This title draws inspiration from the chameleon's ability to adapt and blend into its surroundings, which parallels

For these systems, a common issue is the difficulty of collecting sufficiently refined timescale data.

Post-training quantization (PTQ) reduces the memory footprint of LLMs by quan-tizing weights to low-precision datatypes. Since LLM inference is usually memory-bound, PTQ methods can improve inference throughput.

The circles and crosses represent the object and goal positions, respectively.

This approach seeks to modify other agents'