LLM Inference

LLM: Large Language Model

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30 papers in the last four weeks, up 233% on the four weeks before. 0.3% of all new papers.

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Latest papers 187

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Anchorless Diversification for Parallel LLM Ideation
May 28, 2026Fares Nabil Ibrahim, Nafis Saami Azad, Raiyan Abdul BatenLLM Inference Efficiency LLM Inference
Bellini College of Artificial Intelligence, Cybersecurity, and Computing, University of South Florida, USA
Fingerprinting Inference Systems of Large Language Models
May 28, 2026Anna Wimbauer, Jonas Möller, Erik Imgrund +1LLM Inference Language Model Fingerprinting
BIFOLD & TU Berlin
How Far Can Disaggregation Go? A Design-Space Exploration of Attention-FFN Disaggregation for Efficient MoE LLM Serving
May 27, 2026Hanjiang Wu, Abhimanyu Rajeshkumar Bambhaniya, Sarbartha Banerjee +9LLM Serving LLM Inference
1Georgia Institute of Technology · 5Infravana · 2Intel +2
A Paired Testing Protocol for Batch-Conditioned Refusal Robustness in LLM Serving
May 26, 2026Sahil KadadekarLLM Inference Language Model Safety Evaluation
Independent Researcher.
Beyond Inference-Only Deployment: Comparing Weight-Based Consolidation Against Cascading Compaction
May 23, 2026Simon Dennis, Kevin Shabahang, Hao Guo +1Continual Learning for LLMs LLM Personalization
i14 / University of Melbourne · i14
Summoning the Oracle to Slay It: Mitigating Look-Ahead Bias in Financial Backtesting with Large Language Models
May 23, 2026Weixian Waylon Li, Mengyu Wang, Tiejun MaLanguage Model Decoding Quantitative Finance
University of Edinburgh
Identifying and Mitigating Systemic Measurement Bias in Production LLM Inference Benchmarks
May 22, 2026Ashok Chandrasekar, Jason KrambergerLLM Evaluation LLM Serving
Google
Unextractable Protocol Models: Collaborative Training and Inference without Weight Materialization
May 22, 2026Alexander Long, Chamin Hewa Koneputugodage, Thalaiyasingam Ajanthan +5LLM Inference Decentralized Learning
Pluralis Research
FastKernels: Benchmarking GPU Kernel Generation in Production
May 22, 2026Gabriele Oliaro, Yichao Fu, May Jiang +5GPU Kernel Optimization GPU Acceleration
1Snowflake AI Research · 2CMU · 3UCSD +1
ModeSwitch-LLM: A Lightweight Phase-Aware Controller for Cross-Mode LLM Inference on a Single GPU
May 21, 2026Aman Sunesh, Ali Alshehhi, Hivansh DhakneLLM Inference Efficiency LLM Serving
Department of Computer Engineering NYU Abu Dhabi Abu Dhabi, United Arab Emirates · Courant Institute of Mathematical Sciences New York University New York, United States · Department of Computer Engineering New York University New York, United States
Runtime-Certified Bounded-Error Quantized Attention
May 20, 2026Dean CalverLLM Inference KV Caching
Independent Researcher
Llamas on the Web: Memory-Efficient, Performance-Portable, and Multi-Precision LLM Inference with WebGPU
May 20, 2026Reese Levine, Rithik Sharma, Nikhil Jain +5GPU Acceleration LLM Inference
UC Santa Cruz · Microsoft Research
Towards Multi-Model LLM Schedulers: Empirical Insights into Offloading and Preemption
May 19, 2026Mert Yildiz, Pietro Spadaccino, Alexey Rolich +2LLM Inference Efficiency LLM Serving
Sapienza University of Rome, Rome, Italy
The Silent Hyperparameter: Quantifying the Impact of Inference Backends on LLM Reproducibility
May 19, 2026David Pape, Jonathan Evertz, Lea SchönherrLLM Evaluation LLM Inference
CISPA Helmholtz Center for Information Security, Saarbrücken, Germany
Computational Challenges in Token Economics: Bridging Economic Theory and AI System Design
May 17, 2026Ou Wu, Yingjun DengLLM Inference Cost-Aware Inference
Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, China · Hefei Institutes of Physical Science, Chinese Academy of Sciences, China
Charon: A Unified and Fine-Grained Simulator for Large-Scale LLM Training and Inference
May 16, 2026Mengtian Yang, Zhekun Zhang, Mingheng Wu +3LLM Inference LLM Inference Acceleration
ByteDance Seed · University of Texas at Austin
Lever: Speculative LLM Inference on Smartphones
May 16, 2026Tuowei Wang, Fengzu Li, Yanfan Sun +2On-Device Language Model Inference LLM Inference
Tsinghua University · Beijing, China · Beihang University +1
EnergyLens: Predictive Energy-Aware Exploration for Multi-GPU LLM Inference Optimization
May 14, 2026Zhiye Song, Kyungmi Lee, Eun Kyung Lee +3LLM Inference Efficiency Energy-Efficient ML
Massachusetts Institute of Technology Cambridge, MA 02139 · IBM Research Yorktown Heights, NY 10598
Diagnosing Training Inference Mismatch in LLM Reinforcement Learning via a Zero-Mismatch Reference
May 14, 2026Tianle Zhong, Neiwen Ling, Yifan Pi +5Reinforcement Learning LLM Inference
ByteDance · The University of Virginia
LLMs Know When They Know, but Do Not Act on It: A Metacognitive Harness for Test-time Scaling
May 13, 2026Qi Cao, Yufan Wang, Peijia Qin +2LLM Inference Test-Time Scaling
University of California, San Diego
Attention Once Is All You Need: Efficient Streaming Inference with Stateful Transformers
May 13, 2026Victor NorgrenTransformer Inference LLM Inference
LayerScale, Inc.
MARLIN: Multi-Agent Game-Theoretic Reinforcement Learning for Sustainable LLM Inference in Cloud Datacenters
May 13, 2026H. Moore, S. Qi, D. Milojicic +2LLM Serving LLM Inference
Colorado State University Fort Collins, CO, USA · Hewlett Packard Labs Milpitas, CA, USA
CR^2: Cost-Aware Risk-Controlled Routing for Wireless Device-Edge LLM Inference
May 12, 2026Nan Xue, Shengkang Chen, Zhiyong Chen +4On-Device Language Model Inference LLM Inference
Cooperative Medianet Innovation Center, Shanghai Jiao Tong University, Shanghai 200240, China · Department of Broadband Communication, Pengcheng Laboratory, Shenzhen 518000, China · Future Network of Intelligent Institute (FNii), the Chinese University of Hong Kong (Shenzhen), Shenzhen 518172, China +1
EnergyLens: Interpretable Closed-Form Energy Models for Multimodal LLM Inference Serving
May 11, 2026Vittorio Palladino, Gianluca Palermo, Michael E. Papka +1Efficient Multimodal Inference LLM Inference
Politecnico di Milano & UIC · Politecnico di Milano · University of Illinois Chicago
Extending Confidence-Based Text2Cypher with Grammar and Schema Aware Filtering
May 11, 2026Makbule Gulcin OzsoyLLM Inference Text-to-Cypher
Neo4j, London, UK
CheckSupport: A Local LLM-Powered Tool for Automated Manuscript Submission Checklist Selection and Completion
May 10, 2026Satvik Tripathi, Don Enwerem, Kevin Song +3LLM Inference Scientific Reproducibility
Department of Radiology, Perelman School of Medicine at University of Pennsylvania, Philadelphia, PA, USA · Department of Computer Science, Drexel University, Philadelphia, PA, USA · Department of Computer and Information Science, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA 19104 +2
Non-Monotonic Latency in Apple MPS Decoding: KV Cache Interactions and Execution Regimes
May 9, 2026Willy Fitra HendriaLLM Inference KV Caching
1Independent Researcher, Seoul, South Korea.
Large Language Models over Networks: Collaborative Intelligence under Resource Constraints
May 9, 2026Liangqi Yuan, Wenzhi Fang, Shiqiang Wang +2LLM Inference Efficiency On-Device Language Model Inference
L. Yuan, W. Fang, and C. G. Brinton are with Purdue University, IN, USA. · S. Wang is with University of Exeter, UK. · H. V. Poor is with Princeton University, USA.
Dooly: Configuration-Agnostic, Redundancy-Aware Profiling for LLM Inference Simulation
May 8, 2026Joon Ha Kim, Geon-Woo Kim, Anoop Rachakonda +1LLM Inference Efficiency LLM Inference
University of Texas at Austin
Litespark Inference For CPUs: Ultra-Fast SIMD Framework for Ternary (1.58-bit) Language Models
May 7, 2026Nii Osae Osae Dade, Tony Morri, Moinul Hossain Rahat +2LLM Quantization Efficient Neural Network Inference
Mindbeam AI

May 28, 2026, cs.AIJ/K move · Enter open · S save

Anchorless Diversification for Parallel LLM Ideation

Fares Nabil Ibrahim, Nafis Saami Azad, Raiyan Abdul Baten

Bellini College of Artificial Intelligence, Cybersecurity, and Computing, University of South Florida, USA

LLM Inference Efficiency LLM Inference LLM Prompting Computational Creativity Diverse Text Generation

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LLMs are increasingly used to generate candidate-idea pools for creative tasks where broad exploration is valuable. Parallel inference can be attractive in this setting when it broadens the pool while retaining quality and cost efficiency. We study inference-time controls for candidate-pool diversification, asking whether anchorless methods can rival methods that depend on observed seed ideas. Across three creative task families, we compare independent generation and semantic direction stratification with self-, peer-, and representative-anchor baselines, under neutral and population-referential divergent instructions. Population-referential divergence is a strong low-cost baseline, increasing semantic diversity while preserving quality proxies. Semantic direction stratification is stronger: a single planning call organizes generations across broad semantic directions, yielding the best diversity--quality--compute frontier. Anchored regeneration can be strong in final-pool diversity, but its advantage shrinks under full-pipeline token accounting. These results establish practical anchorless baselines for open-ended LLM ideation.

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