Call for Papers

Foundation models are transforming AI, yet their resource demands are widening the gap between research breakthroughs and practical deployment. Closing this gap requires collaboration across traditionally separate communities of ML, systems, and hardware. AdaptFM offers a dedicated space for interdisciplinary exchange, bringing together diverse perspectives to tackle resource-efficient foundation model inference.

We welcome contributions in the following research areas:

• Flexible & Elastic Architectures: Once-for-all and supernet approaches, slimmable & any-width/any-depth networks, Matryoshka representation learning, MatFormer and elastic transformers, layer skipping and dynamic width, modular and reconfigurable architectures
• Adaptive Test-Time Compute: Input-adaptive inference, adaptive reasoning, universal transformer & recursive inference
• Dynamic Networks & Routing: Early-exit & adaptive-depth networks, MoEs & conditional computation, cascade & routing systems
• Efficient Decoding & Token-Level Adaptation: Speculative & parallel decoding, token pruning and merging, adaptive token computation, adaptive attention mechanisms, dynamic KV cache compression
• Model Compression & Optimization: Quantization, pruning, and sparsity, knowledge distillation, low-rank decomposition and factorization, hardware-aware model optimization
• Systems & Hardware: H/W–S/W co-design for adaptive inference, runtime systems for flexible computation, benchmarking and profiling across resource budgets
• Analysis & Trade-offs: Quality-resource tradeoff analysis, energy-efficient and sustainable inference, emerging applications of adaptive inference

Submissions

Any questions regarding submission issues should be directed to: Deepak Gupta or Stefanos Laskaridis.

Important Dates