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arxiv: 2605.12178 · v1 · submitted 2026-05-12 · 💻 cs.AI · cs.CL· cs.LG

Recognition: 1 theorem link

Do Enterprise Systems Need Learned World Models? The Importance of Context to Infer Dynamics

Jishnu Sethumadhavan Nair , Patrice Bechard , Rishabh Maheshwary , Surajit Dasgupta , Sravan Ramachandran , Aakash Bhagat , Shruthan Radhakrishna , Pulkit Pattnaik
show 9 more authors
Johan Obando-Ceron Shiva Krishna Reddy Malay Sagar Davasam Seganrasan Subramanian Vipul Mittal Sridhar Krishna Nemala Christopher Pal Srinivas Sunkara Sai Rajeswar
Authors on Pith no claims yet

Pith reviewed 2026-05-13 05:23 UTC · model grok-4.3

classification 💻 cs.AI cs.CLcs.LG
keywords enterprise systemsworld modelsruntime discoverytransition dynamicsdeployment shiftCascadeBenchconfigurable environmentsbusiness logic
0
0 comments X

The pith

In configurable enterprise systems, agents should discover transition dynamics by reading configurations at runtime rather than relying solely on internalized world models.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper asks whether agents need to learn world models when transition rules can be read directly at inference time in enterprise settings. It demonstrates that models trained offline on historical transitions lose accuracy when tenant-specific business logic shifts across deployments, whereas agents that read the active configuration recover the current dynamics and stay robust. This matters for enterprise applications because rules vary by tenant and evolve over time, making fixed internalized models brittle. The work introduces enterprise discovery agents and the CascadeBench benchmark to show that runtime discovery grounds predictions in the specific system instance.

Core claim

When the rules can be read at inference time, an agent still benefits from discovering them rather than learning them entirely; runtime discovery of configurable dynamics complements offline training by grounding predictions in the active system instance, as shown by higher robustness under deployment shift on CascadeBench compared to purely learned models.

What carries the argument

Enterprise discovery agents that recover relevant transition dynamics at runtime by reading the system's configuration.

Load-bearing premise

The system's configuration files or APIs accurately and completely encode the transition dynamics without hidden state, side effects, or version-specific behavior that cannot be read at inference time.

What would settle it

An observation that an offline-trained world model maintains accuracy after a shift in tenant business logic while a discovery agent fails to extract correct dynamics from the readable configuration would falsify the robustness advantage.

read the original abstract

World models enable agents to anticipate the effects of their actions by internalizing environment dynamics. In enterprise systems, however, these dynamics are often defined by tenant-specific business logic that varies across deployments and evolves over time, making models trained on historical transitions brittle under deployment shift. We ask a question the world-models literature has not addressed: when the rules can be read at inference time, does an agent still need to learn them? We argue, and demonstrate empirically, that in settings where transition dynamics are configurable and readable, runtime discovery complements offline training by grounding predictions in the active system instance. We propose enterprise discovery agents, which recover relevant transition dynamics at runtime by reading the system's configuration rather than relying solely on internalized representations. We introduce CascadeBench, a reasoning-focused benchmark for enterprise cascade prediction that adopts the evaluation methodology of World of Workflows on diverse synthetic environments, and use it together with deployment-shift evaluation to show that offline-trained world models can perform well in-distribution but degrade as dynamics change, whereas discovery-based agents are more robust under shift by grounding their predictions in the current instance. Our findings suggest that, in configurable enterprise environments, agents should not rely solely on fixed internalized dynamics, but should incorporate mechanisms for discovering relevant transition logic at runtime.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 0 minor

Summary. The paper claims that in enterprise systems where transition dynamics are configurable and readable, runtime discovery agents—which recover transition logic by reading system configurations at inference time—complement offline-trained world models by grounding predictions in the active instance. This is supported by the introduction of CascadeBench (a reasoning-focused benchmark adopting World of Workflows methodology on synthetic environments) and deployment-shift evaluations showing that offline world models perform well in-distribution but degrade under shifts, while discovery agents remain robust.

Significance. If the empirical contrast holds, the result is significant for agent design in dynamic enterprise environments, as it provides a concrete test of when learned world models can be augmented or replaced by runtime context mechanisms. The introduction of CascadeBench as a new benchmark for enterprise cascade prediction is a clear strength that could enable reproducible follow-up work on configurable systems.

major comments (2)
  1. [Abstract] Abstract: the claim of empirical demonstration on CascadeBench and deployment-shift tests provides no details on methods, data splits, statistical significance, baselines, or implementation of the discovery agents. This is load-bearing for the central robustness claim, as it prevents verification that the advantage is free of post-hoc selection or baseline issues.
  2. [Abstract] Abstract: the complementarity argument requires that readable configurations or APIs fully and accurately specify transition dynamics. The manuscript does not address how discovery agents handle partial/ambiguous configs, hidden state, data-dependent side effects, or version-specific behavior, which directly risks the grounding claim if such elements exist even in 'configurable' systems.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive comments, which help clarify the presentation of our empirical claims and assumptions. We address each major comment below and will incorporate revisions to improve the manuscript.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the claim of empirical demonstration on CascadeBench and deployment-shift tests provides no details on methods, data splits, statistical significance, baselines, or implementation of the discovery agents. This is load-bearing for the central robustness claim, as it prevents verification that the advantage is free of post-hoc selection or baseline issues.

    Authors: The abstract is necessarily concise, but we agree it should better support the central claims. The full manuscript details CascadeBench in Section 3 (adopting World of Workflows on synthetic environments with explicit configuration generation), the deployment-shift protocol in Section 4 (including train/test splits across configuration variants, 5 independent runs with mean/std reporting for significance, and baselines such as standard world-model RL agents plus ablations), and discovery-agent implementation (runtime parsing of readable configs to recover transition rules). We will revise the abstract to concisely reference these elements and the key robustness result. revision: yes

  2. Referee: [Abstract] Abstract: the complementarity argument requires that readable configurations or APIs fully and accurately specify transition dynamics. The manuscript does not address how discovery agents handle partial/ambiguous configs, hidden state, data-dependent side effects, or version-specific behavior, which directly risks the grounding claim if such elements exist even in 'configurable' systems.

    Authors: This correctly identifies a core assumption of our study. Our evaluations use synthetic environments constructed so that configurations fully and accurately define dynamics (by design of the benchmark), allowing isolation of runtime discovery benefits. We do not claim universality for all enterprise systems. We will add a new Limitations paragraph discussing partial/ambiguous configs, hidden state, side effects, and version issues, along with potential extensions such as hybrid discovery-plus-learning fallbacks. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical contrast on new benchmark is self-contained

full rationale

The paper advances an empirical argument that runtime discovery agents complement offline world models in configurable enterprise settings by showing degradation of learned models under deployment shift while discovery agents remain robust. This is demonstrated via the introduced CascadeBench benchmark (adopting World of Workflows methodology) and shift evaluations, without any equations, fitted parameters renamed as predictions, or load-bearing self-citations. No derivation reduces to its own inputs by construction; the central claim rests on observable performance differences rather than definitional equivalence or ansatz smuggling.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 1 invented entities

The central claim rests on the premise that enterprise transition dynamics are fully or largely encoded in readable configuration; this is treated as a domain assumption rather than derived.

axioms (1)
  • domain assumption Enterprise transition dynamics are configurable and can be read at inference time without loss of fidelity
    Stated in the abstract as the key condition under which runtime discovery is preferable; no derivation is provided.
invented entities (1)
  • enterprise discovery agents no independent evidence
    purpose: Agents that recover transition dynamics by reading system configuration at runtime
    New term introduced to describe the proposed alternative to purely learned world models; no independent evidence of existence or performance outside the paper's argument.

pith-pipeline@v0.9.0 · 5614 in / 1374 out tokens · 48624 ms · 2026-05-13T05:23:48.197944+00:00 · methodology

discussion (0)

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Reference graph

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