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STaR-KV: Spatio-Temporal Adaptive Re-weighting for KV Cache Compression in GUI Vision-Language Models

Announce Type: new Abstract: Vision-language-model-based graphical user interface (GUI) agents have shown broad automation capabilities, yet deployment is bottlenecked by a key-value (KV) cache that grows linearly with interaction steps. For instance, UI-TARS-1.5-7B consumes 76 GB of GPU memory on merely five screenshots, approaching the capacity of mainstream 80 GB accelerators. Existing KV compression methods share two structural assumptions: aggregating visual-token importance into a...

arXiv CS 8d ago

Beyond Generative Decoding: Discriminative Hidden-State Readout from a Native Omni-Modal LLM for Multimodal Sentiment Analysis

arXiv:2606.05713v1 Announce Type: new Abstract: Multimodal sentiment analysis (MSA) infers human affect from language, acoustic, and visual signals. Recent methods increasingly adapt large multimodal models (LMMs) via generative readout: prompting the model to emit a sentiment score as a text string. While convenient, this ties continuous regression to discrete autoregressive decoding, incurring unmeasured costs.

arXiv CS 5d ago

Teacher-Free Self-Training Amplifies but Does Not Compound: A Pass@$K$ Crossover on a Free-Verifier Domain

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arXiv CS 1d ago

Hallucination Is Linearly Decodable from Mid-Layer Hidden States in Quantized LLMs

arXiv:2606.02628v1 Announce Type: new Abstract: We investigate whether open-source LLMs encode a linearly separable truthfulness signal in their hidden states, and at which network depth this signal is strongest. Across three $7$B--$8$B instruction-tuned models (Llama-3.1-8B, Mistral-7B, Qwen2.5-7B) loaded in $4$-bit NF4 quantization, we extract per-layer hidden states on four hallucination benchmarks (TruthfulQA, HaluEval-QA, FEVER, and a controlled synthetic set) and compare four detection...

arXiv CS 7d ago

Rotary GPU: Exploring Local Execution for Large MoE Models Under Limited VRAM

Performance [Submitted on 27 May 2026] Title:Rotary GPU: Exploring Local Execution Paths for Large Mixture-of-Experts Models Under Limited GPU Memory View PDF HTML (experimental)Abstract:Large language models have achieved remarkable capabilities through scaling, and this paper does not challenge that. It instead investigates a different question: once large models already exist, can they become more accessible to environments with substantially smaller hardware resources?

Hacker News 11d ago

A 10 year old Xeon is all you need (for 26B-A4B MTP Drafters without GPU)

A 10 year old Xeon is all you need 17 minutes read The previous post covered getting Gemma 4’s MTP drafters quantized and paired with a verifier. This one is about running the result on a machine that has no business running it. I have a recycled server.

Hacker News 9d ago

Semantic-decoupled Spatial Partition Guided Point-supervised Oriented Object Detection

arXiv:2506.10601v2 Announce Type: replace Abstract: Given its ability to reduce annotation costs, weakly supervised learning based on single-point annotations has emerged as a research focus in oriented object detection. Compared with the classical teacher-student paradigm, the simple model paradigm (e.g., PointOBB-v2) can substantially further reduce resources required for training while ensuring strong performance.

arXiv CS 5d ago

FreqLite: A Lightweight Frequency-Decomposed Linear Model with Adaptive Reversible Normalization for Robust Long-Term Time-Series Forecasting

arXiv:2606.01339v1 Announce Type: new Abstract: Long-term time-series forecasting needs models that are accurate yet efficient enough for commodity hardware. Lightweight linear forecasters are remarkably strong in this regime, yet they leave two openings: reversible instance normalization (RevIN) de-normalizes the entire horizon with a single lookback statistic, which is inaccurate under non-stationarity, and time-domain trend/seasonal decomposition relies on a fixed, non-adaptive filter. We...

arXiv CS 8d ago

Topologically Consistent Multi-view 3D Head Reconstruction via Coarse-Guided Layered Surface Sampling

arXiv:2605.31283v1 Announce Type: new Abstract: We present SHELLS (Semantic Head Estimation via Layered Local Sampling), an efficient feed-forward framework for 3D head reconstruction in dense semantic correspondence from multi-view images. Existing methods typically refine vertices independently via localized feature volumes. This approach couples memory-intensive feature sampling to mesh resolution, which limits scalability for dense topologies (> 10k vertices) and introduces surface noise.

arXiv CS 9d ago

HP Discount Codes: 60% Off June 2026

If you don't know where to start—and use—your HP coupon code, there’s a wide variety of options available at HP.com in terms of budget and use case, but my eye goes first to the high-end HP Omen gaming monitors, like the fantastic HP Omen Transcend 32. This 4K 240Hz monitor is a favorite among PC gamers, even among the huge amounts of OLED options out there. It can hit a peak brightness of over 1,000 nits in HDR, bringing scenes in games to life in vivid detail.

Wired 5d ago