Compressed Deep Learning (DL) models are essential for deployment in resource-constrained environments. But their performance often lags behind their large-scale counterparts. To bridge this gap, we propose Alignment Adapter (AlAd): a lightweight, sliding-window-based adapter. It aligns the token-level embeddings of a compressed model with those of the original large model. AlAd preserves local contextual semantics, enables flexible alignment across differing dimensionalities or architectures, and is entirely agnostic to the underlying compression method. AlAd can be deployed in two […]

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arXiv:2603.05559v1 Announce Type: new Abstract: Decision makers exploiting photonic chaotic dynamics obtained by semiconductor lasers provide an ultrafast approach to solving multi-armed bandit problems by using a temporal optical signal as the driving source for sequential decisions. In such systems, the sampling interval of the chaotic waveform shapes the temporal correlation of the resulting time series, and experiments have reported that decision accuracy depends strongly on this autocorrelation property. However, it remains unclear whether the benefit of autocorrelation […]

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Monitoring is not a dashboard only problem In the last couple of years, I have moved across a few product teams. Every time I walk into an engineering team and ask how monitoring works. I get a standard response. There is a dashboard Usually, Grafana. Sometimes Kibana or Splunk. Team members have passed me links buried in wiki pages and nobody updates them anymore. But dashboards don’t make a system observable. I have worked on large scale cloud […]

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arXiv:2601.19908v1 Announce Type: new Abstract: The proliferation of large language models (LLMs) is accelerating the integration of multimodal assistants into edge devices, where inference is executed under stringent latency and energy constraints, often exacerbated by intermittent connectivity. These challenges become particularly acute in the context of multimodal LLMs (MLLMs), as high-dimensional visual inputs are transformed into extensive token sequences, thereby inflating the key-value (KV) cache and imposing substantial data movement overheads to the LLM backbone. To address these […]

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Author(s): Kamrun Nahar Originally published on Towards AI. What Even Is OpenClaw, Though? Let me tell it straight. OpenClaw is an open-source, self-hosted AI agent built by Peter Steinberger. It started life as “Clawdbot” in November 2025, got renamed to “Moltbot” after Anthropic raised an eyebrow about trademark stuff, then finally settled on “OpenClaw” a few days later. The lobster logo stuck around through every name change, because apparently crustaceans are the spirit animal of autonomous computing. Here’s […]

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arXiv:2604.16936v1 Announce Type: new Abstract: Feature reconstruction techniques are widely applied for few-shot fine-grained image classification (FSFGIC). Our research indicates that one of the main challenges facing existing feature-based FSFGIC methods is how to choose the size of the receptive field to extract feature descriptors (including spatial and frequency feature descriptors) from different category input images, thereby better performing the FSFGIC tasks. To address this, an adaptive receptive field-based spatial-frequency feature reconstruction network (ARF-SFR-Net) is proposed. The designed […]

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arXiv:2602.18579v1 Announce Type: new Abstract: Developers often extract methods to improve readability, understanding, and reuse, while inlining keeps logic in one block. Prior work based on static metrics has not shown clear differences between these practices, and the human side of comprehension and navigation remains underexplored. We investigate Inline Method vs. Extract Method refactorings using a dynamic approach: eye tracking while participants read and solve tasks. We analyze key code areas and compare visual effort and reading behavior […]

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