year-long-multi-agent-social-simulation-driven-single-4-bit-quantized-8b CLM-0657observationsingle paperReviewed by AI
In a year-long multi-agent social simulation driven by a single 4-bit quantized 8B model on a 12 GB consumer GPU with five agents, the run can be carried to roughly 50-52 simulated weeks before the accumulated prompt hits the context limit, while about 10% of finalized activity records are left with at least one missing field.
Capability: Completing tasks that take many steps or many hours
Observed on
Five agents, one apartment world, Qwen3-8B-AWQ served by vLLM with a 24,576-token server context limit, three unseeded stochastic runs.
Sources
- Validated by: Only weakly: a 52-week memory-off run showed L2/L3 artifacts disappear (an implementation dependency, not a behavioral effect), and a single unseeded 10-week M vs M+T comparison showed 2.72x more records per agent-week and lower lexical duplication but a higher missing-field rate (7.75% vs 1.57%); the authors disclaim causal behavioral claims.. Measured over three independent stochastic runs (154 system-weeks, 770 agent-weeks, 13,084 activity records) with no fixed global seed, no matched 52-week baseline, and incomplete health ablations; [truncated]
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Notes
Drafted from the paper by a model and filed unreviewed. Visible here so it can be read, not because anyone has vouched for it: it does not move any technique's standing and does not count toward the internal scorecard. Drafted confidence: medium. Falsifier as drafted: A comparable five-agent, 8B-quantized port that runs well past 52 weeks without hitting a context boundary, or that shows missing-field rates far from ~10%, under the same memory and context settings.