Thought-Retriever: Don't Just Retrieve Raw Data, Retrieve Thoughts for Memory-Augmented Agentic Systems

Ge Liu; Guanyu Lin; Jiaxuan You; Pengrui Han; Tao Feng

arxiv: 2604.12231 · v1 · submitted 2026-04-14 · 💻 cs.CL · cs.IR

Thought-Retriever: Don't Just Retrieve Raw Data, Retrieve Thoughts for Memory-Augmented Agentic Systems

Tao Feng , Pengrui Han , Guanyu Lin , Ge Liu , Jiaxuan You This is my paper

Pith reviewed 2026-05-10 16:29 UTC · model grok-4.3

classification 💻 cs.CL cs.IR

keywords thought retrievalmemory-augmented agentsself-evolving memoryretrieval-augmented generationlong-context LLMsAcademicEval

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The pith

Retrieving thoughts from past queries lets LLMs handle arbitrarily long external data.

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

Thought-Retriever equips LLM agents with a memory made from their own past intermediate responses rather than raw external data. The system filters those responses to remove redundancy, organizes them, and retrieves the relevant ones when facing new queries. This design removes the hard limit on how much external knowledge an LLM can use at once. The authors created AcademicEval, a benchmark based on real academic papers, to test long-context usage. Tests show the method improves results and that the memory gets better with more interactions while favoring deeper thoughts for abstract questions.

Core claim

By letting LLMs store and retrieve their own processed thoughts from previous tasks, Thought-Retriever creates a self-evolving memory that grows with use. This memory replaces direct retrieval of raw data chunks, allowing the model to condition its answers on far more external information than its context window permits. The process involves an LLM filtering and organizing thoughts, then selecting the right ones for each new query.

What carries the argument

The thought memory, built from filtered intermediate responses generated while solving past queries, which the LLM retrieves to inform answers on new tasks.

If this is right

LLM agents can draw on arbitrarily large external knowledge bases without context length constraints.
The memory system improves its effectiveness as the agent solves more queries over time.
Answers benefit from deeper prior thoughts when dealing with abstract questions.
Results improve on tasks that require using information from very long documents.

Where Pith is reading between the lines

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

Agents using this approach might develop expertise in specific areas through repeated interactions without additional training.
The method could extend to other types of AI systems that generate intermediate reasoning steps.
Future work could explore adding mechanisms to verify the accuracy of stored thoughts.

Load-bearing premise

An LLM can reliably identify and filter out meaningless or redundant thoughts while preserving all critical information during organization and retrieval.

What would settle it

A drop in performance or loss of key information when the number of stored thoughts grows large, or when the system fails to improve after processing additional queries.

Figures

Figures reproduced from arXiv: 2604.12231 by Ge Liu, Guanyu Lin, Jiaxuan You, Pengrui Han, Tao Feng.

**Figure 1.** Figure 1: Why Thought-Retriever helps. (a) A standard RALM is limited by the number of retrieved chunks. The retrieved data fails to cover all the necessary data chunks (red chunks) for a user query. (b) A hierarchical RALM retrieves summaries Si , generated independently from user queries, which could improve recall at the cost of lower precision. (c) Thought-Retriever leverages past LLM thoughts collected from ans… view at source ↗

**Figure 2.** Figure 2: Thought-Retriever Framework. (a) Thought retrieval: Upon receiving a user query, Thought-Retriever retrieves top-K data chunks from the mixture of external knowledge and thought memory based on embedding similarity; (b) Answer and confidence generation: The LLM generates the answer for the user query based on the retrieved data chunks; (c) Thought generation: The LLM further generates thoughts and its conf… view at source ↗

**Figure 3.** Figure 3: Thought and Confidence Generation Prompt. This prompt is used for Thought and Confidence Generation as described in Section 2.3. It evaluates whether the answer is valid and meaningful, and then summarizes the query and answer into a thought. maximum embedding similarity between Ti and all existing items in the memory. If this maximum score exceeds the threshold ϵ, we flag the thought as redundant (settin… view at source ↗

**Figure 4.** Figure 4: Thoughts from other LLMs help respond without fact. It presents an illustrative example in which our LLM communicates with four other LLMs, each an expert in a different field. These expert LLMs are assigned specific roles (e.g., doctor) with different background knowledge. Our LLM is then able to rapidly learn from their thoughts and incorporate them as external knowledge. publication of new papers. We fu… view at source ↗

**Figure 5.** Figure 5: Deeper thoughts help abstract queries. This figure illustrates the correlation between six questions, categorized by their level of abstraction as evaluated by expert LLM (x-axis), and the abstraction level of the corresponding retrieved information (y-axis). The questions are grouped into three categories: high abstraction (top 2 questions), medium abstraction, and low abstraction. Keywords from each ques… view at source ↗

**Figure 6.** Figure 6: Thought-Retriever can indeed help LLM self-evolve after solving more user queries. It illustrates that the performance of LLM across two datasets shows an upward trend as the number of thoughts increases. given the limitation of retrieving only 8 chunks of information at a time. We plotted the findings in [PITH_FULL_IMAGE:figures/full_fig_p011_6.png] view at source ↗

**Figure 7.** Figure 7: Contriever and thoughts filtering are suitable for Thought-Retriever. Ablation study of 6 methods on two datasets helps us decide on important components of Thought-Retriever. 0.2 0.4 0.6 0.8 Recall 0.2 0.4 0.6 0.8 Precision BM25 TF-IDF Contriever DPR DRAGON Thought Retriever Model Type Heuristic-based Deep-Learning-based Thought-Retriever [PITH_FULL_IMAGE:figures/full_fig_p012_7.png] view at source ↗

**Figure 8.** Figure 8: Thought-Retriever performs better in balancing recall and precision. The dotted line indicates the exact balance between precision and recall. The closer the dotted line is, the better the balance is. 5 Additional Related Works LLM Agent Memory and Experiences. Autonomous agents rely on persistent memory mechanisms to maintain coherence over extended interactions, effectively emulating the concept of exper… view at source ↗

**Figure 9.** Figure 9: AcademicEval Usage Instructions. This figure provides a visualization of the usage instructions for the AcademicEval dataset, as described in Section A.2, to aid understanding. retriever. Then, the LLM would generate an abstract based on the retrieved information using the prompt in [PITH_FULL_IMAGE:figures/full_fig_p019_9.png] view at source ↗

**Figure 10.** Figure 10: Prompt for Writing Abstracts. This prompt was used in our experiment to ask the LLM to write an abstract based on the retrieved information. We provided in-context instructions to guide the LLM in producing higher-quality responses. Create a concise, cohesive summary that encapsulates the key points and themes from the following five distinct abstracts. The summary should integrate the main ideas from eac… view at source ↗

**Figure 11.** Figure 11: Abstract Multi Ground Truth Prompt. This prompt was used in our experiment on the Academic-abstract-multi dataset. Specifically, for each data entry, we summarize the abstracts of five papers in the entry to create the ground truth. To ensure high-quality generation, we utilized GPT-4o as the expert LLM to synthesize these summaries based on the provided prompt. B Baseline Details First, we consider 2 heu… view at source ↗

**Figure 12.** Figure 12: Prompt for Writing Related Works. This prompt was used in our experiment to ask the LLM to write a related work section based on the original paper’s abstract and the retrieved related materials. We also provided an example of in-context learning to enable the LLM to perform more effectively on this challenging task. Second, we select 4 deep learning-based retrievers: (3) Contriever (Izacard et al., 2022)… view at source ↗

**Figure 13.** Figure 13: User Query Formation Prompt. This figure presents the prompt used to model real-world user queries. Specifically, it includes two methods: template-based query formation, where general question templates are created to be suitable for a wide range of papers, and LLM-based query formation, where this prompt is used to ask an LLM to generate diverse queries. Given the original abstract:{original},and given … view at source ↗

**Figure 14.** Figure 14: AI Evaluation Prompt. This prompt is used for the AI Evaluation metric Win Rate, as described in Section 4. Given two generated answers and the ground truth answer, we ask the expert LLM to determine which generated answer aligns more closely with the ground truth. that leverages large-scale pre-training to achieve superior semantic understanding and retrieval performance across diverse tasks. Third, we i… view at source ↗

**Figure 15.** Figure 15: Qualitative Example - Original Abstract and Abstract Generated by ThoughtRetriever. This figure presents example outputs from different methods using data from the AcademicEvalabstract-single dataset. Specifically, it shows the original abstract alongside the abstract generated by Thought-Retriever, accompanied by a comment from an expert LLM. Comparison examples generated by DPR and TF-IDF are shown in… view at source ↗

**Figure 16.** Figure 16: Qualitative Example - Abstracts Generated by DPR and TF-IDF. This figure presents example outputs using data from the AcademicEval-abstract-single dataset, generated by traditional methods: DPR and TF-IDF. We also include comments from an expert LLM. The original abstract and the abstract generated by our Thought-Retriever can be found in [PITH_FULL_IMAGE:figures/full_fig_p037_16.png] view at source ↗

**Figure 17.** Figure 17: Qualitative Example - Abstracts Generated by Long Context Model. This figure presents example outputs using data from the AcademicEval-abstract-single dataset, generated by the long context model Nours Hermes - 32k. The original abstract and the abstract generated by our ThoughtRetriever can be found in [PITH_FULL_IMAGE:figures/full_fig_p038_17.png] view at source ↗

**Figure 18.** Figure 18: Arxiv Copilot Demo. This figure shows the demo built based on our proposed ThoughtRetriever, which is publicly available on Hugging Face. It offers personalized academic services, aiming to test the real-world robustness of our algorithm and provide social benefits. 39 [PITH_FULL_IMAGE:figures/full_fig_p039_18.png] view at source ↗

read the original abstract

Large language models (LLMs) have transformed AI research thanks to their powerful internal capabilities and knowledge. However, existing LLMs still fail to effectively incorporate the massive external knowledge when interacting with the world. Although retrieval-augmented LLMs are proposed to mitigate the issue, they are still fundamentally constrained by the context length of LLMs, as they can only retrieve top-K raw data chunks from the external knowledge base which often consists of millions of data chunks. Here we propose Thought-Retriever, a novel model-agnostic algorithm that helps LLMs generate output conditioned on arbitrarily long external data, without being constrained by the context length or number of retrieved data chunks. Our key insight is to let an LLM fully leverage its intermediate responses generated when solving past user queries (thoughts), filtering meaningless and redundant thoughts, organizing them in thought memory, and retrieving the relevant thoughts when addressing new queries. This effectively equips LLM-based agents with a self-evolving long-term memory that grows more capable through continuous interaction. Besides algorithmic innovation, we further meticulously prepare a novel benchmark, AcademicEval, which requires an LLM to faithfully leverage ultra-long context to answer queries based on real-world academic papers. Extensive experiments on AcademicEval and two other public datasets validate that Thought-Retriever remarkably outperforms state-of-the-art baselines, achieving an average increase of at least 7.6% in F1 score and 16% in win rate across various tasks. More importantly, we further demonstrate two exciting findings: (1) Thought-Retriever can indeed help LLM self-evolve after solving more user queries; (2) Thought-Retriever learns to leverage deeper thoughts to answer more abstract user queries.

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.

Desk Editor's Note private letter to a colleague

Thought-Retriever stores LLM-generated thoughts instead of raw chunks to build agent memory, but the gains may come from selective filtering rather than faithful long-context handling.

read the letter

The core move here is to replace top-k chunk retrieval with a memory of past thoughts that the model itself generates, filters, and later pulls from. That is a clean shift from standard RAG and gives agents something that can grow across sessions without immediately blowing up the context window. The new AcademicEval benchmark, built on real academic papers, is a useful addition because it forces models to synthesize across long sources rather than answer from short snippets. The reported lifts in F1 and win rate on that set plus two public datasets are the main empirical result, and the self-evolution observation (performance improves as more thoughts accumulate) is worth checking further. The second finding, that deeper thoughts help with more abstract queries, also lines up with how people actually use long documents. These pieces are straightforward to reproduce if the code and filtering prompts are released. The main weakness is that filtering and organization are left to the same LLM whose context limits are the original problem. Without an explicit retention check (for example, how many key facts from the source papers survive into the stored thoughts), the performance numbers could reflect the model simply answering better on the subset it kept rather than true compression of arbitrary-length input. The abstract gives no numbers on false-negative rates during filtering or on how memory size scales, so it is hard to judge whether the method stays reliable as the thought store grows. This is the kind of paper that belongs in a reading group focused on agent memory and retrieval. People working on long-context agents or RAG variants will find the benchmark and the basic algorithm useful even if they end up tightening the evaluation. It is solid enough to send to peer review; the idea is implementable and the benchmark has clear value, but referees will need to see the filtering details and retention metrics before the central claim can be taken as settled.

Referee Report

3 major / 3 minor

Summary. The manuscript proposes Thought-Retriever, a model-agnostic algorithm for LLM agents that generates intermediate 'thoughts' while solving past queries, filters out meaningless or redundant ones, organizes the remainder into a self-evolving thought memory, and retrieves relevant thoughts to condition responses on new queries. This is claimed to enable faithful use of arbitrarily long external data without being limited by LLM context length or top-K chunk retrieval. The authors introduce the AcademicEval benchmark requiring ultra-long context use on real academic papers, report average gains of at least 7.6% F1 and 16% win rate over baselines on AcademicEval plus two public datasets, and present two findings on self-evolution and deeper-thought usage for abstract queries.

Significance. If the filtering and organization steps can be shown to preserve necessary information without systematic omissions or distortions, the approach would represent a meaningful step toward scalable, evolving memory for agentic systems that operate over unbounded external corpora. The creation of AcademicEval as a faithfulness-oriented benchmark for long academic documents is a constructive contribution, and the reported self-evolution results, if rigorously supported, would be of interest for continual-learning research in LLMs.

major comments (3)

[Experiments section (AcademicEval results)] Experiments section (AcademicEval results): the reported F1 and win-rate improvements are presented without any information-retention metric (e.g., fact-level recall or span coverage between original paper content and the filtered thought set actually retrieved at inference time). This leaves open the possibility that gains arise from selective retention rather than faithful conditioning on the full ultra-long source.
[Method description (Thought-Retriever pipeline)] Method description (Thought-Retriever pipeline): the central assumption that the same LLM can reliably filter redundant/meaningless thoughts while preserving all query-relevant facts is load-bearing for the 'arbitrarily long external data' claim, yet no ablation or error-propagation analysis is provided on how filtering omissions or hallucinations affect downstream answer quality.
[Results on self-evolution] Results on self-evolution: the claim that the system 'self-evolves' after processing more queries lacks controls for confounding factors such as simply accumulating more raw thoughts or changes in retrieval hyperparameters, making it difficult to attribute improvements specifically to the thought-memory mechanism.

minor comments (3)

[Abstract] Abstract: the statement 'at least 7.6% in F1 score and 16% in win rate' would be clearer if the exact per-task averages, standard deviations, and number of runs were reported.
The manuscript would benefit from a diagram or pseudocode explicitly showing the data flow among thought generation, filtering criteria, memory organization, and retrieval steps.
Notation for the thought memory structure (e.g., whether it is flat, hierarchical, or indexed by query type) is introduced informally and could be formalized in a dedicated subsection.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive feedback, which identifies key areas where additional evidence can strengthen our claims regarding faithfulness and self-evolution. We address each major comment below and outline the revisions we will incorporate.

read point-by-point responses

Referee: Experiments section (AcademicEval results): the reported F1 and win-rate improvements are presented without any information-retention metric (e.g., fact-level recall or span coverage between original paper content and the filtered thought set actually retrieved at inference time). This leaves open the possibility that gains arise from selective retention rather than faithful conditioning on the full ultra-long source.

Authors: We agree that an explicit information-retention metric would provide stronger support for the claim of faithful conditioning on ultra-long sources. In the revised manuscript, we will add a fact-level recall metric (and optionally span coverage) computed between the original paper content and the filtered thought set retrieved at inference time on AcademicEval. This analysis will quantify preservation of necessary information and rule out systematic selective retention as the source of gains. revision: yes
Referee: Method description (Thought-Retriever pipeline): the central assumption that the same LLM can reliably filter redundant/meaningless thoughts while preserving all query-relevant facts is load-bearing for the 'arbitrarily long external data' claim, yet no ablation or error-propagation analysis is provided on how filtering omissions or hallucinations affect downstream answer quality.

Authors: The filtering assumption is indeed central. While the end-to-end results demonstrate net benefits, we acknowledge the absence of targeted ablation on error propagation. In the revision, we will include an error-propagation analysis: we will introduce controlled synthetic omissions and hallucinations into the thought set on a subset of AcademicEval queries and measure the resulting degradation in answer quality, thereby quantifying sensitivity to filtering imperfections. revision: yes
Referee: Results on self-evolution: the claim that the system 'self-evolves' after processing more queries lacks controls for confounding factors such as simply accumulating more raw thoughts or changes in retrieval hyperparameters, making it difficult to attribute improvements specifically to the thought-memory mechanism.

Authors: Our self-evolution experiments compare the full Thought-Retriever pipeline against a raw-thought accumulation baseline while holding retrieval hyperparameters fixed; however, we agree that explicit controls and clearer exposition would reduce ambiguity. In the revised version, we will add a dedicated subsection that (i) fixes all retrieval hyperparameters across conditions, (ii) reports performance curves for both the organized thought memory and an unfiltered raw-thought accumulator, and (iii) includes statistical tests isolating the contribution of the filtering/organization steps. revision: partial

Circularity Check

0 steps flagged

No significant circularity detected

full rationale

The paper describes an algorithmic pipeline for thought generation, filtering, organization, and retrieval rather than any mathematical derivation or first-principles proof. Claims of improved performance rest on empirical results across AcademicEval (a benchmark prepared by the authors) and two public datasets, with no equations, fitted parameters renamed as predictions, or load-bearing self-citations that reduce the central result to its own inputs by construction. The method is presented as model-agnostic and self-evolving through interaction, but its validity is treated as an empirical question rather than a definitional tautology. No steps match the enumerated circularity patterns.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

No equations, free parameters, or new physical entities are described in the abstract. The proposal rests on the unstated assumption that thoughts can be meaningfully extracted, filtered, and reused across queries.

pith-pipeline@v0.9.0 · 5617 in / 1118 out tokens · 44932 ms · 2026-05-10T16:29:06.716454+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

6 extracted references · 6 canonical work pages

[1]

SMART-LLM: Smart Multi-Agent Robot Task Planning Using Large Language Mod- els,

Check specifically for SE track proceedings. 14 Published in Transactions on Machine Learning Research (04/2026) Shyam Sundar Kannan, Vishnunandan LN Venkatesh, and Byung-Cheol Min. Smart-llm: Smart multi-agent robot task planning using large language models.arXiv preprint arXiv:2309.10062, 2023. Jared Kaplan, Sam McCandlish, Tom Henighan, Tom B Brown, Be...

work page arXiv 2026
[2]

What are the practical applications of the research in ’title’?

template-based query formation, and 2) LLM-based query formation. The prompts are shown in Figure 13 Template-based Query Formation.We construct general and broadly applicable templates for all papers. For example,"What are the practical applications of the research in ’title’?"and"What new per- spectives does ’title’ offer in its field?". During experime...

work page 2023
[3]

Data Collection.We initiated our data pool by sourcing raw research papers from arXiv, specifically targeting the Computer Science domain (e.g., cs. CL, cs. LG, cs.AI). To align with the capabilities of modern LLMs, we focused on recent high-quality publications to serve as the external knowledge source

work page
[4]

This step allows us to cleanly separate themain body(used as external knowledge context) from theabstract(used as ground truth), while effectively removing non-textual elements

Preprocessing and Parsing.Since raw PDFs contain noise (headers, footers, citation indices) that can disrupt LLM ingestion, we employed a parsing pipeline to convert PDF documents into a structured text format. This step allows us to cleanly separate themain body(used as external knowledge context) from theabstract(used as ground truth), while effectively...

work page
[5]

This ensures that the retrieval and reasoning challenge is non-trivial

Filtering Criteria.From the parsed corpus, we applied strict filtering criteria to select the final test set for AcademicEval: •Length Constraint:To strictly evaluate long-context capabilities (a core motivation of Thought- Retriever), we filtered for papers that exceed a substantial token threshold. This ensures that the retrieval and reasoning challenge...

work page
[6]

Utility is in the Eye of the User: A Critique of NLP Leaderboards

Test Set Sampling.Crucially, consistent with our analysis in Section 4.4, we performedstratified samplingbased on the abstract’s abstraction level. This strategy ensures a balanced representation of difficulty levels across the benchmark, allowing us to evaluate the model’s performance on both fact-based and reasoning-heavy queries. 35 Published in Transa...

work page 2026

[1] [1]

SMART-LLM: Smart Multi-Agent Robot Task Planning Using Large Language Mod- els,

Check specifically for SE track proceedings. 14 Published in Transactions on Machine Learning Research (04/2026) Shyam Sundar Kannan, Vishnunandan LN Venkatesh, and Byung-Cheol Min. Smart-llm: Smart multi-agent robot task planning using large language models.arXiv preprint arXiv:2309.10062, 2023. Jared Kaplan, Sam McCandlish, Tom Henighan, Tom B Brown, Be...

work page arXiv 2026

[2] [2]

What are the practical applications of the research in ’title’?

template-based query formation, and 2) LLM-based query formation. The prompts are shown in Figure 13 Template-based Query Formation.We construct general and broadly applicable templates for all papers. For example,"What are the practical applications of the research in ’title’?"and"What new per- spectives does ’title’ offer in its field?". During experime...

work page 2023

[3] [3]

Data Collection.We initiated our data pool by sourcing raw research papers from arXiv, specifically targeting the Computer Science domain (e.g., cs. CL, cs. LG, cs.AI). To align with the capabilities of modern LLMs, we focused on recent high-quality publications to serve as the external knowledge source

work page

[4] [4]

This step allows us to cleanly separate themain body(used as external knowledge context) from theabstract(used as ground truth), while effectively removing non-textual elements

Preprocessing and Parsing.Since raw PDFs contain noise (headers, footers, citation indices) that can disrupt LLM ingestion, we employed a parsing pipeline to convert PDF documents into a structured text format. This step allows us to cleanly separate themain body(used as external knowledge context) from theabstract(used as ground truth), while effectively...

work page

[5] [5]

This ensures that the retrieval and reasoning challenge is non-trivial

Filtering Criteria.From the parsed corpus, we applied strict filtering criteria to select the final test set for AcademicEval: •Length Constraint:To strictly evaluate long-context capabilities (a core motivation of Thought- Retriever), we filtered for papers that exceed a substantial token threshold. This ensures that the retrieval and reasoning challenge...

work page

[6] [6]

Utility is in the Eye of the User: A Critique of NLP Leaderboards

Test Set Sampling.Crucially, consistent with our analysis in Section 4.4, we performedstratified samplingbased on the abstract’s abstraction level. This strategy ensures a balanced representation of difficulty levels across the benchmark, allowing us to evaluate the model’s performance on both fact-based and reasoning-heavy queries. 35 Published in Transa...

work page 2026