LightNews — News, ranked by scholars

Bridging LLM Serving and CXL-SSDs with Chunk-Aware KV Cache Management
tech_blogs_arxiv | Author: Hyunsun Chung, Taewan Noh, Minji Kim, Joo-Young Hwang, Hong-Yeon Kim, Youngjae Kim

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Bridging LLM Serving and CXL-SSDs with Chunk-Aware KV Cache Management

NAND-backed storage offers the capacity needed to scale LLM prefix caching, but its block I/O path incurs CPU cache contention and host-DRAM staging in addition to NAND latency. Our characterization shows that these interface costs persist even with DRAM as the storage medium, motivating CXL-SSDs fo

arxiv.org

September 24, 2026 at 4:27 AM

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What Makes a Terminal-Bench Task Hard? Separating Genuine Hardness from Fake-Hardness on an Adjudicated Agentic Corpus
tech_blogs_arxiv | Author: Edward Lue Chee Lip, Boden Moraski, Tim Knappe, Lang Xiong, Sarvesh Gharat, Antonio Mari, Ivan Bercovich

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What Makes a Terminal-Bench Task Hard? Separating Genuine Hardness from Fake-Hardness on an Adjudicated Agentic Corpus

Frontier benchmarks need tasks that current models cannot solve. But a task that no model solves is not automatically a hard task. The same zero pass rate can come from a real capability gap, but it can also come from missing context, a broken reference solution, infrastructure failure, or a verifie

arxiv.org

September 24, 2026 at 4:27 AM

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Text Scores Can Miss Waveform Use: A Qwen2-Audio Quantization Case Study
tech_blogs_arxiv | Author: Mengzhe Geng, Jinxi Jin, Junhao Xu

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Text Scores Can Miss Waveform Use: A Qwen2-Audio Quantization Case Study

Post-training quantization of speech language models is often summarized with text-output scores and nominal bit widths. Those numbers alone do not establish behavior that depends on information missing from a transcript, or efficiency for a particular runtime. We introduce an evaluation protocol th

arxiv.org

September 24, 2026 at 4:27 AM

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HARN: Hierarchical Associative Resonance Network for Event-Driven Multi-Timeframe Forecasting
tech_blogs_arxiv | Author: Nabeel Ahmad Saidd

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HARN: Hierarchical Associative Resonance Network for Event-Driven Multi-Timeframe Forecasting

Financial time series evolve across multiple temporal resolutions, challenging forecasting systems to incorporate newly available information without repeatedly recomputing unchanged representations. We introduce HARN, a Hierarchical Associative Resonance Network for event-driven multi-timeframe for

arxiv.org

September 24, 2026 at 4:27 AM

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Signal2Symbol: Neuro-Symbolic Temporal Reasoning for Explainable Physiological Time-Series Anomaly Detection
tech_blogs_arxiv | Author: Naser Mansour, Sidahmed Benabderrahmane, Ameer Rahwan

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Signal2Symbol: Neuro-Symbolic Temporal Reasoning for Explainable Physiological Time-Series Anomaly Detection

Physiological time series such as electrocardiograms (ECG) and electroencephalograms (EEG) exhibit complex temporal structure, substantial acquisition variability, and a strong need for transparent decision-making. Although deep models can achieve high detection performance, they often provide limit

arxiv.org

September 24, 2026 at 4:27 AM

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FedCoT-VQA: A Federated Learning and Unlearning Framework for Chain-of-Thought Planners in VideoQA
tech_blogs_arxiv | Author: Rui Lu, Tao Ling, Dan Wang, Qing Li

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FedCoT-VQA: A Federated Learning and Unlearning Framework for Chain-of-Thought Planners in VideoQA

Chain-of-Thought (CoT) planners have emerged as an effective design for VideoQA, where a lightweight planner first generates intermediate reasoning steps to guide temporal evidence selection before answer prediction. This modularity makes CoT-based VideoQA attractive for federated learning, since on

arxiv.org

September 24, 2026 at 4:27 AM

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The Drift Contract: Spectral Updates for Depth-Robust Local Learning
tech_blogs_arxiv | Author: Fabien Polly

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The Drift Contract: Spectral Updates for Depth-Robust Local Learning

Local learning trains each layer with its own auxiliary loss and no global backward pass, which makes layer updates structurally parallel. Two problems have kept it marginal: accuracy degrades as depth grows, and hyperparameters are fragile. We apply Muon-style spectral update geometry (momentum ort

arxiv.org

September 24, 2026 at 4:27 AM

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AgroBench: A Reproducible Multimodal Benchmark for Weakly Supervised Crop Yield Learning from County Statistics and Pixel Observations
tech_blogs_arxiv | Author: Udaiveer Singh, Rajiv Ranjan, Shashank Tamaskar, Dharmendra Saraswat

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AgroBench: A Reproducible Multimodal Benchmark for Weakly Supervised Crop Yield Learning from County Statistics and Pixel Observations

Reliable agricultural yield statistics are typically reported at coarse administrative scales, whereas modern geospatial machine learning methods require spatially explicit, pixel level supervision. This mismatch has limited the development of large-scale benchmarks for crop yield learning using mul

arxiv.org

September 24, 2026 at 4:27 AM