Agents
Pay attention to this one, AI devs. This is particularly interesting if you work with long-horizon terminal agents that often drown in their…
Pay attention to this one, AI devs. This is particularly interesting if you work with long-horizon terminal agents that often drown in their own observations. TACO is a self-evolving framework that au
Pay attention to this one, AI devs. This is particularly interesting if you work with long-horizon terminal agents that often drown in their own observations. TACO is a self-evolving framework that automatically discovers and refines context compression rules directly from interaction trajectories. In other words, you let the agent figure out the rules itself. It plugs into existing terminal agents as a wrapper. Tested on TerminalBench, SWE-Bench Lite, and CompileBench with MiniMax-2.5, it cuts token overhead by ~10% while preserving or improving accuracy. Why does it matter? Context is the real bottleneck for long-horizon coding agents. Hand-tuned compression does not generalize across environments. Letting the agent learn what to keep and what to drop is a much cleaner path forward. Paper: https://arxiv.org/abs/2604.19572 Learn to build effective AI agents in our academy: https://academy.dair.ai/
Related
- A Self-Evolving Framework for Efficient Terminal Agents via Observational Context Compression
- GenericAgent: A Token-Efficient Self-Evolving LLM Agent via Contextual Information Density Maximization (V1.0)
- Autogenesis: A Self-Evolving Agent Protocol
- Experience Compression Spectrum: Unifying Memory, Skills, and Rules in LLM Agents
Source: DAIR.AI (X) | 2026-04-22