TokenCast: Forecasting Token Consumption During LLM Agent Execution
cs.LG, cs.AI, cs.SE
Submitted: 2026-09-28
Updated: 2026-09-29
Code: https://github.com/DEFENSE-SEU/TokenCast
Sources
- How Do AI Agents Spend Your Money? Analyzing and Predicting Token Consumption in Agentic Coding Tasks
- Token Economics for LLM Agents: A Dual-View Study from Computing and Economics
- Search-R1: Training LLMs to Reason and Leverage Search Engines with Reinforcement Learning
- DSPy: Compiling Declarative Language Model Calls into Self-Improving Pipelines
- LiveClawBench: Benchmarking LLM Agents on Complex, Real-World Assistant Tasks
- How Much is Left? LLMs Linearly Encode Their Remaining Output Length
- Chimera: Latency- and Performance-Aware Multi-agent Serving for Heterogeneous LLMs
- CASTILLO: Characterizing Response Length Distributions of Large Language Models
- Efficient Interactive LLM Serving with Proxy Model-based Sequence Length Prediction
- Tokenomics: Quantifying Where Tokens Are Used in Agentic Software Engineering
- Robust Length Prediction: A Perspective from Heavy-Tailed Prompt-Conditioned Distributions
- Executable Code Actions Elicit Better LLM Agents
- Predicting LLM Output Length via Entropy-Guided Representations
- Pythia: Exploiting Workflow Predictability for Efficient Agent-Native LLM Serving
- TraceLab: Characterizing Coding Agent Workloads for LLM Serving
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