Thought-Retriever: Don't Just Retrieve Raw Data, Retrieve Thoughts for Memory-Augmented Agentic Systems
Authors: Tao Feng
Pengrui Han
Guanyu Lin
Ge Liu
Jiaxuan You
Venue: Accepted by TMLR ()

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.

Published in Transactions on Machine Learning Research (04/2026)
Thought-Reetriveer Don't Just Retrieve Raw Data, Retrieve Thoughts for Memory-Augmented Agentic Systems
Tao Feng"
taofeng2@fllimois.eed edu
University of Mlinois Urbena- Channn-g
Pengrui Han"
phan3 @omät. phann@0mit...ommm
edu
University of Blinois Urbama-Charn Champaign
Guanyu Lin" Carnegie Mellon University
guangul@ondrew.ccmt edu
Ge Liu University of Ilinois Urbana- Chaamaaa
geliu@illinois. edu
Jiaxuan You
jaruanEilinooi.c edu
University of Mlinois Urbona- Channaagn
Reviewed on OpenReview: https: // opwnrevie..eeeeee.e net/ forum? id= emCoul LENL
Abstract
Large language models (LLMSs) have transformed AT research thanks to their powerful in- ternal capabilities and knowledge. However, existing LLMs still fail to effectively incorpo- rate the massive enternal 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- Rettggtt-etrieeer,, novel model-agnostic algorithm that helps LLMs generate output con- ditioned 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 (thoughls), filtering mean- ingless and redundant thoughts, organizing them in thought memory, and retrieving the relevant thoughts when addressing new querics. This effectively oquips LLM-basod agents with a self-evolving long-term memory that grows more capable through continuous inter- action. Besides algorithmic innovation, we further meticulously prepare novel benchmark, AcademicEvall, which requires 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--Retrieerr remarkably outperforms state-of- tho-art basclines, achieving an average increase of at least 7.6%
score and 16% in win rate across various tasks. More importantly, we further demonstrate two exciting findings: (1) Thought-Retriewe can indeed help LLM self-evolve after solving more user queries; (2) Thought- Rettrivver learns to leverage deeper thoughts to answer more abstract user queries.
ulab Shab-uuuuchhhouht--Rtrreee
1 Introduction and Related Work
Large language models (LLMs) have revolutionized AI rescarch thanks to their powerful internal capabilities
Zhao et al. 2023 Wang et al. 2023 and knowledge [Peng et al. 2023a). When building LLMs, researchers
"Equal contribution. Corresponding author.

---

Published in Transactions on Machine Learning Research (04/2026)
(a) Standard RALM is limited by number retrieved chuncks
(c) Thought- leverages past LLM thoughts, balancing
Retrieved Data
Preccison==1 Recall:0,5)
low-level facts and high-level thoughts, without computational overhead
Precision=0.8 Recall=1;
K;
K
K--1
K
T,
Retrieved Data
Thoughts created by LLM
(bb Hlerarchical RALM is limited by the low precision recall
Retrieved Data
|Precision R.call-0.75
LLM
S1
Ss
Query
K
K
K4
K5
K K
K1
K2
K₃ K4 Ks
K
K2
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 S generated independently from user queries, which could improve recall at the cost of lower precision. (c) Thought-Retrinnn Retriever leverages past LLM thoughts collected from answering user queries, with little computational overhead. Thought-Retriever balances low-level facts and high-level thoughts, leading to high precision and recall.
further expect LLMs to interact with the world by effectively incorporating the external knowledge as their long-term memories, e.g., collected from facts (Sun et al. 2023 interactions with other Als (Wu et al, 2023 Kannan et al. 2023). Importantly, the scale of the external knowledge for LLMs could be arbitrarily large; ultimately, all the digitized information within our universe could serve as the external knowledge for these LLMs. In practice, when building personalized LLM applications (Bill & Eriksson 2023) or LLM- powered domaín experts Thirunavukarasuu et al.] 2023 Liu ct al., 2023), e.g., Al doctor, the relevant external knowledge for the LLMs could also easily get extremely large, e.g., billions of tokens. Therefore, our paper aims to raise attention to the pressing research question: how to effectively and efficiently help L.LMs and LLM-based agents utilize (arbitrurily) rich enternal knowledge long-term memory.
To help LLMs better incorporate external knowledge, existing research mainly falls into two categories: long-context I.I.ss and relrieval-augmmented LLMs (RAT.Ms). (1) Long-contert L.Mss, such as MPT (Mo- 2023 and LongChat (LM-SYS 2023), aim to expand the LLM's context window, e.g., via novel training algorithms (Tay et al.] 2022). inference algorithms (Xiao et al. 2023), new architectures (Peng al., 2023b Gu & Dao 2023), or system optimization (Xu al.) 2023ch. Although these methods improve the
saicML
working memory size of LLMs, they cannot fundamentally address the issue of interacting with ultra-rich external knowledge using LLMs, since the computational complexity is often quadratic to the context length. (2) RALMs retrieve pertinent information from external knowledge bases using retrievers, such as BM-25 [Robersoon et al.] 2009), Contriever (Izacard et al. 2022), and DRAGON (Lin et al., 2023). However, these algorithms are still constrained by LLMs context length, since they can only retrieve top-K raw data chunks from the external knowledge that fits within an LLM's 's context. limit. (3) Hierarchical RALMs, e.g., creating a tree-structured memory for an LLM (Chen et al. 2023). Despite its potential to help LLMs incorporate more abstract knowledge, mamually summarizing closed chunks and rigidly forming tree structure proves to be a costly and inefficient method. This approach demands significant resources and lacks the flexibility to adapt to specific inputs in LLMs. Overall, existing methods in attempting to include external knowledge for LLMs still exhibit fundamental limitations in efficiency and effectiveness. Meanwhile, recent LLM-based agentic systems (Park et al.) 2023 Wang et al.) 2024 Packer et al. 2023) also require persistent memory to maintain coherence across extended interactions. However, these frameworks typically store raw observations or unprocessed interaction logs, which are noisy and difficult to retrieve effectively. This highlights the neod for more compressed and reasoning-awan memory mechanism for agents.
Here, propose Retrieve,, an LLM-agnostic self-evolving retrieval framework that leverages his- torical LLM responses to answer new queries. Our key insight is that LLM responses can be transformed into thoughts with little computational overhead and that the thoughts can be organized as a thought mem- ory for the LLM to facilitate future tasks. Psychological studies (Kurzweil 2013 Snell, 2012) support our insight, revealing that human memory is organized hierarchically, which not only aids in retrieving relevant information for problem-solving but also gradually deepens our understanding of the world through con- tinuous processing and summarizing these interactions into complex cognitive thoughts. Notably, through
2

---

Published in Transactions on Machine Learning Research (04/2026)
(a) Thought retrieval
(b) Answer generation
Query
Retrieved Retrieved_Chunks...
Query Embed
T context RALM is method that
Eq
Top-K
T
T
The Author mainly solves the following
problema...
Context
Ti
LLM
K
Generate
Retriever
Retrieval Augmenteed Language
Modeling (RALM) methods...
Answer A,
What is the central theme of the paper?
Thought Memory
T
T T2 Kg
T
Query Q
LLM
Kg
K
K2
K3
Kg
Ks
K
Confidence D/1: Whether generated answer is valid
Chunk
cc
External Knowledge
User
Embed
Thought: In-context text can be defined
Update
(e) Thought memory update Similarity 8
like
T
(c) Thought and confidence generation
K
(d) Thought merge
Figure 2: Thought-Retriever Framework. (a) Thought retrieval: Upon receiving user query, Thought Reeight-eetrieerr 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 confidence based on the user query and the generated answer; (d) Thought merge: The calculation of similarity is used measure whether the generated thought will cause redundancy in data chunks; (c) Thought memory update: Meaningless and redundant thoughts are removed and the remaining novel thoughts are used to update the thought memory.
continuous interaction with diverse user queries, Thought--Rtrieeerr Retriever progressively generates more novel and expansive thoughts. This is achioved by organizing new data chunks from external knowledge into thoughts after addressing each query, filtering out meaningless and redundant thoughts, and ultimately incorporat- ing high-quality thoughts into the thought memory. Therefore, potential to utilize arbitrarily rich external knowledge as long-term memories and achieve self-evolution in capabilities.
Retriever gives an L.M agent the
In addition to algorithmic advancements, we also meticulously developed novel benchmark, AcademicFual, which challenges an LLM to accurately utilize extensive context to answer queries based on real-world academic papers. Our comprehensive experiments on AcademicEval and two additional datasets confirm that Thought-Retriever significantly surpasses state-of-the-art baselines, achieving an average increase of least 7.6% in F1 score and 16% in win rate across various tasks. Furthermore, we present two intriguing discoveries: (1) Thought-Retriiver can indeed facilitate the self-evolution of LLM after addressing more is capable of harnessing deeper insights to respond to more abstract user
user queries; (2) queries.
In summary, our main contributions are as follows: (1) Thought-Retrriver framework enables an LLM agent to efficiently and effectively utilize external knowledge as long-term memory, and further allowing to self-evolve through continuous interactions.
(2) AcademicEval, a real-world benchmark for testing LLM's understanding of ultra-long context. (3) Thought- Retriever -horight-Retriever consistently outperforms all state-of-the-.. retrieval-uugmented and long-context baselines and presents exciting findings.
3

---

Published in Transactions on Machine Learning Research (04/2026)
2
Thought- Thought-Retrieverr Retriever: Effectively Equip LLMs with External Knowledge
2.1
Preliminaries
An agent's external knowledge base KC = (K1, K2,,., Kn) consists of n data chunks. Instead of treating model outputs merely as transient responses, we formally define a thought (T;) as a persistent, query-driven cognitive unit derived from the interaction between a query Q and retrieved context K, Formally, a Thought T; is a validated abstraction T; A Abstract(Q, A, K2) that satisfies three key properties: (1) Query-Conditioned: it captures the logical link between specifie query and the data, unlike static summaries; (2) A Abstractive: it distills a coherent knowledge point from the conversation; and (3) Validated: it passes confidence check (c:) and novelty check (s;) to ensure it is meaningful and non-redundant.
We define the immediate source of a thought Tj as the set of retrieved items R3 (which may contain both raw chunks and other thoughts) used to generate it, To rigorously trace information provenance, we define the root source mapping Ö(-) recursively:
Base Case: For a raw data chunk K E K, the root source the chunk itself: Ō(K) = (K).
Recursive Step: For a thought generated from a retrieved set R, the root source is the union of the root sources of its components: Õ(T) U.er Ö(r).
Example: Consider thought Taew generated based on an existing thought Told and a raw chunk K3 (i.e. R = (Toid, K3)). If Tord was originally derived from raw chunks {K1, K2), then the root source of the new thought is ((Tnew) = O(Tota) U ((K)) (K,, K2, Kz}. This ensures that we can always verify the factual grounding of any high-level thought.
2.2 Motivating Examples
To measure how effectively an LLM agent can utilize external knowledge, we propose to extend the retricval metric, precision, and recall, with the root source mapping Ō(.). Assuming that answering a user query Qthik
requires a set of data chunks KG E K, and an LLM's response is T.. We have Precision [K,nō(T))
[Ó(Ta) Recall
(KnóóTa), As a motivating example, in Figure we assume K. = {K2, K3, K4, Ks} is required to answer a user query and an LLM can only fit 2 data chunks in its context window. A standard RALM (Figure I [a))
IC,
can achieve perfect precision by retrieving the correct data chunks; however, it has a lower recall since it does not have the context window to hold all the relevant data chunks.
To address the limited context window of RALM, researchers (Chen et al. 2023 proposed hierarchical RALMs (Figure [b)), where similar data chunks are summarized into S, via LLM as preprocessing step. However, the treo-structured summary structure is rigid, since the summaries S, are static compressions generated independently from user queries. In contrast, our Thought-Retriever generates dynamic thoughts that are conditioned on specific user interactions, allowing the memory to evolve based on actual data usage patterns. In Figure [b), idcally, chunks (K2, K3} and {K,, Ks} should be grouped together to answer the user query, where Precision 1, Recall could be achieved; however, the tree construction happened before user query, and the generated tree fail to adapt to the diverse future user query.
To stress the above limitations of existing RALMs, is shown in Figure [cc,, we propose the Thought- Retriever that leverages past LLM thoughts and balances low-level facts and high-level thoughts to answer user queries. In real-world applications, user queries are often sufficiently diverse, lcading to numerous diverse thoughts to meet the demands of new user queries. This valuable observation iilferentiates Thought- Retriever from existing tree-structured RALMs: (1) Thought-Retrrever Retriever offers a more flexible structure of are byprod- uets from the standard RALM response, making it easy to implement and bringing little computational overhead.
thoughts that depends on past user queries, and (2) the thoughts leveragod by Thought-
4

---

Published in Transactions on Machine Learning Research (04/2026)
Algorithm 1 Thouuhht-ettiien Retriever Inference Algorithm
Input: User queries 2, external knowledge K,, thought memory T, language model L, retriever R and threshold of similarity 6.
Output: Answers to user queries A, updated thought memory T.
A+ 0
for Qi NEEQ =Qdo do
T KUTT
Thought retrieval Answer generalion
A; + L(Q,,T) T))
5:
6:
Ti + L(Qi, A,)
b. Thought and confidence generation Thought merge Thought memory update
7:
8 1 (T/
if 11,8
end for
10: return A.T
2.3 Thought-Retries Framework
Method Overview. Figure 2 offers an overview of the proposed Thought-Retriever framework, which serves as a general-purpose memory module for LLM-based agents and consists of four major components: (1) Thought retrieval, where data chunks from external knowlodge and thought memory are retrieved;
(2) Answer generation, where an LLM generates the answer for the user query based on the retrieved data chunks; (3) Thought. and confidence generation, where LLM further generates thought and its confidence in validation to avoid hallucination based on the user query and the generated answer; (4) Thought merge, where similarity is calculated to mcasure whether generated thought will cause redun- dancy in data chunks; (5) Thought memory update, where meaningless and redundant thoughts are removed; the thought memory is updated with the remaining novel thoughts, rather than adopting all the new thoughts. We summarize the pipeline of Thought-Retriever in Algorithm [ whose details are shown as follows. Detailed prompts for this section can be found in Appendix A.2
Thought Retrieval. After receiving user query Qs, Thought Retriever R retrieves relevant information T, from external knowledge K. and previously generated thought memory T via embedding similarity ranking. This process is formulated as T, + R(Q,,KCTT).
Answer Generation. Based on the retrieved information T, we design a prompt to combine Te and user query Q; and feed the prompt lo an LLM L. to get the answer A, can be articulated as A, + L(1,, T:).
Thought and Confidence Generation. We can generate thoughts via LLM L. using the obtained answer A, and its query Q; (an example is shown in Table 5}. However, meaningless thoughts during the generation process may cause hallucinations for LLM and harm performance since some queries may be irrelevant to the external knowledge and thought memory. To solve this issue, we design a special prompt so that LLM L can generate thought T; and thought quality confidence & based on the user's query Q; and corresponding answer A,. This can be described as T, G + (.).,A)). Crucially, this step differentiates a thought from a standard response (A.). While A; aims satisfy the user's immediate request, T, explicitly generated distill the reasoning logic into "coherent knowledge point" (as shown in the prompt Figure 3 for the system's 's long-term memory. Specifically, is a discrete binary value, where indicates that the generated thought T; is meaningful, and 0 indicates that it is meaningless hallucinated. This confidence generation is also validated through our experiment in Sec E.1
Thought Merge. Redundant thoughts may cause LLM to retrieve duplicate information, which is also harmful the performance of L.M. Therefore, we calculate the similarity between the generated thought T, and data chunks KT) measure whether the generated thought T; will cause redundancy in data chunks. Instead of using complex notation, implement this as direct threshold check: compute the
5

---

Published in Transactions on Machine Learning Research (04/2026)
Given question:quession)) given answer;(context) Based on the provided question and its corresponding answer, perform the following steps:
Input:
Determine if the answer is an actual answer or if merely indicates that the question cannot be answered due to insufficient information. If the latter is true, just output 'O' without any extra words, otherwise output '1'.
Step 1:
If it is a valid answer, succinctly summarize both the question and answer into a coherent knowledge point, forming a fluent passage.
Step 2:
Figure 3: Thought and Confidence Generation Prompt. This prompt is used for Thought and Confi- dence Generation as described in Section 2.3 It evaluates whether the answer is valid and meaningful, and then summarizes the query and answer into thought.
maximum embedding similarity between T, and all existing items in the memory. If this maximum score exceods the threshold c, flag the thought rodundant (sctting 8; 1); otherwise, consider it novel (setting 8; = 0). Here, the embedding similarity is calculated based Contriever (Izacard et al. 2022) (same as retriever used elsewhere in Thought- Rettih--ee
Thought Memory Update. The confidence of thought quality e and the similarity 8 determine whether the newly generated thought should be updated into the thought memory T. Here, we design that the LLM is confident about its answer and the generated thought is not redundant, where c; 1, = 0, will be updated.
3 AcademicEval: New Benchmark for Long-Context LLM Understanding
Current benchmarks for assessing LLM long-context memory utilization involve tasks such question- answering, long-context summarization, and classification. Despile being well-constructed, they are limited in flexibility and real-world impact and are costly to acquire due to human labeling. To address these issues, we introduce an innovative benchmark, AcademicEval. basod on academic papers from arXiv updated daily. AcademiccEaal comes with two datasets: AcademicEvul-abehin abslractt and AcudemiccEul- launched a public platform that will enable users to easily create similar datasets or utilize L.MSs for academic tasks (see details in Appendix Hb. In addition, the detailed dataset introduction and usage instructions can be found in Appendix A.1 and respectively.
We also
This dataset focuses on the summarization of single (Abstract-single in Table D or multiple ( (Abstract-mulli in Table T academic papers. The LLM is presented with one or more papers with the abstract and conclusion sections removed and is tasked with writing an abstract. For Abstract-single, the generated abstract directly compared with the paper's original abstract. For Abstract- multi, the generated abstract is compared with a summary of abstracts from all the provided papers, which is generated by expert LLM as label. (2) AcademicEval-relater.
(1) AcademicEval-abstracd
This dataset. (Related-malti, in
Table D introduces challenging task for assessing LLM's ability to understand the connections between heterogencous segments of its long-context memory. The task is to write related work section based on the title and abstract of a larget paper. The LLM needs to use the title and abstraci as the query to relrieve memory chunks to complete this task. To be specific, memory chunks depict the abstracts of several papers (cach memory chunk corresponds the abstract of paper), where some papers are cited in the related work section of the target paper, while others are randomly sampled from the same broader field. The generated related work is then compared to the original related work of the target paper for evaluation.
6

---

Published in Transactions on Machine Learning Research (04/2026)
Table Overview of Datasets: task types, average length, and number of cases.
Dataset
Task Type
Avg. len
Cases
AcademicEval
Abstract-single
Single Sum
8,295
100
Abstract-mulli
Multi Sum
33,637
30
Related-multi
Multi Related
22,107
30
Public Datasets
Guv Report
Single QA
8,910
100
WCEP
Multi QA
8,176
30
4 Experiment
4.1 Experiment Setup
Additional Datasets. Besides AcademieEval, we further evaluate Thought-Retriever against state-of- the-art baselines two public datasets. (1) GovReport (Cao & Wang 2022): This dataset comprises 19,466 reports and associated labels prepared by government research agencies to verify if the LLM is capable of extracting salient words and useful information from a single lengthy governmental document. (2) WCEP GGallandari et al., 2020):: This dataset contains 10,200 entries, each containing multiple news articles associated with an event sourced from the Wikipedia Current Events Portal. requires the LLM to understand and extract useful information from a cluster of documents. Table summarizes the statistics for all the datasets. Additionally, we discuss the computational details in Appendix N It. important to note that the current experimental validation is conducted in English. However, the Thought- Retriever framework is inherently language-agnostic. We acknowledge that exploring its application to multilingual settings and low-resource languages is a promising direction, which we plan to investigate in future work to further demonstrate the framework's broad applicability.
ThouivetRRerriveer
Baselines. To gain a comprehensive understanding of our thought retriever's performance on LLM long- term memory tasks, we have adopted several baselines. All experiments with these baselines are conducted under the same LLM: Mistral-8x7B with LLM context length of 4,096 (Jiang et al.. 2024). Note that we set chunk size=500, K=8, 0.85, and maximum context length=2,000 tokens for all RALMs. We employ Con- triever (Izacard ct al. 2022 as our primary retriever due to its unsupervised design, which provides strong zero-shot performance across diverse domains without requiring labeled training data (unlike supervised alternatives like DPR). This aligns with our goal of building a general-purpose framework. Its empirical superiority over other retrievers is further verified in our ablation study in Sec 4.5. First, we consider 2 heuristic-based based retricvers BM25 (Robertson et al., 2009) and TF-IDF (Ramos al.) 2003). Second, we retrievers: Contriever (Izacard al., 2022), DPR (Karpukhin al. 2020 DRAGON Lin et al. 2023), and the state-of-the-art decoder-only embedding model @wen3-Embed-8it (Zhang et al., 2025). which leverages the Qwen3 foundation model for enhanced multilingual text under- standing and retrieval. Third, to evaluate advanced retrieval strategies, we include IRCoT (Trivedi et al. 2023a) an iterative method that interleaves retrieval with chain-of-thought reasoning to dynamically guide the informatioon-seeking process. Fourth, we employ RECOMP (Xu et al., 2023a),, a context compression technique that generates textual summaries of retrieved documents to reduce computational cost while main- taining information Coansiyy.Fitth we consider full context window baselines with document truncation Full Context (left) (Chen et al. 2023 and Full Context (right) (Chen et al. 2023 Lastly, we selected two long-context LLMs OpenOrca-Sk (Mukherjee et al.] 2023) and Nous Hermes-324 (Shen et al. 2023) Note that we do not compare with MEMWALKER (Chen et al. 2023), since it is costly to run and cannot scale to tasks with many data chunks. The details of baselines can be found in Appendix B
select deep learning-
Evaluation Metrics. Our ovaluation approach encompasses both traditional metric and AI-based assess- ments: (1) F1 (Lin) 2004): This metric computes the semantic similarity between the generated text and the ground truth reference through ROUGE-L (F1). An F1 score closer to 1 indicates higher alignment
7

---

Published in Transactions on Machine Learning Research (04/2026)
Table 2: Thought-Retriever consistently outperforms all the baselines in fact retrieval datasets. Bold and underline denote the best and second-best results. F1 score evaluates the similarity with the ground truth, higher is better. Win Rate represents the frequency with which a method's response preferred over Thought-Retriever by the evaluator. The 50% entry for tie); consequently, for other baselines, win rate lower than 50% indicates that they are outperformed by Thought Retriever. Note that the maximum context length is 2,000 tokens for all setriever-basee methods and Thought- Retriever employs Contriever as its retriever.
Retriever serves as the reference point (a
Type
AcademicEval Ahstracf-muih Win Rate
Public
Dataset
Abstract-singlo Win Rate
Related-mull
Gov Report
WCEP
Method
F1
Win Rate
F1
Win Rate
F1
Win Rate
BM25
0.212
7%
0.232
1%
0.203
40%
0.211
30%
0.178
31%
IDF
0.202
4%
0.225
4%
0.207
40%
0.195
35%
0.223
34%
Contriever
0.242
13%
0.232
15%
0.201
35%
0.223
10%
0.211
10%
DPR
0.206
0.226
4%
0.196
30%
0,188
20%
0:201
33%
DRAGON
0.236
7%
0.226
8%
0.208
30%
0.210
40%
0.231
35%
Owen3-Embed--Si
0.245
28%
0.240
20%
0.211
35%
0.229
12%
0.235
44%
IRCoT
0.243
25%
0.235
18%
0.209
33%
0.225
41%
0.233
42%
RECOMP
0.237
7%
0.202
8%
0.198
10%
0.215
35%
0.205
33%
Full Context (left)
0.118
2%
0.135
0%
0.193
13%
0.234
45%
0.207
35%
Full Context (right)
0.118
1%
0.149
0%
0.188
8%
0.220
10%
0,210
11%
OrenOrra-Sk
20%
0.135
3%
0.135
13%
0.244
41%
0.169
30%
Nous Hermes-32k
0.247
30%
0.204
7%
0.183
15%
0.238
37%
0.214
37%
Reerrvee Retriever
0.290
50%
0.275
50%
0.216
50%
0.232
50%
0.238
50%
with the reference text, signifying the better quality of the generated content. (2) Win Rate: Alongside F1, incorporate feedback from the AI evaluator for a more comprehensive assessment. Here, we choose Oweel..-72B-chhl as our AI evaluator, since it has superb alignment with human preferened This evalua- tion process involves presenting various responses to the LLM evaluator, who then ranks the quality of the responses. The percentage represents the frequency of a response being chosen over our thought retriever. A rate below 50% suggests that our thought retriever is outperforming the compared baseline.
4.2 Retrieve Context from Factual Knowledge
This section is to verify the performance of Thought-Retrrieer interaction with facts. We report the performance of our model and baselines in Table 2 Major observations are as follows:
Retriever when the external knowledge comes from
First, in both AcademicEval and public benchmarks, Thought-Retriewe significantly outperforms most baselines on two metrics. For example, achieves an average increase of at least. 7.6% F1 score and 16% in win rate across all datasets. This suggests that thoughts formed through interaction with the environment can effectively enhance an LLM's performance in different lasks. Moreover, the comparison and analysis of abstracts generated by different methods the Abstract-single task (Appendix F) also verify the effectiveness of Thought-Retriever. Second, we observe that the performance of methods that use the entire text directly have many features on two different benchmarks differs greatly, which contain Full Context baselines and long-context LLMs baselines. However, the performance of retriever-based methods stable across two benchmarks. This is due to two reasons: (1) AcademicEval is a more challenging benchmark. It contains modlti-modal' information, such as tables, different chapters, different symbol formats, etc. Directly putling this complicated information in a context makes it difficult for the LLM to process and analyze. For retriever-basee methods, they extract the most important information for respond the query from the entire memory, so they can filter out the influence of some redundant information and get better results; (2) Some Long-context LLMs may have contimuuusyy train on the public benchmarks, which causes the leak of the label and the overfitting of the model. In contrast to this, AcademicFval is a good benchmark for evaluating the zero-shot performance of LLM and has no risk of label leakage and overfitting. Since the benchmark is formed using papers from arXiv, it is dynamic and always up-to-date, benefiting from the continuous
8

---

Published in Transactions on Machine Learning Research (04/2026)
Table 3: Thought-Retriever can help the LLM quickly learn from other LLMs. Retriever-origin is the golden setting that retrieves original facts, others are comparative settings without original facts.
Setting Retriever-origin Respoue-direct Retriever-ather Thought-Retriee Retrrevee
0:25
0.19
0.22
0.24
Thoughts from our
Given the "Exploring Specific Blackbox Faimess Algorithms in Multimodal Clinical NLP", please write abstract summary.
LLM
This study addresses imperative of fair and unblased Natural Language Processing (NLP) algorithms in the clinical context, where the heterogeneit
Interaction with other LLMs
" examining the unique challenges posed by the clinical context,
significant legal and reputational repercussions for healthcare providers
Role: Doctor Model: GPT-4
Role: Health Expert Model: LLaMA-2 70B
techniques like deep learning, can be highly effective yet difficult to Interpret.
..E implementation text-specific fairness algorithms ....."
Role: NLP Expert Model: GPT-4
Role: Programmar
Model: GPT-3.5
Figure 4: Thoughts from other LLMs help respond without fact. Tt 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 (c.g., doctor) with different background knowledge. Our LLM is then able to rapidly learn from their thoughts and incorporate them external knowledge.
publication of new papers. We further show the comparison between Thought-Rotriever and other SOTA baselines in Appendix L
4.3 Retrieve Context Generated from other LLMs
Forming thoughts can be a lengthy process. When a new LLM lacks relevant memory or external knowledge, it is challenging to develop high-quality thought memories from scratch. Consequently, we aim to investigate whether Thought-Retrieverr Retriever can help the LLM quickly learn from other LLMs that have already formed expert knowledge. To answer this question, we design experiment on Abstract-single, and the goal of the LLM is to write an abstract summary based on its title. Our LLM builds its memories based on interaction with other LLMs, which include different roles of an LLM or different LLMs, as shown in Fig 4 To verify the effectiveness of Retrieee under this setting, we design four different comparison settings: (a) Retriever-origin retrieves knowledge based on the original context the papers and then uses this knowledge to respond to queries, which serves as golden setting; (b) Response-direct feods the query directly to the L.I.M to get the responses; (c) Retriever-other let other LLMs provide some relevant data based on a query, then memories of our LLM, and finally retrieves and gets response based retriever; (d) Thought- Retrrivve utilizes Shouuhh-Rettreewe to construct thought memories then retrieve thoughts for responding queries based on the setting of Retriever-other. We evaluate with metric F1 in 30 cases of Abstract-single, and the results shown in Table 3 demonstrate that the rank of them from good to bad Retriever-other, Response-dinn direct. Moreover, the response quality of Betught-Retriever is very close to that of Retricver-origin. These obscrvations verify the effect and efficiency of Thought-Retin Retriever when learning from other LLMs. Further results on QA and Reasoning tasks Li et al. 2023 be found in Appendix
uses this knowledge
is: Retriever-origin,
9

---

Published in Transactions on Machine Learning Research (04/2026)
4.4 New Findings from Thought- Retrive
(1) Thought Retriever learns to leverage deeper thoughts to answer more abstract user queries. We conduct a case study to explore the relationship between the abstraction levels of queries and the retrieved information. Specifically, we created set of questions with varying levels of abstraction and ranked them according to their abstraction level using an expert LLM (exact queries can be found in Appendix
To quantify the depth of retrieved information, we introduce a formal mcasure of Abstraction Level, denoted as ((r). The calculation follows a recursive definition:
Base Case (Raw Data): For any raw data chunk K E KC from the external knowledge basc, we assign a baseline abstraction level of [(K)= =1
Recursive Step (Thoughts): For generated thought T, let Rr be the set of items (either raw chunks or existing thoughts) retrieved to generate it. The abstraction level of T is calculated as the average level of its sources plus one:
=1+
Σ [(r) FERT
(1)
RT
This recursive formula ensures that a thought derived solely from data has level of (i.e., 1 1+ 1), while thought synthesized from other high-level thoughts will achieve strictly higher abstraction score (e.g..
> 2), reflecting deeper cognitive processing.
where the y-axis represents the abstraction level of the question and the x-axis As shown in Figure 5
represents the average abstraction level all information retrieved by our method. It can be observed that more abstract questions tend to retrieve information with higher abstraction levels.
(2) Reeriie helps LLM self-evolve after solving more user queries new type of scaling law. To investigate the relationship between the performance of Thought-Retriever and the number of thoughts, we design an experiment using varying numbers of thoughts on Abstract-multi and Related-multi of AcademicEval. As depicted in Figure 0 there is a distinct trend of increasing F1 scores correlating with the growing number of thoughts, which indicates improved performance. Therefore, more interactions with the users enable TToought-Retriee deeper understandings, demonstrating a new type of scaling law (Kaplan et al., 2020) for agentic memory. We also verify the diversity of thoughts and effectiveness of thought. filtering mechanisms in Appendix K and M
to assist LLM agents in self-evolving and developing
4.5 Ablation Study
We conduct a series of experiments investigate the impact of various retrievers. (1) w/wo TF- IDF ID//DPR/DRAGON In these variants, we replace the retriever (Contriever) in our method with other representative retrievers to assess their effectiveness compared to current retriever. (2) w/wo NousH- ermes: Here, we utilize NousHHermes to construct the thoughts. generation and thought merge in our framework to assess the importance of filtering meaningless and redun- dant thoughts. We report the evaluation results on Abstract-single and Abstract-mulli datasets in Figure These comparisons clearly show that our method consistently outperforms all the variants, suggesting that Contriever is most suitable for Thought- and filtering meaningless and redundant thoughts can bring great improvement the performance.
(3) w/o Filter: We remove the confidence
4.6 Qualitative Analysis based on Precision and Recall
In our motivation example in Sec 2.2 we highlighted where traditional methods struggle with recall and precision. Here, using the Relatod-multi datasct, we show that Thought-Retriever outperforms other base- lines in balancing both metrics. In the experiment, the abstracts of the real citations are regarded as ground truth. We aimed to assess how well different retrievers could retrieve information to cover the ground truth,
10

---

Published in Transactions on Machine Learning Research (04/2026)
1.7
Future Impact
Abstract Levels
High Abstract Level
Summarization
1.6
Medium Abstract Level Methodology
Retrieved Level
Low Abstract Level
1.5
Limits Overview
1.4
1.3
Minerva Overview
Benchmark Count
1.2
3
5
Query Abstraction
Figure Deeper thoughts help abstract queries. This figure illustrates the correlation between six questions, calegorized by their level of abstraction as evaluated by expert LLM (x-axis), and the raction level of the corresponding retrieved information (y-axis). The questions are grouped into three categories: high abstraction (top questions), medium abstraction, and low abstraction. Keywords from each question are displayed next to their corresponding data points for clarily.
0.280
0.260
E1
0.240
0.220
Abstract-multi
Related-multi
0.200
5
15
20
25
30
35
40
45
Number of Thoughts
Figure 6: Thought-Retriewe 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 time. We plotted the findings in Figure where the x-axis is the recall value and the y-axis represents the precision. It can be observed that all tra- ditional retrieval methods displayed significantly low recall values. This is primarily attributed to the top-K retrieval limit since K-8 is far less than the number of ground truth citations. In comparison, Thought- Retriever demonstrates a notable improvement in recall value. This is because il leverages thoughts that are constructed from multiple papers, thereby allowing Thought-Retrieve Retriever to achieve a much higher recall. More importantly, the ReorievtRetriever also exhibits moderately high precision compared to other retriev- Thought-Reeriiver Retriever does not significantly compromise its ability to retrieve the most relevant information.
ers. This suggests that, despite a minor trade-off, Thought
11

---

Published in Transactions on Machine Learning Research (04/2026)
1
w/wo TF-IDF
w/wo NousHermes
Relative Performance
w/wo DPR
W/o Filter
w/wo DRAGON
Thought-Retriever
0.8
0.6
0.4
Abstract-single
Related-multi
Datasets
Figure 7: Contriever and thoughts filtering are suitable for Thought- Retriever. Ablation study of methods on two datasets helps us decide on important components Thought-Retnnng Retriever.
Thought-Retriever
Contriever
BM25
DRAGON
0.8
TF-IDF
Thought Retriever
Precision
DPR
0.6
0.4
Model Type
Heuristii-bbsse
0.2
Deep-Learning-bun bbssdd
Thought-Retriewe
0.2
0.4
0.6
0.8
Recall
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 maintain coherence over extended interactions, effectively emulating the concept of experience replay continual learning. Prominent frameworks like Generative Agents (Park al. 2023) and Voyager (Wang et al. 2024) utilize memory streams skill libraries to store past observations and behaviors, enabling agents to evolve based on historical experiences. Similarly, MemGPT (Packer et al. 2023) manages context as operating system manages hicrarchical memory. While long-context LLMs attempt to address this by encom- passing all history within a massive context window, they suffer from quadratic computational complexity and the pes--i-thee-middee phenomenon, often failing to effectively utilize distant information. Our approach bridges these paradigms but with a distinct advantago: instead of storing raw observations (as in standard agents) or processing exhaustive raw contexts (as long-contest models), Thought-Retriever Retriever stores distilled thoughts-intermeede intermediate reasoning results. This allows the model to retrieve and reuse high-level cognitive
12

---

Published in Transactions on Machine Learning Research (04/2026)
patterns from past experiences efficiently, avoiding the noise and latency bottlenecks inherent in processing ultra-long raw contexts. In summary, unlike existing agent memory systems that store raw observations or skill descriptions, Thought- Retrit--etrieeer stores validated and deduplicated reasoning abstractions, offering a more information-dense and retrioval-friendly memory representation for agentic systems.
Long-context LLMs. In response to the challenge of long-context processing in LLMss the most intu- itive strategies involve expanding the LLM's context window. These methods include training larger, more advanced models (MosaicML 2023 LM-SYS 2023), fine-tuning existing language models to handle wider windows (Tay et a 2022), applying positional encoding to extend the context window size (Xiao et al, 2023), and compressing context via user embeddings (Ning et al., 2024). However, these methods often fall short due to the high costs associated with model training and a lack of flexibility. Moreover, simply extending the context does not guarantee faithful generation, as large models still suffer from issues like object hallucination, necessitating specific interventions (Li et al., 2025 2024bp, failing to fully address the fundamental reliability issucs of long context.
Intrieval-Augmentie Language Models. RALM offers flexible, cost-effective alternative to long- context LLMs by retrieving relevant information potentially assessed by LLMs themselves (Rahmani et al. from context chunks. Current methods employ techniques like token embeddings (Izacard et al.
2024
2022
Lin et al. 2023 keyword searches (Robertson et al.] 2009), fine-tuned rerankers (Ram et al. 2023
and parameterized retrieval verification losses (Fu et al., 2023). ) Recent paradigms, exemplified by IRCoT (Trivedi et al.j (023b) and emerging approaches like RankCoT (Wu et al.) 2025) and Rationale-Guided RAG (Sohn et al., 2025), attempt to enhance retrieval by interleaving or ranking step-by-step reasoning paths. However, these methods typically rely on-the-fly generation of reasoning, which incurs prohibitive latency and computational overhead. Despite advancements like hierarchical tree structures (Chen et al., 2023) to manage context, existing solutions remain rigid or costly. We propose the Thouuht-Retrieeee framework using RALM, which efficiently condenses context into stored thoughts, addressing these efficiency challenges. To rigorously validate our approach against the evolving landscape of
LLM capabilities (Pang el al. 22225) and evaluation standards (Li et al. 2024a Kang el al. 2024), ) we also introduce a comprehensive benchmark.
Context Compression for LLMs. To alleviate context window constraints, context comprossion tech- niques aim to condense long inputs into compact representations. Early approaches focused on token-level pruning (Jiang et al., 2023). More recently, generative compression methods have gained traction. For instance, RECOMP (Xu et al. 2023b) and AutoCompressors (Chevalier et al., 2023) propose learning to synthesize compressive summaries or dense vectors to represent long documents. However, a significant draw- back of these methods is the necessity to train additional compression modules or fine-tune the backbone LLMs, which incurs substantial computational overhead. Furthermore, methods relying on soft prompts or internal embeddings (Mu et al., (023) are often incompatible with black-box APIs where model weights are inaccessible. In contrast, our
is lightweight, training- free framework. It is en- tirely model-agnostic, capable of scamlessly integrating with both locally deployed open-source models and closed-source commercial APIs, thus offering superior efficiency and broader real-world applicability while preserving high-level reasoning paths.
6 Conclusion
We introduce Thought-RRtriever thoughts, enabling efficient use of external knowledge beyond context limits. For evaluation, we further propose AcademicEval, a benchmark for academic tasks like abstract and related work generation. Thought- Retriever outperforms existing methods, evolves through interaction, and shows strong potential for real- world applications. As a lightweight, model-agnostic memory module, Thouuhh--Retrieve can be readily integrated into LLM-based agentic systems to serve as their persistent, self-evolving long-term memory.
Retriever to enhance LLMs by dynamically generating and retrieving intermediate
7 Limitations
Despite the promising results and contributions of our work, we would like to discuss some limitations. Our experiments and the AcademicFval dataset primarily utilize papers from Al-related fields, which could limit
13

---

Published in Transactions on Machine Learning Research (04/2026)
the generalizability of our findings. Future work should consider extending the scope broader range of disciplines.
Additionally, our experiments and evaluations are conducted in English. This focus on English may overlook the nuances and challenges associated with other languages. Expanding our approach to include multilingual datasets and evaluations could provide a more comprehensive assessment of its effectiveness.
While AcademicEval provides a dynamic and continuously updated dataset from arXiv, it is reliant on the availability and quality of the papers uploaded to the platform. We assume and hope that researchers will continue to produce novel and high-quality work.
Lastly, while our framework shows effectiveness in our experiments, its robustness, scalability, and adapt- ability to realdworld, extremely large-scale applications have yet to be fully tested. We are actively working on our demos and hope to provide more exciting updates on this front the near future.
References
Jinze Bai, Shuai Bai, Yunfci Chu, Zeyu Cui, Kai Dang, Xiaodong Deng, Yang Fan, Wenbin Ge, Yu Han, Fei Huang, et al. Qwen technical report. Xiv preprint urXim:2309. 16609, 2023.
Desirée Bill and Theodor Eriksson. Fining a Ilm using reinforcement learning from human leedback for a therapy chatbot application, 2023.
Shuyang Cao and Lu Wang, Hibrids: Attention with hierarchical biases for structure-awar long document summarization, 2022.
Howard Chen, Ramakanth Pasumuru, Jason Weston, and Asli Celikyilmaz. Walking down the memory maze:
Beyond context limit through interactive reading. Xiv preprint orXiv:2310.05022 2023.
Alexis Chevalier et al. Autocompressors: Adapting pre-trained language models to compress long contexts. arXiv preprint ar Xrr::23305 12469, 2023.
Abhimanyu Dubey, Abhinav Jauhri, Abhinav Pandey, Abhishek Kadian, Ahmad Al-Dahle, Aiesha Letman, Akhil Mathur, Alan Schelten, Amy Yang, Angela Fan, et al. The Ilama 3 herd of models. arXiv preprint arXiv:2407.21758 2024.
Chaowei Fu et al. Searching parameterized retrieval & verification loss for re-identification. IEEE Journal of Selected Topics in Signal Processing, 17(3):560-55) 574, 2023.
Demian Gholipour Ghalandari, Chris Hokamp, Nghia The Pham, John Glover, and Georgiana Ifrim. A large- scale multi-document summarization datasct from the wikipedia current events portal. arXiv preprint arXiv:2005. 10070, 2020.
Albert Gu and Tri Dao. Mamba: Linear-tine sequence modeling with selective state spaces. arXiv preprinl arXiv:2312.0077 007522 2023.
Gautier Izacard, Mathilde Caron, Lucas Hosseini, Sebastian Riedel, Piotr Bojanowski, Armand Joulin, and Edouard Grave. Unsupervised dense information retricval with contrastive learning, 2022.
Albert Q Jiang, Alexandre Sablayrolles, Antoine Roux, Arthur Mensch, Blanche Savary, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Emma Bou Hanna, Florian Bressand, el. al. Mixtral of experts. arXiv preprint ar Xrv::22401.00 00000000000088, 2024.
Huiqiang Jiang et al. Limlingua: Compressing prompts for accelerated inference of large language models. In Proceedings of EMNLP, 2023.
Hong Jin Kang et al. Can 1mss replace human evaluators? an empirical study of ilm-as-a-judge in software engineering. In Proceedings of the IEEE/ACM International Conference on Software Engineering (ICSE), 2024. 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 Cheol Min. Smart-llm: Smart multi-agent robot task planning using large language models. ar Xiv preprint arXiv:2309.1000 10062, 2023.
Jared Kaplan, Sam McCandlish, Tom Henighan, Tom B Brown, Benjamin Chess, Rewon Child, Scott Gray, Alec Radford, Jeffrey Wu, and Dario Amodei. Scaling laws for neural language models. arXie preprint arXiv:2001.0883 2020.
Vladimir Karpukhin, Barlas Oguz, Sewon Min, Patrick Lewis, Ledell Wu, Sergey Edunov, Danqi Chen, and Wen-tau Yih. Dense passage retrieval for open-domain question answering. arXiv preprint 04X::200400496, 2020.
Ray Kurzweil. How to create a mind: The secret of human thought revealed. Penguin, 2013.
Chankyu Lee, Rajarshi Roy, Mengyao Xu, Jonathan Raiman, Mohammad Shoeybi, Bryan Catanzaro, and Wei Ping. Nv-embed: Improved techniques for training llms as generalist embedding models. arXiv preprint (kfe:22455.1425 2024.
Jiaqi Li, Mengmeng Wang, Zilong Zheng, and Muhan Zhang. Loogle: Can long-context language models understand long contexts? arXiv preprint arXiv:2311. 1.04939, 2023.
Junyi Li et al. Llm-based nlg evaluation: Current status and challenges. arXiv preprint orXiv:2402.01388,
2024a. Also appearing in ACT, Anthology / Commutational Linguistics resources.
Ye Li et al. Claim: Mitigating multilingual object hallucination in large vision-language models with cross- lingual attention intervention. In Proceedings of the 63rd Annual Meeting of the Association for Compu- tational Linguistics (ACL), 2025. Long Paper.
Zhaoyang Li et al. Cai: Caption-sensitive attention intervention for mitigating object hallucination in large vision-languag models. ai preprint arXiv:2407.nrxxz 2024b. Please check for recent conference acceptance (e.g, EMNLP/NeurIPS))
Chin-Yew Lin. Rouge: A package for automatic evaluation of summaries. In Tert summarization branches out, pp. 74-81, 2004.
Sheng-Chich Lin, Akari Asai, Minghan Li, Barlas Oguz, Jimmy Lin, Yashar Mehdad, Wen-tau Yih, and Xilun Chen. How to train your dragon: Diverse augmentation towards generalizable dense retrieval. anXiv preprint arXiv:2302.074: 07452, 2023.
Zhe Liu, Chunyang Chen, Junjie Wang, Mengzhuo Chen, Boyu Wu, Xing Che, Dandan Wang, and Qing Wang. Make Ilm a testing expert: Bringing like interaction to mobile gui testing via functionality- aware decisions. arXiv preprint arXiv:2510.15790, 2023.
LM-SYS. Longchat-13b-16k htttes//gthhb..cccc/aaaaaa////gp June 2023. GitHub Repository.
MosaicML. Taking language models to the next level with mpt-7b. https://fusionchat. ai 2023.
Jesse Mu et al. Learning to compress prompts with gist tokens. In Proceedings of NewrIPS, 2023.
Subhabrata Mukherjee, Arindam Mitra, Ganesh Jawahar, Sahaj Agarwal, Hamid Palangi, and Ahmed Awadallah. Orca: Progressive learning from complex explanation traces of gpt-4, 2023.
Lin Ning, Luyang Liu, Jun Xie, et al. User-llm: Efficient 11m contextualization with user embeddings. In Proceedings of the ACM Web Conference (WWW), 2024.
Charles Packer, Vivian Fang, Shishir Patil, Kevin Lin, Sarah Wooders, and Joseph Gonzalez. Memgpt: Towards iims as operating systems. arXiv preprint orXiv:2310.08500, 2023.
Rock Yuren Pang, Hope Schroeder, Kynnedy Simone Smith, Solon Barocas, Ziang Xiao, Emily Tseng, and Danielle Bragg. Understanding the llm-ification of chi: Unpacking the impact of 1mms chi through a systematic literature review. In Proceedings of the CHI Conference on Human Factors in Computing Systems (CHT), 2025. Conditionally Accepted.
15

---

Published in Transactions on Machine Learning Research (04/2026)
Joon Sung Park, Joseph C O'Brien, Carrie Jun Cai, Meredith Ringel Morris, Percy Liang, and Michael S Bernstein. Generative agents: Interactive simulacra of human behavior. In Proceedings of the 36th Annuul ACM Symposium on User Interface Software and Technology (UIST), 2023.
Baolin Peng, Michel Galley, Pengcheng He, Hao Cheng, Yujia Xie, Yu Hu, Qiuyuan Huang, Lars Liden, Zhou Yu, Weizhu Chen, et al. Check your facts and try again: Improving large language models with external knowledge and automated feedback. arXiv preprint Xrtf::2302.1985 2023a.
Bo Peng, Eric Alcaide, Quentin Anthony, Alon Albalak, Samuel Arcadinho, Huanqi Cao, Xin Cheng, Michael Chung, Matteo Grella, Kranthi Kiran GV, et al. Rwkv: Reinventing rons for the transformer era. arxiv preprint Xree:22305. 13048, 2023b.
Hossein A. Rahmani al. Llm-assisted relevance assessments: When should we ask 1hms for help? arXiv preprint trXfv:2411.06877, 2024.
Ori Ram, Yoav Levine, Itay Dalmedigos, Dor Muhlgay, Amnon Shashua, Kevin Leyton- Brown, and Yoar Shoham. In-context retrieval-augmented language models. arXiv preprint arXiv:2002.000: 2023.
Juan Ramos al. Using tf-idf to determine word relevance in document. queries. In Proceedings of the first instructional conference machine learning, volume 242, pp. 29 48. Citeseer, 2003.
Stephen Robertson, Hugo Zaragoza, et al. The probabilistic relevance framework: Bm25 and beyond. Foundations and Trends in Information Retrieval, 3(4):333-333 389, 2009.
Yongliang Shen, Kaitao Song, Xu Tan, Wenqi Zhang, Kan Ren, Siyu Yuan, Weiming Lu, Dongsheng Li, and Yueting Zhnang, Taskbench: Benchmarking large language models for task automation. arXiv preprint arXiv:2311.18760 2023.
Bruno Snell. The discovery of the mind. Courier Corporation, 2012.
Jiwoong Sohn et al. Rationale-guided retrieval augmented generation for medical question answering. In Proceedings of NAACL, 2025.
Kai Sun, Yifan Ethan Xu, Hanwen Zha Yue Liu, and Xin Luna Dong. Head-to-tail: How knowledgeable are large language models (Im))? aka will Ilms replace knowledge graphs? arXiv preprint orXiv:2308.1010 2023.
Tay, Mostafa Dehghani, Vinh Q Tran, Xavier Garcia, Jason Wei, Xuezhi Wang, Hyung Won Chung, Dara Bahri, Tal Schuster, Steven Zheng, al. U12: Unifying language learning paradigms. In The Eleventh International Conference on Learning Representations, 2022.
Arun James Thirunavukarasu, Shathar Mahmood, Andrew Malem, William Paul Foster, Rohan Sanghera, Refaat Hassan, Sean Zhou, Shiao Wei Wong, Yee Ling Wong, Yu Jeat Chong, et al. Large language models approach expert-level clinical knowledge and reasoning in ophthalmology: A head-to-head cross-sectional study. medRziv, pp. 2023-07, 2023.
Harsh Trivedi, Niranjan Balasubramanian, Tushar Khot, and Ashish Sabharwal. Interleaving retrieval with chain-of-though reasoning for kuowledge-intensiv multi-step questions. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 10014-10037, 2023a.
Harsh Trivedi et al. Interleaving retrieval with shain-of-thought reasoning for knowledge-intensive multi-step questions. In Proceedings of ACL, 2023b.
Guanzhi Wang, Yuqi Xie, Yunfan Jiang, Ajay Mandlckar, Chaowei Xiao, Yuke Zhu, Linxi Fan, and Anima Anandkumar. Voyager: An open-ended embodied agent with large language models. In Proceedings of the International Conference on Learning Representations (ICI.R,, 2024.
16

---

Published in Transactions on Machine Learning Research (04/2026)
Lei Wang, Chen Ma, Xueyang Feng, Zeyu Zhang, Hao Yang, Jingsen Zhang, Zhiyuan Chen, Jiakai Tang, Xu Chen, Yankai Lin, et al. A survey on large language model based autonomous agents. ar preprinl arXiv:2308.1148, 2023.
Mingyan Wu et al. Rankcot: Refining knowledge for retrieval-augmennted generation through ranking chain- of-thoughts. In Proceedings of ACL, 2025.
Qingyun Wu, Gagan Bansal, Jieyu Zhang, Yiran Wu, Shaokun Zhang, Erkang Zhu, Beibin Li, Li Jiang, Xiaoyun Zhang, and Chi Wang. Autogen: Enabling next-gen Ilm applications via multi-agent conversation framework. Xiv preprint arXiv:2308.081 2023.
Guangxuan Xiao, Yuandong Tian, Beidi Chen, Song Han, and Mike Lewis. Efficient streaming language models with attention sinks. arXiv preprint arXim:2309.1744 17453, 2023.
Fangyuan Xu, Uri Alon, and Graham Neubig, Recomp: Improving retrieval-augmented Ims with compression and selective augmentation. arXiv preprint arXio:2310. 04408, 2023a.
Fangyuan Xu, Weijia Shi, and Eunsol Choi. Recomp: Improving retrieval-angmenned Ims with compression and selective augmentation. arXiv preprint arXiv:2310.44408 2023b.
Peng Xu, Wei Ping, Xianchao Wu, Lawrence McAfee, Chen Zhu, Zihan Liu, Sandeep Subramanian, Evelina Bakhturina, Mohammad Shoeybi, and Bryan Catanzaro. Retrieval meets long context large language models. arXiv preprint arXiv:2310.0888 030022, 2023c.
Yanzhao Zhang, Mingxin Li, Dingkun Long, Xin Zhang, Huan Lin, Baosong Yang, Pengjun Xie, An Yang, Dayiheng Liu, Junyang Lin, Fei Huang, and Jingren Zhou. Qwen3 embedding: Advancing text embedding and reranking through foundation models. arXie preprint orXiv:2506. 05116, 2025.
Andrew Zhao, Daniel Huang, Quentin Xu, Matthicu Lin, Yong-Jin Liu, and Gao Huang. Expel: Lim agents are experiential learners. arXiv preprint orXiv:2308.100 1014 2023.
17

---

Published in Transactions on Machine Learning Research (04/2026)
A Details of AcademicEval
In this section, provide the data format documentation for the datasets in proposed AcademicEval benchmark in Section A.1. and detailed instructions and prompts for its usage in Section A.2
A.1 Dataset Documentation
For AcademicFyallabsttaact the single document setting (Abstract-single), each case includes the paper title, abstract as the label, and main content, excluding the abstract and conclusion. For the multiple docu- ment setting (Abstract-multi) each case includes five papers' titles, abstracts, and main contents excluding the abstracts and conclusions. We utilize an expert LLM to summarize the five abstracts of one case into a fluent summary as its label using the prompt in Figure I For AcademicEvallrelateed (Related-multi), each paper includes a title, its abstract, its related work as the label, the abstracts of its real citations, and the abstracts of other random papers.
Attribute
Description
'title'
The title the academic paper. The abstract of the academic paper.
Abstract-Single
'abstract' as label main_content'
The content of the paper excluding the abstract and the con- clusion.
title
The title the first academic paper.
'abstract 1'
The abstract of the first academic paper.
'main content 1'
The content of the first paper excluding the abstract and the conclusion.
Abstract-Multi
...
ttitle 5'
The title of the fifth academic paper. The abstract of the fifth academic paper.
aasstacc 5'
'main content 5'
The content the fifth paper excluding the abstract and the conclusion.
'label'
The summary of five abstracts as fluent passage.
'title'
The title the academic paper.
Related-MMnn Multii
'own abstract' 'own related work as label'
The abstract of the academic paper for wiring-related work.
The related work of the academic paper for wiring-related work.
'citations' abstracts' 'other random ab- stracts'
The abstracts of the target paper's real citations.
The abstracts of other random papers.
Table 4: AcademicEval Dataset Documentation. This table presents the specific format of the data in our AcademicEval dataset.
A.2 Usage Instruction and Prompt Utilization.
Here we offer detailed instructions for utilizing the datasets in the AcademicEval benchmark. We also provide all the necessary prompts we utilized in our experiment.
A Abstract-Single. For the lask of single paper abstract summarization, as shown in Figure 9 (a), we first provide a prompt 'Please craft an abstract summarizing the key points from the provided text. The abstract should be of appropriate length and include the main theme, significant findings or arguments, and conclusions of the tert. Ensure it captures the essence of the content in clear, succinct manner' for the retrieval purpose. We then retrieve information from the paper's main content based on this prompt using
18

---

Published in Transactions on Machine Learning Research (04/2026)
(a) Abstract-Singe
(b) Related-Muuft
Dataset Format: (title), (abstract), condn_t
Dataset Format:
Query: Write abstract for (title)
Generated Abstract
Query: Original paper
Abstracts of Real Citations
Abstracts Random Paper
LLM
Retrieval
Compare
(title 1) (abstract
(abstract 2)
(abstract N+1)
(abstract)
Label: Own related work
(related work)
(abstract N)
(abstract 100)
(c) Abstract-Multi Dataset Format: (title 1), (abstract 1), (main__cotennl 1) (title 5), (abstract (main_come content
Write Related Work section for this:
(title (abstract
Generated Related Work
Query: Write an abstract (title
Generated
LLM
Retrieval
1) (title 5)
Abstract
Compare
Retrieval
Summary
abstract
Compare
(abstract
(abstract
(own related work)
cociiet_ amaint_ccnnent 5)
(abstract N+1)
(abstract 100)
abstract
Figure 9: AcademicEval Usage Instructions. This figure provides a visualization of the usage instruc- tions for the AcademicEval dataset, as described in Section A.2 to aid understanding.
retriever. Then, the LLM would generate abstract based on the retrieved information using the prompt in Figure 10 Finally, compare the LLM-generated abstract with the original abstract to do the evaluation.
Abstract-Multt. For the multiple paper abstracts summarization task, shown in Figure (b), we first provide a prompt "Plense craft an absivacl summarizing the key points from the provided text. The abstruct should be of appropriate length and include the main theme, significant findings or arguments, and conclusions of the text. Ensure it caplures the essence of the content in a cleur, succinel manner for the retrieval purpose. Then retrieve information from the main content of the 5 papers based this prompt. Further, the LLM would generate abstract based on the retrieved information with the prompt in Figure [0] The generated abstract is compared with the ground truth, which is a summary of the five abstracts created using the prompt. in Figure I1
Related-Multi. In the related work task, as shown in Figure 9 (c), we provide the LLM with prompt "Could you please wrile a related work for introducing this paper? Its abstract is: {paper abs)', where {paper_abe a is sustibute with the paper's real abstract. Following this prompt, the LLM retrieves informa- tion from collection of paper abstracts, comprising the abstracts of real citations in its related work section and random papers. The LLM then generates the related works section based on this retrieved information using the prompt in Figure 12 This generated related work is then compared with the real related work section of the paper to perform evaluation.
Benefits and Contributions. AcademicEval offers several advantages existing benchmark datasets. Firstly, maintain up-to-date dataset from arXiv that benefits from the continuous publication of new papers. This dynamic nature cases overfitting and label lcakage problems in static benchmarks and enables the evaluation of LLM self-adaptability. Secondly, high-quality labels can be generated with no extra cost as opposed to manually crafted datasets that require human effort. Thirdly, our dataset is not only valuable for evaluating LLM but also serves as practical academic tool the real world to assist rescarchers in better understanding their fields and boost productivity. We developed a highly automated codebase for dataset construction that will be released soon.
19

---

Published in Transactions on Machine Learning Research (04/2026)
Please craft an abstract summarizing and connecting the key points from the provided Text.
The text should be composed of content extracted from different papers, potentially spanning varied disciplines, but all addressing overlapping themes or subjects."
The abstract should be of appropriate length (around 300 words), encompassing the main theme, significant findings or arguments, and conclusion of the Text.
Ensure the abstract captures the essence of the content in clear, succinct manner, providing a coherent summary that bridges the various papers."
Here is the Text: (context)
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-que sighhr-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 each abstract to provide comprehensive overview. It should be about 300 words.
Abstract 1: {abs1}
Abstract 2: (abs2)
Abstract 3: {abs3}
Abstract 4: {abs4)
Abstract 5: {abs5)
Figure 11: Abstract Multi Ground Truth Prompt. This prompt was used in our experiment the Academic-absttract-mii 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 heuristic-based retrievers: (1) BM25 (Robertson et al. 2009): widely-used ranking function in information retrieval. (2) TF-IDF RRams al. 2003): A statistical measure that evaluates the importance of word in memory.
20

---

Published in Transactions on Machine Learning Research (04/2026)
Given the abstract and related work of a research article, along with a sample material, write paragraph about its related work. Use the following as guidance:
This research paper investigates the impact of climate change on global agricultural productivity. The study employs a comprehensive dataset of temperature and precipitation changes over the past century, combined with historical crop yield data. Through advanced statistical modeling and machine learning techniques, the research identifies significant correlations between temperature and precipitation fluctuations and variations in crop yields. Furthermore, it predicts future scenarios of agricultural productivity under different climate change scenarios, providing valuable insights for policymakers and stakeholders in the agricultural sector to develop adaptive strategies.
Abstract:
Related Work: Previous studies in the field have explored the relationship between climate change and agriculture but have primarily focused on specific regions or crops. Smith et al. (2017) conducted a comprehensive analysis of the impact of temperature on wheat yields in North America, highlighting the vulnerability of wheat crops to warming temperatures. Additionally, Johnson et al. (2019) investigated the effects of changing precipitation patterns on rice production in Southeast Asia, emphasizing the importance of water management in mitigating climate-related risks to agriculture. While these studies contribute valuable insights, our research extends their scope by considering a global perspective and employing advanced modeling techniques to provide more accurate predictions of future agricultural productivity under climate change scenarios.
Based on the abstract of this article and related materials, write a paragraph
about its related work:
Abstract: (abstract)
Related materials: {context)
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 enable the LLM to perform more effectively on this challenging task.
Second, we select deep learning-based retrievers: (3) Contriever (Izacard el. al. 2022): leveraging con- textualized embeddings and neural networks to understand and retrieve relevant memory chunks. (4) DPR (Karpukhin et al. (2020): retrieving memory chunks by encoding chunks and queries into dense vectors. (5) DRAGON (Lin et al.] 2023): employing contrastive learning train its ability to retrieve memory chunks. (6) Qwen:-EEmbed--S) (Zhang et al. 2025 A state-of-the-art decoder-only embedding model
21

---

Published in Transactions on Machine Learning Research (04/2026)
Template-based Query Formation:
What new perspectives does "(title)' offer in its field
How might the findings in "ttitle)' influence future research?
What are the practical applications of the research in ?title}"?
In what ways does (title)' challenge existing theories or beliefs?
How does "ttitle) contribute to our understanding of its subject matter?
What does the statement "(sentence) imply in the context of '(title)'?
How does the sentence (senteece)) relate to the overall theme of "ttilee"?
LLM-based Query Formation:
Given the paper title: (title); and its abstract (abstract), please ask 20 questions that would be helpful for writing its related work section. Each questions should have a number at the begining. For example:\n 1.<Put Your Question Here>In2. <Put Your Question Here>, etc. The questions should be diverse and with different level of abstraction.
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 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:(orggina)..an given the two generated abstracts:
Generated Abstract 1: and Generated Abstract 2: (gen2), plase evaluate which one is closer to the original abstract.
Just output 'Abstract 1 is better' or 'Abstract 2 is better', no extra words.
AI Evaluation Prompt. This prompt is used for the AT Evaluation metric Win Rate, as described in Section 4 Given two generated answers and the ground truth answer, we ask the export LLM determine which generated answer aligns more closely with the ground truth.
Figure 14:
that leverages large-scale pre-training to achieve superior semantic understanding and retrieval performance across diverse tasks.
Third, we include 2 advanced retrieval and compression strategies: (7) IRCoT (rrieedi et 2023a
An iterative framework that interlcaves retrieval with Chain-of-Thought reasoning, allowing the model dynamically retrieve information based partial reasoning steps for complex queries. (8) RECOMP (Xu et al. 2023a): A retrieval angmentation method that compresses retrieved documents into concise summaries or selects key segments to maximize information density within the context window.
Fourth, we consider full context window baselines with document truncation: (9) Full Context (left) (Chen et al. )023): This approach uses the initial segment of a document, truncated to fit within a 4.096-token window. Focusing on the first 4,096 tokens, prioritizes carly content in the document. (10) Full Context (right) (Chen et al. 2023): In contrast to Full Context (left), it utilizes the final segment of document, also truncated to 4,096-token window.
22

---

Published in Transactions on Machine Learning Research (04/2026)
Lastly, selected two long-context LLMs: (11) OpenOrea-8k [Mukherjee et al. 2023 is fine-tuned the Mistral 7B model using the OpenOrca dataset. At its release time, it was ranked the best model among all models smaller than 30B on Hugging Face, with a maximum context length of 8,192 tokens. (12) Nous
Hermes-32k (Shen et al. 2023 trained on Mixtral8x7B MoE LLM. It boasts a maximum context length of 32,768 tokens.
2023 since it is costly to run and cannot scale to tasks with many data chunks. We use Contriever Thought Reatghtteetrieerr's retriever.
Note that we do not compare with MEMWALKER (Chen
C Retrieve Context Generated from other LLMs
We utilize an example shown in Figure 4 to illustrate our LLM interacting with four other specialized LLMs, each an expert in distinct field. These expert LLMs assume designated roles (such as a doctor) and possess unique background knowledge. Consequently, our LLM can quickly assimilate its insights and integrate this external knowledge.
User Query Formation
To model user queries in real-world scenarios for guiding thought generation, we primarily use two approaches: 1) semplate-based query formation, and 2) LLM-based query formation. The prompts are shown Figure
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 experiments, we substitute 'title" with the actual paper title to form specific queries.
LLM-based Query Formation. Another approach we use to generate more specific queries is by leveraging LLMs. Spocifically, we utilize models such as Mistral 8x7B and expert LLM. By providing these models with the paper title and abstract, we ask them to generate diverse questions at varying levels of abstraction. These questions are tailored to each specific paper, allowing for more nuanced and targeted queries.
E
Specific Queries of Abstract Level
This section lists the specific queries utilized in our case study in Section 4.4
demonstrating how Thought-
Retriever leverages deeper thoughts for more abstract user queries. Each query is categorized by its general level of abstraction, ranked according to its abstraction level assessed by an expert LLM, and detailed with its exact content in Table G
E.1 Algorithm Adaptability and Filter Effectiveness
Thought-Retriever Retriever is adaptable to various LLM backbones. While we use carefully designed prompt templates, Thought-RRttrivee is not tailored to any specific model. The algorithm is adaptable and effective across various LLM backbones, as shown by the consistent top performance on both Qwen-7B Bai et al. 2023) and Llama-3-70B Dubey et al. (2024) in multiple tasks (Table 7
F Example Outputs Comparison of Different Methods
We present examples of outputs generated using different methods on the Prademiccvallabsttaatt datasct. Specifically, in Figure [5] we provide the original paper title and abstract, along with the abstract generated by our
accompanied by a comment from an expert LLM. In Figure 16 show abstracts generated using DPR and TF-IDF, also accompanied by expert LLM comments for
23

---

Published in Transactions on Machine Learning Research (04/2026)
Table 5: Example of thought construction. For given query, Thought-Retrieee retrieves the corre- sponding data chunks (thoughts and original data chunks) from the knowledge corpus get the response. Then, Thhought-Retriever integrates the query and response into a prompt through the prompt template and obtains the final thought candidate through the output of LLM. This thought candidate is then evaluated to determine whether it is correct and not redundant.
Query
What has driven significant progress in various NLP tasks in recent years?
Response
According to the texl, significani progress in various NLP lasks in recent years has been driven in part by benchmarks such as GLUE, whose leaderboards rank models by how well they perform on these diverse tasks.
Prompt tem- plate
Input: Given query:[query] [query]; given response: (repoosse. Based on the provided query and its corresponding response, perform the following step: succinctly summarize both the question and answer into coherent knowledge point, forming a fluent passage.
Thought
Here summarized knowlodge point: In recent years, significant progress has been made in various Natural Language Processing (NLP) tasks. A key driver of this progress is the development of benchmarks, such GLUE, which provide stan- dardized way to evaluate and compare the performance of different models on range of diverse NLP tasks. These benchmarks, which often take the form of leaderboards, rank models based on their performance, fostering competition and innovation in the field. As a result, rescarchers and developers have been motivated to improve their models, leading to significant advancements in NLP capabilities.
candidate
Abstraction
Rank (expert Query LLM)
High
(Most Ab-
'What are the broader future implications of user-centric utility in NLP model evaluation?"
stract)
High
"Please craft an abstract summarizing the key points from the provided text."
Medium
4
'What are some of the limitations of this study?'
Medium
"What are the key methods introduced in this paper?"
Low
'Please explain the term Minerva me."
Low
(Least Ab-
'How many benchmarks are used to test the model's long context understanding ability in this paper?"
stract)
Table 6: Sample Queries Used in Abstraction Level Case Study. This table presents sample queries from the case study conducted in Section 4 which demonstrates how Thought-Retriewe learns to leverage deeper thoughts to answer more abstract user queries.
comparison. In Figure 17 we showed the example abstract generated by the long context model Nous Hermes 32k and the corresponding comments from the expert LLM. It is evident that the abstract generated by our Thought- is more comprehensive and coherent, with better management of specification and abstraction levels. Below, we include comprehensive comment from the expert LLM:
"The Thought-Retriever Retriever abstract is the best and most aligned with the original abstract. It effec- tively captures all key points, including the critique of leaderboard metrics and the need to consider factors beyond accuracy, such as energy efficiency, model size, and inference latency. It also calls for increased trans- parency leaderboards, emphasizing a holistic approach to NLP evaluation that includes practical statistics
24

---

Published in Transactions on Machine Learning Research (04/2026)
Table 7: Thought-Retriever demonstrates adaptability across different LLMs. This table com- pares Teeiivrs performance against baselines on Abstract-single and Abstract-multi tasks using Qwen-7B and Llama-3-708 B0B models. Thought- Retrriver consistently delivers the best results, highlighting its adaptability to various LLMs.
Type
A Abstract-single
A Abstract-multi
LLM
Q wwn--P 7b
Alama-3-70b
Qwen-7b 7b
Llama-3 3-70b
Method
F1
Win Rate
F1
Win Rate
F1
Win Rate
F1
Win Rate
BM25
0.196
3%
0.22
13%
0.232
7%
0.233
14%
TF-IDF
0.192
3%
0.21
17%
0.220
3%
0.224
12%
Contriever
0.231
10%
0.238
18%
0.229
4%
0.228
19%
DPR
0.209
4%
0,215
18%
0.222
3%
0.222
11%
DRAGON
0.209
3%
0.225
13%
0.224
4%
0.236
19%
Full Context (left)
0.069
0%
0.102
0%
0.061
0%
0.107
0%
Full Context (right)
0.073
0%
0.104
0%
0.065
0%
0.103
0%
OpenOrca-83 8k
0.175
17%
0.175
23%
0.135
17%
0.135
10%
Nous Hermes-32k
0.247
20%
0.247
37%
0.204
13%
0.204
7%
Retrivet
0.259
50%
0.285
50%
0.253
50%
0.266
50%
This abstract is clear, well-organized, and in- cludes call to action for changes in leaderboard reporting to better serve the practical needs of NLP practitioners."
to provide comprehensive measure of model udilily.
'In contrast, the DPR and long context model abstract, while touching on similar points, is less comprehensive and focuses more on specific. suggestions like user-specific leaderboards and revealed preference theory without fully encapsulating the broader argument about the divergence between leaderboard metrics and practitioner needs. The TFIDF abstract diverges the most, discussing related topics like brittleness, bias, and out-of- distribution data, but it does not focus specifically on the central argument about leaderboard metrics versus practical utility, making it less aligned with the original abstract's intent."
G Discussion
Transformative Impact and Real-World Applications. The Reereeet represents a paradigm shift in Al systems, transforming them from static repositories knowledge to dynamic, intelligent frameworks that interact and learn. Its unique architecture not only processes and retrieves information but also evolves with each user interaction, effectively 'thinking' and adapting over time. Such an intelligent system crucial for scenarios where real-time learning and context-aware responses are vital. For instance, existing AI service systems could be significantly enhanced by incorporating our approach. By storing orig- inal guidelines and regulations as part of the external knowledge base and recording each human query and its results as thoughts, these systems can evolve into more intelligent entities capable of continuous improvement and learning. This adaptive capability makes the Thought-Retriever an invaluable tool for dynamic and ever-changing industrial enviromments, where quick decision-making based on historical data and evolving information crucial. In sectors like customer service, healthcare, and legal advisory, where personalized and informed responses aware, and efficient solutions. Its ability to continuously learn and adapt from user interactions positions it as groundbreaking tool for transforming how industries interact with and utilize AI technology.
key, the Thought-Retriever
provide more accurate, context-
Future Research. Inspired by human thinking, Thought- Thought-Retriever represents a solid step toward general Al agents. Building on this foundation, future research could address several key challenges. Firstly, scalability and efficiency in processing incrcasingly complex datascts will be crucial. This involves not only enhancing computational power but also refining algorithms for greater precision and speed. Secondly, understanding and mimicking human-like reasoning remains a pivotal goal. This includes grasping muances in language, emotion, and cultural contexts, and pushing the boundarics of what AI can comprehend and respond to. Moreover, ensuring ethical considerations in AI decision-makte is significant. As the retriever evolves, its impact on privacy, security, and societal norms must be rigorously evaluated and guided. Finally,
25

---

Published in Transactions on Machine Learning Research (04/2026)
explore new domains of application, such as personalized education, mental health analysis, and advanced robotics.
Arxiv Copilot Demo
Based on the Shought-Retrieven we further propose a demo named Arxiv Copilot and deploy it on the Huggingface shown in Figure T8 which aims to provide personalized academic service. More specifically, it consists of three main parts as follows. Firstly, in the first "Profile' part, users can enter the researcher's name and generate a rescarch profile. Secondly, the rescarch trend part, users can select a time range and gel. relevant topic trends and ideas. Finally, in the 'Chat and Feedback' part, users chat with Arxiv Copilot and choose the better response from two answers. Here, appreciate any further feedback.
Profile In this part, as shown in Figure 18 (a), the user can input his/her name in a standard format to get the profile from Xiv here.
Research Trend As shown in Figure 18 (b), Arxiv Copilot will give the user personalized research trends and ideas if the user has set his/her profile. Otherwise, general research trends will be provided.
Chat and Feedback As shown in Figure 18 (c), each time Arxiv Copilot will give two answers. If the user prefers the second answer, he/she can click 'like" below the second answer, and the first answer will be removed. If the user clicks 'dislike', the second answer will be removed.
Further Results on QA and Reasoning Task
We evaluated Thought-Retriever As shown in the Table S consistently outperformed baselines, demonstrating strong performance in both QA and reasoning accuracy.
Retriever on the recent LooGLE dataset Li et al. (0233 for QA and reasoning tasks.
Table 8:
Effectiveness QA and Reasoning Tasks. This table presents results from LooGLE, a recent and widely used QA and reasoning benchmark. Our Thought Retriever consistently outperforms all other baselines.
Accuracy Reasoning Accuracy
BM25
10%
30%
TF-IDF
13%
33%
Contriever
20%
50%
DPR
17%
20%
Dragon
13%
27%
Full Context (left))
7%
20%
Full Coutext (right)
7%
13%
OpenOrca 8K
0%
10%
Nous Hermes-32K
3%
17%
Thought Retriever
27%
57%
J API Acknowledgement
We used Together AI's API conduct our experiments. There are no specific requirements to run our code. Essentially, our experimental setup can be replicated by anyone with standard laptops or desktop computers and any compatible API, not necessarily Together AT's APT.
26

---

Published in Transactions on Machine Learning Research (04/2026)
Table 9: Ablation with latest retriever models. We substitute the Contriever component Thought-
Retriever with NV-Embed, one of the latest retriever models on MTEB, and evaluate their performance comparatively on the Abstract Single task.
Method
F1
Contriever
0.242
Thought-Retrien Retriever using Contriever
0.290
NV-Embed
0.268
Thought-Retriever Retriever using NV-Embed
0.326
Table 10: Comparison between Thought-Retrieve and RECOMP in Abstract-single and
Abstract-multi.
Dataset
Model
F1
Win Rate
Abstract-single
RECOMP
0.237
7%
A Abstracl-single
Thouiee-Relrieeer
0.290
50%
Abstract-multi
RECOMP
0.202
8%
Abstract-multi
Retrive
0.275
50%
K Diversity of user queries and thoughts in real-world applications
We discuss the diversity of user queries and thoughts in real-world applications from the following three perspectives.
Redundancy Filtering for Diversity. As stated section 2.3 in our framework, when new thought is generated, we explicitly check its cosine similarity against existing thoughts in the pool. If it is too similar to any existing thought, it is discarded. This ensures that unique, non-redundant thoughts are retained, maintaining the diversity of the thought pool.
Empirical Diversity Analysis. To quantify diversity, we further conducted an analysis where we ran- domly sampled 50 thoughts from the thought pool in the AcademicEval task and computed their pairwise cosine similarities. This was repeated times. The average pairwise similarity was 0.32, indicating a high level of semantic diversity among the thoughts.
Preliminary Real-World Usage Data. As introduced in Appendix H we have built Arxiv Copilot system based on the RhoriertRetrieverr and it is capable of interacting with real users. From actual usage data collected through our deployed system, we observe a wide variety of user query types and thought interactions. This real-world evidence further supports the claim that our system encourages and handles diverse, meaningful memory retrieval in practical scenarios.
L Comparison between Thought-Retriever Retriever and other SOTA baselines
A Ablation with latest retriever models. We conducted an experiment on the Abstract Single task using the latest retriever models on MTEB- NV Embed Lee et al. (2024), and the results are shown in Table g These results further support that our framework and retrievers mutually reinforcing. On side note, many of the latest retriever models MTEB, including NV-Embed (7.85B parameters), are substantially larger than Contriever (110M parameters) and require significantly more computational resources. For our initial experiments, we prioritized models that balance performance with computational efficiency lo ensure broader accessibility and practical implementation of our framework.
Comparison with newest long-context understanding models. We introduced an advanced retrieval algorithm named RECOMP (Xu et al. 2023b) for comparison. RECOMP is strong baseline that tackles the
27

---

Published in Transactions on Machine Learning Research (04/2026)
long-content issue by combining an extractive compressor, which selects key sentences from documents, with abstractive compressor; which creates summaries by synthesizing information across multiple documents. We compared Thought-Retries Retriever with RECOMP on the Abstract-single and Abstract-multi tasks. The results, presented in Table 10 further validate the effectiveness of our method in understanding long-context.
M Details of our thought filtering mechanisms
We discuss thought filtering mechanisms from the following three aspects.
Thought Quality Filtering. When generating answers based a given query and retrieved information, explicitly instruct the LLM to assess whether the retrieved content is sufficient to answer the question. If not, it is prompted to return "No" rather than produce speculative answer. Furthermore, when generating thoughts based on the question and answer, we use specialized prompt that includes binary confidence check check-asking the model to indicate whether the generated thought is logical and non-hallueinated. Tf not meaningful, the thought is discarded. This process is detailed section 2.3 and the full prompt is included in Figure
Thought Redundancy Filtering. As illustrated in section 2.3 before adding a new thought to the pool, we compute its cosine similarity against existing thoughts. If is too similar to any existing entry, is filtered out. This ensures that unique, non-redundant thoughts are stored, promoting diversity and reducing duplication.
Robustness to Low-Quality Thoughts Added Empirical Study. We further add an experiment that shows that our Thought- Retrievr is robust even when some low-quality or distracting thoughts are present. Specifically, we designed an experiment using the Related-malli dataset, where the ground truth of retrieval is known (ie.,, the abstract chunks of the real citation paper). We compared two methods to assess the impact of low-quality thoughts:
Without relevant thoughts the retrieval process does not include the ground-truth abstract chunks, and irrelevant thoughts are added.
Raw data chunks only no thoughts are addod at all.
The F1 performance of the two methods (Without relevant thoughts: 0.207; Raw data chunks only: 0.208) demonstrates that the performance of the Thought- Thought-Retriever Retriever is not significantly affected by extremely low-quality thoughts.
N Computational analysis
In the RAG system, the majority of computation is allocated to LLM inference, while retrieval operations can be executed on the CPU. The Thought-Retriever maintains nearly identical retrieval compute costs across all experiments by using the same retriever. The cost of LLM inference is directly proportional to the number of retrieved tokens. To illustrate the efficiency of Therieee in real-world applications, we use the Arxiv Copilot introduced in Appendix H as example. We have constructed 100,000 paper abstracts as the original data chunks, and we pre-calculate and store the embeddings of these chunks. Additionally, we need to construct index files to establish the connection between data chunks and thoughts. The storage of this data requires less than 1.5 GB of memory. On this basis, we utilize FAISST a high-efficiency vector processor capable of handling billions vectors, as the similarity retriever, and implement the entire framework on CPUs. When a user query arrives, we use the Thought-Retrievee based on FAISS to retrieve historical thoughts data chunks and save the newly derived thoughts. The average inference time for generating user responses is approximately 5 seconds, which is mainly limited by the API's response speed.
28

---

Published in Transactions on Machine Learning Research (04/2026)
Table II: Human evaluation instructions for thought quality assessment.
Task Description:
You will be shown a query and several intermodiate thoughts generated by a large language model (LLM). Please evaluate each thought independently based on its usefulness, relevance, and correct- ness. Your rating should reflect how well the thought contributes answering the query.
Scoring Criteria (per thought):
Relevant, helpful, logically coherent, and factually correct. Score 1 (Medium Quality): Somewhat relevant/helpful, may have minor issues vague content. Irrelevant, incorrect, incoherent, or hallucinated content.
Score 2 (High Quality):
Score 0 (Low Quality):
Instructions:
Evaluate each thought individually, without comparing it to others.
Focus on content quality, not writing style or fluency.
You only need to assign a score (0, 1, or 2); no explanation required.
Example:
Query: What are the key challenges in training large-scale language models?
Thought A: One major challenge the need for large-scale, high-quality datasets that accurately reflect diverse linguistic contexts.
Thought B: Transformer models were introduced in 2017 by Vaswani et al., and they changed NLP.
Thought C: LLMs are expensive to train and often face issues like overfitting and gradient instability.
Recommended Scores:
Thought A + 2 Thought B +
Thought C c+>2 2
O
Human evaluations on the quality of retrieved thoughts
We conduct human evaluations on the quality of retrieved thoughts from the following three aspects.
Human Evaluation of Thought Quality. We conducted a human evaluation study (the detailed human instructions can be seen in Table II to directly assess the quality and reliability of the generated thoughts and our confidence-based filtering mechanism. Ten volunteers were recruited to review generated thoughts, with each thought independently evaluated by five annotators. On the Abstract-single dataset, the thoughts matched human judgment 96% the time. On the Related-mults dataset, the agrecment rate was 98%. These results confirm that our L.M-generated confidence scores are highly reliable and effective for filtering out low-quality hallucinated thoughts.
Robustness to Low-Quality Thoughts (Empirical Study). We further evaluated how the system performs in the presence of low-quality thoughts. Using the Related-multi dataset, we compared two settings: (a) Without relevant thoughts, where only unrelated thoughts were injected, and (b) Raw data chunks only, without any thoughts. The Fl scores in both settings were nearly identical (0.207 vs. 0,208), indicating that our framework remains robust even in the presence of noisy or irrelevant thoughts.
Real-Worle Deployment Validation. As introduced in Appendix H, we have built real-world ap- plication named Arriv Copilot with Thought-Retricn Retriever. Arxiv Copilot can interact with real users and provides two types of answers: one based solely on retrieved original data chunks, and the other incor- porating both original data chunks and historical thoughts. We collected feedback from 500 real users to compare preferences for these two types of responses. Approximately 75% of users preferred answers that included both original data chunks and historical thoughts. This observation demonstrates the effectiveness of "Thought-Retrieeer in real-world scenarios involving large query requests.
29

---

Published in Transactions on Machine Learning Research (04/2026)
P Comparison with other baselines
P.1 Comparison with MemWalker
In this section, we compare Thought- with MemWalker (Chen et al. 2023 a representative mothod designed lor processing ultra-long contexts through interactive reading.
MemWalker Setting. MemWalker operates by first segmenting the long context and constructing a hi- erarchical tree of summarics. During the inference phase, it treats the retrieval process decision-making task. The LLM acts as agent that 'walks' down this memory tree: at each step, the model reads the current summary node, reasons about which child node to visit next, and iteratively nav- igates until it locates the relevant leaf nodes. For our baseline implementation, we utilized the standard configuration with a branching factor of 3 and a maximum depth of as proposed in (Chen et al., 2028 We justify this configuration based on three factors: 1) Coverage: A depth of 3 yields a capacity of 3d = 27 leaf nodes (covering ~33,00 tokens), which aligns well with the average length of academic papers in our dataset; 2) Structure: The hicrarchy mimics the natural "Title - Section + Content" structure of academic texts; 3) Contezt Window: A branching factor of 3 ensures that the parent summary context each decision step remains small enough to fit within the LLM's effective window, preventing navigation errors caused by information overload.
interactive
Retriever consistently outperforms MemWalker in both F1 score and Win Rate across all datascts (c-g., 0.290 vs. 0.268 Abstract-single). More critically, in terms of inference time, MemWalker suffers from significant latency due its sequential nature; each query requires multiple rounds of LLM inference to navigate the tree structure. For instance, the Abstract-multi task, MemWalker takes 85.0 seconds per query. In contrast, Retriever completes the same task in just 3.20 achievin a speedup of over 25x. This demonstrates that while MemWalker is effective, Thought-Retrieve offers a superior balance of accuracy and efficiency for real-world applications.
Performance and Efficiency Analysis. Table 12 presents the detailed comparison.
Performance and Inference Time Comparison across Datasets, Note that MemWalker incurs high latency due to its interactive, multi-step tree navigation process.
Table 12:
MemWalker
Thought-Retrieve (Ours)
Type
Dataset
F1
Win Rate
Time
F1
Win Rate
Time
Abs-single
0.268
40%
25.0s
0.290
50%
1.85s
Academic Eval
Abs-multi
0.255
35%
85.0s
0.275
50%
3.20s
Rel-multi
0.212
44%
65.0s
0.216
50%
2.40s
Gov Report
0.229
45%
30.0s
0.232
50%
3.50s
Public
WCEP
0.228
39%
28.0s
0.238
50%
3.10s
P.2 Comparison with Oracle
To rigorously evaluate the effectiveness of our proposed method, we introduce an Oracle baseline which serves as a thcoretical upper bound. Specifically, we utilize the ground truth answers to calculate the ROUGE-L overlap scores with all corpus chunks, selecting the top-K chunks with the highest overlap to serve the 'perfect" input context. Crucially, context window constraints for the Oracle, Thought-Retrievar and all baselines. This ensures that any performance difference is solely attributed to the quality and format of the retrieved context.
employ the same generator (Mistral-8x7B) and
consistently outperforms the Oracle bascline on AcademicEval datasets (c.g., F1 0.290 vs. 0.278 on Abstract-single), while maintaining a competitive >50% win rate against traditional baselines. Although the Oracle utilizes ground- truth-derived gold chunks, these raw texts inherently contain significant redundancy and irrelevant, syntactic
Table 13 reveals counter counter-intutiiv yet compelling result: Thought-Retnn
30

---

Published in Transactions on Machine Learning Research (04/2026)
Table 13: Performance comparison with the SOTA baseline and the Oracle upper bound. We select Swen3-EEbeed as the strongest bascline. Win rate compares each method's response with Thought- Retriever (50% represents a tie). Remarkably, Shhought-Retrievee achieves a >50% win rate against the Oracle on AcademicEval tasks, demonstrating that generated thoughts provide superior reasoning guidance than raw gold chunks.
Abstract-single
Abstract-multi
Related-muli
Gov Report
WCEP
Method
F1
Win Rate
F1
Win Rate
F1
Win Rate
F1
Win Rate
F1
Win Rate
SOTA Baseline (Qwen3)
0.245
28%
0.240
20%
0.211
33%
0.229
42%
0.235
44%
Ovacle (Gold Chunks)
0.278
46%
0.255
42%
0.214
49%
0.255
62%
0.245
55%
0.290
50%
0.275
50%
0.216
50%
0.232
50%
0.238
50%
noise. Given the fixed context window (e.g., 2,000 tokens), filling the prompt with raw chunks limits the total volume of distinct information the model can ingest.
In contrast, our method generates thoughts that act as highly compressed, information representations of the retrieved content. This compression allows Thoughtt-Rerieeer to "pack' a broader scope of relevant details into the same context limit compared to the verbose raw text used by the Oracle. Consequently, in method provides
complex multi-hop scenarios (AcademicEval) where information coverage is paramount, the generator with more comprehensive evidence set than even the gold raw chunks. While the Oracle regains slight lead in pure extraction tasks (Public datasets), Thought-Retrinn Retrreveer significantly narrows the gap compared to the strongest baseline (Qwen3-Embed-8i) Embed-&b), demonstrating the robustness of thought- based retrieval in maximizing information density.
P.3 Retrieval Granularity and Chunk Coverage Analysis against HRALM
To rigorously evaluate the retrioval granularity, assess the quality of the retrieved context by mcasuring its coverage of the ground truth evidence. Since both HRALM (we use MemWalker (Chen et al. 2023 here) and Thought-Retriever retrieve higher-level abstractions (summarics S or thoughts 7) rather than raw text, simply counting retrieved items is insufficient. As illustrated in Figure 1 and defined in Section 2.1, we utilize the root source mapping function Ô(.) to trace retrieved abstractions back to their constituent raw data chunks Kyetrieved- We then compare these mapped chunks against the Gold Chunks set Kgoldd (identified by the Oracle having the highest ROUGE-L overlap with the ground truth answer).
Precision: The proportion of mapped raw chunks that are relevant: [rettreved (Kreri/ve/rr
Recall: The proportion of gold chunks covered by the retrieval: [Kretriened Kootta/ KKootl-
As shown in Table 14 determining the root source reveals structural trade-off between the two methods. In focused tasks like Abstract-single, HRALM achieves slightly higher precision (0.82 vs. 0.80) because its summarization process creates clean boundaries around specific semantic clusters, introducing less noise when the query falls neatly into one cluster. However, its rigid hierarchy struggles with queries requiring cross-cluster information, leading to low recall in Rel-multi (0.52). In contrast, Thought-Rotnn Retricver leverages thoughts to bridge semantic gaps by retrieving content that inherently connects disparate raw chunks. This capability allows it to achieve significantly higher recall (0.82) and F1 (0.79), demonstrating superior coverage for complex reasoning tasks.
Causal Analysis of Self-Evolution on Held-out Sets
To verify that the performance improvement is causally linked to the accumulation of thoughts rather than mere correlation, we conducted a rigorous held-out evaluation on both Abstract-muuti and Related-multi datascts.
Experimental Setup. We partition each dataset into two disjoint subsets with 50%/50% split ratio:
31

---

Published in Transactions on Machine Learning Research (04/2026)
Table 14: Retrieval Quality Comparison: HRALM vs. Reerieet- We evaluate the Chunk Cov- erage performance based on the precision and recall of retricved raw data chunks. Note: While HRALM achieves slightly higher precision in single-document tasks (e.g., Gov Report) due its ierractionbasse summarization, Retriiet-Retrieeer significantly outperforms it in recall and overall F1 score by bridging semantic gaps with generated thoughts.
HRALM (Baselines)
Thought- Reeegee (Ours)
Type
Dataset
Prec.
Recall
F1
Prec.
Recall
F1
Abs-single
0.82
0.65
0.72
0.80
0.88
0.84
AcademicEval
Abs-multi
0.74
0.58
0.65
0.78
0.84
0.81
Rel-multi
0.65
0.52
0.58
0.76
0.82
0.79
Gov Report
0.78
0.60
0.68
0.75
0.82
0.78
Public
WCEP
0.71
0.62
0.66
0.77
0.83
0.80
Evolution Set (Qevol): The first 50% of queries used solely for generating and accumulating thoughts into the memory T. No performance metrics are recorded during this phase.
Held-out Test Set (Qtest): The remaining 50% of queries are used for evaluation. These queries striclly unseen during the evolution phase.
Results. We compare the F1 performance on the Test Set under two experimental conditions:
Baseline (Cold Start): The model processes the test querics starting with an empty thought memory ():
2. Ours (Evolved): The model processes the test queries with thought memory pre-filled by the Evolution Set (T < Thoughts from Qevot).
As presented in Table 15 the model utilizing the evolved memory achieves consistent performance gains across both datascts. Specifically, on Abstract-multi, the pre-accumulated thoughts yicld 6.4% relative improvement in F1 score compared to the cold-start baseline. This provides strong causal evidence that the system learns transferable reasoning patterns from past interactions that generalize to novel, unseen queries.
Table 15: Cansal Evidence Self-Evolution on Disjoint Sets. Comparison of F1 scores on the held-out Test Set (50% of data) with and without prior evolution on the disjoint Evolution Set (50% of data). The consistent improvement confirms that accumulated thoughts provide transferable benefits novel queries.
Memory Status
F1 Score Held-out. Test Set Abstract-multi Related-mult
Method
(at start of testing)
Bascline (Cold Start)
Empty
0.235
0.198
Ours (Evolved)
Pre-filled (from Evolution Set)
0.250
0.210
Relative Improvement
+6.4%
+6.1%
R Inference Time Comparison
In this section, we analyze the computational efficiency of Thought-Retriewe compared to bascline mothods. We measured the average end-to-end inference latency per query on a single NVIDIA A6000 GPU. The results summarized in Table 16
32

---

Published in Transactions on Machine Learning Research (04/2026)
Table 16: Inference time comparison across different datasets. Values represent the average latency in seconds (s) per query. Lower is better. While heuristic and simple dense retrievers are the fastest, Beought-Retriveer maintains a competitive inference speed compared to advanced baselines like IRCoT and long-context LLMs (Nous Hermes-32k), achieving balance between performance and efficiency.
Type
Inference Time (Seconds)
Dataset
Abstract-single
Abstract-multi
Related-multi
Gov Report Time (s)
WCEP
Method
Time (s)
Time (s)
Time (s)
Time (s)
BM25
0.42
0.45
0.48
0.75
0.62
TF-IDF
0.40
0.43
0.46
Contriever
0.65
0.72
0.68
1.10
0.95
DPR
0.62
0.70
0.66
1.05
0.92
DRAGON
0.68
0.75
0.70
1.12
Qwen3- Embeed-8b
2.15
2.30
2.25
2.85
2.60
IRCoT
4.80
10.50
6.20
7.50
6.80
RECOMP
1.25
1.55
1.40
2.50
2.10
Full Context (left.)
2.80
3.10
3.05
5.20
4.10
Full Context (right.)
2.80
3.10
3.05
5.20
4.10
OpenOrea-8k
4.50
5.20
5. 10
9.80
7.50
Nous Hermes-32k
12.50
14.20
13.80
28.50
19.20
T'hought-Reerieverr Retriever
1.85
3.20
2.40
3.50
3.10
Comparison with Heuristic and Dense Retrievers. As expected, lightweight heuristic methods (e.g.) BM25, TF-IDF) and single-step dense retrievers (c.g., Contriever, DPR) exhibit the lowest latency (c.g.. < 1.0s). Thought-Relrieeer incurs moderate overhead (e-g.; 1.85s Abstract-single) compared to these methods. This additional cost stems from the ieneration-based nature of our approach, where the model must query the thought memory and potentially generate new thoughts, rather than simply matching static vectors. However, given the significant performance gains demonstrated in the main results, this latency increase is acceptable for most applications.
Comparison with Advanced Retrieval and Long-Context Baselines. Critically, Thought- Retriever demonstrates superior efficiency against advanced reasoning- heavy and long-context baselines:
vs. IRCoT: Iterative retrieval methods like IRCoT suffer from high latency due to their multi-step loop. For instance, on the complex Abstract-mulit dataset, IRCoT requires 10.50s per query. contrast, Thought-Retrinne generated thoughts directly from memory, avoiding repetitive retrieval cycles at inference time.
achieves a 3x speedup (3.20s) by leveraging pre-
vs. Long-Context LLMs: Processing ultra-long conlexts imposes a heavy computational burden on the attention mechanism. The long-context model Nous Hermes-32h exhibits the highest latency, reaching 28.50s on Gov Report. Shoought-Retrieee bypasses this bottleneck retrieving concise, high-level thoughts instead of raw, long documents, reducing the input token load significantly and achieving an 8x speedup (3.50s) on the same dataset.
Conclusion. Thought-Retriewe strikes a favorable balance between efficiency and performance. sig- nificantly outperforms fast but weak retrievers in reasoning capability, while remaining much faster than computationally expensive long-context models and iterative agents.
S Sequential Interaction Case Study: Visualizing Self-Evolution
To address the qualitative dynamics of how Thought-Retriever interacts with different user queries to achieve 'self-evolution," we present a sequential case study using our Arxiv Copilot system.
It is important to clarify that in our framework, self-evolution refers to the continuous expansion and refinement of the Thought Memory (T), as defined in Algorithm 1, rather than the updating of LLM parameters. This qualitative analysis complements the quantitative "scaling law" of thoughts demonstrated in Figure 5.
33

---

Published in Transactions on Machine Learning Research (04/2026)
Scenario: A user is researching the topic of 'Hallucination Mitigation in LLMs." The system starts with emply Thought Memory.
Turn 1: Initial Knowledge Acquisition
User Query: "What are the primary causes of hallucinations in Large Language Models?'
Retrieval: The system retrieves raw data chunks from the external knowledge base (e.g., papers discussing data bias and sourco-reference divergence).
Answer Generation: The model explains that hallucinations often stem from a conflict between the model's parametric memory and the provided context, or noise the training data.
Self-Evolution (Memory Update): Based on this interaction, the system generates and stores new thought:
Thought Ti: Hallucinations in LLMs are primarily caused by the divergence between inter- nal parametric knowledge and external context, often exacerbated by noisy training data.
Turn 2: Leveraging Past Thoughts
"How does Retrieval-Augmenned Generation (RAG) attempt to solve generation er- rors?"
User Query:
Retrieval: The system retrieves raw chunks related to RAG. Crucially, it also retrieves Thought Ti because ggencration errors' are semantically linked 'Hallucinations' in Tı.
Answer Generation: Leveraging Tı, the model connects RAG's mechanism directly to the root cause identified in Turn 1. It explains that RAG aligns the "external context" (mentioned in T2) with the generation process.
Seff-Fvolution (Memory Update): The system synthesizes deeper insight:
Thought T2: RAG mitigates hallucinations by grounding generation in the retrieved exter- nal context. However, to be effective, it must ensure the retrieved context is relevant to avoid exacerbating the sofrce-reference divergence.
Turn 3: Handling Abstract/Applicatiee Queries
User Query: "Propose robust architecture for a trustworthy medical Al assistant."
Retrieval: The query is abstract and does not explicitly mention "hallucination' or 'RAG." However, the semantic vector for 'trustworthy' matches with Thought Tr (mitigating errors).
Result: The system retrieves Thought T2 and immediately proposes RAG-based architecture with strict relevance filtering.
Without the self-evolution in Turns 1 and 2, the system might have just retricved generic papers on medical AI. By retrieving T2, it applies the high-level principle (RAG + Relevance Check) derived from previous interactions, demonstrating how the system has "evolved" handle more complex, application-orienteed tasks efficiently.
Analysis:
T Data Construction and Filtering Process
To ensure the high quality and academic rigor of AcademicEval, we followed systematic data construction pipeline. The process consists of three main stages: collection, preprocessing, and filtering.
34

---

Published in Transactions on Machine Learning Research (04/2026)
1. Data Collection. We initiated data pool by sourcing raw research papers from arXiv, specifically targeting the Computer Science domain (e.g., cs. CL. cs. L.G, es.AT). To align with the capabilities of modern LLMs, we focused on recent high-quality publications to serve as the external knowledge source.
2. 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 cleanly separate the main body (used as external knowlodge context) from the abstract (used ground truth), while effectively removing non-textual elements.
3. Filtering Criteria. From the parsed corpus, applied strict filtering criteria to select the final test set for AcademicFval:
Length Constraint:
To strictly evaluate long-content capabilities (a core motivation of Thought- Retriever), filtered for papers that exceed substantial token threshold. This ensures that the retrieval and reasoning challenge is non-trivial.
Structure Integrity: We selected papers with clear section demarcations to ensure consistent evaluation formats.
4. Test Set Sampling. Crucially, consistent with our analysis in Section 4.4 sampling based 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-basod and aasooing-heavyy queries.
we performed stratified
35

---

Published in Transactions on Machine Learning Research (04/2026)
Original Abstract:
Benchmarks such as GLUE have helped drive advances in NLP by incentivizing the creation of more accurate models. While this leaderboard paradigm has been remarkably successful, historical focus on performance based evaluation has been at the expense of other qualities that the NLP community values in models, such compactness, fairness, and energy efficiency. In this opinion paper, we study the divergence between what is incentivized by leaderboards and what is useful in practice through the lens of microeconomic theory. We frame both the leaderboard and NLP practitioners as consumers and the benefit they get from a model as its utility to them. With this framing, we formalize how leaderboards community at large. For example, highly inefficient model would provide less utility to practitioners but not to leaderboard, since it is a cost that only the former must bear. To allow practitioners to better estimate model's utilityy to them, we advocate for more transparency leaderboards, such as the reporting of statistics that are of practical concern (e.g., model size, energy efficiency, and inference latency).
their current form can be poor proxies for the NLP
Abstract by Thought Retriever:
The paper "Utility is in the Eye of the User: A Critique of NLP Leaderboards' critiques the prevalent assumption that leaderboard metrics accurately reflect the utility of NLP models. It emphasizes the need consider multiple factors beyond predictive accuracy, including energy efficiency, model size, and inference latency, when evaluating NLP models. The paper argues that leaderboard values often diverge from the values of practitioners, callinq for increased transparency on leaderboards to better aliqn these metrics with the practical concerns of the NLP community. highlights the importance of holistic approach NLP evaluation, advocating for the inclusion of practical statistics leaderboards to provide more comprehensive measure of model utility. The research suggests that this shift towards transparency and comprehensive evaluation will encouraqe the development of models that are not only accurate but also efficient and fair, ultimately better serving the needs of diverse users in the NLP community.
Comments from Expert LLMs:
This abstract effectively captures critical aspects of the oriqinal while emphasizing the divergence between leaderboard metrics and practical utility. It maintains clarity and completeness, making it an excellent summary of the paper's main arguments. Specifically, it includes key elements such as energy efficiency, model size, and the need for holistic approach, which are well-aliqned with the oriqinal abstract. The Thought Retriever abstract is comprehensive and aligns well with the original content, making it the best among the generated abstracts.
Figure 15: Qualitative Example Original Abstract and Abstract Generated by Thought- Retriever. This figure presents example outputs from different methods using data from the AcademicEval- abstract-single datasct. Specifically, it shows the original abstract alongside the abstract generated by Thought-Retrieverr accompanied by a comment from an expert LLM. Comparison examples generated by DPR and TF-IDF are shown in Figure [6] while comparison examples by the long context model can found in Figure
36

---

Published in Transactions on Machine Learning Research (04/2026)
Abstract by DPR:
discusses the importance including costs model size, energy efficiency, training time, and inference latency leaderboard rankings NLP models, which currently prioritize accuracy above else. focus on accuracy can result models that provide high utility to leaderboards but not to practitioners, as they may come with unaccounted costs, The text suggests that model creators could voluntarily submit these cost statistics, but without a requirement, there is no explicit incentive to do so. proposes creating user-specific leaderboards with desired thresholds and reporting performance on the worst-off group to incentivize improvements. The use of revealed preference theory is suqgested to estimate model's utility by examining its real-world use. The text also advocates for making usaqe statistics public and allowing users to adjust metric weightings to create personalized leaderboards, aligning rankings with practical utility.
Comments from Expert LLMs:
This abstract is detailed and covers the essential points but tends to be repetitive and slightly verbose. effectively communicates the need for including various costs in leaderboard rankings, but it lacks the concise clarity found in the Thought Retriever abstract. Additionally, the abstract somewhat incoherent lack clear flow between ideas. It does not explicitly mention holistic approach evaluation or the practical applications of this research, which are important elements from the original abstract.
repetitive structure
Abstract by TFIDF:
Recent research has focused on the evaluation and development of NLP models, highlighting issues such as brittleness, bias, and lack of robustness. Datasets like SQUAD 2.0 have addressed challenges like unanswerable questions, while biases in NLP systems lead to better performance issue in evaluation is aligning priorities between leaderboard rankings and practitioners' preferences. While accuracy valued by both, practitioners also consider out-of- distribution data, fairness, and real-works applicability, which leaderboards often ignore. The study suggests that leaderboard design should include prediction costs and fairness measures better align with real-worf requirements. Alternative evaluation methods that reflect the complexities of NLP model usage also recommended.
gender-stereotypica inputs and inanimate objects. A key
Comments from Expert LLMs:
This abstract concise and well-organized, addressing key points effectively. communicates the need for better alionment between leaderboard rankings and practical utility but does not delve as deeply into specific suqgestions for improvement the Thought Retriever abstract. While provides a detailed discussion on issues brittleness and bias, might too detailed, potentially overwhelming the reader with too many aspects without a clear focus on the main argument of the original abstract.
Additionally, it lacks specific elements like the advocacy for increased transparency and the detailed exolanation of how leaderboards can be poor proxies for the NLP community, which are crucial parts of the original abstract.
Figure 16: Qualitative Example- Abstracts Generated by DPR and TF-IDF. This figure presents example outputs using data. from the aaaddemicFvallaasttat---iiii ods: DPR and TF-IDE. We also include comments from expert LLM. The original abstract and the abstract generated by our Reeieerr can be found in Figure 15
dataset, generated by traditional meth-
37

---

Published in Transactions on Machine Learning Research (04/2026)
Abstract by Nours Hermes 32k:
The text addresses the limitations of performance-based evaluation in NLP tasks and advocates for more comprehensive leaderboard designs. It points out the disparity between leaderboard priorities and the values of the NLP community, such as fairness, energy efficiency, and robustness. By viewing leaderboards and NLP practitioners as consumers of models, the text suggests using utility functions to align their preferences. Three main limitations identified: non-smooth utility, prediction cost, and robustness. Leaderboards only increase utility from accuracy improvements that affect rankings, while practitioners value consistent accuracy gains. Leaderboards ignore prediction costs, such as model size, energy efficiency, and inference latency. They also overlook robustness, crucial for real-world applications. To address these issues, the text proposes that leaderboards require transparency, reporting statistics like training time, model size, inference latency, and energy efficiency. This transparency would help practitioners estimate model utility and re-rank models based on their needs. The text also suggests personalized leaderboards, allowing users to adjust metric weightings creating leaderboards tailored to different user types based on their preferences or actual usage.
Comments from Expert LLMs:
The long context model's abstract is detailed but overly verbose, making it harder to quickly grasp the core arguments. In contrast, the thought retriever's abstract more succinct and focused, effectively summarizing the critique of leaderboard metrics and the need for comprehensive evaluation factors. This makes the thought retriever's abstract clearer and better aligned with the original abstract's intent.
Qualitative Example Abstracts Generated by Long Context Model. This figure presents example outputs using data from the AcademicFvallaasttrat-iiiii dataset, generated by the long context model Nours Hermes 32k. The original abstract and the abstract generated by our Thought- Retriever can be found in Figure 15
Figure 17:
38

---

Published in Transactions on Machine Learning Research (04/2026)
prottle!
imput your
your
benglo, Yoshus benglo, yoshua Bengio.
Impul your name
Cenerated profiee tran be edited)
Edit Profile
Set Profile
(a) Profile.
Get research trend and idees!
We will give you personalized research trenz and idtess you have set your profile. Otherwise, general research trend will be provided
Se lot Time Rango
Trend Papers
dayy
wook
all
Confirm
Topic Trend
Iceas Related to Topic Trend
(b) Research Trend.
Chat with AxploCt
Chuttet
Message Ar Coplict here..
Send
Clear
With Eopllo, how mony minutes you own abtain theoome smount infermation
(c) Chat and Feodback.
Figure 18: Arxiv Copilot Demo. This figure shows the demo built. based on our proposed Thought- Retriever, which is publicly available on Hugging Face. It offers personalized academic services, aiming test the real world robustness of our algorithm and provide social benefits.
39


Reviews:
[{"review_id": "e3TmxQKypu", "reviewer": "Reviewer_bn2M", "summary": "", "questions": "", "limitations": "", "rating": null, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "No", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "paper_topic_and_main_contributions": "", "excitement": null, "reproducibility": null, "ethical_concerns": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "summary_of_contributions": "This paper tackles the task of external knowledge retrieval for LLMs, which often struggle to fully take advantage of the whole knowledge base due to context size limits. The idea of the paper is to summarise the large context into \"thoughts\" which can also be refined e.g. by interacting with the user queries\n\nContributions:\n  - AcademicEval, a new benchmark for retrieval augmented tasks, as well as a platform to help users create similar datasets\n  - ThoughtRetriever augments hierarchal RALMs in a way that the way thoughts/knowledge chunks are organised is now user-query dependent", "claims_and_evidence_yn": "No", "claims_explanation": "- The main missing link is the omission of MemWalker as a baseline. MemWalker is a hierarchical RALMs which is the main work referenced by the authors in related work. I understand that \"it is costly to run and cannot scale to tasks with many data chunks\" but I think it's really difficult to judge the potential improvement of ThoughtRetriever without this baseline or a similar one, since ThoughtRetriever is in essence very closely related to hierarchical RAG \n\n- More generally, he related work section is quite short which makes it hard to place `ThoughtRetriever` in the retrieval-augmented literature. In particular, things like contextual compression are only briefly addressed (only Hierarchical RALMs are developed), although it does seem to be the most obvious way to address the challenge presented in the introduction\n\n- Maybe a more minor comment but the paper mentions the \"little computational overhead of ThoughtRetriever\" however it is not clear what it is being compared to. For instance, In Figure 1, compared to HRALMs, ThoughtRetriever utilises an extra LLM to summarize/connect past thoughts and queries, which has to bring some overhead ?", "audience_yn": "Yes", "audience_explanation": "- The problem of making RAG more scalable and efficient is an interesting one\n\n- As shown in Section 4.3 Toughtretriever can be extended to retrieve contexts generated by multiple experts LLMs which is an interesting use case", "requested_changes": "- More thorough/explicit related work, in particular on the topic of context compression for RAG, to extend the discussion beyond hierarchical RALMS\n\n- Add a hierarchical RALMs (MemWalker or other) baseline, even if done on a smaller scale experiment just for ablation, as it would greatly support the story presented in the introduction\n\n- Minor change: More clearly describe what \"little computational overhead\" means for ThroughtRetriever. Maybe this could also be explicated with timings/FLOps/etc measurement in experiments ?\n\n- Minor change: Ideally Figure 1 should come with an actual use case/example; If I understood correctly, the assumption is that Hierarchical RALMs may struggle to group knowledge chunks into the optimal structure (optimal as in, all relevant chunks are part of the top-K retrieved groups), while ThoughtRetriever does so by making the group user-query dependent. However, without context, it's hard to understand how often this happens in practice and how many failure cases of HRALMs are linked to this.\n\n- In a similar way, it is not fully clear to me how ThoughtRetriever can interact with different user queries to \"self-evolve\" on a real use case: In particular the main qualitative example in Table 11 only showcases an example with a single query.", "broader_impact_concerns": "No very strong concerns.  I guess one possible implication (but more of a stretch) of organising LLM \"thoughts\" based on past user queries is the potential impact of leveraging this design across different users to let a LLM self-evolve. On the one hand, it could improve the model performance to leverage past user interaction, on the other hand, this might mean certain user queries could influence how the LLM organise thoughts", "additional_comments": "", "extra_scores": {}, "extra_text": {}}, {"review_id": "mKK6MUaIMi", "reviewer": "Reviewer_ovuJ", "summary": "", "questions": "", "limitations": "", "rating": null, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "Yes", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "paper_topic_and_main_contributions": "", "excitement": null, "reproducibility": null, "ethical_concerns": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "summary_of_contributions": "This paper introduces Thought-Retriever, a novel framework that enables Large Language Models (LLMs) to utilize vast external knowledge beyond their context limit. Its core innovation is having the LLM generate and store condensed \"thoughts\" from past query responses, creating a dynamic, retrievable memory. This allows the model to self-evolve and leverage deeper abstractions for complex queries. The authors also contribute AcademicEval, a challenging benchmark using real academic papers to test long-context understanding. Extensive experiments show Thought-Retriever significantly outperforms state-of-the-art baselines, demonstrating strong performance gains and novel scaling capabilities through continuous interaction.\n\n1. Although the concept is intuitive, a more rigorous and formal definition of a \"thought\" is needed. The current definition $T_i = L(Q_{think}, K_i)$ is a start, but it should be explicitly differentiated from a simple \"summary\" or \"response.\" What are the necessary and sufficient properties that make an LLM's output a \"thought\" in this framework?\n\n2. The claim of self-evolution is exciting but needs stronger causal evidence. Figure 5 shows correlation, not causation. An ablation is needed where the model is evaluated on a held-out set of queries after being trained on a separate set of queries, demonstrating that the accumulated thoughts from the training queries genuinely improve performance on novel, unseen test queries, rather than just performing better on queries similar to those it has already answered.\n\n3. The AcademicEval benchmark is a significant contribution, but its construction process needs more detail. How were papers selected and filtered? What was the prompt and the specific \"expert LLM\" used to generate ground-truth summaries for Abstract-multi (Figure 10)? A discussion of potential biases introduced by this automated labeling process is necessary.\n\n4. The paper uses Contriever as the primary retriever, validated by an ablation study. However, the reasoning for initially selecting Contriever over other dense retrievers like DPR or Dragon is not provided. A brief justification based on its performance or design (e.g., unsupervised, general-purpose) would strengthen the methodological setup.\n\n5. The confidence generation mechanism $c_i$ is crucial for filtering. The paper should include an analysis of its accuracy. For instance, a sample of thoughts labeled $c_i=0$ and $c_i=1$ could be manually evaluated to report the precision/recall of this self-evaluation step. The prompt in Figure 13 should also be moved from the appendix to the main body.\n\n6. The experiments lack a comparison to an \"oracle\" or upper-bound baseline. For instance, a baseline that retrieves the ideal set of K raw chunks (as determined by human annotation or maximal F1 score with the ground truth) would help contextualize the performance gap that remains between Thought-Retriever and the theoretical optimum.\n\n7. The claim of \"little computational overhead\" (Page 2) is potentially misleading. While the retrieval cost is similar, the framework introduces significant inference overhead by requiring two additional LLM calls per user query (for answer+confidence and thought generation). This should be explicitly quantified and discussed as a trade-off for improved performance.\n\n8. The methodology for calculating the abstraction level of a thought (Page 8) is unclear. How is the \"average abstraction level of all the segments it retrieves\" computed when a thought is built upon other thoughts? This recursive process needs a clearer formal definition or algorithm description.\n\n9. The related work section should be expanded to discuss connections with other areas. For example, how does Thought-Retriever relate to \"Memory-Augmented Neural Networks\" and research on \"Experience Replay\" in continual learning? Drawing these connections would better position the work within the broader machine learning literature. In addition,  the following related references should be discussed in the introduction and related work, and some methods should be compared in experiments.\n\n[1] CLAIM: Mitigating Multilingual Object Hallucination in Large Vision-Language Models with Cross-Lingual Attention Intervention\n[2] CAI: Caption-Sensitive Attention Intervention for Mitigating Object Hallucination in Large Vision-Language Models\n[3] Searching parameterized retrieval & verification loss for re-identification\n[4] Understanding the LLM-ification of CHI: Unpacking the Impact of LLMs at CHI through a Systematic Literature Review\n[5] Llm-based nlg evaluation: Current status and challenges\n[6] LLM-assisted relevance assessments: When should we ask LLMs for help?\n[7] User-LLM: Efficient LLM Contextualization with User Embeddings\n[8] Can llms replace human evaluators? an empirical study of llm-as-a-judge in software engineering\n\n10. Several figures, particularly the pipeline in Figure 2, are small and difficult to read. The labels and text should be enlarged. In Table 2, the \"Win Rate\" column's description is confusing; it should be explicitly stated that for the Thought-Retriever row, the 50% is a reference point, and values for other baselines are their rates of being chosen over Thought-Retriever.\n\n11. Page 3, Sec 2.1: The root source mapping $Ô(T_i)$ should be more explicitly defined, perhaps with a simple recursive example.\n\n12. The description of the Thought Merge step (Page 4) uses an indicator function. It would be clearer to describe it as a simple check against a similarity threshold ε.\n\n13. The limitation regarding English-only evaluation should be moved from the \"Limitations\" section (which is often skimmed) to the experimental setup or a dedicated discussion paragraph, as it is a significant constraint on the claimed generality.", "claims_and_evidence_yn": "Yes", "claims_explanation": "See Summary Of Contributions.", "audience_yn": "Yes", "audience_explanation": "See Summary Of Contributions.", "requested_changes": "See Summary Of Contributions.", "broader_impact_concerns": "See Summary Of Contributions.", "additional_comments": "See Summary Of Contributions.", "extra_scores": {}, "extra_text": {}}, {"review_id": "0TK9mRwoeS", "reviewer": "Reviewer_Rtrk", "summary": "", "questions": "", "limitations": "", "rating": null, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "Yes", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "paper_topic_and_main_contributions": "", "excitement": null, "reproducibility": null, "ethical_concerns": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "summary_of_contributions": "The paper introduces Thought-Retriever, which builds a searchable thought memory from an LLM’s intermediate responses and retrieves from the mixture of external chunks and prior thoughts at inference time. It also releases AcademicEval, a long-context benchmark for single/multi-paper abstracting and related-work writing. Experiments on AcademicEval plus GovReport/WCEP show consistent gains, and analyses show better precision-recall balance, scaling with #thoughts, and higher-level thoughts for abstract queries. Ablations highlight the impact of Contriever and filtering, with preliminary checks against NV-Embed and RECOMP.", "claims_and_evidence_yn": "Yes", "claims_explanation": "Results across multiple tasks show consistent gains; precision/recall plots and ablations substantiate the mechanism (thought retrieval + filtering) and scaling with more thoughts.", "audience_yn": "Yes", "audience_explanation": "Researchers in RAG, memory architectures, and long-context evaluation will find the idea and benchmark relevant and practical.", "requested_changes": "Please consider to include or justify the following:\n\n1. Include at least one recent long-context/base LLM and expand retriever/reranker coverage beyond Contriever in main experiments; report latency/cost. \n2. Move/include multi-stage/agentic RAG (planner/reflect/rewrite pipelines), not only single-stage and truncation; consider move RECOMP to main text from the appendix. \n3. Release code + scripts (indexing, prompts, memory ops) and minimal library API for reproducibility.\n4. Expand related work on retrieving rationales/thoughts and contrast with hierarchical/compression methods.", "broader_impact_concerns": "N/A", "additional_comments": "", "extra_scores": {}, "extra_text": {}}]

Author rebuttal:

Decision: Accept (poster)