LLM Agent Efficiency

LLM: Large Language Model

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Latest papers

26 papers11 papers in the last four weeks, against 1 the four weeks before. 0.1% of all new papers.

Heartbeat Thinking: Speculative Reasoning and Tool Execution While Listening in Realtime Voice Agents
Oct 19, 2026Zhenyu Zhang, Kewei Fu, Yongqi Tong +4Tool-Using Agents LLM Agent Efficiency
Ant International
NIOCIS: Typed-Decision Next-Hop Routing with Node-Conditioned Option Spaces for Tool-Use Agents
Oct 19, 2026Jin Han, Mingfeng Xin, Feng Liang +1LLM Agent Routing LLM Agent Efficiency
Independent Researcher
Learning Who Talks to Whom: Role-Aware Communication Topology Scheduling for Efficient LLM Multi-Agent Collaboration
Oct 19, 2026Haibin Li, Wenya Yu, Xuefeng Jiang +4Multi-Agent Communication Topology Multi-Agent LLMs
Beihang University · Nanyang Technological University · University of Chinese Academy of Sciences
Design and Cross-Domain Evaluation of an Agent Execution Harness
Oct 18, 2026Saurav Joshi, Manas Gupta, Ashwin Venkatesh Raman +1LLM Agent Harnesses LLM Agent Evaluation
Spine AI
StopLoss: Teaching Agents to Predict Failure and Stop Early
Oct 18, 2026Jiarong Liang, Xueguang Ma, Dongfu Jiang +3Early Stopping AI Agent Monitoring
University of Waterloo
Bellman Language Models: Solving the Recurring Decisions of LLM Agents
Oct 17, 2026Adarsh Vatsa, William EiersLLM Decision-Making LLM Agent Efficiency
Stevens Institute of Technology
Good Judgment, Bad Advice: Decision-Model Controllers Raise Agent Cost Without Improving Success
Oct 17, 2026Naman VatsLLM Agents LLM Agent Efficiency
Write Once, Check Many: Reusable Ledgers for Post-Hoc Policy Compliance Auditing of Agent Trajectories
Oct 17, 2026Ashwin Nagarajan, Gursimran Panesar, Alicia Yang +3AI Agent Auditing LLM Agent Efficiency
Carnegie Mellon University
IF-MAP: Influence-Function-Inspired Agent Prioritization in Multi-Agent Systems
Oct 17, 2026Jie Mao, Pingchen Lu, Zikun Qu +6Multi-Agent LLM Systems LLM Agent Workflow Optimization
The Chinese University of Hong Kong, Shenzhen · Harbin Institute of Technology · School of Engineering and Applied Sciences, Harvard University
Who Plans, Who Edits? Verified, Cost-Aware Division of Labor for LLM-Driven Analog Layout Repair
Oct 16, 2026Xilai Dai, Saeed MohammadiLLM Agents Electronic Design Automation
Elmore Family School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN 47907, USA
Tool Recommendation for Multi-Step Agents: Finding Every Tool a Task Needs
Oct 16, 2026Sam Partee, River Stevens, Thierry Damiba +1Tool Selection for Language Model Agents Tool Discovery for LLM Agents
Agent in a Bottle: Can LLM Agents Turn Their Capabilities Into Cheap, Scalable Artifacts?
Oct 6, 2026Ankit Sonthalia, Haritz Puerto, Alexander Rubinstein +2LLM Agent Efficiency Cost-Aware Inference
University of Tübingen · ELLIS Institute Tübingen, Max Planck Institute for Intelligent Systems, Tübingen AI Center · École Polytechnique, Institut Polytechnique de Paris, CNRS +1
SquidAgent: Parallelize Wisely, Coordinate Efficiently
Oct 6, 2026Yexiong Lin, Shanshan Ye, Yu Yao +3LLM Agent Orchestration LLM Agent Workflow Optimization
The University of Sydney · Mohamed bin Zayed University of Artificial Intelligence · University of Technology Sydney +1
What Does a Harness Buy? Tokens, Mostly
Oct 3, 2026Yangze Liu, Zhongyi HanLLM Agent Efficiency LLM Agent Harnesses
Shandong University
StepKV: Step-Aware KV Cache Compression for LLM Agents
Aug 26, 2026Boyu Feng, Jiahong Liu, Yifan Li +7KV Caching KV-Cache Compression
The Chinese University of Hong Kong · Huawei Technologies Co., Ltd
The Scaffolding Matters More Than the Interface: A Controlled Comparison of MCP and CLI Tool Use Across Seven Agent Scaffoldings, Five Language Models, and One Software Task
Aug 9, 2026Marc Alier Forment, María José Casañ Guerrero, Francisco José García-Peñalvo +1AI Coding Agents LLM Agent Evaluation
Universitat Politècnica de Catalunya (UPC), Barcelona, Spain · Universidad de Salamanca (USAL), Salamanca, Spain · Universidad del País Vasco / Euskal Herriko Unibertsitatea (UPV/EHU), Donostia-San Sebastián, Spain
Do AI Agents Know When a Task Is Simple? Toward Complexity-Aware Reasoning and Execution
Jul 14, 2026Junjie Yin, Xinyu FengSoftware Engineering Agents LLM Agent Evaluation
University of Tennessee, Knoxville, TN 37996, USA · Microsoft, Redmond, WA 98052, USA
Auto: The AGI Compiler
Jul 5, 2026Jaber Jaber, Osama JaberContinual Learning for LLM Agents LLM-Based Program Synthesis
RightNow AI
Specialize Roles, Mix Deployments: Pushing the Cost-Accuracy Frontier of LLM Agent Teams
May 28, 2026Yinsicheng Jiang, Liang Cheng, Yeqi Huang +6Multi-Agent LLM Systems LLM Agent Evaluation
University of Edinburgh · 2Microsoft Research
SkillSmith: Compiling Agent Skills into Boundary-Guided Runtime Interfaces
May 12, 2026Duling Xu, Zheng Chen, Zaifeng Pan +4LLM Agent Workflow Optimization LLM Agent Skill Retrieval
AetherHeart Tech Co., Ltd., Shanghai, China · Renmin University of China, Beijing, China · University of California San Diego, CA, USA
LLM Agents Already Know When to Call Tools -- Even Without Reasoning
May 10, 2026Chung-En Sun, Linbo Liu, Ge Yan +2Tool-Augmented Language Model Agents AI Agent Benchmarks
University of California, San Diego
Agent Capsules: Quality-Gated Granularity Control for Multi-Agent LLM Pipelines
May 1, 2026Aninda RayLLM Inference Efficiency Multi-Agent LLM Systems
AgentOpt v0.1 Technical Report: Client-Side Optimization for LLM-Based Agent
Apr 7, 2026Wenyue Hua, Sripad Karne, Qian Xie +4LLM Agent Workflow Optimization LLM Agent Efficiency
Microsoft Research, AI Frontiers · Columbia University · Cornell University
CirrusBench: Evaluating LLM-based Agents Beyond Correctness in Real-World Cloud Service Environments
Mar 30, 2026Yi Yu, Guangquan Hu, Chenghuang Shen +15LLM Agent Efficiency LLM Agent Evaluation
School of Mathematics and Sciences, Fudan University, Shanghai, China · Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China · Shanghai Center for Mathematical Sciences, Fudan University, Shanghai, China +2
HotelQuEST: Balancing Quality and Efficiency in Agentic Search
Feb 27, 2026Guy Hadad, Shadi Iskander, Oren Kalinsky +3LLM Agent Efficiency Agentic Search
Ben-Gurion University · Amazon
Toward Efficient Agents: Memory, Tool learning, and Planning
Jan 20, 2026Xiaofang Yang, Lijun Li, Heng Zhou +12LLM Agent Memory AI Agent Benchmarks
Shanghai Artificial Intelligence Laboratory · Fudan University · University of Science and Technology of China +5

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Heartbeat Thinking: Speculative Reasoning and Tool Execution While Listening in Realtime Voice Agents

Zhenyu Zhang, Kewei Fu, Yongqi Tong, Chenzhi Zheng+3

Ant International

Tool-Using Agents LLM Agent Efficiency Full-Duplex Spoken Dialogue Systems

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Tool-using voice agents often leave users waiting after speech ends, even when actionable information arrives earlier in the request. Speculative approaches begin work during speech, but chunk-bounded or interrupt-on-update generation can truncate a tool call before its arguments are complete. This couples computation time to speech updates, limiting useful work completed in advance. We introduce Heartbeat Thinking, which handles updated input through new Thinker invocations while allowing ongoing invocations and dispatched tools to finish. Periodic invocations read the latest partial transcript and a shared record of completed reasoning and tool results. These results help resolve missing arguments and initiate dependent calls, allowing preparation to progress across speech updates. Across SpeechFCEval, FDBench v3, and AudioCRAG, Heartbeat improves aggregate speculative tool coverage or retrieval reuse over a SHANKS-style windowed baseline, with benchmark-dependent effects on final task quality. On FDBench v3, it increases tool coverage by 16 percentage points and reduces mean final-response latency by 36.4%. The results show that heartbeat thinking paradigm preserving ongoing computation during speech can turn listening time into reusable preparation for faster and more capable voice-agent responses.