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arXiv:2604.15356v1 Announce Type: new Abstract: Recent work on KV cache quantization, culminating in TurboQuant, has approached the Shannon entropy limit for per-vector compression of transformer key-value caches. We observe that this limit applies to a strictly weaker problem than the one that actually matters: compressing the KV cache as a sequence. The tokens stored in a KV cache are not arbitrary floating-point data — they are samples from the exact formal language the model was trained on, and […]

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arXiv:2604.15352v1 Announce Type: new Abstract: Modern methods of environmental monitoring are deficient in the lack of ability to take measurements of energy flows since traditional readings involve capturing parameters such as temperature, pressure, and humidity without considering their physical causes. The present research describes Differential Temporal Derivative Soft-Sensing (DTDSS), a physics-based approach which enables any ordinary low cost sensor array to infer estimates of the energy exchange in the environment by modeling its radiative heat fluxes. In particular, […]

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arXiv:2604.15351v1 Announce Type: new Abstract: Low-Rank Adaptation (LoRA) has become the dominant parameter-efficient fine-tuning method for large language models, yet standard practice applies LoRA adapters uniformly to all transformer layers regardless of their relevance to the downstream task. We introduce Aletheia, a gradient-guided layer selection method that identifies the most task-relevant layers via a lightweight gradient probe and applies LoRA adapters only to those layers with asymmetric rank allocation. Across 81 experiment rows covering 14 successful models from […]

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arXiv:2604.15350v1 Announce Type: new Abstract: We discover that large language models exhibit emph{spectral phase transitions} in their hidden activation spaces when engaging in reasoning versus factual recall. Through systematic spectral analysis across textbf{11 models} spanning textbf{5 architecture families} (Qwen, Pythia, Phi, Llama, DeepSeek-R1), we identify textbf{seven} core phenomena: (1)~textbf{Reasoning Spectral Compression} — 9/11 models show significantly lower $alpha$ for reasoning ($p < 0.05$), with larger effects in stronger models; (2)~textbf{Instruction Tuning Spectral Reversal} — base models show reasoning […]

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arXiv:2604.15348v1 Announce Type: new Abstract: Conventional mobile eye-tracking maps gaze to static screen coordinates, failing to capture user attention when content is dynamic. As users pinch, zoom, and rotate images, static coordinates lose their semantic meaning relative to the underlying visual content. To address this methodological gap, we present textit{GazeSync}, a reusable mobile system that synchronizes on-device gaze estimation with real-time image transformation matrices (scale, rotation, and translation). By logging gaze coordinates alongside precise UI states, GazeSync enables […]

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arXiv:2604.15347v1 Announce Type: new Abstract: Autism Spectrum Disorder (ASD) affects more than 75 million people worldwide. However, scalable support for practicing everyday conversation is scarce: Low-cost activities such as story reading yield limited improvement. At the same time, effective role-play therapy demands expensive, in-person sessions with specialists. SocialWise bridges this gap through a browser-based application that pairs LLM conversational agents with a therapeutic retrieval augmented generation (RAG) knowledge base. Users select a scenario (e.g., ordering food, joining a […]

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arXiv:2604.15345v1 Announce Type: new Abstract: Despite public motivation to recycle, significant barriers hinder effective household recycling in the UK. Decentralised local authority waste management creates citizen confusion and “wishcycling” (disposing of non-recyclable items in recycling bins). The recent Simpler Recycling Policy further complicates this landscape by mandating new identification, sorting, and cleaning requirements that will require citizen guidance to ensure they understand how these will impact their recycling practices. This mixed methods study (surveys n=50, expert interviews, design […]

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arXiv:2604.15344v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly integrated into design and development workflows, yet decisions about their use are rarely binary or purely technical. We report findings from a constructivist grounded theory study based on interviews with 33 designers and developers across three large technology organisations. Rather than evaluating LLMs solely by capability, participants reasoned about the role an LLM could occupy within a workflow and how that role would interact with existing structures […]

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arXiv:2604.15343v1 Announce Type: new Abstract: We report a detailed autoethnographic case study of a single-subject who deliberately constructed and operated a multi-modal prompt-engineering system (System A) designed to externalize cognitive self-regulation onto a large language model (LLM). Within 48 hours of the system’s completion, a cascade of observable behavioral changes occurred: voluntary transfer of decision-making authority to the LLM, use of LLM-generated output to deflect external criticism, and a loss of self-initiated reasoning that was independently perceived by […]

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arXiv:2604.15342v1 Announce Type: new Abstract: ProvenanceWidgets is an existing JavaScript library that tracks the recency and frequency of user interactions with individual UI controls (e.g., range sliders and dropdowns) and dynamically overlays this provenance onto them. In this work, we introduce SuperProvenanceWidgets, an extension to ProvenanceWidgets featuring a new SuperWidget that similarly tracks and visualizes provenance but across multiple UI controls, enabling users to understand how, when, and whether different UI controls were used. Through three example usage […]

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