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arxiv: 2604.19837 · v1 · submitted 2026-04-21 · 💻 cs.AI · cs.MA

Recognition: unknown

Forage V2: Knowledge Evolution and Transfer in Autonomous Agent Organizations

Huaqing Xie

Authors on Pith no claims yet

Pith reviewed 2026-05-10 03:11 UTC · model grok-4.3

classification 💻 cs.AI cs.MA
keywords autonomous agentsknowledge transferorganizational memoryopen-world tasksdenominator estimationknowledge accumulationAI agent organizations
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The pith

Weaker agents seeded with stronger agents' accumulated knowledge close most of a coverage gap while halving costs and converging faster.

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

Autonomous agents in open-world tasks systematically underestimate the full scope of what counts as complete work. This paper demonstrates that building an organization around accumulated knowledge lets experience carry over across runs and across different model capabilities. When a weaker model receives the stronger model's stored knowledge as readable documents, it narrows the performance difference dramatically, spends less, and reaches stable estimates sooner. The same transferred knowledge produces identical results in independent runs, showing that the organizational record itself calibrates what counts as finished. This approach turns isolated agent attempts into persistent, model-agnostic institutional memory that any new agent can inherit.

Core claim

The paper establishes that an autonomous agent organization accumulates knowledge entries over successive runs, growing from zero to dozens of entries while denominator estimates stabilize. It further shows that transferring those entries as readable documents from a stronger model to a weaker one reduces a 6.6 percentage point coverage gap to 1.1 points, cuts cost in half, and shortens convergence time, with three independent seeded runs all arriving at the identical denominator value.

What carries the argument

Organizational memory: knowledge entries stored as readable documents together with institutional safeguards such as audit separation and contract protocols that keep the record accurate and transferable.

If this is right

  • Knowledge entries grow and estimates stabilize over multiple runs as domain understanding deepens.
  • Seeded weaker agents achieve nearly the same coverage as stronger agents at lower cost and in fewer rounds.
  • The same transferred knowledge produces identical denominator estimates in independent runs.
  • The accumulated experience is model-agnostic and can be inherited by any future agent regardless of provider.
  • Institutional design elements like audit separation prevent knowledge degradation during transfer.

Where Pith is reading between the lines

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

  • Persistent organizations could let teams of agents improve indefinitely without each new member starting from scratch.
  • The approach may generalize beyond the tested tasks to any setting where completeness is hard to define in advance.
  • Future tests could examine whether the same documents remain effective when transferred to models with even larger capability differences.

Load-bearing premise

Knowledge entries captured as readable documents remain accurate and usable by agents of different capabilities without loss of context or introduction of errors when transferred across runs and models.

What would settle it

A weaker agent given the transferred documents still produces a large remaining coverage gap or inconsistent denominator estimates across repeated runs.

Figures

Figures reproduced from arXiv: 2604.19837 by Huaqing Xie.

Figure 1
Figure 1. Figure 1: Forage V2 architecture. Top: within a single run, the Evaluator and Planner operate in isolated workspaces with no mutual visibility; they coordinate through shared artifacts (dataset, metrics, eval_contract). Bottom: across runs, each run’s post-mortem extracts knowledge into a persistent base. Subsequent runs—and weaker models via transfer—inherit accumulated organizational experience. enables weaker age… view at source ↗
Figure 2
Figure 2. Figure 2: Anatomy of a single run (NVIDIA Opus, run 002). Coverage and denominator co [PITH_FULL_IMAGE:figures/full_fig_p011_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Cumulative knowledge entries across six runs for three Opus conditions. All tasks [PITH_FULL_IMAGE:figures/full_fig_p012_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: NVIDIA knowledge transfer effect across six runs per condition. [PITH_FULL_IMAGE:figures/full_fig_p013_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Denominator convergence across NVIDIA conditions. The shaded band marks Opus’s [PITH_FULL_IMAGE:figures/full_fig_p018_5.png] view at source ↗
read the original abstract

Autonomous agents operating in open-world tasks -- where the completion boundary is not given in advance -- face denominator blindness: they systematically underestimate the scope of the target space. Forage V1 addressed this through co-evolving evaluation (an independent Evaluator discovers what "complete" means) and method isolation (Evaluator and Planner cannot see each other's code). V2 extends the architecture from a single expedition to a learning organization: experience accumulates across runs, transfers across model capabilities, and institutional safeguards prevent knowledge degradation. We demonstrate two claims across three task types (web scraping, API queries, mathematical reasoning). Knowledge accumulation: over six runs, knowledge entries grow from 0 to 54, and denominator estimates stabilize as domain understanding deepens. Knowledge transfer: a weaker agent (Sonnet) seeded with a stronger agent's (Opus) knowledge narrows a 6.6pp coverage gap to 1.1pp, halves cost (9.40 to 5.13 USD), converges in half the rounds (mean 4.5 vs. 7.0), and three independent seeded runs arrive at exactly the same denominator estimate (266), suggesting organizational knowledge calibrates evaluation itself. V2's contribution is architectural: it designs institutions -- audit separation, contract protocols, organizational memory -- that make any agent more reliable upon entry. The accumulated experience is organizational, model-agnostic, and transferable, stored as readable documents that any future agent inherits regardless of provider or capability level.

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 / 2 minor

Summary. The manuscript introduces Forage V2, which builds on Forage V1 by adding organizational memory to autonomous agent systems. This allows knowledge to accumulate over multiple runs and transfer across agents of different capabilities (e.g., from Opus to Sonnet models). The central claims are that this architecture mitigates denominator blindness in open-world tasks, with evidence from knowledge growth (0 to 54 entries) and transfer benefits including reduced coverage gap (6.6pp to 1.1pp), lower costs (9.40 to 5.13 USD), faster convergence (7.0 to 4.5 rounds), and exact replication of denominator estimates (266) across runs.

Significance. Should the empirical results prove robust to controls and ablations, the paper offers a novel architectural approach to building learning organizations of agents. The emphasis on model-agnostic, readable knowledge documents and institutional safeguards (audit separation, contract protocols) could have broad implications for scalable, reliable autonomous systems in AI.

major comments (2)
  1. [Abstract] The interpretation of the exact match in denominator estimate (266) across three independent seeded runs as evidence of calibration via organizational knowledge is not supported without additional evidence. An ablation study removing entries related to the denominator or an external check against ground truth would be necessary to rule out direct propagation of specific values from the transferred knowledge.
  2. [Abstract / Results] Specific quantitative claims are made (e.g., coverage gap narrowing, cost halving, round reduction) but the abstract and available description provide no information on experimental methods, controls, statistical details, or data. This is a load-bearing issue for verifying the soundness of the transfer results.
minor comments (2)
  1. [Abstract] The three task types are listed but not described; adding one-sentence examples for each would improve clarity.
  2. Consider adding a figure or table summarizing the knowledge accumulation over the six runs.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback on our manuscript. We address each major comment point by point below, indicating where we will revise the paper to strengthen the presentation and evidence.

read point-by-point responses

  1. Referee: [Abstract] The interpretation of the exact match in denominator estimate (266) across three independent seeded runs as evidence of calibration via organizational knowledge is not supported without additional evidence. An ablation study removing entries related to the denominator or an external check against ground truth would be necessary to rule out direct propagation of specific values from the transferred knowledge.

    Authors: We acknowledge that the exact replication of the denominator estimate alone is suggestive rather than conclusive, as it could in principle reflect indirect propagation of related insights. The transferred knowledge consists of readable, high-level documents containing domain insights and procedural guidelines, not explicit numerical targets. To address this directly, we will add an ablation study to the revised manuscript in which we remove or mask all entries potentially related to denominator estimation and re-run the transfer experiments to test whether convergence to 266 persists. Where ground truth is determinable (particularly in the mathematical reasoning tasks), we will also report direct comparisons between the estimated and true denominators. These additions will appear in the Results and Discussion sections. revision: yes

  2. Referee: [Abstract / Results] Specific quantitative claims are made (e.g., coverage gap narrowing, cost halving, round reduction) but the abstract and available description provide no information on experimental methods, controls, statistical details, or data. This is a load-bearing issue for verifying the soundness of the transfer results.

    Authors: We agree that the abstract is concise by design and omits methodological specifics, which can hinder immediate verification of the quantitative claims. The full manuscript contains a dedicated Experimental Setup section that details the three task types, the specific models (Claude Opus and Sonnet), seeding procedures, run counts, transfer protocols, and metrics for coverage, cost, and convergence. We will revise the abstract to include a brief summary sentence on the experimental framework and will add explicit references to the methods section. In addition, we will expand the Results section with further statistical details, including the exact number of independent runs and any observed variance, to make the evidence more transparent and load-bearing. revision: yes

Circularity Check

0 steps flagged

No significant circularity; results are empirical observations without derivations or self-referential fits.

full rationale

The paper presents an architectural extension of prior agent systems through experimental runs on web scraping, API queries, and mathematical reasoning tasks. Knowledge growth is reported as a direct count (0 to 54 entries) and performance metrics (coverage gaps, costs, convergence rounds, and a repeated denominator value) are framed as observed outcomes across seeded and unseeded runs. No equations, fitted parameters, or derivation chains appear in the provided text. Claims rest on measured experimental results rather than quantities defined in terms of themselves or load-bearing self-citations that reduce to unverified premises. This is self-contained empirical work with no reduction of predictions to inputs by construction.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 1 invented entities

Claims rest on domain assumptions about knowledge capture and transferability plus the V1 mechanisms of co-evolving evaluation and method isolation; no free parameters or invented entities with independent evidence are introduced in the abstract.

axioms (2)
  • domain assumption Agents suffer from denominator blindness in open-world tasks where completion boundaries are undefined
    Core premise carried from V1 and addressed by the organizational design.
  • domain assumption Knowledge can be stored as readable documents that transfer effectively across model capabilities without degradation
    Central architectural assumption enabling the transfer results.
invented entities (1)
  • organizational memory no independent evidence
    purpose: Accumulated knowledge entries stored for inheritance by future agents
    New construct in V2 for persistence and transfer.

pith-pipeline@v0.9.0 · 5561 in / 1493 out tokens · 61538 ms · 2026-05-10T03:11:15.354718+00:00 · methodology

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Reference graph

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