Verification-Aware Training for Speculative Decoding
Speculative decoding accelerates large language model inference by using a draft model to generate candidate tokens, ...
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Speculative decoding accelerates large language model inference by using a draft model to generate candidate tokens, ...
Strategic deception by LLM and VLM agents has emerged as a central AI alignment and safety concern. Social-deduction ...
Visual instruction tuning is crucial for advancing the vision-language alignment and instruction-following capabiliti...
Latent diffusion models have emerged as a dominant framework for high-fidelity image and video synthesis, operating i...
VLM-driven self-improvement of web code has a structural flaw: the model that proposes the repair is the model that j...
Embodied navigation requires agents to translate heterogeneous goals and visual observations into actions across task...
While Large language models (LLMs) incorporate user personalization signals to improve usability and helpfulness, the...
Multimodal safety moderation requires distinguishing risks arising from visual content, user intent, and assistant be...
Organizations often develop and maintain portfolios of related applications: independently deployable codebases that ...
Recent work on image content manipulation based on vision-language pre-training models has been effectively extended ...
Recent video generators often omit audio or synthesize it in a separate stage, limiting reciprocal modeling of visual...
Loop Engineering is emerging as a practice for organizing development work around coding agents. Instead of writing e...