Alignment Is All You Need: Instruction-Free Training for General Audio-Language Models
A new approach to training multimodal large language models (MLLMs) eliminates the need for extensive task-specific supervision.
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A new approach to training multimodal large language models (MLLMs) eliminates the need for extensive task-specific supervision.
This work examines emoji-augmented prompts as a test case for gaps in safety evaluation of large language models (LLMs).
Modern e-commerce platforms often operate search, recommendation, personalization, and CRM systems independently, limiting opportunities for proactive customer re-engagement.
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A new arXiv survey proposes viewing an AI agent’s memories, tools, skills, workflows and relationships as a graph that changes over time, and calls for graph-aware evaluation and governance.
A new arXiv preprint presents FACET, a framework for generating executable terminal tasks whose instructions, environments, solutions and verifiers are designed to remain consistent.
An arXiv preprint introduces SESSE, a training-free framework that breaks an LLM judge’s preference into sub-questions. The authors report near-parity with a chain-of-thought baseline on 1,000 RewardBench examples and criterion-level vote records; generalization, cost, and independent validation remain open questions.
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An Apple research paper describes a three-phase iterative pseudo-labeling method for Mandarin-English code-switching automatic speech recognition and reports Mix Error Rate reductions on two SEAME development subsets.
Google says users will soon be able to tell Discover what topics and links they want to see more or less of, while new controls also personalize Search and Google News audio briefings.
Amazon Bedrock now offers OpenAI’s GPT-5.6 Sol, Terra, and Luna models in more than 25 AWS Regions, with geographic and global routing options that expand the available compute pool.
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