AI Research

AI & ML Research Papers

A comprehensive index of artificial intelligence and machine-learning research with AI-generated summaries, citation metrics, and direct links to papers and code.

All Papers

21 papers

Preprint2026

Fastkv: Decoupling of context reduction and kv cache compression for prefill-decoding acceleration

Unknown

FastKV decouples context reduction from KV cache compression to accelerate both prefill and decoding phases in LLM inference.

0Jan 1, 2026

Preprint2023

Fast inference from transformers via speculative decoding

Unknown

Introduces speculative decoding, an algorithm for faster sampling from autoregressive models by computing multiple tokens in parallel without altering outputs.

0Jan 1, 2023Transformers

Preprint2024

Unlocking efficiency in large language model inference: A comprehensive survey of speculative decoding

Unknown

A comprehensive survey of speculative decoding techniques for efficient large language model inference.

0Jan 1, 2024Large Language Models

Preprint2021

Mqbench: Towards reproducible and deployable model quantization benchmark

Yuhang Li, Mingzhu Shen, Yan Ren, et al.

Mqbench provides a reproducible and deployable benchmark for model quantization to accelerate deep learning inference.

71Jan 1, 2021BenchmarksQuantization

arXiv

Preprint2026

OpenForgeRL: Train Harness-native Agents in Any Environment

Xiao Yu, Baolin Peng, Ruize Xu, et al.

OpenForgeRL is an open-source framework that enables end-to-end training of harness-based AI agents in any environment by decoupling training and inference via a lightweight proxy and Kubernetes orchestrator.

0Jul 23, 2026MultimodalReasoning

arXiv

ConferenceAnnual Meeting of the Association for Computational Linguistics2023

Parameter-efficient fine-tuning without introducing new latency

Baohao Liao, Yan Meng, C. Monz

This paper proposes a parameter-efficient fine-tuning method that matches full fine-tuning performance without introducing additional inference latency.

84Jan 1, 2023Fine Tuning

arXiv

Preprint2111

An explanation of in-context learning as implicit bayesian inference

Unknown

This paper explains in-context learning in large language models as a form of implicit Bayesian inference over latent concepts.

0Jan 1, 2111In Context Learning

arXiv

Preprint2026

Differentiable Logic Gate Networks for Low-Latency EEG Classification on Edge Devices

Shyamal Y. Dharia, Stephen D. Smith, Camilo E. Valderrama

Diff-Logic networks enable low-latency EEG classification on edge devices by compiling models into pure Boolean circuits, achieving competitive accuracy with constant inference time across scales.

0Jul 20, 2026Neural Networks

arXiv

Preprint2026

A survey of test-time compute: From intuitive inference to deliberate reasoning

Unknown

A comprehensive survey tracing test-time compute scaling from System-1 models to deliberate reasoning.

0Jan 1, 2026ReasoningScaling Laws

Preprint2026

Shorterbetter: Guiding reasoning models to find optimal inference length for efficient reasoning

Unknown

This paper introduces ShorterBetter, a method to train reasoning models to dynamically determine optimal inference length, improving efficiency without sacrificing accuracy.

0Jan 1, 2026Reasoning

Preprint2504

Efficient reasoning models: A survey

Unknown

This survey explores efficient reasoning models and decoding strategies to accelerate inference in complex logic tasks.

0Jan 1, 2504Reasoning

arXiv

Preprint2603

MemFactory: Unified Inference & Training Framework for Agent Memory

Unknown

MemFactory unifies inference and training for agent memory by applying RL to optimize memory-augmented LLMs.

0Jan 1, 2603Large Language ModelsReinforcement Learning

arXiv