arXiv AI July 13, 2026 By Peng Kuang, Haibo Jin, Xiaoyu Han, Yanli Wang, Xiaopeng Yuan, Ye Yu, Kaidi Xu, Haohan Wang

KV-PRM: Efficient Process Reward Modeling via KV-Cache Transfer for Multi-Agent Test-Time Scaling

arXiv:2607. 09153v1 Announce Type: new Abstract: Process Reward Models (PRMs) have been proven to be highly effective in guiding test-time scaling (TTS) methods, which significantly boost the capabilities of LLM-based multi-agent systems.

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arXiv Machine Learning

Jun 29

Learning to Evict from Key-Value Cache

arXiv:2602. 10238v2 Announce Type: replace-cross Abstract: The growing size of Large Language Models (LLMs) makes efficient inference challenging, primarily due to the memory demands of the autoregressive Key-Value (KV) cache.

By Luca Moschella, Laura Manduchi, Ozan Sener

llms agents reinforcement-learning efficiency benchmarks

arXiv Computation and Language

Sep 15

When Agents Slow Down: Understanding LLM Agents' Test-Time Strategies via Elo-per-token Analysis

arXiv:2609.15309v1 Announce Type: new Abstract: Large language model (LLM) agents allocate test-time compute adaptively as they revise solutions, use tools, explore alternatives, and decide when to s...

By Kaiyuan Liu, Qiuyang Mang, Bo Peng, Wenhao Chai, Hanchen Li, Shreyas Pimpalgaonkar, Luke Zettlemoyer, Alex Dimakis, Alvin Cheung

llms agents benchmarks

Hugging Face Trending Papers

Sep 3

Random Attention: Rethinking KV Cache Eviction for Efficient Reasoning

The paper introduces Random Attention, a method that evicts KV cache entries uniformly at random within each attention head while preserving the prompt. It demonstrates that this simple strategy matches or surpasses more complex eviction schemes across four models and six reasoning tasks, achieving 32‑43% higher throughput in vLLM deployments. Experiments reveal that the prompt is the most fragile cache component and that redundancy in the reasoning trace across text and attention heads protects against random eviction, eliminating the need for a selection score.

llms efficiency

arXiv AI

Jun 30

Coverage-Driven KV Cache Eviction for Efficient and Improved Inference of LLM

arXiv:2606. 29563v1 Announce Type: cross Abstract: Large language models (LLMs) excel at complex tasks like question answering and summarization, thanks to their ability to handle long-context inputs.

By Shuvendu Roy, Mengyao Zhai, Hossein Hajimirsadeghi, Golnoosh Samei

llms nlp efficiency

arXiv Machine Learning

Jun 3

Multi-Segment Attention: Enabling Efficient KV-Cache Management for Faster Large Language Model Serving

arXiv:2606. 02964v1 Announce Type: cross Abstract: Large Language Model (LLM) inference relies on key-value (KV) caches to avoid redundant attention computation.

By Chunan Shi, Yilei Chen, Yilin Chen, Xupeng Miao, Bin Cui

llms rag agents efficiency

arXiv Computation and Language

Aug 21

ReCache: Efficient KV Cache Reuse and Compression for Tool-Augmented LLM Agents

arXiv:2608. 19662v1 Announce Type: new Abstract: Agentic language models repeatedly encode tool and skill schemas that recur across requests in different combinations and orders, preventing standard prefix caching from reusing their key--value (KV) states.

By Yichu Fang, Sitong Wei, Haozhe Hu, Xiaoyu Shen

llms agents nlp efficiency benchmarks