Introduction Writing with AI assistance is not the same as writing by AI. When you write directly with AI, you’re prompting it to generate content from scratch. When you write with AI assistance, you remain the author—AI simply helps refine your work rather than doing most of it for you. The Problem with Full AI Generation Yes, you can create a story from scratch using AI prompts, but the result will likely be short and won’t capture exactly […]

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arXiv:2603.26752v1 Announce Type: new Abstract: Tight matching with the environment is key to effective robot operation in complex settings. Situated robots that build their bodies in situ (e.g. by spinning) are uniquely positioned to exploit their surroundings, yet functionalization of these structures remains an integration challenge – multimaterial spinning requires complex spinneret multiplexing, and mixture doping is limited by additive availability and chemical stability. We propose instead using materials available in the environment to functionalize in situ spun […]

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arXiv:2602.23388v1 Announce Type: new Abstract: The rising demand for inclusive speech technologies amplifies the need for multilingual datasets for Natural Language Processing (NLP) research. However, limited awareness of existing task-specific resources in low-resource languages hinders research. This challenge is especially acute in linguistically diverse countries, such as India. Cross-task profiling of existing Indian speech datasets can alleviate the data scarcity challenge. This involves investigating the utility of datasets across multiple downstream tasks rather than focusing on a single […]

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arXiv:2603.00542v1 Announce Type: new Abstract: In real-world vision systems,haze removal is required not only to enhance image visibility but also to meet the specific needs of diverse downstream tasks.To address this challenge,we propose a novel adaptive dynamic dehazing framework that incorporates a closed-loop optimization mechanism.It enables feedback-driven refinement based on downstream task performance and user instruction-guided adjustment during inference,allowing the model to satisfy the specific requirements of multiple downstream tasks without retraining.Technically,our framework integrates two complementary and innovative […]

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Split learning recently emerged as a solution for distributed machine learning with heterogeneous IoT devices, where clients can offload part of their training to computationally-powerful helpers. The core challenge in split learning is to minimize the training time by jointly devising the client-helper assignment and the schedule of tasks at the helpers. We first study the model where each helper has a memory cardinality constraint on how many clients it may be assigned, which represents the case of […]

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Agentic Engineering Patterns > The defining characteristic of a coding agent is that it can execute the code that it writes. This is what makes coding agents so much more useful than LLMs that simply spit out code without any way to verify it. Never assume that code generated by an LLM works until that code has been executed. Coding agents have the ability to confirm that the code they have produced works as intended, or iterate further […]

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