AI Inspires New Research Topics In Materials Science - miragenews.com
<a href="https://news.google.com/rss/articles/CBMihwFBVV95cUxQRlVFdkRBaHRvYkJJdFRlMTZmajEzeFRPU0hGWWdfbi02V1FnTUdVQ2pmY2VZLUV2NlB4V3BFdEVlSVZkUlhRSTZaNWFKMmcyWXJYbnNqbUhMTmp0NnFtMEppOXlPZkJSNHJfck5VSEVYcmUtX1k2QkJlR1BvUEdTTkp3UmlYRkk?oc=5" target="_blank">AI Inspires New Research Topics In Materials Science</a> <font color="#6f6f6f">miragenews.com</font>
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Exclusive | Caltech Researchers Claim Radical Compression of High-Fidelity AI Models - wsj.com
<a href="https://news.google.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?oc=5" target="_blank">Exclusive | Caltech Researchers Claim Radical Compression of High-Fidelity AI Models</a> <font color="#6f6f6f">wsj.com</font>
Exclusive | Caltech Researchers Claim Radical Compression of High-Fidelity AI Models - WSJ
<a href="https://news.google.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?oc=5" target="_blank">Exclusive | Caltech Researchers Claim Radical Compression of High-Fidelity AI Models</a> <font color="#6f6f6f">WSJ</font>
Sweden brings urgency—and royalty—to Montréal as it seeks AI research partnership - BetaKit
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From brain scans to alloys: Teaching AI to make sense of complex research data - Penn State University
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Locating Risk: Task Designers and the Challenge of Risk Disclosure in RAI Content Work
arXiv:2505.24246v4 Announce Type: replace Abstract: As AI systems are increasingly tested and deployed in open-ended and high-stakes domains, crowdworkers are often tasked with responsible AI (RAI) content work. These tasks include labeling violent content, moderating disturbing text, or simulating harmful behavior for red teaming exercises to shape AI system behaviors. While prior research efforts have highlighted the risks to worker well-being associated with RAI content work, far less attention has been paid to how these risks are communicated to workers by task designers or individuals who design and post RAI tasks. Existing transparency frameworks and guidelines, such as model cards, datasheets, and crowdworksheets, focus on documenting model information and dataset collection process
Togedule: Scheduling Meetings with Large Language Models and Adaptive Representations of Group Availability
arXiv:2505.01000v5 Announce Type: replace Abstract: Scheduling is a perennial-and often challenging-problem for many groups. Existing tools are mostly static, showing an identical set of choices to everyone, regardless of the current status of attendees' inputs and preferences. In this paper, we propose Togedule, an adaptive scheduling tool that uses large language models to dynamically adjust the pool of choices and their presentation format. With the initial prototype, we conducted a formative study (N=10) and identified the potential benefits and risks of such an adaptive scheduling tool. Then, after enhancing the system, we conducted two controlled experiments, one each for attendees and organizers (total N=66). For each experiment, we compared scheduling with verbal messages, shared c
Dynamic Cogeneration of Bug Reproduction Test in Agentic Program Repair
arXiv:2601.19066v2 Announce Type: replace Abstract: Bug Reproduction Tests (BRTs) have been used in many Automated Program Repair (APR) systems, primarily for validating promising fixes and aiding fix generation. In practice, when developers submit a patch, they often implement the BRT alongside the fix. Our experience deploying agentic APR reveals that developers similarly desire a BRT within AI-generated patches to increase their confidence. However, canonical APR systems tend to generate BRTs and fixes separately, and focus on producing only the fix in the final patch. In this paper, we study agentic APR in the context of cogeneration, where the APR agent is instructed to generate both a fix and a BRT in the same patch. We evaluate the effectiveness of different cogeneration strategies
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