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arxiv: 2606.06960 · v1 · pith:2MK65EDJnew · submitted 2026-06-05 · 💻 cs.CL

Tree-of-Experience: A Structured Experience-Management Solution for Self-Evolving Agents under Low-Repetition and Implicit-Reward Environments

Zihao Deng , Yining Zhu , Leiming Wang , Jingfei Lu , Junbo Wang , Chuncheng Ran , Yu Yang , Dixuan Yang
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Jikun Shen
This is my paper

Pith reviewed 2026-06-27 22:22 UTC · model grok-4.3

classification 💻 cs.CL
keywords self-evolving agentsexperience managementimplicit rewardslow-repetition tasksLLM agentsfinancial sentimentTree-of-Experienceagent benchmarks
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The pith

A tree-structured experience system lets LLM agents improve where tasks rarely repeat and feedback is delayed and noisy.

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

The paper studies LLM agents that must evolve without repeated tasks or clear immediate rewards, a setting where past experience is hard to apply and feedback arrives late and noisy. It creates FinEvolveBench, a benchmark that turns daily financial news into predictions whose quality is judged by later excess returns. The authors introduce Tree-of-Experience, which stores, retrieves, validates, and revises experiences inside a tree rather than a flat list or vector store. Experiments find that ordinary experience mechanisms usually fail to beat a no-experience baseline, yet the tree method produces higher performance across the benchmark tasks. The result indicates that the way experience is organized can determine whether agents make progress in these difficult conditions.

Core claim

In low-repetition implicit-reward environments, general-purpose experience mechanisms do not consistently outperform no-experience baselines, while Tree-of-Experience achieves stronger overall performance by organizing, retrieving, validating, and updating agent experience.

What carries the argument

Tree-of-Experience (ToE), a structured method that organizes, retrieves, validates, and updates agent experience inside a tree.

If this is right

  • General-purpose experience mechanisms often match or fall below no-experience performance in the studied setting.
  • Tree-of-Experience delivers measurably stronger results on the financial sentiment benchmark.
  • Structured organization of experience becomes necessary once tasks do not repeat and rewards stay implicit.
  • Without such structure, simply accumulating experience provides little or no advantage to self-evolving agents.

Where Pith is reading between the lines

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

  • The same tree organization could be tested on non-financial tasks such as medical decision sequences or adaptive planning where repetition is low.
  • Agents using Tree-of-Experience might maintain useful memory over longer horizons without explicit reward signals.
  • Combining the tree with retrieval-augmented generation could further reduce the impact of noisy outcome feedback.

Load-bearing premise

FinEvolveBench, which scores news-based predictions by later excess returns, is a valid general model of low-repetition implicit-reward environments.

What would settle it

An experiment in which Tree-of-Experience fails to outperform both no-experience and generic experience baselines on a second low-repetition implicit-reward task outside finance.

Figures

Figures reproduced from arXiv: 2606.06960 by Chuncheng Ran, Dixuan Yang, Jikun Shen, Jingfei Lu, Junbo Wang, Leiming Wang, Yining Zhu, Yu Yang, Zihao Deng.

Figure 1
Figure 1. Figure 1: Overview of FINEVOLVEBENCH. FINEVOLVEBENCH consists of a market environment dataset with implicit rewards and a workflow that converts a backbone LLM into an agent equipped with a general self-evolution mechanism. agents in a broader market-wide information environment with dense, noisy, and weakly structured public opinion signals. 3 FinEvolveBench In this section, we present FINEVOLVEBENCH, a benchmark c… view at source ↗
Figure 2
Figure 2. Figure 2: Overview of TREE-OF-EXPERIENCE. The upper panel shows the end-to-end learning workflow: given a task τt, the agent retrieves an experience context mctx from the experience memory E, generates the output yt, and updates the utility values Q of the retrieved experiences according to the environment reward R. The lower-left panel illustrates the two-stage experience query process, where a predefined experienc… view at source ↗
Figure 3
Figure 3. Figure 3: Main results on the 20-trading-day prediction [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Industry distribution of the selected Shenwan [PITH_FULL_IMAGE:figures/full_fig_p011_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: News corpus profile of FINEVOLVEBENCH, including monthly source distribution, major industry coverage, importance score distribution, and primary topic distribution. 2024-01 2024-02 2024-03 2024-04 2024-05 2024-06 2024-07 2025-01 2025-02 2025-03 2025-04 2025-05 2025-06 2025-07 2025-08 2025-09 2025-10 2025-11 2025-12 2026-01 2026-02 2026-03 2026-04 2026-05 Month Computer Electronics Petrochem. Transport Non… view at source ↗
Figure 6
Figure 6. Figure 6: Industry-month news coverage heatmap. Col [PITH_FULL_IMAGE:figures/full_fig_p013_6.png] view at source ↗
read the original abstract

Experience-based self-evolution is crucial for LLM agents, but existing benchmarks often assume explicit goals, stable task patterns, and clear feedback. We study a more challenging setting: low-repetition tasks with implicit rewards, where past experience is difficult to reuse and feedback is delayed, noisy, and outcome-level. We introduce \textsc{FinEvolveBench}, a temporally controlled benchmark for financial sentiment prediction that links daily news-driven predictions to future excess returns. We further propose Tree-of-Experience (ToE), a structured experience-management method that organizes, retrieves, validates, and updates agent experience. Experiments show that general-purpose experience mechanisms do not consistently outperform no-experience baselines, while ToE achieves stronger overall performance. These results highlight the importance of structured experience management for self-evolving agents in implicit-reward environments.

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 FinEvolveBench, a temporally controlled benchmark linking daily news-driven financial sentiment predictions to future excess returns, to study LLM agent self-evolution in low-repetition tasks with implicit, delayed, and noisy rewards. It proposes Tree-of-Experience (ToE), a structured experience-management approach that organizes, retrieves, validates, and updates experiences, and reports experiments showing that general-purpose experience mechanisms do not consistently outperform no-experience baselines while ToE achieves stronger overall performance.

Significance. If the experimental results hold under a benchmark that genuinely enforces low repetition and implicit rewards, the work provides concrete evidence that unstructured experience mechanisms are insufficient in such regimes and that structured management yields measurable gains. This could inform the design of self-evolving agents for domains like finance where task patterns do not repeat and feedback is outcome-level only.

major comments (2)
  1. [§3, §4] §4 (Experiments) and §3 (FinEvolveBench): the central claim that general experience mechanisms fail to beat no-experience baselines while ToE succeeds rests on the benchmark enforcing minimal transferable patterns across days and purely outcome-level rewards. The manuscript must include explicit analysis (e.g., overlap statistics on news features or reward-signal leakage checks) demonstrating that daily tasks remain low-repetition and that excess-return computation introduces no intermediate explicit signals; without this, the performance gap could be an artifact of a standard prediction task rather than evidence for the stated regime.
  2. [§4] §4.1–4.3: the abstract and experimental summary state that ToE outperforms baselines, yet no information is supplied on number of runs, statistical tests, variance across seeds, or precise baseline implementations (e.g., how retrieval-augmented or memory-augmented agents are instantiated). These omissions prevent verification that the reported superiority is robust and load-bearing for the claim.
minor comments (1)
  1. [§2] Notation for experience nodes and validation steps in the ToE description could be formalized with pseudocode or a diagram to improve reproducibility.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive comments, which help clarify the requirements for validating our benchmark and experimental claims. We address each major point below and will revise the manuscript to incorporate the requested analyses and details.

read point-by-point responses

  1. Referee: [§3, §4] §4 (Experiments) and §3 (FinEvolveBench): the central claim that general experience mechanisms fail to beat no-experience baselines while ToE succeeds rests on the benchmark enforcing minimal transferable patterns across days and purely outcome-level rewards. The manuscript must include explicit analysis (e.g., overlap statistics on news features or reward-signal leakage checks) demonstrating that daily tasks remain low-repetition and that excess-return computation introduces no intermediate explicit signals; without this, the performance gap could be an artifact of a standard prediction task rather than evidence for the stated regime.

    Authors: We agree that explicit validation of the benchmark's low-repetition and implicit-reward properties is necessary to support the central claim. In the revised version, we will add overlap statistics (e.g., average cosine similarity and Jaccard index on daily news embeddings and entity features across consecutive days) and explicit checks confirming that excess-return labels provide no intermediate signals or leakage. These additions will be placed in §3 and referenced in §4 to demonstrate that transferable patterns are minimal. revision: yes

  2. Referee: [§4] §4.1–4.3: the abstract and experimental summary state that ToE outperforms baselines, yet no information is supplied on number of runs, statistical tests, variance across seeds, or precise baseline implementations (e.g., how retrieval-augmented or memory-augmented agents are instantiated). These omissions prevent verification that the reported superiority is robust and load-bearing for the claim.

    Authors: We acknowledge these omissions limit verifiability. The revised manuscript will expand §4.1–4.3 with: (i) number of independent runs (5 random seeds), (ii) statistical tests (paired t-tests with p-values and effect sizes), (iii) variance/std across seeds for all metrics, and (iv) precise baseline instantiations, including retrieval parameters, memory buffer sizes, and update rules for the retrieval-augmented and memory-augmented agents. This will be summarized in a new experimental-details subsection. revision: yes

Circularity Check

0 steps flagged

No circularity; claims rest on external experimental comparisons

full rationale

The paper introduces FinEvolveBench and the ToE method, then reports experimental results comparing experience mechanisms against no-experience baselines. No equations, derivations, or first-principles results are presented that reduce to fitted parameters, self-definitions, or self-citation chains. Performance claims are grounded in benchmark outcomes rather than quantities defined in terms of themselves. The benchmark construction and method are described independently of the reported metrics, satisfying the criteria for a self-contained experimental contribution.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 1 invented entities

The central claim rests on the domain assumption that structured experience management provides measurable benefits in implicit-reward settings and on the validity of the new benchmark as a proxy for those settings.

axioms (1)
  • domain assumption Experience from past tasks can be structured to improve future performance in low-repetition implicit-reward environments
    This premise underpins both the benchmark design and the proposal of ToE.
invented entities (1)
  • Tree-of-Experience no independent evidence
    purpose: Organize, retrieve, validate, and update agent experience in a tree structure
    Newly proposed method introduced to address the identified limitations of general experience mechanisms.

pith-pipeline@v0.9.1-grok · 5701 in / 1283 out tokens · 28777 ms · 2026-06-27T22:22:28.097700+00:00 · methodology

discussion (0)

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Reference graph

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    The Statistic Value Market universe 31 SW first-level industry indices Processed span 2024-01-01 to 2026-05-03 Online evaluation span 2025-01-01 to 2026-03-31 Trading days in evaluation 300 Training split None News articles 177,324 Active news days 696 Avg. news / day 254.78 Avg. news / industry / day 6.08 Prediction horizons 1, 5, 10, 20 trading days Tab...

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