Paper Citation Record · LEDGER

TradingGPT: Multi-Agent System with Layered Memory and Distinct Characters for Enhanced Financial Trading Performance

As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 18 inbound Pith citation observations for arXiv:2309.03736.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2309.03736 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 18 of 18 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 18 of 18 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T14:02:22.596425Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-07-04T15:09:54.468733Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 56e4e5ba-4578-4db1-bd54-064c2bd88957 · inbound

A Survey on the Memory Mechanism of Large Language Model based Agents cites this paper.

A Survey on the Memory Mechanism of Large Language Model based Agents TradingGPT: Multi-Agent System with Layered Memory and Distinct Characters for Enhanced Financial Trading Performance

Reference 154

Resolution: verified exact; arxiv_id, observed 2026-05-15T07:21:39.627093Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 2a786ac2-118a-4e0e-a5af-1d02a255a2de · inbound

FinTSB: A Comprehensive and Practical Benchmark for Financial Time Series Forecasting cites this paper.

FinTSB: A Comprehensive and Practical Benchmark for Financial Time Series Forecasting TradingGPT: Multi-Agent System with Layered Memory and Distinct Characters for Enhanced Financial Trading Performance

Reference 38

Resolution: verified exact; arxiv_id, observed 2026-05-23T02:52:26.559899Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 7987977e-e1c9-4ac5-9707-c60ecfd717e7 · inbound

Large Language Model Agent: A Survey on Methodology, Applications and Challenges cites this paper.

Large Language Model Agent: A Survey on Methodology, Applications and Challenges TradingGPT: Multi-Agent System with Layered Memory and Distinct Characters for Enhanced Financial Trading Performance

Reference 294

Resolution: verified exact; arxiv_id, observed 2026-05-22T21:52:10.544056Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 44ab68b5-dc4f-4723-a284-5b32d5667c51 · inbound

From Human Memory to AI Memory: A Survey on Memory Mechanisms in the Era of LLMs cites this paper.

From Human Memory to AI Memory: A Survey on Memory Mechanisms in the Era of LLMs TradingGPT: Multi-Agent System with Layered Memory and Distinct Characters for Enhanced Financial Trading Performance

Reference 71

Resolution: verified exact; arxiv_id, observed 2026-05-17T11:05:09.744779Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 6fd76012-0cc6-4b3e-ac42-e9d55ef94401 · inbound

LLM-Powered AI Agent Systems and Their Applications in Industry cites this paper.

LLM-Powered AI Agent Systems and Their Applications in Industry TradingGPT: Multi-Agent System with Layered Memory and Distinct Characters for Enhanced Financial Trading Performance

Reference 24

Resolution: verified exact; arxiv_id, observed 2026-05-22T14:06:37.855203Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 00736b61-2dbf-4cdd-a8fc-d78634cf2ab1 · inbound

Agents Require Metacognitive and Strategic Reasoning to Succeed in the Coming Labor Markets cites this paper.

Agents Require Metacognitive and Strategic Reasoning to Succeed in the Coming Labor Markets TradingGPT: Multi-Agent System with Layered Memory and Distinct Characters for Enhanced Financial Trading Performance

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-07T14:02:22.596425Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1962d965-c2a9-44db-8c25-6967305bcde5 · inbound

Efficient Leave-one-out Approximation in LLM Multi-agent Debate Based on Introspection cites this paper.

Efficient Leave-one-out Approximation in LLM Multi-agent Debate Based on Introspection TradingGPT: Multi-Agent System with Layered Memory and Distinct Characters for Enhanced Financial Trading Performance

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-07T13:16:20.583371Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c9138890-dced-4e21-bf5e-b70bcb61d2fc · inbound

Doc2Agent: Scalable Generation of Tool-Using Agents from API Documentation cites this paper.

Doc2Agent: Scalable Generation of Tool-Using Agents from API Documentation TradingGPT: Multi-Agent System with Layered Memory and Distinct Characters for Enhanced Financial Trading Performance

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-06T23:05:09.554803Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 511a3013-1482-450d-a059-7669b3110af9 · inbound

To Trade or Not to Trade: An Agentic Approach to Estimating Market Risk Improves Trading Decisions cites this paper.

To Trade or Not to Trade: An Agentic Approach to Estimating Market Risk Improves Trading Decisions TradingGPT: Multi-Agent System with Layered Memory and Distinct Characters for Enhanced Financial Trading Performance

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-06T18:22:15.824389Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 274edb4e-0158-4d73-875f-a848a24096be · inbound

FinTeam: A Multi-Agent Collaborative Intelligence System for Comprehensive Financial Scenarios cites this paper.

FinTeam: A Multi-Agent Collaborative Intelligence System for Comprehensive Financial Scenarios TradingGPT: Multi-Agent System with Layered Memory and Distinct Characters for Enhanced Financial Trading Performance

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-06T20:02:14.160845Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 37b183e5-1487-4e34-9e3c-c2a9bbb783fd · inbound

ContestTrade: A Multi-Agent Trading System Based on Internal Contest Mechanism cites this paper.

ContestTrade: A Multi-Agent Trading System Based on Internal Contest Mechanism TradingGPT: Multi-Agent System with Layered Memory and Distinct Characters for Enhanced Financial Trading Performance

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-06T10:12:19.808982Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 483fdc68-ab5a-4950-bf88-1fa0b87de80d · inbound

Forecasting Commodity Price Shocks Using Temporal and Semantic Fusion of Prices Signals and Agentic Generative AI Extracted Economic News cites this paper.

Forecasting Commodity Price Shocks Using Temporal and Semantic Fusion of Prices Signals and Agentic Generative AI Extracted Economic News TradingGPT: Multi-Agent System with Layered Memory and Distinct Characters for Enhanced Financial Trading Performance

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-06T14:35:42.123602Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 26c9f5c0-f458-4a2d-8cdc-68ab1cfd9e97 · inbound

SHARP: A Self-Evolving Human-Auditable Rubric Policy for Financial Trading Agents cites this paper.

SHARP: A Self-Evolving Human-Auditable Rubric Policy for Financial Trading Agents TradingGPT: Multi-Agent System with Layered Memory and Distinct Characters for Enhanced Financial Trading Performance

Reference 12

Resolution: verified exact; arxiv_id, observed 2026-05-11T05:06:04.018699Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 03484088-9cb1-4c0a-a8f8-6ff2bf69d3f1 · inbound

Is a team only as strong as its weakest link? Quantifying the short-board effect with AI Agents cites this paper.

Is a team only as strong as its weakest link? Quantifying the short-board effect with AI Agents TradingGPT: Multi-Agent System with Layered Memory and Distinct Characters for Enhanced Financial Trading Performance

Reference 19

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T02:30:55.347619Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation bbce3a00-3bfa-4ebc-b064-d9c2b5006767 · inbound

MadEvolve: Evolutionary Optimization of Trading Systems with Large Language Models cites this paper.

MadEvolve: Evolutionary Optimization of Trading Systems with Large Language Models TradingGPT: Multi-Agent System with Layered Memory and Distinct Characters for Enhanced Financial Trading Performance

Reference 7

Resolution: verified exact; arxiv_id, observed 2026-05-25T05:25:23.362382Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 7e4a5570-ffe1-4018-8f51-b4309649f91d · inbound

PandaAI: A Practical Agent CQ2 for Neuro-symbolic Data Analysis And Integrated Decision-Making in Quantitative Finance cites this paper.

PandaAI: A Practical Agent CQ2 for Neuro-symbolic Data Analysis And Integrated Decision-Making in Quantitative Finance TradingGPT: Multi-Agent System with Layered Memory and Distinct Characters for Enhanced Financial Trading Performance

Reference 13

Resolution: verified exact; arxiv_id, observed 2026-07-02T16:17:09.260277Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 1fd1fb4b-6568-49b3-bda2-1260af3cd6f8 · inbound

Beyond Agent Architecture: Execution Assumptions and Reproducibility in LLM-Based Trading Systems cites this paper.

Beyond Agent Architecture: Execution Assumptions and Reproducibility in LLM-Based Trading Systems TradingGPT: Multi-Agent System with Layered Memory and Distinct Characters for Enhanced Financial Trading Performance

Reference 2

Resolution: verified exact; arxiv_id, observed 2026-07-02T21:37:25.623496Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation bb68ee89-2853-4d43-a932-058f03188a8a · inbound

AlgoEvolve: LLM-driven Meta-evolution of Algorithmic Trading Programs cites this paper.

AlgoEvolve: LLM-driven Meta-evolution of Algorithmic Trading Programs TradingGPT: Multi-Agent System with Layered Memory and Distinct Characters for Enhanced Financial Trading Performance

Reference 17

Resolution: verified exact; arxiv_id, observed 2026-07-04T15:09:54.470801Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.