Socio-Traffic Thermodynamics v0.3.2 is a claim-bounded public repository refresh of a reduced toy-network diagnostic framework for congestion-like non-equilibrium patterns, stop-and-go waves, and information-induced synchronization risk in finite-capacity traffic surrogate models. The archived framework studies a limited question: in simplified finite-capacity traffic toy networks, can route information reduce mean social cost while avoiding synchronized reaction, overload amplification, or fragile congestion-like dynamics? The package combines the original Socio-Traffic Thermodynamics manuscript, the v0.2.0–v0.2.9 audit sequence, the v0.3.0 synthesis outputs, and the v0.3.1 integrated revision into a cleaner public-facing repository structure. The model should be understood as a reduced surrogate and hypothesis-generating diagnostic, not as a real-world traffic simulator. It uses simplified Optimal Velocity / car-following and toy-network settings to examine how socially motivated motion, finite perceptual bandwidth, stochastic response, information compression, and dissipative stop-and-go wave formation can interact under finite capacity. The central claim is conservative: within the tested toy-network setting, route information is not automatically beneficial. In the frozen holdout tests, risk-budgeted low-rate, coarse, staggered, or guarded information release reduced mean toy cost relative to a no-information null without increasing overload severity, while common precise or delayed common information increased synchronization, overload, and cost. This result is limited to the tested surrogate environment and should be interpreted as a diagnostic observation about synchronization risk and information buffering, not as a traffic-policy recommendation. This v0.3.2 release does not introduce new theoretical claims beyond the archived v0.3.1 synthesis. Its main purpose is to reorganize the repository for public readability, strengthen claim-boundary statements, add a GitHub Pages landing structure, refresh metadata, regenerate manifests, and improve discoverability. This release does not claim real-world traffic prediction, calibrated urban simulation, city-scale deployment readiness, route-guidance product readiness, traffic-policy validation, public-infrastructure recommendation, behavioral-control method, legal or safety approval, or a universal congestion solution. It does not recommend withholding real-world traffic information. Any future empirical extension would require externally defined benchmark networks, real or public traffic scenarios, predeclared metrics, status-quo baselines, overload-risk measures, synchronization metrics, and frozen holdout protocols.