AI Impact on the Interface
How artificial intelligence is fundamentally reshaping user interactions, and what it means for the future of design Continue reading on Paperclip Design »
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Inter-Speaker Relative Cues for Two-Stage Text-Guided Target Speech Extraction
arXiv:2603.01316v2 Announce Type: replace Abstract: This paper investigates the use of relative cues for text-based target speech extraction (TSE). We first provide a theoretical justification for relative cues from the perspectives of human perception and label quantization, showing that relative cues preserve fine-grained distinctions that are often lost in absolute categorical representations for continuous-valued attributes. Building on this analysis, we propose a two-stage TSE framework in which a speech separation model first generates candidate sources, followed by a text-guided classifier that selects the target speaker based on embedding similarity. Within this framework, we train two separate classification models to evaluate the advantages of relative cues over independent cues
MIMO Capacity Enhancement by Grating Walls: A Physics-Based Proof of Principle
arXiv:2604.01786v1 Announce Type: new Abstract: This paper investigates the passive enhancement of MIMO spectral efficiency through boundary engineering in a simplified two dimensional indoor proof of principle model. The propagation channel is constructed from the electromagnetic Green's function of a room with boundaries modeled as free space, drywall, perfect electric conductor (PEC), or binary gratings. Within this framework, grating coated walls enrich the non line of sight (NLoS) multipath field, reduce channel correlation, and enhance spatial multiplexing over a broad range of receiver locations. Comparisons with the drywall and PEC reference cases further reveal that the observed capacity enhancement arises not from diffraction alone, but from the combined effects of effective wall
RIFT: Entropy-Optimised Fractional Wavelet Constellations for Ideal Time-Frequency Estimation
arXiv:2501.15764v3 Announce Type: replace Abstract: We introduce a new method for estimating the Ideal Time-Frequency Representation (ITFR) of complex nonstationary signals. The Reconstructive Ideal Fractional Transform (RIFT) computes a constellation of Continuous Fractional Wavelet Transforms (CFWTs) aligned to different local time-frequency curvatures. This constellation is combined into a single optimised time-frequency energy representation via a localised entropy-based sparsity measure, designed to resolve auto-terms and attenuate cross-terms. Finally, a positivity-constrained Lucy-Richardson deconvolution with total-variation regularisation is applied to estimate the ITFR, achieving auto-term resolution comparable to that of the Wigner-Ville Distribution (WVD), yielding the high-res
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ToolMisuseBench: An Offline Deterministic Benchmark for Tool Misuse and Recovery in Agentic Systems
arXiv:2604.01508v1 Announce Type: new Abstract: Tool using agents often fail for operational reasons even when language understanding is strong. Common causes include invalid arguments, interface drift, weak recovery, and inefficient retry behavior. We introduce ToolMisuseBench, an offline deterministic benchmark for evaluating tool misuse and recovery under explicit step, call, and retry budgets. The benchmark covers CRUD, retrieval, file, and scheduling environments with replayable fault injection. It reports success, invalid call behavior, policy violations, recovery quality, and budgeted efficiency. We release a public dataset with 6800 tasks and a reproducible evaluation pipeline. Baseline results show fault specific recovery gains for schema aware methods, while overall success remai
GAP-URGENet: A Generative-Predictive Fusion Framework for Universal Speech Enhancement
arXiv:2604.01832v1 Announce Type: new Abstract: We introduce GAP-URGENet, a generative-predictive fusion framework developed for Track 1 of the ICASSP 2026 URGENT Challenge. The system integrates a generative branch, which performs full-stack speech restoration in a self-supervised representation domain and reconstructs the waveform via a neural vocoder, along with a predictive branch that performs spectrogram-domain enhancement, providing complementary cues. Outputs from both branches are fused by a post-processing module, which also performs bandwidth extension to generate the enhanced waveform at 48 kHz, later downsampled to the original sampling rate. This generative-predictive fusion improves robustness and perceptual quality, achieving top performance in the blind-test phase and rank
Frequency-switching Coherent Reception for Hardware-efficient High-baud-rate Optical Transmission Experiments
arXiv:2604.01623v1 Announce Type: new Abstract: Signal gating combined with local-oscillator-frequency switching enables bandwidth scaling of offline coherent reception without costly receiver parallelization. We experimentally verify this concept at symbol rates of up to 288 GBaud.
MOVis: A Visual Analytics Tool for Surfacing Missed Patches Across Software Variants
arXiv:2604.01494v1 Announce Type: new Abstract: Clone-and-own development produces families of related software variants that evolve independently. As variants diverge, important fixes applied in one repository are often missing in others. PaReco has shown that thousands of such missed opportunity (MO) patches exist across real ecosystems, yet its textual output provides limited support for understanding where and how these fixes should be propagated. We present MOVis, a lightweight, interactive desktop tool that visualizes MO patches between a source and target variant. MOVis loads PaReco's MO classifications and presents patched and buggy hunks side-by-side, highlighting corresponding regions and exposing structural differences that hinder reuse. This design enables developers to quickly
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