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University of Regensburg

Academic institutioneurope · de
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Research library44linked papers
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Representative Papers

"Can You Tell Me?": Designing Copilots to Support Human Judgement in Online Information Seeking

Jan 16, 2026

This study addresses the risk that generative AI in information retrieval may foster user overreliance, thereby undermining critical thinking and independent verification skills. To counter this, the authors propose a large language model–based conversational collaborator that eschews direct answers in favor of Socratic questioning, employing cognitive scaffolding to prompt users to reflect on the credibility of information and cultivate digital literacy. Evaluated through a randomized controlled trial and mixed-methods analysis, the system elicited significantly enhanced metacognitive reflection among users. However, it did not yield measurable improvements in answer accuracy or search engagement, highlighting an inherent tension between efficiency-oriented search behaviors and the cultivation of critical information literacy. This approach offers a novel paradigm that supports—rather than supplants—user judgment.

1 citations1 influentialRead paper

DSLR-CNN: Efficient CNN Acceleration Using Digit-Serial Left-to-Right Arithmetic

Jan 03, 2025IEEE Access

To address energy-efficiency and latency bottlenecks in CNN hardware acceleration, this paper proposes DSLRCNN, a domain-specific accelerator leveraging left-to-right (LR) digit-serial arithmetic. It innovatively integrates LR digit-serial computation—operating in most-significant-digit-first (MSDF) mode—into CNN accelerator design, enabling fine-grained digit-level pipelining and parallel multiply-accumulate (MAC) operations under low interconnect overhead and small area constraints. Implemented in Verilog and synthesized in GSCL 45nm CMOS technology, DSLRCNN incorporates custom LR multipliers/adders and a digit-level pipelined convolution engine. Evaluated on AlexNet, VGG-16, and ResNet-18, it achieves 4.37×–569.11× higher peak throughput and 3.58×–44.75× improved energy efficiency (TOPS/W) over baseline accelerators, while significantly reducing inference latency, silicon area, and power consumption.

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