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arxiv: 2605.12087 · v1 · submitted 2026-05-12 · 💻 cs.AI · cs.MA

Recognition: 2 theorem links

· Lean Theorem

Intermediate Artifacts as First-Class Citizens: A Data Model for Durable Intermediate Artifacts in Agentic Systems

Josh Rosen , Seth Rosen
Authors on Pith no claims yet

Pith reviewed 2026-05-13 04:34 UTC · model grok-4.3

classification 💻 cs.AI cs.MA
keywords intermediate artifactsagentic systemsdata modeldurable staterevisable AI workartifact lineageinspectable outputsmulti-step reasoning
0
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The pith

AI agentic systems should store intermediate work products as durable, typed, versioned data objects that later steps and humans can inspect and revise.

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

AI systems often run loops of reasoning and tool use to produce final outputs such as plans or analyses, yet discard the steps that shaped those outputs. The paper argues that these intermediate results should instead be kept as persistent, structured artifacts that carry explicit type, structure, address, version, dependencies, and authority. Such artifacts differ from private chain-of-thought or chat logs; they are consumable work products like evidence maps, claim structures, and partial syntheses. If the claim holds, downstream agents and people can examine, edit, supersede, or extend prior work without reconstructing lost context. The authors supply a systems data model that defines how these artifacts receive additive or superseding updates and how their lineage maintains state across revisions.

Core claim

The paper presents a data model in which intermediate artifacts become first-class, durable objects: typed, structured, addressable, versioned, dependency-aware, authoritative, and consumable by other computation. These objects receive explicit additive and superseding update semantics with current-state resolution rules. Lineage tracking keeps the artifacts consistent across revisions. The model treats evaluation as a question of maintained-state quality rather than final-output quality alone.

What carries the argument

Durable intermediate artifact: a structured, addressable, versioned data object that represents maintained work products such as evidence maps or plans and carries explicit dependencies and update semantics so downstream computation can consume or revise it directly.

If this is right

  • Artifact lineage supplies durable intermediate state that survives revisions without requiring regeneration.
  • Evaluation of agentic systems must measure quality of the preserved artifacts, not only the final output.
  • Downstream agents can consume and extend specific artifacts directly rather than re-deriving lost context.
  • Humans gain direct access to edit or supersede individual artifacts instead of rewriting entire final products.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The model could be added to existing agent runtimes to give persistent, queryable memory of reasoning steps.
  • Collaborative workflows become possible when multiple agents and users edit the same shared artifact set over time.
  • A practical test would measure whether agents that read prior artifacts produce fewer redundant steps than agents that start from scratch each time.

Load-bearing premise

Final outputs lose important details from earlier steps, and preserving the intermediates as durable objects will improve inspectability and revisability without adding meaningful overhead.

What would settle it

Compare revision time and error rate for a human editor fixing a multi-step agent task when only the final artifact is available versus when the full set of typed, versioned intermediate artifacts and their lineage are also present.

Figures

Figures reproduced from arXiv: 2605.12087 by Josh Rosen, Seth Rosen.

Figure 1
Figure 1. Figure 1: A final-answer loop collapses intermediate state into a transcript and answer. An artifact-first system pre [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
read the original abstract

Many AI systems are organized around loops in which models reason, call tools, observe results, and continue until a task is complete. These systems often produce final artifacts such as memos, plans, recommendations, and analyses, while the intermediate work that shaped those outputs remains ephemeral. For multi-step, revisable AI work, final artifacts are often lossy projections over upstream state. We argue that such systems should preserve durable, inspectable intermediate artifacts: typed, structured, addressable, versioned, dependency-aware, authoritative, and consumable by downstream computation. These artifacts are not the model's private chain-of-thought. They are maintained work products such as evidence maps, claim structures, criteria, assumptions, plans, transformation rules, synthesis procedures, unresolved tensions, and partial products that later humans and agents can inspect, revise, supersede, and improve. The contribution is a systems-level data model. We distinguish intermediate artifacts from chat transcripts, memory, hidden chain-of-thought, narration, thinking, and final answers; formalize additive and superseding update semantics with explicit current-state resolution; describe how artifact lineage supports durable intermediate state across revisions; and argue that evaluation must target maintained-state quality, not only final-output quality. The claim is not that artifacts make models smarter. It is that durable intermediate artifacts make AI-generated work more inspectable, revisable, and maintainable over time.

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

1 major / 2 minor

Summary. The paper claims that agentic AI systems organized around reasoning-tool-observation loops often produce final artifacts that are lossy projections of upstream state because intermediate work remains ephemeral. It argues that such systems should instead preserve durable, inspectable intermediate artifacts that are typed, structured, addressable, versioned, dependency-aware, authoritative, and consumable by downstream computation. These artifacts include evidence maps, claim structures, criteria, assumptions, plans, transformation rules, synthesis procedures, unresolved tensions, and partial products. The contribution is a systems-level data model that distinguishes intermediate artifacts from chat transcripts, memory, hidden chain-of-thought, narration, thinking, and final answers; formalizes additive and superseding update semantics with explicit current-state resolution; describes how artifact lineage supports durable intermediate state across revisions; and advocates evaluating maintained-state quality rather than only final-output quality.

Significance. If the distinctions and update semantics hold in practice, the framework could meaningfully shift agentic system design toward greater long-term inspectability, revisability, and maintainability by treating intermediate work products as persistent, queryable entities. The paper's strengths include its self-contained conceptual framework, clear separation of artifact types, and explicit formalization of additive/superseding semantics and lineage mechanics, which provide a reusable vocabulary for future implementations even without empirical validation or machine-checked proofs.

major comments (1)
  1. [Data model and lineage mechanics] The manuscript describes the data model and lineage mechanics at a systems level but supplies neither asymptotic bounds, storage growth estimates under realistic revision patterns, nor any prototype measurements for maintaining the artifact graph in long-running agent loops. This is load-bearing for the central claim that durable intermediates will improve inspectability and maintainability without introducing prohibitive overhead or complexity.
minor comments (2)
  1. The abstract and introduction would benefit from one or two concrete, worked examples contrasting an ephemeral intermediate state with its durable counterpart to illustrate the claimed lossiness of final artifacts.
  2. [Data model description] The terms 'authoritative' and 'consumable' are introduced without explicit definitions or operational criteria, which could lead to inconsistent interpretations when the model is applied.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for the constructive review and for identifying a point that merits clarification. The manuscript is a conceptual contribution focused on a systems-level data model, distinctions among artifact types, and update/lineage semantics. We address the major comment below and have made a targeted revision to acknowledge practical considerations without altering the paper's scope.

read point-by-point responses

  1. Referee: The manuscript describes the data model and lineage mechanics at a systems level but supplies neither asymptotic bounds, storage growth estimates under realistic revision patterns, nor any prototype measurements for maintaining the artifact graph in long-running agent loops. This is load-bearing for the central claim that durable intermediates will improve inspectability and maintainability without introducing prohibitive overhead or complexity.

    Authors: We agree that concrete performance data would strengthen any claim about non-prohibitive overhead. However, the manuscript's central claim concerns the semantic and structural properties that make durable intermediates inspectable, revisable, and authoritative, rather than a performance guarantee for a particular implementation. The data model is deliberately implementation-agnostic: it specifies typed, versioned, dependency-aware entities with additive and superseding update rules, but leaves storage representation, indexing, and retention policies to concrete systems. Lineage mechanics explicitly support superseding and current-state resolution, which in principle permit pruning of obsolete versions and delta-based storage—standard techniques that avoid unbounded growth. Because the paper does not present or evaluate an implementation, we do not supply asymptotic bounds or prototype measurements; doing so would require choosing specific data structures and workloads outside the stated contribution. We have added a brief discussion paragraph noting that storage overhead is governed by implementation choices (e.g., retention policies, garbage collection of superseded artifacts) and that the model's explicit versioning and dependency tracking are intended to make such management feasible rather than to eliminate it. revision: partial

Circularity Check

0 steps flagged

No circularity in conceptual data model proposal

full rationale

The paper is a systems-level conceptual proposal that defines durable intermediate artifacts, distinguishes them from chat transcripts and chain-of-thought, formalizes additive/superseding semantics, and argues for improved inspectability without any equations, derivations, fitted parameters, or predictions. No load-bearing steps reduce to self-definition, self-citation chains, or renamed known results; the framework is self-contained against external benchmarks and contains no internal circularity.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 1 invented entities

This is a conceptual proposal. The central claim rests on domain assumptions about the value of preserved intermediate state rather than on fitted parameters or new entities with independent evidence.

axioms (2)
  • domain assumption Final artifacts in AI systems are lossy projections that discard useful upstream state
    Invoked in the opening argument that intermediate work should be preserved.
  • domain assumption Durable, typed, versioned artifacts will improve inspectability and maintainability
    Core premise of the proposed data model and evaluation shift.
invented entities (1)
  • Durable intermediate artifacts data model no independent evidence
    purpose: To formalize preservation, versioning, and consumption of intermediate work products
    The model itself is the proposed contribution; no independent evidence or falsifiable predictions are supplied in the abstract.

pith-pipeline@v0.9.0 · 5552 in / 1370 out tokens · 75693 ms · 2026-05-13T04:34:12.816504+00:00 · methodology

discussion (0)

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Reference graph

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