ICM
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Radial and angular evolution of magnetic cloud signatures in the turbulent solar wind: virtual spacecraft analysis
arXiv:2606.07209v1 Announce Type: cross Abstract: Interplanetary coronal mass ejections (ICMEs) carry magnetic clouds (MCs), large-scale structures with average radial widths about a fifth of an astronomical unit at Earth's orbit. ICMEs display substructures in white light images and reveal rich dynamics across many spatial scales when directly measured by spacecraft. A spacecraft encounter with an ICME can result in smoothly rotating MC intervals or less organised magnetic obstacle (MO) ones.
Coherence Maximization Improves Pluralistic Alignment
arXiv:2606.03110v1 Announce Type: new Abstract: Aligning AI systems with diverse human values requires value specifications grounded in concrete examples, but generating such examples without extensive human supervision remains an open challenge. We investigate what makes these examples effective, using Internal Coherence Maximization (ICM) -- which infers labels by maximizing their mutual predictability -- to generate persona-specific examples that steer a model toward a target group's...
Coherence Maximization Improves Pluralistic Alignment
arXiv:2606.03110v2 Announce Type: replace Abstract: Aligning AI systems with diverse human values requires value specifications grounded in concrete examples, but generating such examples without extensive human supervision remains an open challenge. We investigate what makes these examples effective, using Internal Coherence Maximization (ICM) -- which infers labels by maximizing their mutual predictability -- to generate persona-specific examples that steer a model toward a target group's...
Creative Artists, TPG Back $250 Million Fund for YouTube Talent
The Creative Artists Agency (CAA) logo is displayed outside their headquarters on June 28, 2022 in Los Angeles, California. CAA has acquired rival ICM Partners in a move predicted to transform the Hollywood talent agency industry.
Attention, May I Have Your Decision? Localizing Generative Choices in Diffusion Models
Announce Type: replace Abstract: Text-to-image diffusion models exhibit remarkable generative capabilities, yet their internal operations remain opaque, particularly when handling prompts that are not fully descriptive. In such scenarios, models must make implicit decisions to generate details not explicitly specified in the text. This work investigates the hypothesis that this decision-making process is not diffuse but is computationally localized within the model's architecture.
Debugging the Debuggers: Failure-Anchored Structured Recovery for Software Engineering Agents
arXiv:2605.08717v2 Announce Type: replace Abstract: Software engineering agents are increasingly deployed in evaluable engineering environments, yet post-failure recovery remains costly, manual, and ad hoc. Existing systems expose traces or generate follow-up feedback, but they do not convert heterogeneous runtime evidence into grounded, bounded recovery guidance for a subsequent attempt. We present PROBE, a failure-anchored framework for structured recovery in software engineering agents.
Attention, May I Have Your Decision? Localizing Generative Choices in Diffusion Models
arXiv:2604.06052v2 Announce Type: replace Abstract: Text-to-image diffusion models exhibit remarkable generative capabilities, yet their internal operations remain opaque, particularly when handling prompts that are not fully descriptive. In such scenarios, models must make implicit decisions to generate details not explicitly specified in the text. This work investigates the hypothesis that this decision-making process is not diffuse but is computationally localized within the model's...
Making the Most of Limited Data: Score-Aware Training for Text-to-Music Generation
Announce Type: new Abstract: State-of-the-art text-to-music generation systems rely on massive proprietary datasets and industrial-scale compute, making it impossible to disentangle architectural contributions from resource advantages. We propose \textit{score-aware training}, which treats audio-caption alignment score as a direct supervision signal throughout the pipeline. Rather than discarding low-scoring segments, we repurpose them via a CLAP-conditioned Beta noise timestep schedule that...