:2603.17099v1 Announce Type: new Abstract: Vectorization is a compiler optimization that replaces multiple operations on scalar values with a single operation on vector values. Although common in traditional compilers such as rustc, clang, and gcc, vectorization is not common in the Verilog ecosystem. This happens because, even though Verilog supports vector notation, the language provides no semantic guarantee that a vectorized signal behaves as a word-level entity: synthesis tools still resolve multiple individual assignments and a single vector […]

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