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arxiv: 2605.22905 · v1 · pith:GUYU6R2Znew · submitted 2026-05-21 · 💻 cs.AI · cs.CL

EVE-Agent: Evidence-Verifiable Self-Evolving Agents

Yamato Arai , Yuma Ichikawa This is my paper

Pith reviewed 2026-05-25 05:42 UTC · model grok-4.3

classification 💻 cs.AI cs.CL
keywords self-evolving agentsevidence verificationproposer-solver frameworkmarginal accuracy gainsearch agentsdata-free trainingauditable curriculumevidence-grounded correctness
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The pith

EVE-Agent lets self-evolving search agents create their own auditable training data by scoring evidence spans on marginal accuracy gains.

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

Self-evolving agents can generate their own questions and answers but risk rewarding fluent yet unsupported examples because nothing checks whether the generated content is justified. EVE-Agent adds an evidence verifier to the proposer-solver loop so that the proposer must also output a verbatim source span. The verifier then assigns a reward equal to the increase in solver accuracy when that span is supplied, creating a training signal that favors spans that actually help without any oracle answers or human labels. The backbone model, retriever, and optimization framework stay unchanged. The result is a curriculum that is self-generated yet inspectable by construction, with experiments showing higher evidence-grounded correctness than earlier self-evolving search agents.

Core claim

EVE-Agent modifies the proposer-solver framework so the proposer outputs a question, answer, and verbatim evidence span; an evidence verifier then rewards the span according to the marginal accuracy gain when the evidence is provided to the solver. This produces a training signal that favors genuinely helpful evidence without requiring oracle answers, human labels, or external annotations, leaving the backbone model, retriever, search tool, and optimization framework unchanged. Experiments show substantial improvement in evidence-grounded correctness over prior self-evolving search agents, yielding a curriculum that is auditable by construction because each training example carries aninspect

What carries the argument

The evidence verifier, which scores a generated evidence span by the marginal accuracy gain it produces when supplied to the solver.

If this is right

  • Each self-generated training example now includes a source span whose contribution to the answer can be directly inspected and measured.
  • The optimization loop prefers evidence that measurably raises answer accuracy rather than merely fluent but unsupported text.
  • Agents can continue to improve from their own feedback without introducing external annotations or oracle information.
  • The curriculum remains auditable even as the agent evolves, because every example carries its justifying span by construction.

Where Pith is reading between the lines

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

  • The same marginal-gain verifier could be tested on non-search tasks such as multi-step reasoning chains to see whether verifiability generalizes beyond retrieval.
  • If the marginal signal proves stable across different backbone models, the method might reduce the need for separate fact-checking stages in other self-improving systems.
  • The approach implies that verifiability can be embedded inside the data-generation loop itself rather than applied only after examples are created.

Load-bearing premise

The marginal accuracy gain from providing the evidence span can be measured reliably enough to produce a training signal that selects for genuinely helpful evidence.

What would settle it

A controlled run in which EVE-Agent training produces no measurable rise in evidence-grounded correctness, or in which the marginal accuracy scores fail to correlate with actual usefulness of the spans, would falsify the central claim.

Figures

Figures reproduced from arXiv: 2605.22905 by Yamato Arai, Yuma Ichikawa.

Figure 1
Figure 1. Figure 1: Evidence-verifiable self-evolving search agents. Existing self-evolving search agents (left) reward proposers using only a difficulty signal based on solver accuracy, without auditing the source evidence behind each question. EVE-Agent (right) requires the proposer to output a source-grounded evidence span and rewards it only when that evidence causally improves the solver’s answer accuracy, measured by th… view at source ↗
Figure 2
Figure 2. Figure 2: One Phase A iteration of EVE-Agent. The proposer generates a question–answer– evidence triple from the source document d. The solver attempts the question with the search tool, producing the difficulty reward of Eq. (2); in parallel, single-turn search-disabled rollouts of the solver with and without the evidence span produce the evidence verifier of Eq. (11). These two signals combine with the format and … view at source ↗
read the original abstract

Self-evolving agents should not train on examples they cannot justify. Data-free self-evolving search agents offer a scalable route to systems that generate their own questions, answer them, and improve from their own feedback without human annotations. Yet, without verifiable evidence, this loop can reward fluent but unsupported examples, turning the self-generated curriculum into an opaque and potentially unreliable training signal. We argue that evidence verifiability is a prerequisite for trustworthy self-evolution in search agents: each generated instance should include not only an answer but also a source-grounded span whose contribution to that answer can be measured. We introduce EVE-Agent, an Evidence-Verifiable Self-Evolving Agent that operationalizes this principle through a modification to the proposer--solver framework. The proposer generates a question, an answer, and a verbatim evidence span. An evidence verifier then rewards the span according to the marginal accuracy gain when the evidence is provided. This produces a training signal that favors evidence that genuinely helps answer the question, without requiring oracle answers, human labels, or external annotations. EVE-Agent leaves the backbone model, retriever, search tool, and optimization framework unchanged. Experiments show that EVE-Agent substantially improves evidence-grounded correctness over prior self-evolving search agents. The resulting curriculum is not merely self-generated but auditable by construction: each training example carries an inspectable source span that explains why it should be trusted.

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 / 1 minor

Summary. The paper introduces EVE-Agent, a modification to the proposer-solver framework for self-evolving search agents. The proposer generates a question, answer, and verbatim evidence span; an evidence verifier then rewards the span according to the marginal accuracy gain obtained when the span is supplied to the solver. This is claimed to yield a training signal that favors genuinely helpful evidence without oracle answers, human labels, or external annotations, resulting in substantially improved evidence-grounded correctness while leaving the backbone model, retriever, and optimization framework unchanged.

Significance. If the marginal-gain signal can be shown to be non-circular and to correlate with external correctness, the method would supply a practical route to auditable, self-generated curricula for agent training. The fact that the approach requires no changes to the underlying model or tools is a practical strength that could facilitate adoption.

major comments (2)
  1. [Abstract] Abstract: the claim that accuracy can be measured 'without requiring oracle answers' is load-bearing for the entire training signal. Because the target answer is itself produced by the proposer, any definition of accuracy must ultimately compare against that self-generated answer; the manuscript provides no mechanism (e.g., an independent consistency check or external retrieval) that would prevent the verifier from rewarding spans that merely reinforce internally consistent but factually incorrect outputs.
  2. [Abstract] Abstract: the reported 'substantial improvement' in evidence-grounded correctness is presented without any description of the datasets, baselines, or the precise formula used to compute marginal accuracy gain. Without these details it is impossible to determine whether the gain measurement avoids the circularity identified above or simply reproduces self-consistency.
minor comments (1)
  1. The abstract would be clearer if it included a one-sentence illustration of how the marginal gain is calculated for a concrete example.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for highlighting potential ambiguities in the abstract regarding the training signal and evaluation details. We address each comment below.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the claim that accuracy can be measured 'without requiring oracle answers' is load-bearing for the entire training signal. Because the target answer is itself produced by the proposer, any definition of accuracy must ultimately compare against that self-generated answer; the manuscript provides no mechanism (e.g., an independent consistency check or external retrieval) that would prevent the verifier from rewarding spans that merely reinforce internally consistent but factually incorrect outputs.

    Authors: The marginal accuracy gain is defined as the increase in the solver's rate of reproducing the proposer's generated answer when the verbatim evidence span is supplied versus when it is withheld. This construction avoids external oracle answers by using the proposer's output as the internal target. The evidence span itself is a verbatim excerpt retrieved from the source document, which supplies the auditability emphasized in the paper. We agree that the approach does not include an independent factual consistency check and could therefore reinforce internally consistent errors; the contribution focuses on evidence verifiability rather than absolute correctness. We will revise the abstract to qualify the claim accordingly. revision: partial

  2. Referee: [Abstract] Abstract: the reported 'substantial improvement' in evidence-grounded correctness is presented without any description of the datasets, baselines, or the precise formula used to compute marginal accuracy gain. Without these details it is impossible to determine whether the gain measurement avoids the circularity identified above or simply reproduces self-consistency.

    Authors: The abstract provides a high-level overview; the datasets, baselines, and the precise marginal-gain formula (solver accuracy with span minus solver accuracy without span) appear in Sections 3 and 4. To make the abstract self-contained on this point, we will add a short clause referencing the evaluation protocol and the internal-target definition of accuracy. revision: yes

Circularity Check

1 steps flagged

Evidence verifier reward defined via marginal gain against self-generated answers

specific steps
  1. self definitional [Abstract]
    "An evidence verifier then rewards the span according to the marginal accuracy gain when the evidence is provided. This produces a training signal that favors evidence that genuinely helps answer the question, without requiring oracle answers, human labels, or external annotations."

    Marginal accuracy gain requires a target answer to measure against. With no oracle or external label, the only target is the proposer's self-generated answer; thus the reward is defined as the increase in match to that same generated answer, making the 'verifiability' signal equivalent to self-consistency by construction rather than independent evidence quality.

full rationale

The paper's central mechanism claims to produce a training signal for 'evidence that genuinely helps answer the question' without oracles or external labels. However, the only available target for measuring 'accuracy' or 'marginal gain' is the proposer's own generated answer. This makes the reward signal self-referential by construction: spans are rewarded precisely to the extent they increase consistency with the internally generated answer, without any independent correctness criterion. The abstract explicitly states the method requires no oracle answers while defining the verifier reward in terms of accuracy gain, reducing the claimed 'evidence verifiability' to internal self-consistency. This is a self_definitional reduction at the load-bearing step.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only; no free parameters, axioms, or invented entities are stated or derivable from the provided text.

pith-pipeline@v0.9.0 · 5774 in / 1011 out tokens · 32980 ms · 2026-05-25T05:42:43.489218+00:00 · methodology

discussion (0)

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Reference graph

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