SwarmWorld: Stigmergic technological evolution in societies of language-model agents

Abstract: Collective intelligence can emerge when individuals coordinate through a shared environment, allowing local actions to accumulate into durable social organization. Language-model agents offer a new substrate for this process, yet most multi-agent systems rely on direct conversation, predefined roles, or centralized workflows. It remains unclear whether decentralized agents can build functional technologies and outperform independent search. Here, initially homogeneous LLM agents in SwarmWorld self-organize without assigned roles or recipes into evolving technological societies. Agents explore a spatial environment, process resources, test materials, construct persistent artifacts, and write executable controllers evaluated by a deterministic simulator under unseen disturbances after the agents are removed. SwarmWorld splits cognition from consequence: agents propose architectures and controllers within fixed action and material schemas, while the simulated world determines function. Shared societies develop broader, more resilient technological portfolios than a strong best-of-N isolated-search baseline, although isolated search remains competitive for the strongest artifact. Agents differentiate into exploration, construction, maintenance, and coordination behaviors, transitioning as the world matures. Technologies accumulate through collaborative construction, executable inheritance, and persistent agent-artifact networks, with most reuse beginning through physical observation rather than communication. Explicit cultural mechanisms amplify collaboration and organization, but functional benefits depend on outcome and timescale. Physical stigmergy alone supports capable societies, while interaction drives persistent technological ecologies rather than universally superior individual inventions.

• The paper integrates multiple research fields such as swarm intelligence, artificial societies, and agent systems to show how decentralized LLM agents can generate a rich technological ecology through stigmergic evolution, outperforming independent searches in resilience and breadth of technological portfolios.
• Demonstrates how shared worlds favor portfolio resilience and validated invention counts, but independent search retains the strongest final individual artifact, highlighting the ecological optimization target of SwarmWorld.
• The analysis provides that a majority of artifacts in SwarmWorld settings are the result of multiple-agent contributions, emphasizing the role of physical stigmergy in technological evolution.

Research question and conceptual contribution

“SwarmWorld: Stigmergic technological evolution in societies of language-model agents” (2608.26081) examines whether decentralized LLM agents can generate a cumulative technological ecology rather than merely perform parallel independent search. The central comparison is deliberately demanding: a society of initially homogeneous agents operating in one persistent world is evaluated against an endpoint-wise best-of-$N$ envelope of isolated agents receiving matched decision opportunities. Collective advantage therefore cannot be attributed simply to a larger number of model calls.

The paper integrates four research traditions. From biological collective behavior and swarm intelligence, it adopts decentralized coordination, local feedback, adaptive task differentiation, and stigmergy. From artificial societies and cellular automata, it adopts persistent worlds whose local modifications alter later dynamics. From LLM-agent systems, it adopts memory, language-mediated interaction, planning, embodiment, and executable skill generation. The resulting architecture differs from systems that prescribe agent roles, workflows, or specialist capabilities. It instead asks what organization emerges when equivalent agents must explore, acquire resources, fabricate technologies, and respond to environmental disturbances.

The conceptual lineage is important because the paper defines collective intelligence operationally through durable consequences. Agents do not merely exchange proposals or produce textual descriptions. They leave behind material artifacts and executable controllers that continue operating after the agents are removed. This allows the study to distinguish claims made by agents from consequences generated by an authoritative simulator.

Figure 1: Conceptual lineages converging on SwarmWorld, combining decentralized swarm coordination, persistent artificial worlds, LLM-based planning and memory, and executable technological artifacts.

The work is situated among several recent LLM-agent systems. “Generative Agents” (Park et al., 2023) demonstrated memory-mediated social behavior in a simulated town, while Project Sid (AL et al., 2024) studied large populations exhibiting specialization and collective rule formation. Voyager (Wang et al., 2023) made executable skills persistent and reusable in an embodied environment. Other systems such as DiscoveryWorld (Jansen et al., 2024), SciAgents, and Sparks (Ghafarollahi et al., 26 Apr 2025) couple LLM reasoning to scientific experimentation. SwarmWorld’s distinct contribution is to combine these ingredients with decentralized physical stigmergy, a persistent material substrate, program inheritance, and agent-free functional evaluation.

Simulator architecture and experimental controls

SwarmWorld separates cognition from consequence. At scheduled macroturns, an agent receives a local observation, private memory, and condition-dependent shared records. It returns a schema-constrained research-state update and an action queue. The simulator validates actions against spatial, energetic, material, ownership, and permission constraints. The world then advances independently: resources renew or deplete, environmental fields diffuse, disturbances occur, and installed artifact programs execute.

This separation prevents an LLM from declaring that a technology works. Agents can propose names, mechanisms, material compositions, processing routes, and controllers, but artifact function is determined by numerical material state, geometry, local environment, stored matter, health, and program actuation. Textual descriptions contribute to provenance and interpretation but do not directly determine performance.

Artifacts are persistent, spatially situated objects. They contain recorded material inputs, fabrication history, geometry, claimed function, provenance, and optionally a deterministic controller. Controllers contain 1–64 instructions over 16 registers and can read local environmental and artifact sensors. Their actuators are bounded and capability-scoped. There are no loops, imports, network access, file access, or dynamic code execution. This restriction makes executable inheritance auditable: a program fork must modify at least one instruction, and parent-child relationships are content-addressed using SHA-256 identifiers.

The main 800-tick experiment crosses four conditions and three population sizes:

Condition	Available coordination substrate
Full culture	Shared world, explicit communication, teaching, publication, trade, program reuse, program forking, and physical stigmergy
No communication	Shared world, physical stigmergy, executable reuse and forking, but no direct cultural channels
No explicit culture	Shared world and physical stigmergy only; cross-agent cultural and executable inheritance removed
Independent search	$N$ isolated one-agent worlds; endpoint-wise best result used for each metric

The primary design contains 48 episodes: four matched seeds, three population sizes, and four interaction conditions. The long-horizon study contains 12 additional episodes at $N=100$ over 3,200 ticks. At checkpoints, agents are removed and frozen technological states are cloned into eight unseen disturbance schedules. Only deterministic physics and installed programs continue during evaluation.

The independent-search baseline is stronger than a single-agent comparison. Its winning member can differ for discovery AUC, final artifact performance, invention count, portfolio resilience, and held-out resilience. This makes the central claim narrow: social interaction must improve a population-level endpoint beyond the best isolated result available for that endpoint.

Shared worlds improve technological portfolios, not necessarily the best artifact

The strongest result is a bounded rather than universal swarm advantage. Shared-world societies generally produce broader, more resilient technological portfolios than the independent-search envelope, but they do not consistently produce the highest-performing individual artifact.

In the 800-tick population study, discovery-frontier AUC increased with population but showed condition-dependent rankings. At $N=200$, the no-explicit-culture condition achieved the largest paired discovery gain over independent search, $+0.069$. By contrast, held-out resilience, portfolio resilience, and validated invention counts showed more consistent shared-world benefits. The no-explicit-culture condition reached a mean paired gain of six validated inventions at $N=200$.

The distinction becomes clearer over 3,200 ticks:

Endpoint at tick 3,200	Full culture	No explicit culture	Independent envelope
Portfolio resilience	0.2474	0.2365	0.1794
Validated inventions	5.75	7.00	2.75
Held-out resilience	comparable to no explicit culture	0.0446	0.0356
Best final artifact	0.2380	—	0.3488

The isolated envelope retains the strongest final single artifact, with mean performance $0.3488$ versus $0.2380$ under full culture. Shared societies instead produce complementary technologies covering multiple services. The implication is that interaction changes the optimization target from record-setting individual performance toward portfolio breadth, redundancy, and persistence. This result also qualifies claims that multi-agent interaction inherently improves capability: the advantage depends on whether the endpoint rewards ecological coverage or single-object optimization.

Emergent differentiation without assigned roles

Agents begin homogeneous and receive no role prompts. Nevertheless, trajectory analysis recovers recurring behavioral phenotypes. In the 800-tick study, a two-cluster model separates artifact-centered work from mobile exploration. At $N=200$, the artifact-centered fraction is approximately 27% under full culture, 20% without explicit culture, and 17% without communication.

The long-horizon analysis provides a stronger result because it excludes cultural-action variables from the physical phenotype model. Full culture increases the artifact-centered fraction from 31.0% to 52.8% relative to no explicit culture, a paired difference of 21.8 percentage points with a seed-bootstrap 95% interval of 12.0–33.5 percentage points. The clustering silhouette is 0.472. This supports a descriptive claim that explicit culture reorganizes physical activity around shared technological infrastructure rather than merely changing the content of messages.

A finer analysis of 22,400 agent-windows recovers four recurring states: constructor/operator, artifact-local caretaker, cultural coordinator, and mobile surveyor. The classification is stable under replicate holdout, with adjusted Rand indices of 0.999 for the broad split and 0.921 and 0.980 for the conditional subdivisions. In full culture, the constructor/operator fraction rises from 0.008 to 0.535 over the 3,200-tick $N=100$ runs, while the cultural-coordinator fraction falls from 0.695 to 0.292. No-explicit-culture societies also develop more construction activity, but end with constructors at 0.252 and mobile surveyors still comprising 0.633 of agents.

These states are dynamic rather than fixed identities. Window-to-window switching is 0.270 under full culture versus 0.102 without explicit culture in the 800-tick comparison, and 0.244 versus 0.138 over 3,200 ticks. The result is therefore not the emergence of permanent professions. It is state-dependent task allocation in which agents reorient as the technological environment changes.

Executable cultural evolution and physical stigmergy

The paper’s most consequential methodological distinction is between language-mediated culture and artifact-mediated culture. Explicit cultural mechanisms include communication, publication, teaching, trade, task claims, program reuse, and program forking. Physical stigmergy persists even when these mechanisms are removed: agents can encounter material deposits, artifacts, damage states, environmental gradients, stored matter, and ongoing artifact services.

The artifact record demonstrates that cultural accumulation enters executable technology. Under full culture, 67%, 76%, and 56% of artifacts at $N$0, 100, and 200, respectively, contain contributions from multiple agents. Cross-agent program forking remains common in the no-communication condition because programs remain physically observable and inheritable. It is absent by construction in the no-explicit-culture condition.

The deepest representative executable lineage contains 12 fork edges. Mean maximum program-lineage depth in full culture increases from 3.75 at tick 400 to 9.75 at tick 3,200, with approximately half of eligible forks crossing author boundaries. This is stronger than textual imitation or semantic similarity: the system records exact parent identifiers, instruction-level edits, authorship, installations, and downstream artifacts.

The paper’s diffusion analysis complicates a conventional account of cultural transmission. At least 96.9% of artifacts are eventually reused by noncreators, and reuse occurs earlier and more broadly under full culture. Median time to first reuse is 5 ticks under full culture versus 8 ticks without explicit culture; mean adoption breadth is 13.53 versus 7.49 noncreator agents. Yet approximately 95% of first reuse begins through direct physical observation in both conditions. Creator-to-adopter message motifs are only weakly enriched at a 25-tick lag and fall below parity at longer lags.

The implication is that explicit communication may amplify the technological ecology without serving as the dominant immediate transmission channel. Agents mostly acquire technologies by encountering persistent objects in the world. The world functions as an externalized memory and as a medium through which prior construction changes the opportunities available to later agents.

Network organization: modularity, redundancy, and vulnerability

SwarmWorld does not produce a single globally integrated society. It produces modular technological neighborhoods connected by limited bridges and local hubs. In a representative full-culture society, the complete temporal record contains 389 artifact tracks, 100 agent tracks, 3,924 delivered-message recipient edges, and 358 cross-artifact program-descent events. The matched no-explicit-culture society contains 248 artifacts and lacks the disabled message and executable-descent pathways.

Degree-controlled hypergeometric projections show that full culture yields 1,100 validated agent pairs involving 92 agents, compared with 261 pairs involving 45 agents without explicit culture. The corresponding artifact co-use projection contains 18,563 statistically supported artifact pairs among 385 artifacts. However, node participation coefficients remain below 0.26, and mean participation at tick 3,200 is only 0.0016 under full culture and 0.0044 without explicit culture. NODF nestedness is nearly identical, 0.094 and 0.096, respectively.

These values reject a centralized interpretation of the denser full-culture network. Explicit interaction increases the number of coordination pathways but does not erase local specialization. Cumulative unique agent–artifact ties scale superlinearly with the number of agents plus artifacts, with descriptive densification exponents of 3.47 and 3.48. By tick 3,200, full culture has a mean of 4,031.3 unique ties, approximately twice the 2,027.3 observed without explicit culture. Relationship reuse reaches 0.650 and 0.588, indicating that both conditions develop stable local neighborhoods.

The same topology produces robustness and targeted vulnerability. Randomly removing half the agents leaves 98.3% of full-culture artifacts and 95.2% of no-explicit-culture artifacts connected to at least one surviving agent. Removing high-degree agents reduces these values to 59.6% and 73.9%; betweenness-targeted removal reduces them to 62.9% and 68.4%. Thus, distributed participation provides tolerance to random dropout, while concentration around highly connected agents creates predictable failure modes. The paper appropriately limits this conclusion to graph accessibility: the knockout assay does not measure physical service, adaptive repair, or recovery in a live society.

Transfer across material domains

The authors test whether the observed organization depends on the BioFoundry setting by replacing the resource ecology, processing routes, environmental fields, and functional objectives.

In AshenRealm, agents work with volcanic resources under thermal, ash, and seismic hazards. Absolute performance and resilience are lower than in BioFoundry, but multi-agent construction, cross-agent program reuse, executable lineage formation, and spatial exploration persist. Behavioral analysis again recovers artifact-centered work and mobile exploration, with an additional stationary or low-activity state. Representative technologies include obsidian thermal segments, fracture-localizing shields, iron-based loadshields, and vented interlocks, with simulator performance ranging from 0.116 to 0.248.

This transfer result is appropriately modest. The two environments are not calibrated to equal difficulty, so raw performance values cannot support a cross-domain performance comparison. The evidence instead concerns organizational recurrence: the same world architecture supports persistent construction and behavioral differentiation under a distinct resource topology.

Protein Realms provides a second transfer test, this time in a catalog-conditioned protein–matrix design space. Agents select from 12 versioned sequence variants spanning collagen-like, silk–elastin-like, resilin-like, and mussel-adhesive families. They must obtain resources, execute processing routes, fabricate microbatches, test them at a spatially separate proving ground, and rebuild valid recipes as persistent installations.

The pilot produces only descriptive evidence. A no-communication society installs a collagen-like CLP_PPG10 cellulose composite with tick-800 health 0.906 and performance 0.363, while the selected best isolated member produces a SELP_47K cellulose composite with utility 0.729, heat resistance 0.726, protease resistance 0.754, flood/shear resistance 0.634, health 0.904, and performance 0.418. Full culture and no explicit culture generate proposals but do not complete a valid assay within the 800-tick horizon. Since this is a single-seed pilot with unequal endpoint construction—a selected best of 50 isolated worlds rather than a shared $N$1 society—it demonstrates workflow transfer, not comparative superiority.

Limitations and open questions

The principal inferential limitation is replication. The main comparisons use four matched world seeds per condition, and the authors explicitly avoid agent-level inference because agents, artifacts, ticks, behavioral windows, network edges, and held-out schedules are nested observations. With four paired seeds, the smallest attainable two-sided sign-flip probability is 0.125. Effect sizes, paired consistency, and mechanistic evidence are consequently more informative than conventional significance thresholds.

The results also depend on one model and prompting configuration: gpt-5.6-luna, temperature 0.7, low reasoning effort, fixed memory budgets, and a strict action schema. The paper does not establish that the observed organization is invariant across model families, context windows, inference costs, or prompting regimes. Equal scheduled decision opportunities also do not imply equal token use, since cultural context can lengthen prompts.

The simulator is authoritative but not physically calibrated to real materials. BioFoundry and AshenRealm use normalized game-level surrogate properties, while Protein Realms uses fixed hidden catalog priors rather than biochemical prediction or new measurements. Technology illustrations are visualizations of recorded specifications, not manufactured geometries. Consequently, the paper establishes simulator-grounded technological evolution, not real-world materials discovery.

Some mechanisms are necessarily confounded with their ablations. The cultural-coordinator phenotype partly depends on features disabled in the comparison conditions, although the physical/task-only sensitivity analysis recovers independent differentiation. The no-communication condition retains executable inheritance, whereas no explicit culture removes it, so the two conditions isolate different combinations of mechanisms rather than communication alone.

Finally, structural robustness is topological rather than functional, and diffusion evidence identifies recorded pathways rather than proving causal influence. The main unresolved empirical question is whether the same bounded swarm advantage persists when the consequence layer is replaced by calibrated atomistic or continuum solvers, measured environmental disturbances, robotic embodiment, or laboratory instruments while retaining the same provenance and agent-free evaluation protocol.

Conclusion

SwarmWorld establishes a specific form of collective advantage for LLM-agent societies. Persistent shared worlds enable agents to construct, encounter, inherit, modify, and maintain technologies that outlive individual decisions. Relative to matched independent search, shared societies are better at assembling broad and resilient technological portfolios and at producing validated inventions, while independent search remains competitive—and sometimes superior—for the strongest single artifact.

The results also show that explicit communication is not the sole or even necessary basis of collective organization. Physical stigmergy alone supports substantial coordination, artifact reuse, and behavioral differentiation. Explicit culture increases interaction density, executable lineage depth, adoption breadth, and artifact-centered activity, but its functional benefits are endpoint- and timescale-dependent. The paper’s central contribution is therefore an experimental framework in which LLM collectives can be evaluated as evolving technological ecologies, with claims grounded in persistent artifacts, executable provenance, deterministic consequences, held-out disturbances, and controlled structural intervention rather than in agent-generated descriptions alone.