ICML 2026PastLarge language models
AdaptFM: Resource-Adaptive Foundation Model Inference
AdaptFM
- Submission deadline
- May 9, 2026, 11:59 UTCOpenReview-synced 2026-05-09 11:59 UTC (as of 2026-06-23) — extensions on OpenReview are applied automatically; verify on the website.
- Submission portal
- OpenReview
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- Topics were auto-suggested and may be imprecise — edits welcome.
Accepted papers (124)
Fetched from OpenReview (v2) on 2026-06-10.
A Recipe for an Elastic Mixture: One Mixture-of-Experts for Every Resource Budget
A Tale of Two Temperatures: Simple, Efficient, and Diverse Sampling from Diffusion Language Models
A3: an Analytical Low-Rank Approximation Framework for Attention
AAAC: Activation-Aware Adaptive Codebooks for 4-bit LLM Weight Quantization
Accelerating LLM Inference via Vector Index Based Output Embeddings
Activation Quantization of Vision Encoders Needs Prefixing Registers
Activation- and Influence-Aware Ranks (AIR): Function-Preserving SVD Compression for LLMs
Adaptive Generate-Rank-Verify: Inference-Time Search with Costly Verification
Adaptive Safety Probing for Resource-Efficient Vision-Language-Action Models
AgentKV: Phase-Aware KV Eviction for Agentic LLMs
AgentRouter: Heterogeneous Model Routing for Cost-Optimal Multi-Step Agentic Workflows
Alignment Collapse Under KV Cache Quantization: A 35-Minute Audit for Quantized LLM Deployments
BASTION: Budget-Aware Speculative Decoding with Tree-structured Block Diffusion Drafting
Beyond Imitation: A Resource Adaptive Embedder that Outperforms its 14×Larger Teacher on Financial Retrieval
Block-Based Double Decoders
Block-Level Recursion: Adaptive Test-Time Routing in Large Language Models
Cache You Later: Post-Compression KV Repair for Long-Context Agentic LLM Inference
CafeQ: Calibration-free Quantization via Learned Transformations and Adaptive Rounding
CARES: Context-Aware Resolution Selector for VLMs
Characterizing self-speculative decoding approaches for accelerating LLMs
CLAWS: Calibration-Aware Activation Sparsity for Instruction-Tuned LLMs
COAT: COrrelation-Aware Orthogonal Transform for LLM Quantization
CoDistill-GRPO: A Co-Distillation Recipe for Efficient Group Relative Policy Optimization
Convergence-Gated Distillation for Resource-Adaptive Reinforcement Learning Agents
CoupledNorm: Efficient Normalization via Shared RMS Statistics
Cross-Tokenizer LLM Distillation through a Byte-Level Interface
DECO: Sparse Mixture-of-Experts with Dense-Comparable Performance on End-Side Devices
Decoupling Spatial and Semantic Token Compression for Vision-Language Model Acceleration
DIPA: Difficulty-Informed Probabilistic Allocation of Test-Time Compute via Training-Free Proxies
Distill, Suppress, and Fuse: Cross-Modal Knowledge Integration for Optical Flow-Free Temporal Action Segmentation
DREAM-MoE: Downstream Routing Error-Aware Margin-Preserving Quantization for Mixture-of-Experts Large Language Models
DropKV: Decoupling Residual-Output Perturbation for Near-Optimal KV-Cache Eviction
Dropping the Anchor: Statistical Context Summarization for Distributed Systems via Pulsar Attention
Efficient Encoder-Only Context Compression via Marginal Contribution Scoring
EfficientRollout: System-Aware Self-Speculative Decoding for RL Rollouts
Empirical Analysis of Layer Redundancy in Diffusion Language Models
EntropyCache: Decoded Token Entropy Guided KV Caching for Diffusion Language Models
Fast Inference via Hierarchical Speculative Decoding
Fault Robustness of Custom Floating-Point and Integer Formats: Datatype Selection as a Reliability-Aware Compression Decision
Fixed-Point Reasoning: Stable and Adaptive Deep Looped Models
FlexRank: Nested Low-Rank Knowledge Decomposition for Adaptive Model Deployment
Fully Nested Transformers
Ghosted Layers: Unconstrained Activation Alignment for Recovering Layer-Pruned LLMs
GreenMoE: Exploiting Dynamic Load Imbalance for Energy-Efficient Long-Context MoE Training
Hybrid Linear Attention Done Right: Efficient Distillation and Effective Architectures for Extremely Long Contexts
HYBRIDKV: Exploiting Head-Dominant Reconstruction for Efficient Query-Agnostic KV Cache Compression
HyPER: Bridging Exploration and Exploitation for Scalable LLM Reasoning with Hypothesis Path Expansion and Reduction
Implicit Off-Diagonal Curvature Modeling via Gradient Projection for Post-Training Quantization of Vision Transformers
Improving Cascade Routing for Structured Attribute Generation with Heterogeneous Confidence
IR3DE: A Linear Router for Large Language Models
Is Escalation Worth It? A Decision-Theoretic Characterization of LLM Cascades
Jacobian-guided Noise Injection for Quantization Robustness in Large Language Models
KVgrad: Query-Agnostic KV Cache Eviction via Gradient-based Global Importance Scoring
Latent Cache Flow: Model-to-Model Communication Without Text
Layer Verification Accelerates Speculative Tree Decoding
Layout and Fusion Trade-offs for Mixture-of-Experts Inference under Single-Node Tensor Parallelism
LEAP: Learnable End-to-End Adaptive Pruning of Large Language Models
Learning Adaptive LLM Decoding
Learning Adaptive Reasoning Budgets via Constraint-Rectified Training
Learning When to Attend: Conditional Memory Access for Long-Context LLMs
Leech Lattice Vector Quantization for Efficient LLM Compression
LExI: Layer-Adaptive Active Experts for Efficient MoE Inference
LLM Family Expansion via Distillation and Quantization
LongAttnComp: Cross-Family Context Compression for Long-Context Reasoning
Low Dimensional Embeddings for Model Capability Understanding
MAGE: All-[MASK] Block Already Knows Where to Look in Block Diffusion LLM
MatMLA: Matryoshka Multi-Head Latent Attention
MineDraft: A Framework for Batch Parallel Speculative Decoding
Modality-Aware Block Rotation for Vision-Language-Action Model Quantization
MoNe: Modular Neural Memory for Efficient Long Context Inference
MoSE: Mixture of Slimmable Experts for Efficient and Adaptive Language Models
Multi-Mixer Models: Flexible Sequence Modeling with Shared Representations
Multi-Token Prediction via Self-Distillation
Neural Weight Compression for Language Models
NOSA: Native and Offloadable Sparse Attention
On State Reduction in Linear Attention
On the Optimal Reasoning Length for RL-Trained Language Models
One Simple Trick for Improving the Performance of Energy-Limited Local Inference and Training
OriCache: Orientation-Guided Feature Caching for DiT Acceleration
Prelude: Execution-Class Aware Serving for Decision-Style LLM Inference
PRESTO: Prefix-Aligned Tree Drafting for Diffusion Speculative Decoding
Pruning and Distilling Mixture-of-Experts into Dense Language Models
QJL is 1-bit Compressive Sensing: An Equivalence and Its Consequences for KV Cache Compression in LLMs
Re-evaluating Confidence Remasking in Masked Diffusion Language Models
Recency/Frequency Adaptive KV Caching for Large Language Model Serving
Recovering Selectivity with LTI State Space Operators for Portable Long-Context Inference
Reducing Attention Distribution Error with Unified Tail Aggregation for Sparse Attention
Referring Video Object Segmentation via Language-aligned Track Selection
Relaxed On-Policy Distillation: Selective Credit Allocation for Scaling Reasoning Efficiently
Resource-Adaptive Foundation Model Reasoning via Semantic Coverage
Resource-Adaptivity Beyond the Model: Sensor Control for Quantized On-Device Vision
Rethinking Layer Redundancy in Large Language Models: Calibration Objectives and Search for Depth Pruning
Selective Sinkhorn Routing for Improved Sparse Mixture of Experts
SFPruner: Single-Forward Visual Token Subset Selection for Resource-Efficient Multimodal Foundation Model Inference
ShadowSpec: Towards Zero Speculation Overhead for Substitute Speculative Decoding
Sigmoid Attention as a Better Substrate for Learned KV Cache Eviction
SlimQwen: Exploring the Pruning and Distillation in Large MoE Model Pre-training
SparseSAM: Structured Sparsification of Activations in Segment Anything Models
Speedrunning GPT3: Training an (Almost-) GPT3-175B-Quality Model in Under 10K USD
SpiralFovea: Input-Adaptive Foveated Tokenization as a Third Lever of Resource-Adaptive Inference
SRA-MoE: Output-Aware Selective Router Alignment for MoE Quantization
Stabilizing Extrapolation in Looped Transformers via Learned Stochastic Stopping
Staircase Streaming for Low-Latency Multi-Agent Inference
Step-Tagging Early-Stopping: Toward controlling the generation of Language Reasoning Models through black-box step monitoring
StreamAttention: Energy-Efficient and High-Utilization Attention on Systolic Hardware
StructSAM: Structure- and Spectrum-Preserving Token Merging for Segment Anything Models
Structural Outlier-Aware Post-Training Quantization for Monocular Depth Estimation
Structure-Preserving Adaptive Post-Training Quantization for Monocular Depth Estimation
SubspacePath Pruner: Inference-time Pruning via Probe-based Representation–Parameter Coupling
TEAM: Temporal–Spatial Consistency Guided Expert Activation for MoE Diffusion Language Model Acceleration
TERMINATOR: Learning Optimal Exit Points for Early Stopping in Chain-of-Thought Reasoning
Think Deep, Think Fast: Investigating Inference-Time Scaling And The Reasoning Floor
Training Continuous Chain of Thought Models: A Tale of Two Regimes
Understanding Layer Patching in Model Size Interpolation
VEDJE: Video-Efficient Discriminative Joint Encoder for Scalable Video-Text Retrieval
Vision Token Pruning via Query--Vision Interaction Decomposition
Weight Concentration Regularization for Improving Pruning Robustness Under High Sparsity
What Matters for NVFP4 Training? A Scaling Study of Low-Precision Pre-Training Recipes
When Good Enough Is Optimal: Multiplication-Only Matrix Inversion Approximation for Quantized Gated DeltaNet
Why Limit the Residual Stream to Layers and Not Tokens? Persistent Memory for Continuous Latent Reasoning
WildCat: Near-Linear Attention in Theory and Practice
XShare: Collaborative in-Batch Expert Sharing for Faster MoE Inference
You Had One Job: Per-Task Quantization Using LLMs’ Hidden Representations
Zero-Shot Quantization for Vision-Language-Action Models via Trajectory Curvature and Attention Guidance