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The Information Content of Quasar Variability Light Curves: How Well Can we Infer Stochastic Model Parameters?
arXiv:2606.01496v1 Announce Type: cross Abstract: Quasar variability, driven by multi-scale physical processing within a relativistic accretion disk, is commonly modelled with stochastic time series models. The simplest of these is the Damped Random Walk (DRW), also known as the Ornstein-Uhlenbeck (OU) process. Here, we demonstrate that, when fitting such a model to quasar light curve data, the mean of the light curve, $\mu$, should not be fixed (which is the typical approach), as this leads...
How I Get Free Traffic from ChatGPT in 2025 (AIO vs SEO)
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Active Video Perception: Iterative Evidence Seeking for Agentic Long Video Understanding
Announce Type: replace Abstract: Long video understanding (LVU) is challenging because answering real-world queries often depends on sparse, temporally dispersed cues buried in hours of mostly redundant and irrelevant content. While agentic pipelines improve video reasoning capabilities, prevailing frameworks rely on a query-agnostic captioner to perceive video information, which wastes computation on irrelevant content and blurs fine-grained temporal and spatial information. Motivated by...
Style or Content? Evaluating Style Classifiers with Controlled Content Overlap
Announce Type: new Abstract: Style classifiers can use content cues that correlate with style labels in naturally collected data, yet we lack a systematic way to measure this reliance. We study this problem with a controlled content overlap setup built on parallel Bible translations. Specifically, we define the overlap parameter $\alpha$ as the normalized residual of mutual information between content identity and style label, so that it measures how much content is shared across style...
Entropy Gate: Entropy Quenching for Near-Lossless Token Compression in LLM Pipelines
arXiv:2606.03739v1 Announce Type: new Abstract: LLM pipelines waste substantial token budgets on low-information content: repeated context, verbose responses, and redundant boilerplate. We introduce Entropy Gate, a token compression framework applying entropy quenching $-$ a thermodynamic process that progressively freezes out low-energy tokens while preserving semantic fidelity. Each token receives a multi-factor information energy $E(t)$ combining statistical, structural, and positional...
Adaptive Tokenisation Via Temporal Redundancy Masking And Latent Inpainting
arXiv:2606.06158v1 Announce Type: new Abstract: Adaptive video tokenisation seeks to dynamically allocate token budgets based on the underlying visual complexity of a sequence. Current continuous-regime approaches achieve this via iterative binarised searches or trained neural regressors, while discrete methods often require a full-rate decoder pass to estimate information content. We demonstrate that such computational overheads are not strictly necessary.
Large Models for Time Series and Spatio-Temporal Data: A Survey and Outlook
arXiv:2310.10196v3 Announce Type: replace Abstract: Temporal data, including time series and spatio-temporal data, are pervasive in real-world applications. Generated in massive volumes by physical and virtual sensors, they record dynamic system behaviors and enable a wide range of downstream tasks. Effectively analyzing such data is crucial to unlocking their rich information content.
Japanese Gothic is a gorgeously grotesque ghost story
I'll give the usual caveat: The horror novel Japanese Gothic is best experienced going in with as little information as possible. Content warnings for graphic gore, scenes of domestic violence, self-harm, and mental illness. If you're okay with that, then consider pausing here.
Seeing is Believing? Evaluating Vision-Language Model Susceptibility in Agent-to-Agent Multimodal Persuasion
arXiv:2510.22768v2 Announce Type: replace Abstract: As autonomous agents increasingly interact, they inevitably attempt to influence one another. While prior work in text-only settings has explored the dynamics of Agent-to-Agent (A2A) persuasion, the rise of Vision-Language Models (VLMs) introduces a more complex challenge: multimodal content conveys richer information while integrating subtle, hard-to-detect persuasive cues. To study this vulnerability, we present MMPersuade, a unified...
LongTraceRL: Learning Long-Context Reasoning from Search Agent Trajectories with Rubric Rewards
Announce Type: new Abstract: Long-context reasoning remains a central challenge for large language models, which often fail to locate and integrate key information in extensive distracting content. Reinforcement learning with verifiable rewards (RLVR) has shown promise for this task, yet existing methods are limited by low-confusability distractors and sparse, outcome-only reward signals that cannot supervise intermediate reasoning steps. To address these issues, we introduce...