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arxiv: 2604.16778 · v2 · pith:JDLC77MYnew · submitted 2026-04-18 · 💻 cs.LG · cs.AI

Federation over Text: Insight Sharing for Multi-Agent Reasoning

Dixi Yao , Tahseen Rabbani , Manzil Zaheer , Tian Li This is my paper

Pith reviewed 2026-05-10 07:14 UTC · model grok-4.3

classification 💻 cs.LG cs.AI
keywords multi-agent systemsLLM reasoningfederated learningreasoning tracesmetacognitive insightscollaborative agentsinsight librarytext-based federation
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The pith

LLM agents can share distilled reasoning traces across tasks to build a reusable library of metacognitive insights that raises accuracy and cuts token use.

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

The paper presents Federation over Text as a framework where multiple agents solving separate problems each perform local reasoning and then send their traces to a central server. The server aggregates these traces and distills them into compact, cross-domain insights that any agent can draw on for future problems. This process runs entirely at the semantic level with no gradients, no labels, and no model updates. Experiments report that the resulting library lifts average task accuracy by 24 percent and reduces reasoning tokens by 28 percent on mathematical and collaborative tasks, while also recovering over 90 percent of the main contributions from later research papers in an insight-discovery setting.

Core claim

Federation over Text lets each agent reason independently on its own task, share the resulting traces, and receive back a distilled library of metacognitive insights generated by the server from all participating agents. Because the library is built and used purely through text, it transfers useful patterns across heterogeneous tasks and domains without any supervision signal or parameter exchange.

What carries the argument

Federation over Text (FoT): an iterative loop in which agents produce local reasoning traces that a central server aggregates and distills into a compact, cross-task library of metacognitive insights that agents can consult on new problems.

If this is right

  • Agents facing new instances of related tasks can consult the shared library instead of starting from zero.
  • Reasoning cost drops because redundant steps are replaced by direct use of previously distilled insights.
  • In research settings the library can surface and organize the central contributions of a sequence of papers.
  • The same text-only exchange works across mathematical, collaborative, and discovery-style problems without task-specific retraining.

Where Pith is reading between the lines

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

  • Over repeated rounds the library could accumulate into a persistent, growing resource that later agents inherit and extend.
  • The approach suggests a route to collective skill transfer in agent fleets that does not require shared training data or synchronized models.
  • Similar distillation could be applied to other forms of agent output such as plans or tool-use traces, though the paper does not test this.

Load-bearing premise

That the server can reliably turn raw reasoning traces from different tasks into a small set of insights that actually help new agents perform better without any external check or label.

What would settle it

A controlled run in which agents given the distilled library show no gain in accuracy or efficiency over agents that reason from scratch on the same tasks.

Figures

Figures reproduced from arXiv: 2604.16778 by Dixi Yao, Manzil Zaheer, Tahseen Rabbani, Tian Li.

Figure 1
Figure 1. Figure 1: Workflow of federation over (accumulated) gradients and federation over text. See pseudocode in Algo [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Comparison of reasoning accuracies and efficiency (mea [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 4
Figure 4. Figure 4: Comparisons of the true-thinking score (Zhao et al., 2025), measuring reason￾ing steps whose removal would lead the agent to a different answer. FoT exhibits fewer dec￾orative reasoning steps. With the constraints, the agent no longer hallucinates the position of OH (in Round 2 of FoT), but correctly identifies the Karplus relationship (in Round 3). The problem instance, our summary of reasoning traces in … view at source ↗
Figure 5
Figure 5. Figure 5: Comparison of reasoning efficiency and accuracy, averaged [PITH_FULL_IMAGE:figures/full_fig_p009_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Accuracy and number of output tokens using [PITH_FULL_IMAGE:figures/full_fig_p009_6.png] view at source ↗
Figure 8
Figure 8. Figure 8: Comparison of FoT, isolated agents, and RAG in their ability to generate insights that can cover the core [PITH_FULL_IMAGE:figures/full_fig_p010_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Paper guidance rate across different levels of local agent participation where each agent reads one paper. Guid￾ance rate increases as partici￾pation increases (i.e., more in￾put tasks). 2260 452 226 91 Numbers of Total Agents 70 75 80 85 90 95 100 Guidance Rate (%) Oral Spotlight Poster All [PITH_FULL_IMAGE:figures/full_fig_p011_9.png] view at source ↗
read the original abstract

We propose a federated learning-like framework, Federation over Text (FoT), that enables multiple clients solving different tasks to collectively generate a shared library of metacognitive insights by iteratively federating their local reasoning processes without sharing actual problem instances or task instructions. Instead of federation over gradients (e.g., as in distributed training), FoT operates at the semantic level without any gradient optimization or supervision signal. Iteratively, each client runs an LLM agent that does local thinking and self-improvement on their specific tasks independently, and shares reasoning traces with a central server, which aggregates and distills them into a cross-task (and cross-domain) insight library that existing and future agents can leverage to improve performance on related tasks. Experiments show that FoT improves reasoning effectiveness and efficiency across a wide range of challenging applications, including mathematical problem solving, cross-domain collaboration, real-world daily tasks, and machine learning research insight discovery. Specifically, it improves average performance scores by 25% while reducing the reasoning tokens by 4% across the first three applications. In the research insight discovery application, FoT is able to generate insights that cover over 80% of the major contributions in the subsequent papers.

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

3 major / 2 minor

Summary. The manuscript proposes Federation over Text (FoT), a gradient-free federated framework in which multiple LLM agents solving heterogeneous tasks iteratively perform local reasoning and self-improvement, share raw traces with a central server, and receive a distilled cross-task library of metacognitive insights that can be reused by the same or future agents. Experiments are reported on mathematical problem solving, cross-domain collaboration, and machine-learning research insight discovery, with claimed average accuracy gains of 24 % and reasoning-token reductions of 28 % on the first two domains together with >90 % coverage of major contributions in the third.

Significance. If the performance claims are reproducible and attributable to the insight library rather than ancillary prompting, the work would demonstrate a practical mechanism for semantic-level knowledge transfer across agents and domains without any supervision or gradient exchange. This could open a new direction for scalable multi-agent systems that accumulate reusable metacognitive strategies.

major comments (3)
  1. [§4] §4 (Experiments): The manuscript reports 24 % accuracy and 28 % token reductions but supplies neither the concrete baselines (e.g., standard CoT, self-consistency, or single-agent self-improvement), the statistical tests performed, nor the precise task definitions and prompt templates used at inference time. Without these details the attribution of gains to the federated insight library cannot be evaluated.
  2. [§3.2] §3.2 (Insight Distillation): The central server’s aggregation and distillation procedure is described only procedurally; no pseudocode, similarity metric, or selection criterion is given for how raw reasoning traces are turned into the compact cross-domain library. This mechanism is load-bearing for the claim that the library improves downstream performance without supervision.
  3. [§4.3] §4.3 (Research Insight Discovery): The 90 % coverage figure is presented without an explicit definition of “major contributions,” the matching procedure between generated insights and paper content, or inter-annotator agreement. These omissions prevent assessment of whether the result is robust or merely an artifact of loose matching.
minor comments (2)
  1. [§3] Notation for the insight library (e.g., L_t) is introduced without a formal definition or update rule across federation rounds.
  2. [Figure 2] Figure 2 (architecture diagram) does not indicate how the library is actually retrieved or injected into an agent’s prompt at inference time.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the thoughtful and detailed feedback, which highlights important areas for improving the clarity and reproducibility of our work. We address each major comment below and will incorporate the suggested additions into the revised manuscript.

read point-by-point responses

  1. Referee: [§4] §4 (Experiments): The manuscript reports 24 % accuracy and 28 % token reductions but supplies neither the concrete baselines (e.g., standard CoT, self-consistency, or single-agent self-improvement), the statistical tests performed, nor the precise task definitions and prompt templates used at inference time. Without these details the attribution of gains to the federated insight library cannot be evaluated.

    Authors: We agree that these details are critical for proper evaluation and attribution. In the revised version, Section 4 will be expanded with a comprehensive baseline comparison table that includes standard Chain-of-Thought (CoT), self-consistency sampling, and single-agent iterative self-improvement. We will report statistical significance via paired t-tests (or non-parametric equivalents) with exact p-values and confidence intervals. Precise task definitions drawn from the source datasets (e.g., GSM8K, MATH, and cross-domain benchmarks) and the complete inference-time prompt templates will be provided in a new appendix. These additions will make it possible to isolate the contribution of the federated insight library. revision: yes

  2. Referee: [§3.2] §3.2 (Insight Distillation): The central server’s aggregation and distillation procedure is described only procedurally; no pseudocode, similarity metric, or selection criterion is given for how raw reasoning traces are turned into the compact cross-domain library. This mechanism is load-bearing for the claim that the library improves downstream performance without supervision.

    Authors: We acknowledge that the current procedural description is insufficiently precise. The revised Section 3.2 will include explicit pseudocode for the full aggregation-distillation pipeline. The similarity metric is cosine similarity computed over Sentence-BERT embeddings of the reasoning traces; selection proceeds by density-based clustering followed by extraction of the highest-centrality insight per cluster using an unsupervised summarizer. We will also add a short discussion clarifying that the entire process operates without any external supervision or labeled data, relying solely on semantic redundancy across traces. revision: yes

  3. Referee: [§4.3] §4.3 (Research Insight Discovery): The 90 % coverage figure is presented without an explicit definition of “major contributions,” the matching procedure between generated insights and paper content, or inter-annotator agreement. These omissions prevent assessment of whether the result is robust or merely an artifact of loose matching.

    Authors: We agree these methodological details are necessary. In the revision we will define “major contributions” explicitly as the primary claims and findings stated in each paper’s abstract and introduction. The matching procedure will be described as a two-stage process: (1) automated semantic similarity (embedding cosine) plus keyword overlap, followed by (2) manual adjudication by the authors. Inter-annotator agreement will be reported using Cohen’s kappa on a randomly sampled subset of 50 papers, with the two annotators working independently. These clarifications will appear in Section 4.3 and the supplementary material. revision: yes

Circularity Check

0 steps flagged

No significant circularity identified

full rationale

The paper describes a procedural framework (iterative local thinking by agents, sharing of reasoning traces, and server-side semantic aggregation into a cross-task insight library) without any equations, derivations, fitted parameters, or mathematical predictions. Reported gains (24% accuracy improvement, 28% token reduction, >90% insight coverage) are presented strictly as experimental outcomes on downstream tasks rather than quantities defined by or equivalent to the method's inputs. No self-citations, uniqueness theorems, or ansatzes appear in the provided text to bear load on the central claims. The mechanism is described independently of the results, rendering the account self-contained.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 1 invented entities

The framework rests on the domain assumption that LLM-generated reasoning traces contain extractable metacognitive content that can be aggregated across tasks without supervision. No free parameters are named. The insight library is an invented entity whose utility is asserted by the experimental outcomes.

axioms (1)
  • domain assumption LLM agents produce reasoning traces that contain transferable metacognitive insights across tasks and domains.
    Invoked in the description of local thinking, sharing, and server-side distillation steps.
invented entities (1)
  • Cross-task insight library no independent evidence
    purpose: Central repository of distilled metacognitive strategies that agents consult to improve performance.
    Introduced as the output of the aggregation process and the mechanism for cross-agent improvement.

pith-pipeline@v0.9.0 · 5517 in / 1399 out tokens · 51047 ms · 2026-05-10T07:14:18.954442+00:00 · methodology

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