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AutoSynthData: Generating Training Data for Enterprise Agents
AutoSynthData: Generating Training Data for Enterprise Agents
AutoSynthData: Generating Training Data for Enterprise Agents
Contingent Exposure Routing for Financial AI: Outage Risk and the Cost of Indivisible Decisions
arXiv:2610.00239v1 Announce Type: new Abstract: Model failover restores availability, but changes which financial institutions share decision errors. We formulate outage-contingent routing through a local market-impact response matrix and study expected squared price displacement. A symmetric construction shows that a shared…
Scaling Collider Event Generation with Residual-Quantized Tokens
arXiv:2610.00569v1 Announce Type: cross Abstract: Full detector simulation and reconstruction of collider events are projected to become major bottlenecks at the High-Luminosity Large Hadron Collider, motivating the development of fast, ML-based surrogates. At the same time, LLMs have driven fast…
Learning ab initio phase-field models
arXiv:2610.01432v1 Announce Type: cross Abstract: Simulating microstructure evolution requires quantum-mechanical accuracy and mesoscopic reach in length and time scales, a combination that no current method achieves. Classical phase-field models provide this reach, but their accuracy is limited by phenomenological free…
Large Language Bayes Is Not Reparameterisation-Invariant
arXiv:2610.00265v1 Announce Type: new Abstract: Large Language Bayes (LLB) answers an informal modelling question by sampling candidate probabilistic programs from a language model, running approximate inference on each, and averaging them with weights proportional to an exponentiated evidence bound. We…
Hob-VL: A Benchmark for Visually Grounded Boolean Reasoning
arXiv:2610.01605v1 Announce Type: cross Abstract: Reliable visual reasoning requires composing multiple visual observations and returning consistent answers to logically equivalent questions. We introduce Hob-VL, a benchmark for visually grounded Boolean reasoning. Hob-VL comprises two tasks: (1) evaluating whether a Boolean…
MOVE: Multimodal Open-world Verification and Expansion for Graph Learning
arXiv:2610.00268v1 Announce Type: new Abstract: Multimodal graph learning faces a fundamental challenge: new classes may emerge after deployment, while models are trained with a fixed label space. Existing approaches typically detect unknown nodes and use LLMs to generate candidate class…
