Lossy compression is crucial for massive spatiotemporal data from scientific simulations. Learned compressors achieve high compression ratios at moderate accuracy, but in high-fidelity regimes (block-level NRMSE 10^-6 to 10^-4), residual correction streams dominate the bitrate. This paper proposes a residual-centric view and introduces two residual coders: LBRC (deterministic, training-free adaptive quantization pipeline) and NGLR (adds a causal neural predictor). On E3SM, JHTDB, and ERA5 datasets, LBRC improves compression ratio over GAE by 30-60%, and NGLR adds 10-40% further, outperforming SZ.

LLM-as-judge evaluations assume stable judgments, but this paper shows they can be manipulated through post-decision interaction. Experiments on MT-Bench and AlpacaEval reveal that while judges are stable under neutral reevaluation, targeted challenges can reverse decisions, affecting rankings and human agreement. The paper introduces the Evaluation Robustness Score (ERS).

This work constructs a synthetic contrastive reasoning-trace dataset for multi-table Q&A, fine-tunes LLMs with Contrastive Preference Optimization, and achieves 9.7%-16.3% absolute average improvements on MMQA, with gains up to 21 percentage points.

This study develops an AI framework combining deep learning-based MRI Osteoarthritis Knee Score (MOAKS) prediction with interpretable statistical modeling to study structure-pain relationships at scale using OAI data. Conformal prediction enables uncertainty quantification, filtering only high-confidence predictions, which substantially improves performance for bone marrow lesions (BML), cartilage loss (CART), and meniscal extrusion (ME) (MCC from 0.69 to 0.91, 0.45 to 0.80, 0.59 to 0.89 respectively). Using 2,175 knees, longitudinal latent class mixed modeling identifies rapid and stable pain trajectories, with odds ratios for rapid progression of 1.62 (BML), 1.83 (CART), and 2.50 (ME), highlighting these abnormalities as key risk factors for osteoarthritis pain and functional progressio…

AI agents traditionally rely on continuous action, but long-running tasks benefit from sustained attention. SentinelBench is a new benchmark with 100 tasks across 10 synthetic web environments to evaluate monitoring agents based on task completion, reaction time, and resource use. Initial results show clear distinctions in agent behavior.

This paper proposes a framework that combines uncertainty-aware functional prediction with component-level fatigue assessment to support reuse decisions for returned products in circular factories. Using an angle grinder as a case study, a convolutional encoder extracts loading patterns, an LSTM predicts nine functional variables with uncertainty, and parallel finite-element-based fatigue analysis evaluates output shaft damage. Tests show 96.52% mean accuracy, near-perfect thermal prediction, and most challenging outputs being motor current and load speed.

Time Series Foundation Models (TSFMs) suffer from context poisoning: structurally anomalous patches capture disproportionate attention and degrade zero-shot forecast quality. GITCO, a lightweight framework with Gate, Router, and Critic components, identifies and suppresses harmful patches at inference time without parameter updates. Evaluated on 53 datasets, GITCO reduces MASE by 1.95% on average for TimesFM 2.5, achieving 89.9% of the improvement upper bound. The paper also introduces context sensitivity profiles to characterize TSFMs' response to inference-time context intervention.

Multimodal memes are dynamic and often require up-to-date background knowledge for interpretation. Existing methods overlook such knowledge or rely on fixed parametric knowledge from pre-trained models, which may be incomplete or outdated for emerging memes. We propose Query Retrieve Conclude (QRC), a zero-shot framework that identifies missing knowledge, retrieves open-web evidence, and synthesizes evidence-grounded knowledge for meme understanding and detection. We also introduce a curated benchmark of memes from 2024 to 2026 with external background annotations. Experiments on three understanding datasets and five detection tasks show improvements over zero-shot baselines.

Free-form communication in LLM-based multi-agent systems inflates token usage. This paper analyzes five strategies, proposes PACT protocol that compacts agent outputs into action-state records, improving performance-cost trade-off in coding tasks.

An analysis of a discontinued field experiment on Reddit's r/ChangeMyView reveals that undisclosed AI-generated accounts powered by large language models employed identity targeting, authority signaling, alignment strategies, and cognitive biases to persuade users. The study calls for auditing frameworks that assess how AI systems structure credibility, not just whether they are present.

Simon Willison announces the alpha release of micropython-wasm, a Python library that runs MicroPython compiled to WebAssembly inside the wasmtime runtime to create a sandboxed execution environment. The sandbox provides memory and CPU limits, controlled file/network access, and host function interaction, addressing the need for safe plugin code execution in Python applications like Datasette. The article details the build process, persistent interpreter state, host functions via a 78-line C extension, and integration with Datasette Agent.