Beyond RAG vs. Long-Context: Learning Distraction-Aware Retrieval for Efficient Knowledge Grounding
Authors: Seong-Woong Shim
Myunsoo Kim
Jae Hyeon Cho
Byung-Jun Lee
Venue: ICLR 2026 Poster (2026)
Keywords: Retrieval-Augmented Generation
Language Models
Long-Context
Primary area: unsupervised, self-supervised, semi-supervised, and supervised representation learning
TL;DR: We propose a learning-based retrieval strategy framework that adaptively balances information coverage and distraction in accordance with the capacity of the LLM.

Abstract: Retrieval-Augmented Generation (RAG) is a framework for grounding Large Language Models (LLMs) in external, up-to-date information. However, recent advancements in context window size allow LLMs to process inputs of up to 128K tokens or more, offering an alternative strategy: supplying the full document context directly to the model, rather than relying on RAG to retrieve a subset of contexts. Nevertheless, this emerging alternative strategy has notable limitations: (i) it is token-inefficient to handle large and potentially redundant contexts; (ii) it exacerbates the “lost in the middle” phenomenon; and (iii) under limited model capacity, it amplifies distraction, ultimately degrading LLM output quality. In this paper, we propose LDAR (Learning Distraction-Aware Retrieval), an adaptive retriever that learns to retrieve contexts in a way that mitigates interference from distracting passages, thereby achieving significantly higher performance with reduced token usage compared to long-context approaches. Extensive experiments across diverse LLM architectures and six knowledge-intensive benchmarks demonstrate the effectiveness and robustness of our approach, highlighting the importance of balancing the trade-off between information coverage and distraction.

Published as a conference paper at ICLR 2026
BEYOND RAG VS. LONG-CONTEXT. LEARNING DISTRACTION-AWARE RETRIEVAL FOR EFFICIENT KNOWLEDGE GROUNDING
Seongwoong Shim
Myunsoo Kim Jac Hyeon Choè
Byung-Jun Leeh-t
Korea University, Decision Making Lab 2LG CNS
(ssw030830, m970326, Dyungjunle @ akorea.a...... ac.kr 2 cjh.cho@lgcns.co
ABSTRACT
Getrieval-Augmentte Generation (RAG) is framework for grounding Large Lan- guage Models (LLMs) in external, up-to-date information. However, recent ad- vancements in context window size allow LLMs to process inputs of up to 128K tokens more, offering an alternative strategy: supplying the full document con- text directly to the model, rather than relying RAG to retrieve a subset of con- texts. Nevertheless, this emerging alternative strategy has notable limitations: (i) it is loken-inefficient to handle large and potentially redundant contexts; (ii) it exac- erbates the 'lost the middle' phenomenon; and (iii) under limited model capac- ity, it amplifies distraction, ultimately degrading LLM output quality. In this paper, we propose LDAR (Learning Iitracttio-Awar Retrieval), an adaptive retriever that learns retrieve contexts in way that mitigates interference from distract- ing passages, thereby achieving significantly higher performance with reduced token usage compared to long-context approaches. Extensive experiments across diverse LLM architectures and six nnowledge-intenssee benchmarks demonstrate the effectiveness and robustness of our approach, highlighting the importance of balancing the trade-off between information coverage and distraction.
INTRODUCTION
Despite the remarkable progress of Large Language Models (LLMs), they continue to exhibit factual er- rors (Wi al.] 2024 Lv al.] 20044] Li et al.] 2024a), and their knowledge remains limited to the static dataset which they were trained. To ad- Gener- ation (RAG) has been proposed, enabling models ground their outputs in external, up-to-date informa- tion, thereby enhancing both accuracy and relevance tasks (Zhang al. [0244a] Xu et al., 2024). In practice, RAG retrieves a small set of the most relevant passages from external corpus to ground the LLM's generation process.
0.8
Performance
0.7
0.6
dress these limitations,
0.5
Mistral Nemo 128.
0.3
Qwen-2.5-78-ing
in smowledge-intenss
GPT-4o
0.2
0.0
0.6
Cost (Token Usage Ratio)
Figure 1: Performance of LLMs across to- ken usage ratios. Higher ratio corresponds to retrieving more passages. Lines indicate per- formance when retrieving top-similarity pas- sages within fixed token usage ratio (1.0 full context). ' marks the performance of L.AAR optimized for each LLM, illustrat- ing its ability to strike a balance between in- formation coverage and distraction that sur- passes all fixed token usage baselines.
Recent advancements have substantially increased the context length of LLMs, with some models now supporting inputs of up 128K tokens more (e.g., GPT-40 (OpenAT 2024). Gemini 2.5 (Comanici al. 2025), Qwen 2.5 (Yag al. 2025 ). This ca- pability offers an alternative strategy for grounding model outputs: supplying the full document context directly to the model. rather than relying on RAG
Equal contribution.
Corresponding author.

---

Published as a conference paper at ICLR 2026
to supply only subset of them. With sufficient capacity, LLMs can selectively attend to salient information while disregarding irrelevant content, thereby reducing reliance on explicit retrieval mechanisms. Indeed, empirical evidence from emerging benchmarks indicates that providing LLMs with full long-contexts frequently outperforms RAG-based approaches (Li et al. 2024b Wang et al.) 2024b. Li al. (025a). Nevertheless, this long-context approach has its drawbacks, as it can be token-inefficient to process large, potentially redundant contexts. Moreover, long-context ap- proaches are prone to the 'lost in the middle' phenomenon, where the model struggles to recall information presented in the middle of a long input sequence (Liu et al., 2023). When model ca- pacity limited. supplying the full context may further introduce distraction, thereby degrading answer quality (Li et al.) 2025a). These challenges highlight the need for approaches that integrate the advantages of both paradigms- paradigns-approachiig approaching the performance of long-context approaches while maintaining the token efficiency of RAG.
In this paper, we demonstrate that both open and closed-souree LLMS can still fail to answer ques- tions even when the gold passage is retrieved, due to interference from additionally retrieved pas- sages (i.e., distracting passages) (Shi et al., 2023a Cuconasu et al. 2024; Amiraz et al. 2025). However, retrieving passages to minimize such distraction remains non-trivial challenge, as the optimal strategy depends not only on the capacity of the target LLM, but also on the combinatorial interactions among the retrieved passages. To address this challenge, we introduce LDAR (Learning Distraction-Aware Retrieval), a retriever that learns to select passages to minimize potential inter- ference from distracting passages in accordance with the capacity of the LLM, thereby achieving significantly better performance and lower token usage compared to long-context approaches. In summary, our contributions are as follows:
1. Unlike previous heuristic-based methods, we propose learning-based based retrieval strategy framework that adaptively balances information coverage and distraction in accordance with the capacity of the LLM, achieving better performance with significantly reduced token usage compared to the long-context approach.
2. We empirically demonstrate that retrieving passages in bands (i.e., selecting from contigu- ous ranges along the similarity- -ranked list) is critical for learning a distraction-awar aware re- trieval strategy. The banded retrieval strategy provides form of abstraction that improves generalization and prevents the retriever from converging to suboptimal solutions.
3. We validate our approach across diverse LLM architectures (both open and closed-source) and six knowledge-intensive intensive benchmarks, demonstrating both the effectiveness and robust- ness of the proposed retrieval strategy. Our code is at httpps:/giihuu..cm//e wttphh/bb.coomuuudmmm////ae dmlab/LDAR
RELATED WORKS
RAG vs. Long-context LLM Numerous studies have examined the comparative performance of RAG versus LLMs provided with the entire input context. While some works report that RAG out- performs long-context approaches (Xu et al. 2023b), other works demonstrate the opposite trend, with long-context models surpassing RAG-basee methods cLi al. 2024b). Li et al. (2025a) demon- strates that this divergence in findings largely stems from the capacity of LLMS used to evaluate the results. Open-source L.LMs typically exhibit limited capacity for processing long contexts and there- fore benefit substantially from retrieval mechanisms. On the other hand, closed-source LLMs often possess stronger long-context capabilities and consequently achieve higher performance when given full-context inputs (Li et al. 2025a). These findings suggest that RAG works as stopgap technique to boost models that otherwise struggle with long sequences (Bai et al. 2023 Li et al.) 2025a). Furthermore, several studies highlight that increasing the number of retrieved text chunks yields an inverted-U pattern: performance initially improves but eventually declines as the model becomes distracted by irrelevant or misleading passages (Jin et al. 2024. Leng et al.] [ (024). This observation underscores the need for retrieval strategies that balance information coverage against the risk of distraction, thereby optimizing the trade-off in passage selection.
Bridging the Gap between Retriever and LLM Our method can also be interpreted as bridging the gap between the retriever and the LLM. Since retrievers and LLMs are pretrained under distinct training objectives and architectures, preference gap naturally arises between them (Ke et al. 2024 Ye et al., 2024). The passages retrieved by the retriever can even distract the LLM during answer generation, thereby degrading downstream performance (Shi et al. 2023a Cuconasu et al. 2024
2

---

Published as a conference paper at ICLR 2026
Gold passages
Retrieve all Cumulatively
Retrieve Gold passages First
Retrieve Gold Curulatively
0.413
Codine similakty
6.352
0.323
0.200
0.752
0.375
Cosine smititt
0.42
0.000
15
20
Passage 15
15 Passage Retrieved Passage
10 Passage Rank
25
Passage Rank
Cosine Similarity
Correct Answer
Wrong Answer
Figure 2: Visualization of different retrieval strategies and their impact on performance. A green circle (0) indicates that retrieving the passage yields correct answer, a red cross (X) indicates retrieving the passage yields wrong answer, and purple star (f) denotes passage that has already been incorporated into the retrieved passage set. The black curve represents the cosine similarity between the query and passages. The top row reports results for an open-source model (Llama-3.1 8B), while the bottom row shows results for a closed-source model (GPT-4o) on a reasoning task (Li al. 2025a).
Amiraz et al. 2025). Prior work has attempted to mitigate this gap by fine-tuning the LLM (Izacard Grave, 2020; de Jong et al. 2023), fine-tuning the retriever (Shi et al. 2023b Xu et al. 2023a), jointly fine-tuning both components (Izaaadd al. 2022 Lewis et al. (020), training module
that bridges the gap (Ke et al. 2024 Ye et al., 2024). Whereas bridge modules identify relevant
passages based on textual information within the top-A candidates retrieved by cosine similarity, our method instead targets the retrieval stage itself. Specifically, we aim to retrieve sets of passages that minimize distraction under fixed pretrained retriever and LLM, relying solely the similarity distribution between the query and passages.
MOTIVATION
Although prior works have highlighted the detrimental impact of distracting passages on retrieval performance (Jin al. 2024, Leng et al. 2024), relatively little attention has been paid to retrieval strategies that explicitly mitigate such influence. The columns in Figure FigureBillustradd illlstrate retrieval strate- gies commonly adopted in practice: (1) gold passages that lead correct answer when individually retrieved; (2) retrieving all passages cumulatively from top bottom, which corresponds to the con- ventional top-k similarity-
retrieval approach (Lewis et al. 2020 Karpukhin al. 2020); (3)
retrieving all gold passages first (*)) followed by retrieving additional passages, which corresponds to reranking the retrieved top-k passages to prioritize relevance (Nogucira & Cho 2019; Nogueira
et al. 2020 Glass al. 2022); (4) retrieving gold passages cumulatively from top to bottom, which resembles the successful outcome of the hybrid strategy: first selecting the top-k passages by sim- ilarity and then applying relevance-based top-n selection to retain only the gold passages (Asai
ct al. 2024 Ke et al. 2024 Lee et al., 2025).
As shown in (2), top-k retrieval is susceptible distracting passages. Even when individually correct passages are included, their joint presence with distracting passages can result in incorrect answers. Note that retrieving all available passages effectively equivalent the long-context approach setting, which is likewise prone to errors. In (3). reranking approaches that place highly relevant passages at the front of the retrieved passages still fail when distracting passages are present, as their inclusion can override or obscure the signal from the relevant ones. Finally, (4) shows that even when the hybrid strategy, which combines similarity based top-k retrieval with relevance-based top- selection, successfully retains only the gold passages, their joint retrieval can still lead to incorrect answers. Counterintuitively, even when all passages individually lead to the correct answer, their collective inclusion can complicate the reasoning process and ultimately cause the model generate incorrect outputs. Based on these observations. we define distracting passages as passages that misguide the LLM in generating the correct answer, irrespective of whether they lead to correct
3

---

Published as a conference paper at ICLR 2026
0.90
0.95
Eimiarly
Remeeedd
2:10
Purformance
1..-
Fassage Usage
Cornect Ansowar
Similariy
Whong Araonar
0.5
Bemoull
0-4
0.89
166
150
203
200
53
100
253
105
Passage Rank
Passage Rank
Gradient Step
Gradiem: Step
Figure 3: (Left) Visualization of passages retrieved by my based on the similarity distribution be- tween queries and passages, with retrieved passages marked in green if they contain the correct answer and in red otherwise. (Right) Comparison of performance and passage usage ratio across Bernoulli- and band-based retrieval strategies across gradient steps.
answer when individually retrieved. Furthermore, the differing outcomes in Figure (top bottom) demonstrate that retrieval effectiveness is strongly tied to the capacity of the underlying LLM.
These findings underscore the inherent difficulty of reliably retrieving passages that yield correct answers, motivating the need for retrieval strategies that explicitly account for distracting effects in relation model capacity. Note that brute-force remedy minimize distraction would be em- ploy a high-capacity LLM to exhaustively read and align every passage to the query. However, this approach is prohibitively expensive, as inference cost scales with the number of passages, making infeasible in practice (especially in the long-context setting). To address this challenge, we propose a lightweight adaptive retriever framework that learns to minimize distraction by selecting effective passage sets in accordance with the long-context capability of LLMs. This method relies solely on the cosine similarity distribution between queries and passages, guided by evaluation signals to learn an effective balance between information coverage and distraction.
4
MAIN METHOD
RAG employs a pretrained embedding model So that maps a query g and passages {pc [pr), into shared vector space Rdd, retrieving the top-k passages ranked by semantic similarity. Let 8; denote the similarity (e.g., dot product cosine similarity) between the query and the i-th passage,
30 s.t. 50(1) S 5.(2) S ... S Sa(N), R .P .P(N))
Retrieved passages R with higher similarity scores are semantically closer to the query, as the em- bedding space is trained via contrastive objectives that pull matched query-passage pairs together while pushing apart mismatched pairs (Izacard et al.] 2021 Li et al. 2023 Zhhng et al.) 2024b). This dense retrieval approach has remained the dominant strategy due to its effectiveness in re- trieving relevant passages at low computational cost, spanning from early RAG models to recent applications (Lewis et al. 2020 Tang & Yang 2024 Asai et al. 2024 et al. 2025b).
In this section, we present lightweight retriever Ty that learns select passages to minimize potential interference from distracting passages, solely based on the similarity between the query and passages. As discussed in Section 3, employing a high-capacity LLM to exhaustively align all passages with the query is impractical, particularly in long-context settings. To ensure our ap- proach scales large-scale retrieval scenarios, we deliberately restrict Te from accessing textual information. Moreover, our approach avoids the expense of fine-tuning the large pretrained LLM and embedding model by training only a lightweight neural network reduce distraction, while keeping the larger components fixed.
Our retriever ie operates on the cosine similarity distribution and selects a dynamic set of passages from contiguous quantile interval 9r. qu C [0, 1]. When the retriever To determines that infor- mation coverage should prioritized at the risk of increased distraction, retrieves passages from a wide quantile interval. On the contrary, if the risk of having distraction is higher, retrieves passages from a narrow quantile interval, minimizing the risk of having distraction. Figure 3 (left) illustrates that optimized Ty adapts its retrieval strategy based on the similarity distribution between the query and passages. If passages with high semantic similarity exist, To tends to focus narrowly on that region. In contrast, when no passages exhibit strong semantic similarity, To expands the trieval range to ensure broader information coverage, even at the cost of incorporating more potential distracting passages.
4

---

Published as a conference paper at ICLR 2026
Adaptive Retriever T
Dataset
arXiv, novel, Wikipedia,
Lower Quantile
Upper Quantile
Attention Pooling
Selected Region
Query
Passages
t
Transformer Encoder
Pretrained Retriever
Pretrained LLM
Self Attention
Output
Evaluation
Cosine Similarity Distribution
Periodic Embedding
gradient update
Figure 4: Overview of LDAR, a learning-based retrieval strategy that adapts to each LLM by bal- ancing information coverage and distraction. Given query, a fixed pretrained retriever computes cosine similarity scores between the query and passages. Then periodic embeddings encode each score into a token, followed by a Transformer encoder that processes the tokenized similarity dis- tribution. The encoder representations heads predict the lower and upper quantiles that define the similarity interval used for retrieval. The selected passages are passed to pretrained LLM for prediction, and the evaluation signal is used to update the adaptive retriever through gradient-bases learning.
aggregated via attention pooling, after which two output
Notably, this behavior also depends on the capability the pretrained LLM: models with stronger long-context processing generally exhibit lower susceptibility to distraction compared to those with limited long-context capability (Li al.) (025a). Later in experiments, we demonstrate that the LDAR framework adaptively retrieves fewer passages for open-source models compared to closed- source models on the same task, indicating that our framework aligns retrieval strategies with the long-context capability of the underlying LLM.
4.1 DESIGNING THE ADAPTIVE RETRIEVER
We provide the adaptive retriever Ny with cosine similarity vector 8 E R between the query and the N passages, computed by a pretrained embedding model fØ:
fo( fo(p) 8; [g) [
1=
Since the number of passages associated with each query may differ, the dimensionality of 8 E R not fixed and varies across queries. To accommodate this variability, we employ bidirectional self-attention Transformer that maps the token embedding of each similarity score to a contextu- alized representation. An attention-pooling layer then aggregates these token-level representations into global summary vector, which is fed to output heads that predict the parameters (az, BL) and (av, B8) of two Beta distributions. The lower and upper quantiles qu and qu are then sampled from these distributions respectively, and the resulting band {gr, qu) C [0, is used to select the passages from the similarity distribution.
Intuitively, allowing the adaptive retriever to select passages via independent Bernoulli sampling for each candidate also a valid strategy. However, as shown in Tigure3 the Bernoulli-basse variant of LDAR fails identify a balanced trade-off between the RAG and long-context approach. This limitation arises from its need to explore the entire combinatorial subset selection space, which impedes generalization and ultimately causes convergence a local optimum (corresponding the long-context approach in this case). In contrast, sand-based retrieval reduces the effective search space from combinatorial subset selection low-dimensional and smooth control space, yielding
5

---

Published as a conference paper at ICLR 2026
Algorithm Distraction-Aware Adaptive Retrieval
Require: Instances D {(um, Pms ymm) )M m=1 Embedding model fs, Adaptive Retriever n 1: for m-th query 4m and passages {Pm,3 (Pm.aJi=1 D do
2: 8; 6- fulgm) (u(Pm.))
for i 1, ,,
[ [P
3: (qL. 90) ITQ
4:
4 max(1, N qr.
5:
4 max(e, (f, (.qu))
6:
a - argsort(s) s.t. So(1) S 8o(2) S ... S 8x(N)
7:
Rm (Pa(t), Po( ... Pa(u))
8: end for
9: return {R.m )
temporally abstract retrieval strategy that enables more sample-efficient credit assignment and promotes better exploration (Baranes & Oudeyer 2013; Machado et al. 2023 Kim et al.) 2025). Ultimately, our band-based retrieval strategy allows me achieve effective trade-off between information coverage and distraction, yielding a higher score while maintaining a lower passage- retrieval ratio. (see Figure 3 (right)). We provide an illustration of our adaptive retrieval process in Figure 4 with corresponding pseudo-code in Algorithm
4.2
OPTIMIZING THE ADAPTIVE RETRIEVER
The main goal of Ty is to retrieve set of passages that maximizes the likelihood of the pretrained LLM producing the correct answer to given query. To this end, we formulate the objective as maximizing the prediction accuracy of the LLM conditioned on the passage set retrieved by me:
max J(0) P.).
[r (g. R, v)] where Te (g. R, y) == Lee (q, R), (3)
Here, D denotes the dataset with each instance comprising query q, candidate passage pool P. and a ground-truth answer y. R denotes the set passages retrieved by मe given the similarity scores and con is indicator function that evaluates whether the output of the LLM (g R) matches ground-truth answer y.
By applying the likelihood ratio gradient with log-derivative trick (Sutton et al. 1999. update 9 at k-th gradient update step as:
0 02 + 7 (4, R, v) Vo, log Te, (-1s),
where y denotes the step size. Through this optimization, Ty learns distraction-awan retrieval strategy that reduces the likelihood of distracting passages interfering with the prediction LLM.
EXPERIMENTS
5.1 TASKS AND DATASETS
Six datasets encompassing diverse tasks and contexts are used to evaluate LDAR with other base- lines. Each dataset is partitioned into training and test sets using an 8:2 split ratio, and performance is assessed on the test set. Both the training and test sets are available in our GitHub repository.
Location, Reasoning, Comparison. Hallucination tasks are from the LaRA benchmark (Li al. 2025a), which is delicately designed to compare the performance between RAG and long-context ap- proach. Notably, these tasks include contexts approaching the maximum supported length of main- stream commercial and open-weight models (128K tokens), thereby providing rigorous evaluation of the long-context capabilities LLMs. In LaRA, contexts are drawn from novels, financial state- ments, and academic papers with entity replacement (Li et al. 2020 Zhang al. 2024c) to mitigate the risk of data leakage. The Location task evaluates an LLM ability to identify precise information based on the provided context. The Reasoning task examines the model's 's capacity for logical infer- ence, deduction, or computation within the given context. The Comparison task assesses whether the model can integrate and contrast information across multiple parts of the provided context. The

---

Published as a conference paper at ICLR 2026
Table 1: Comparison of retrieval strategies across context lengths and task types, Each cell reports the average score across LLMs with standard error. White background indicates the average over open-source LLMs and brown background indicates the average over closed-source LLMSS Num- bers in parentheses denote the token-usage ratio relative to L.C. The best performing strategy for each task is highlighted in bold. The open-source and closed-source models used to compute the The full results are available in Appendix D.II
scores are introduced in Section 5.2
Method
Location
Reasoning
Comparison
Hallucination
Overall
Context Length 32k
Top-1
52.7
56.61 0o
13 12
37.1133
20
86.0
89.4
15.65
54,02
(0009)
(000))
(2001)
Top-5
66.7= 12
78,0; ss
38.2 48
61.61
47.6
62.8 33
82.5
84.3
58.75
71.67
(005))
(000))
(00%)
(000)
(000))
(2.95)
(0080)
60.04)
Top-10
75.3g
83.4
41.61
59.51
51,5
65.2g
79.1
80.8
61,87=4a
72.22
1960
(0185
0t 1991
(0.000
(0.10%
166)
Top-25
18
87.41
39.9
61.61.0
50,2
70.9
74.4
77.01
60,65
74.22
(644))
(0,200
(BA5))
(B45))
01449
(0415)
(2415)
Top-50
78.1
87,0+ 15
37 17,44mm 40
63.8+ 36
49, 6
69.3 54
72,
74.7+ 50
59, 3,.30=
73.70
10.000
(0,90)
(0035)
(0,55)
03.8221
(0,02)
(0300)
8.809
L.C
80.3g 14
87 47.41.0 07
36.9 io
62.2 1-
47, 47.7
73.5g 40
69.6 41
73.0
58.62
74.00
(1230)
(1.500)
(LARM)
(2A46)
(2:000
RAG
76,3= 13
82,5+ 6s
43,7+ 12
59.0+ 13
47,0+ 65
61 81.2+53 57
81.5 11.5=1 33
79,8+ 4.7
62,12-
70.62+ S.
(SUnS)
(007))
(009))
(000))
63.8551
1004)
Self-Route
80.6 1
89 6+ 68
40,3+ so
62,7 Ll
47,0 47,0+.4 46
67.6 7.66a:1 19
69 9 a3
76.0+ 52
59 45 49
73.97 A3
Adaptive-k
61.0-2x 21
71,4+12 11
25,4+ 84
51.5+ 14
43,1+ su
59.6+ su8
77 3 2.1
82,2+ 44
51,70= 52
66.17 335
BGM
78.5 8-as
82 22.6+ 68
46,5+ 40
59.0+ is
50.1
6 61.2+ 60
75 4..4
75.74 7.
62.70= 24
RankZophyr
72.4 11 ar0sF)
82.1
36,9. 47 (umm))
44.4;
12.11:4
80.5 (4665)
85.2
58.55
72.2:
(UMM))
(DM94)
LDAR
87,7 14
91.9 12
52,7 47
70.1 DM
63.1 24
78,9 4M
76.5
76.8 1.
70.00 3.
79.42 34
Context Length 128k
Top-1
31.1 L3
31.5+
26,
33.7+33
4.30+
12.1
84.9
83,7
36,60=
40,25
R0M0)
16000:
(000))
10005)
10005)
01605)
(0005)
(000))
(5006)
5
60.1 14
61.71 18
47.41 16
62.611 13
24.3 10
1 1.
74.1
71.61** +4
51.47
57_501
(d02))
(0405)
(2/03)
Top-10
66.9 as (085))
70,8 25 (8425)
53,7 37
67.5 15
29.2 51
42,6 12
70.3 as (0662)
64.3 11
55.02 24
61 61_30
(0120)
(006))
T5p-25
71.3=20 27
80.0g 12
54.5+ 16
75.3+ 19
24.8 12
48, 1 15
65, 1 13.0
55.64 18
53.92 54
64.75 s4
Top-50
67.9 91. 41
80.3 05
52,4 19
74.3 3m
33,61 16
17 47
61,2, n8
52.7 14
53.77 54
64.92 57
LC
56,2+ 10
88. 0s 1s
45, 4 64
76.9+
23.8+
56.6+ 4n
49,5+
58,
43.65=m0
99.97=
RAG
15
7 11
16 (buck)
26.44
42.6 13
70.9
1 65
54.6
42
(ui2))
(uu5))
Self-Route
65.8=26
80.0 80,0+11
51.3+ 4n
59.7
25.3+
52.4+
56.4+
57.5+
49.70=
62.40=
(L187)
(0222)
(1181)
(0.44))
(8025)
(2.89)
(B043)
(2.24)
Adaptive-k
47.7 97.7=40
64, 4+
12,1=
54.
18.5+
44, 4
66.
68, 68,3+sx
41.07
57, 80=
01348
(0-95)
(M075)
(B47))
(0:05)
BGM
68.9 18
72.2
56,5
66.8
30,3
34,2
1
73.4+
57,20
61 6665
(alli))
(Uit5)
ad015)
duilio
(0015)
RunkZephyr
16
62.7+ 69
54,5e 15
71.4+
20.9+
28.0a
73.3
75,8+
51 51.20
59 48
16m00
(0006)
1005l)
(00%))
(0:24)
10025)
LDAR
77.3
90.5
61.7
82.7
42,9
65.8
64.3
65.9
61.55
76.22
0135)
(0/00)
002091
(L31))
(0:22)
1531))
Hallucination task evaluates whether an L.M can appropriately refuse answer when the provided context lacks the required information (which is always the case in this task). We observed that training models with feedback from the Hallucination task leads undesirable strategies in practice (e.g., deliberately retrieving no passage or irrelevant passages to trigger refusal. See Appendix [ for more details). This behavior arises because the Hallucination task, by design, rewards avoid- ance rather than the constructive use of retrieved evidence, leading to degenerate strategies that do not reflect real-world retrieval requirements. To circumvent this issue, we treated the Hallucination task purely as an evaluation benchmark rather than training objective. Consequently, for this task, we employed models trained on the Location task most widely adopted retrieval strategy in practice-and and evaluated them in zero-shot setting on the Hallucination task using the full dataset.
HotpotQA (Yang et al., 2018 and Natural Questions (NQ) (Kwiatkoskki et al. 2019) are widely used open-domain QA benchmarks that are incorporated into the HELMET benchmark (Yen et al.) 2024). Within HELMET, these datasets are adapted for long-context evaluation by extending input lengths to approximately 128K tokens through the inclusion of distractor passages. with all contexts drawn from Wikipedia. Among them, HotpotQA is distinguished as a multi-hop hopp QA task, requiring the model to retrieve and integrate information from multiple passages to derive the correct answer.
7

---

Published as a conference paper at ICLR 2026
5.2 BASELINES AND EXPERIMENTAL SETTINGS
Baselines We compare LDAR with eight top-k retrieval methods (Top-1, Top-5, Top-10, Top-25, Top-50, long-context, RAG (Lewis et al. 2020), RankZephyr (Pradeep et al., 2023)). one baseline designed to minimize the gap between a pretrained retriever and a pretrained LLM (BGM (Ke al.) 20244), and two retrieval baselines that aim to balance the trade-off between RAG and long-context processing (Self-Route (Li al. 2024b), Adaptive-k (Taguchi et al.) 2025)). For the top-k retrieval baselines, we retrieved the top-k passages according to similarity scores. Note that retrieving all passages (i.e., the full document context) corresponds the long-context (LC) approach. For RAG, we applied a bge-reranker-large to reorder the retrieved top-S passages. BGM trains sequence-tor sequence model on textual information to identify the effective passage set among the top-5 candi- dates retrieved by similarity. Self-Route queries the LLM to decide between RAG and long- processing based on the model's assessssssm of answerability. Adaptive-k retrieves passages by identifying the largest gap in the sorted similarity score. The listwise reranker RankZephyr jointly processes the top-50 üimilartt--retriee which it selects the final top-5 passages.
passages and produces a global ranking over them, from
Experimental Settings Following the evaluation metric used in LaRA (Li et al. 2025a) and HEL- MET (Yen et al., 2024). we employed GPT-4o to judge response correctness by providing it with the query, the ground-truth answer, and the model prediction. Throughout the experiments, we used bge-large-en-v1.5 as the embedding model. Note LDAR does not use reranker for reordering the retrieved passages, both to maintain high training efficiency and to demonstrate the effectiveness of our method in isolation. For open-source LLMs, we used -wwen-255-7B-Instruc Qwen-3-4B-25077 -lama-3.I-BB-Instrue Llamm--32-3B-Instruut and Mistral-Nemo--nnstuut---- sults. For closed-source LLMs, we used GPT-4o, GPT-4o-mini, Gemini-2.5-pra Gemini-2.5-flaie to evaluate the results. Across all LDAR experiments, we used the same hyperparameter configura- tion, as summarized in Appendix C.2
evaluate the re-
5.3 MAIN RESULTS
Table demonstrates the performance of LDAR compared baseline methods. To ensure statistical significance, we report the average score across LLMs with standard errors, and provide separate averages for open-source and closed-source models as introduced in Section [..2 LDAR generally achieves significantly higher performance compared to all other baselines, while using only about half the token usage of the long-context approach. It is worth noting that no penalty on token usage was imposed during the training of LDAR; the model was optimized solely for prediction accuracy (see Appendix D.5 for the cost-regularized variant of LDAR). These results suggest that a trade-off' between information coverage and distraction does exist, and that LDAR is able to balance such trade-offs by leveraging the similarity distribution between the query and the passages. As LDAR typically retrieves more passages than RAG but fewer than the long-context approach, it achieves Hallucination scores that are generally higher than those of the long-context approach yet lower than those of RAG. This trend arises because retrieving larger number of passages increases the likelihood of including mislcading but seemingly relevant passages, thereby increasing the risk of hallucination. Nevertheless, LDAR consistently outperforms RAG across all other tasks, resulting in significantly higher overall performance.
The average token usage ratio of LDAR relative the long-context approach is: 0.47 (32K open- source), 0.63 (32K closed-source), 0.25 (128K open-source), 0.52 (128K closed-source), LDAR tends use more tokens for closed-source models, which generally exhibit stronger long-context capability than open-source models. Notably, when the context length extends to 128K, the LDAR framework adapts by retrieving smaller portion of passages compared the context length 32K setting, indicating that the risk of having distraction with the long-context approach increases with longer input contexts. These results indicate that the LDAR framework dynamically aligns its re- trieval strategy to the long-context capability of the underlying LLM, adaptively balancing informa- tion coverage against potential distraction.
Breaking down by task, the average token usage ratio relative to the long-context approach is: 0.45 (Location), 0.43 (Reasoning), 0.50 (Comparison), 0.42 (Hallucination). Since the Comparison task requires integrating information from multiple regions of whole context, LDAR is optimized to
8

---

Published as a conference paper at ICLR 2026
retrieve larger number of passages relative to other tasks. This highlights that the optimal retrieval strategy varies across tasks, and our framework effectively adapts its retrieval strategy accordingly.
5.4 COMPARISON TO BASELINE METHODS
As demonstrated in Table LDAR achieves significantly better performance compared to the Top-k baselines, implying that LDAR executes different retrieval strategy based on the similarity distri- bution between the query and passages (see Figure 3 (left) for visualizations). Heuristic baseline methods that try balance the trade-off between RAG and long-context processing (Self-Route, Adaptive-k) fail to retrieve passages based on the long-context capability of LLMs, leading to worse performance. The reranker baselines (RAG, BGM, RankZephyr) also yield limited performance improvements, as they operate solely within the top-similarity region and only reorder those pas- sages, effectively disregarding the LLM's 's long-context reasoning capability. Specifically, the learn- ing method that selects the top-k passages by similarity and subsequently identifying the optimal subset based on textual information through evaluation signals (BGM) yield only marginal gains in long-context settings. While increasing k might seem like straightforward solution, it substantially enlarges the combinatorial subset selection space, causing the model to converge to a suboptimal strategy. Accordingly, we report the best performance of BGM with kt 5, which is consistent with the original paper setting. In contrast, LDAR is explicitly designed for scalability by reducing the search space now-dimensional, smooth control space (Section 4.1) resulting in significantly better performance across overall settings.
5.5 ZERO-SHOT EVALUATION OF LDAR
Table 2 evaluates whether retrieval strate- gies learned in the LaRA benchmark can generalize in a zero-shot manner to tasks in another benchmark (HELMET). To ensure aligmment across multi-hop and single-hop tasks, LDAR trained on the Comparison task is evaluated zero-shot on long-context HotpotQA task, and LDAR trained on the Location task is evalu- ated zero-shot on long-context NQ task. Although the observed performance gap smaller than in Table ! LDAR still achieves better average performance com- pared to RAG or the long-context ap- proach, while also attaining lower to- ken usage ratio relative to the long- context approach. These results indicate that LDAR's learned retrieval strategies can generalize tasks in another bench- mark in a zero-shot manner.
Table 2: Zero-shot performance results of LDAR on HotpotQA and NQ dataset. White cells denote LC, gray denotes RAG and red denotes LDAR Num bers in parentheses show token-usage ratio relative to LC. The best performing strategy for each task within each LLM is highlighted in bold.
Method
HotpotQA
NQ
Llama-3. 3.1--...-8- --strruct
$2.0
50.0
59.0
43.0
40.0
49.0
Llama Allama-3.2-45 In-structt
54.0
52.0
54.0
37.0
43.0
(1580
Qwen- 2.5-7B IIsttrcctt
30.0
63.0
64.0
25.0
42.0
54.0
(1000
(1580)
01.50
Qwen-3-4B 3-ar------nnstrect Instruct
56.0
$1.0
62.0
54.0
41.0
$3.0
(186))
103500
Mistral-Nemo-128
29.0
63.0
61.0
23.0
45.0
47.0
Open-source Average
55.8
60.0
37.4
41.0
49.2
GPT-40
$1.0
65.0
84.0
61.0
$4.0
60.0
(JAM))
(3.50%
(1:00)
GPT-4o-mini
64.0
65.0
76.0
59.0
$2.0
59.0
(0425
01.001
10121
Gemini-2.5-pro
85.0
$5.0
84.0
62.0
37.0
65.0
Gemini-2.5-faash
82.0
57.0
83,0
54.0
37.0
60.0
Closed-source Average
59.0
(1,000
(0/09))
(0020
00.4511
5.6 COST EFFICIENCY ANALYSIS OF LDAR
While LDAR brings performance improvements, it introduces additional cost, both from its training procedure and from the extra inference-time incurred by the forward pass of the learned retriever wa. To quantify this overhead, we measured the end-to-end inference time per example in Location task with all baseline methods. Specifically, the inference time includes the time required to (1) compute the query and passage embeddings, (2) run the bridge model (e.g.. reranker or LDAR retriever), and (3) process the selected passages through the prediction LLM to generate the final answer.
Table 3 summarizes the average per-example inference time with corresponding performance across all baselines using both open-source LLMs and closed-source LLMs. With open-source models, LC exhibits the highest latency it forwards all retrieved passages to the underlying LLM. Methods that rely on text-based rerankers (RAG, RenkZephyr, BGM) also incur substantial overhead due to heavy cross-encoder computation. In contrast, L.DAR employs lightweight, text-free adaptive

---

Published as a conference paper at ICLR 2026
Table 3: Comparison of retrieval strategies on LaRA Location task. Each cell reports the average per-example inference time or performance with standard error, computed using open-source and closed-source LLMs. Numbers in parentheses denote standard error for inference time and the token- usage ratio for performance metrics, respectively.
Metric
Top-1
Top-5
Top-10
Top-25
Top-50
LCC
RAG
Self- Route
Adap- tive-):
BGM
Rank Zephyr
LDAR
Time (Open-source)
1,3
1.5
don
2.5
4.0
15.4
18.4
63%
8.6
13.3
10.2
3,9
(Closed-soon source)
0.3
3,9
dbl.
636
5.2
(230)
5.8
(220
5.9
0.87)
8.2
22.6
(139)
9.0
17.8
13.6
0330
8.0
Score (Open-source)
31.1
60.1
66.9
71.3
67.9
56.2
71.6
65.8
47.7
68.9
56.1
77,3
Scure (Closed-aae source)
31.5
080
R00
70.8
(08)
80.0
9001
80.3
(027)
88.0
0.30
75.2
(040)
80.0
(030
64,4
(940)
72.2
62.7
(3.00
90.5
(030)
LDAR
*
LC
CAG
Open 100K Inferences
500K Interences
Closed 100K Inferences
Clased 500K Inferences
Perormance
0.7
5353
3125
0.25
Training Inference GPU Hours
Training Inference GPU Hours
Tranning Inference GPU
Training Inferencce GPU Hours
Figure 5: Average performance plotted against total cumulative computational cost (GPU hours) for both training and inference. The left two panels report the total cost when applying LDAR at different training epochs under 100K and 500K inference calls using open-source LLMs; the right two panels show the corresponding results for closed-source LLMs.
trieval mechanism, selecting passages minimize potential interference from distracting passages. As result, LDAR achieves the fastest inference time among LC and all reranker baselines, while simultaneously achieving the best overall performance. In the closed-souree LLMs, the underly- ing LLM is highly optimized for fast inference, making total inference time less sensitive to input length. Even under these conditions, L.ARR remains faster than LC and all reranker baselines while also achieving better performance.
In addition, we include an analysis illustrating how the cost benefits accumulate over time when deploying the learned lightweight adaptive retriever Te in Figure 5 For each LDAR training epoch, we compute (1) the cumulative training cost up to that epoch and (2) the inference cost when deploy- ing Te learned until that epoch with certain number of inference calls (100K and 500K inference calls). We include CAG (Chan et al., 2025), a cache-augmenntd generation method that accelerates inference by preloading documents and reusing a precomputed K KVccchee, as additional baseline to compare inference-time efficiency with LDAR
Since LDAR has lower inference-tine time overhead compared to LC, its cost advantage becomes in- creasingly pronounced as the number of deployments grows. As LDAR trains, it learns to identify the most effective passage set that minimizes distraction. This only improves performance but also reduces token usage during both training and inference, As shown in the right panels of Figure LDAR progressively selects fewer tokens over training epochs, thereby reducing the per-epoch train- ing cost. These results demonstrate that LDAR ensures a favorable bost-benefit trade-off in practice.
6 CONCLUSION
In this paper. we demonstrated retrieving passages to minimize distraction remains challenging problem, as the optimal strategy depends on both the capacity of the target L.LM and the interactions among retrieved passages. Although a high-capacity LLM could exhaustively align all passages with the query to minimize distraction, the cost grows prohibitively with passage count, making this ap- proach impractical. To this end, we present LDAR, an adaptive retriever that selects passages in accordance with LLM's long-contex. capability to minimize potential interference from distracting passages, relying solely on the similarity distribution. Experiments across diverse LLM architectures and knowledge- -inteniiv benchmarks demonstrate that LDAR achieves significantly better perfor- mance compared baselines with lower token usage compared the long-context approach.
10

---

Published as a conference paper at ICLR 2026
7 ACKNOWLEDGEMENTS
This work was partly supported by Institute of Information & Communications Technology Plan- ning & Evaluation (IITP) grant funded by the Korea government (MSIT) (No. RS-2022-1120311 Development of Goal-Oriented Reinforcement Learning Techniques for Contact-Rich Robotic Ma- nipulation of Everyday Objects (31%), No. RS-2024-00457882, Al Research Hub Project, No. RS- 1019-I1190079, Artificial Intelligence Graduate School Program (Korea University), the IITP (In- stitute of Information Communications Technology Planning Evaluation)- (Information Technology Research Center) grant funded by the Korea government (Ministry of Science and ICT) (ITPP-205-RS) 00025----2024-0043335 (S$-002--0043685) (31 %),, the NRF (RS-2024-00451162 funded by the Ministry of Science and ICT, Korea, BK21 Four project of the National Research Foundation of Korea, the National Research Foundation Korea (NRF) grant funded by the Korea government (MSIT) (RS- 2025-00560367), the IIT under the Artificial Intelligence Star Fellowship support program to nur- ture the best talents (ITTP2022544 0---22225-23000 (MSIT), and KOREA HYDRO & NUCLEAR POWER CO., LTD (No. 202--Tcch.
(32%) grant funded by the Korea government
REFERENCES
Chen Amiraz, Florin Cuconasu, Simone Filice, and Zohar Karnin. The distracting effect: Under- standing irrelevant passages in rag. arXiv preprint arXiv:2505.06914 2025.
Akari Asai, Zeqiu Wu, Yizhong Wang, Avirup Sil, and Hannaneh Hajishirzi. Self-rag: Learning to retrieve, generate, and critique through self-reflection. 2024.
Yushi Bai, Xin Lv, Jiajie Zhang. Hongchang Lyu. Jiankai Tang, Zhidian Huang, Zhengxiao Du, Xiao Liu, Aohan Zeng, Lei Hou, et al. Longbench: A bilingual, multitask benchmark for long context understanding. arviv preprint arXiv:2308. 01033088.4556 14508, 2023.
Adrien Baranes and Pierre-Y Yves Oudeyer. Active learning of inverse models with intrinsically mo- tivated goal exploration in robots. Robotics and Autonomous Systems, 61(1):49-73 2013.
Brian J Chan, Chao-Ting Chen, Jui-Hung Cheng, and augmented generation is all you need for knowledge tasks. In Companion Proceedings of the ACM on Web Conference 2025, pp. 893-897, 2025.
Huang. Don't 't do rag: When cache-
Gheorghe Comanici, Eric Bieber, Mike Schaekermann, Ice Pasupat, Noveen Sachdeva, Inderjit Dhillon, Marcel Blistein, Ori Ram, Dan Zhang, Evan Rosen, et al. Gemini 2.5: Pushing the frontier with advanced reasoning, multimodality, long context, and next generation agentic capa- bilities. arXiv preprint arXiv:2507.06261 2025.
Florin Cuconasu, Giovanni Trappolini, Federico Siciliano, Simone Filice, Cesare Campagnano, Yoelle Maarek, Nicola Tonellotto, and Fabrizio Silvestri. The power of noise: Redefining re- trieval for rag systems. In Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 719-729, 2024.
Michiel de Jong, Yury Zemlyanskiy, Nicholas FitzGerald, Sumit Sanghai, William W Cohen, and Joshua Ainslie. Glimmer: generalized late-interaction memory reranker. arXiv preprint arXiv:2306. 10231, 2023.
Michael Glass, Gaetano Rossiello, Md Faisal Mahbub Chowdhury, Ankita Rajaram Naik, Pengshan Cai, and Alfio Gliozzo. Re2g: Retrieve, rerank, generate. arXiv preprint arXiv:2207.06333 2022.
Yury Gorishniy, Ivan Rubachev, and Artem Babenko. On embeddings for numerical features in tabular deep learning. Advances in Neural Information Processing Systems, 35:24991 -55004 2022.
Gautier Izacard and Edouard Grave. Leveraging passage retrieval with generative models for open domain question answering. arXiv preprint arXiv:2007.01282 2020.
Gautier Izacard, Mathilde Caron, Lucas Hosseini, Sebastian Riedel, Piotr Bojanowski, Armand Joulin, and Edouard Grave. Unsupervised dense information retrieval with contrastive learning. arXiv preprint arXiv:21 12.09118, 18, 2021.
11

---

Published as a conference paper at ICLR 2026
Gautier Izacard, Patrick Lewis, Maria Lomeli, Lucas Hosseini, Fabio Petroni, Timo Schick, Jane Dwivedi Yu, Armand Joulin, Sebastian Riedel, and Edouard Grave. Few-shot learning with re- trieval augmented language models. arXiv preprint arXiv:2208.0322 1(2):4 2022.
Bowen Jin, Jinsung Yoon, Jiawei Han, and Sercan O Arik. Long-context Ilms meet rag: Overcoming challenges for long inputs in rag. arXiv preprint arXiv:2410.05983, 2024.
Vladimir Karpukhin, Barlas Oguz, Sewon Min, Patrick SH Lewis, Ledell Wu, Sergey Edunov, Danqi Chen, and Wen-tau Yih. Dense passage retrieval for open-domain question answering. In EMNLP (I), pp. 6769-6781, 2020.
Zixuan Ke, Weize Kong, Cheng Li, Mingyang Zhang, Qiaozhu Mei, and Michael Bendersky, Bridg- ing the preference gap between retrievers and llms. arXiv preprint arXiv:2401.0693 2024.
Myunsoo Kim, Hayeong Lee, Seong-Wooe Woong Shim, JunHo Seo, and Byung-Jum Lee. Nbdi: A simple and effective termination condition for skill extraction from task-agnostic demonstrations. arXiv preprint arXiv:2501. 12668, 2025.
Tom Kwiatkowski, Jennimaria Palomaki, Olivia Redfield, Michael Collins, Ankur Parikh, Chris Alberti, Danielle Epstein, Illia Polosukhin, Jacob Devlin, Kenton Lee, et al. Natural questions: benchmark for question answering research. Transactions of the Association for Computational Linguisties, 7:453 7:453-466, 2019.
Dahyun Lee, Yongrae Jo, Haeju Park, and Moontae Lee. Shifting from ranking to set selection for retrieval augmented generation. arXiv preprint arXiv:2507.0688 2025.
Quinn Leng, Jacob Portes, Sam Havens, Matei Zaharia, and Michael Carbin. Long context rag performance of large language models. arXiv preprint arXiv:2411 1..05324 2024.
Patrick Lewis, Ethan Perez, Alcksandra Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Heinrich Küttler, Mike Lewis, Wen-tau Yih, Tim Rocktäschel, et al. oetrieval-augmente gener- ation for knowledge-intene intgee-ntensivveeeeeeeeeeeeeeeee nlp tasks. Advances in neural information processing systems, 33: 9459-9474, 2020.
Jing Li, Aixin Sun, Jianglei Han, and Chenliang Li. A survey on deep learning for named entity recognition. IEEE transactions on knowledge and data engineering, 14(1):50-70,, 2020.
Junyi Li, Jie Chen, Ruiyang Ren, Xiaoxue Cheng, Wayne Xin Zhao, Jian Jian-Yun Nie, and Ji-Rong Wen. The dawn after the dark: An empirical study on factuality hallucination in large language models. arxiv preprint arXiv:2401.03205. 2024a.
Kuan Li, Liwen Zhang, Yong Jiang, Pengjun Xie, Fei Huang, Shuai Wang, and Minhao Cheng. Lara: Benchmarking retrieval-augeee augmented generation and long-context llms-no no silver bullet for lc rag routing. arXiv preprint ar arXiv:2502.09977 2025a.
Xiaoxi Li, Guanting Dong, Jiajie Jin, Yuyao Zhang, Yujia Zhou, Yutao Zhu, Peitian Zhang, and Zhicheng Dou. Search-ol: Agentic search-enhanced large reasoning models. arXiv preprint arXiv:2501.0536 2025b.
Zehan Li, Xin Zhang, Yanzhao Zhang, Dingkun Long, Pengjun Xie, and Meishan Zhang. Towards general text embeddings with multi-stage contrastive learning. arXiv preprint 032201 2023.
Zhuowan Li, Cheng Li, Mingyang Zhang, Qiaozhu Mei, and Michael Bendersky. Retrieval aug- mented generation long-context llms? comprehensive study and hybrid approach. arXiv preprint 1rXiv:2407.16833 2024b.
Nelson F Liu, Kevin Lin, John Hewitt, Ashwin Paranjape, Michele Bevilacqua, Fabio Petroni, and Percy Liang. Lost in the middle: How language models use long contexts. arXiv preprint arXiv:2307.03112. 2023.
Qitan Lv, Jie Wang, Hanzhu Chen, Bin Li, Yongdong Zhang, and Feng Wu. Coarse-to-fine highlighting: Reducing knowledge hallucination in large language models. arXiv preprint 1rXiv.2410.15116, 2024.
12

---

Published as a conference paper at ICLR 2026
Marlos C Machado, Andre Barreto, Doina Precup, and Michael Bowling. Temporal abstraction in reinforcement learning with the successor representation. Journal of machine learning research, 24(80):1-69, 2023.
Rodrigo Nogueira and Kyunghyun Cho. arXiv:1901.04085 2019.
Passage re-ranking with bert. arXiv preprint
Rodrigo Nogueira, Zhiying Jiang, and Jimmy Lin. Document ranking with a pretrained sequence- to-sequence model. arXiv preprint arXiv:2003.06713, 2020.
OpenAl.
gpt-too/ 2024.
Ronak Pradeep, Sahel Sharifymoghaddam, and Jimmy Lin. Rankzephyr: Effective and robust zero- shot listwise reranking is a breeze! arXiv preprint arXiv:2312.02724, 2023.
Noam Shazeer, Azalia Mirhoseini, Krzysztof Maziarz, Andy Davis, Quoc Le, Geoffrey Hinton, and Jeff Dean. Outrageously large neural networks: The sparsely-gated mixture-of- layer. arxiv preprint arXiv: 170.00663, 2017.
Freda Shi, Xinyun Chen, Kanishka Misra, Nathan Scales, David Dohan, Ed H Chi, Nathanael Schärli, and Denny Zhou. Large language models can easily distracted by irrelevant context. In International Conference on Machine Learning, pp- 31210-31227 PMLR, 2023a.
Weijia Shi, Sewon Min, Michihiro Yasunaga, Minjoon Seo, Rich James, Mike Lewis, Luke Zettle- moyer, and Wen-tau Yih. Replug: Retrieval-augmennted black-box language models. arXiv preprint arXiv:2301. 2023b.
Richard S Sutton, David McAllester, Satinder Singh, and Yishay Mansour. Policy gradient meth- ods for reinforcement learning with function approximation. Advances neural information processing systems, 12, 1999.
Chihiro Taguchi, Seiji Mackawa, and Nikita Bhutani. Efficient context selection for long-context qa: No tuning, iteration, just adaptive-k. arXiv preprint arXiv:2506.08479 2025.
Yixuan Tang and Yi Yang. Multihop-ragg Benchmarking retrieval-augmentte generation for multi- hop queries. arXiv preprint arXiv:2401.1533) 2024.
Chonghua Wang, Haodong Duan, Songyang Zhang, Dahua Lin, and Kai Chen. Ada-leval: Eval- uating long-context Ilms with length-adaptable benchmarks. arXiv preprint arXiv:2404.06480. 2024a.
Minzheng Wang, Longze Chen, Cheng Fu, Shengyi Liao, Xinghua Zhang, Bingli Wu, Haiyang Yu, Nan Xu, Lei Zhang, Run Luo, et al. Leave no document behind: Benchmarking long-context Ilms with extended multi-doc qa. arXiv preprint arXiv:2406. 17419, 2024b.
Jerry Wei, Chengrun Yang, Xinying Song, Yifeng Lu, Nathan Hu, Jie Huang, Dustin Tran, Daiyi Peng, Ruibo Liu, Da Huang, et al. Long-form factuality in large language models. Advances in Neural Information Processing Systems, 37:80756-80822 2024.
Shitao Xiao, Zheng Liu, Peitian Zhang, Niklas Muennighoff, Defu Lian, and Jian-Yun Nie. C-pack: Packed resources for general chinese embeddings. In Proceedings of the 47th international ACM SIGIR conference on research and development information retrieval, pp. 641-649, 2024.
Fangyuan Xu, Weijia Shi, and Eunsol Choi. Recomp: Improving retrieval-augmented Ims with compression and selective augmentation. arXiv preprint arXiv:2310.04400. 2023a.
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:2310.03025 2023b.
Rongwu Xu, Zehan Qi, Zhijiang Guo, Cunxiang Wang, Hongru Wang. Yue Zhang, and Wei Xu. Knowledge conflicts for Ilms: A survey. arXiv preprint arXiv:2403.083. 2024.
13

---

Published as a conference paper at ICLR 2026
An Yang, Baosong Yang, Beichen Zhang, Binyuan Hui, Bo Zheng, Bowen Yu, Chengyuan Li, Day- iheng Liu, Fei Huang, Haoran Wei, Huan Lin, Jian Yang, Jianhong Tu, Jianwei Zhang. Jianxin Yang, Jiaxi Yang, Jingren Zhou, Junyang Lin, Kai Dang, Keming Lu, Keqin Bao, Kexin Yang, Le Yu, Mei Li, Mingfeng Xue, Pei Zhang. Qin Zhu, Rui Men, Runji Lin, Tianhao Li, Tianyi Tang, Tingyu Xia, Xingzhang Ren, Xuancheng Ren, Yang Fan, Yang Su, Yichang Zhang, Yu Wan, Yuqiong Liu, Zeyu Cui, Zhenru Zhang, and Zihan Qiu. Qwen2.5 technical report, 2025. URL https:/ arxiv. org/
Zhilin Yang. Peng Qi, Saizheng Zhang. Yoshua Bengio, William W Cohen, Ruslan Salakhutdinov, and Christopher D Manning. Hotpotqa: A dataset for diverse, explainable multi-hop question answering. arXiv preprint (XXi:1800.006000 2018.
Fuda Ye, Shuangyin Li, Yongqi Zhang, and Lei Chen. R 2ag: Incorporating retrieval information into retrieval augmented generation. arXiv preprint ar arXiv:2406.133 132449 2024.
Howard Yen, Tianyu Gao, Minmin Hou, Ke Ding, Daniel Fleischer, Peter Izsak, Moshe Wasserblat, and Danqi Chen. Helmet: How to evaluate long- cong-context language models effectively and thor- oughly. arXiv preprint arXiv:2410.02694, 2024.
Manzil Zaheer, Satwik Kottur, Siamak Ravanbakhsh, Barnabas Poczos, Russ R Salakhutdinov, and Alexander Smola. Deep sets. Advances in neural information processing systems, 30, 2017.
Peitian Zhang, Zheng Liu, Shitao Xiao, Zhicheng Dou, and Jian- Nie. A multi-task embedder for retrieval augmented llms. In Proceedings of the 62nd Annual Meeting the Association for Computational Linguistics (Volume I: Long Papers), pp. 3537-3553, 2024a.
Xin Zhang, Yanzhao Zhang, Dingkun Long, Wen Xie, Ziqi Dai, Jialong Tang, Huan Lin, Baosong Yang, Pengjun Xie, Fei Huang, et al. mgte: Generalized long- long-context text representation and reranking models for multilingual text retrieval. arXiv preprint arXiv:2407.19669, 2024b.
Xinrong Zhang, Yingfa Chen, Shengding Hu, Zihang Xu, Junhao Chen, Moo Khai Hao, Xu Han, Zhen Leng Thai, Shuo Wang, Zhiyuan Liu, et al. Infty bench: Extending long context evaluation beyond 100k tokens. arXiv preprint arXiv:2402.1 13718, 2024c.
14

---

Published as a conference paper at ICLR 2026
A THE USE OF LLM IN PAPER WRITING
We utilized large language models to improve the clarity and phrasing of the text.
B LIMITATIONS AND FUTURE WORK
While LDAR was trained using task task-specific signal, retrieval scenarios in practice are often diverse and may not align neatly with a single task formulation. As our results in Section 5.3 indicate that the optimal retrieval strategy varies across tasks, a promising direction for future research would be to develop meta-classifier capable of identifying the underlying retrieval task and employing mixture-of-expert. framework (Shazeer et al., 2017), where task-specialized retrieval strategies are adaptively combined.
Moreover, LDAR neither explicitly models the ordering retrieved passages nor employs a reranker to reorder them, which may affect downstream performance. While rerankers were deliberately ex- cluded to maintain high training efficiency, an important direction is to explore learning-bate re- trieval strategies that jointly optimize both passage selection and ordering in long-context settings.
C EXPERIMENTAL DETAILS
C.I IMPLEMENTATION DETAILS OF THE ADAPTIVE RETRIEVAL PROCESS
2022 for encoding numeric features, which projects batch of raw scalar inputs E BxN*I to the embedding dimension Bxxxx followed by layer normalization. The embedded tokens are then processed by self-attention Transformer model and outputs h E RBxNxD, A learned linear token scorer projects each Transformer output to a scalar, which normalized with a softmax to obtain attention weights w E BxN, We then form a global summary by attention pooling over the token dimension N: Zo,d = Σn Wo and pass z E B through a small MLP head to obtain g. From g, four linear heads produce the parameters of two Beta distributions: (aLz, (L)) for the lower quantile and (as,, Ba) for the band width aa to ensure that lower quantile to be smaller than upper quantile qu- In addition, we apply softplus to ensure all parameters of Beta distributions are strietly positive.
We employ periodic embedding layer (Gorishniy
We train LDAR on 4 NVIDIA RTX 3090 GPUs for 32K context length settings and NVIDIA RTX PRO 6000 GPU for 128K context length settings.
C.2 HYPERPARAMETER SETTINGS
Our hyperparameter settings are summarized in Table The same configuration is used consistently across all experiments.
Table 4: Hyperparameter settings used for LDAR
Hyperparameter
Setting
Batch Size
32
Embedding Dimension
256
Transformer Hidden Dimension
256
Baseline EMA coefficient
0.5
Transformer Layer
Transformer Head
Optimizer
Adam(3 = [0.9, 0.999], e = le-8)
learning rate 7
3e-4
C.3
IMPLEMENTATION DETAILS OF TEXT CHUNKING PROCEDURE
We followed the standard LaRA benchmark chunking procedure, forming 600-token passages with 100-token overlap. As the size of each passage is equal in length, compute the number
15

---

Published as a conference paper at ICLR 2026
of passages directly from the information provided in Table | This results in approximately 64 passages for the 32k context-length setting and 256 passages for the 128k setting, which are used consistently across all methods. Since the reported token-usage ratio corresponds exactly to the fraction of passages retrieved, the number of passages retrieved by each baseline can be computed directly from the table.
C.4 IMPLEMENTATION DETAILS OF BASELINE METHODS
Self-Route Li et al. 2024b) leverages the LLM itself to decide whether to use the RAG or long- context approach. The method consists of two simple steps: (1) the query and Top-k retrieved pas- sages are provided to the LLM, which is prompted to predict whether the query can be answered with the given passages; (2) if the LLM predicts the query is answerable, the LLM generates the an- swer directly. Otherwise, the full passage pool is passed to the LLM to produce the final prediction using the long-context approach. To implement this routing process, we adapt the prompt used in the LaRA benchmark (Li et al. 2025a), following the design of Self-Routt (see Appendix C in et al. 2024b).
Here are some chunks retrieved from some {datatype): ). Read these chunks to answer question. Be concise. If the question cannot be answered based on the information in the article, write "unanswerable". (context) Question: {question} Only give me the answer and do not output any other words. the question cannot be answered based the information the article, write "unanswerable". Answer:
Table 5: Prompt we used for Self-Route baseline.
Adaptive-k Taguchi et al. (2025) retrieves passages by locating the largest gap in the sorted simi- larity scores. Following the pseudo-code deseribed in the original paper, we compute the difference between consecutive similarity scores in sorted order, identify the index corresponding to the maxi- mum gap, and retrieve all passages preceding this index.
BGM Ke et al. 2024) proposed learning method that first selects the top-k passages based on similarity scores and subsequently identifies the optimal subset using textual information guided by evaluation learning signals. Since the official implementation is not publicly available, we imple- mented BGM by following the procedure described in the paper. Since the paper does not specify the number of silver passages required for the supervised learning stage, we constructed 50 silver passages per task and used the evaluation signal to further fine-tune the sequence-to-sequene model.
CAG Chan al. 2025) operates by first computing KV-cache over combined set of documents D = {d1, d2.,, that corresponds to a set of queries {g1, 42. .. Once this large unified K V-cache constructed, the model can process each instance efficiently by reusing the cached representations. In their experiments on HotPotQA, the largest-scall setting aggregates 64 documents (approximately 85k tokens) build this cache. However, long-contes benchmarks such as LaRA, a single docu- ment associated with single query already reaches the context-length limit of LLMs (which is 128k tokens in our experiments). Therefore, computing a KV-cache over multiple documents infeasi- ble under long-context setting, and CAG effectively exhibits the same computational complexity as standard LC. That said, the LaRA benchmark has a particular characteristic: each document is paired with multiple queries (c.g., 10 queries per document). This enables CAG to gain some benefit by caching individual documents and reusing their cached representations across queries that refer- implemented CAG by caching each
ence the same document. Accordingly, in our experiments, document separately and applying the cache to all associated queries. We report results based on this fair and practically feasible adaptation of CAG for long-context evaluation. With such experimental settings, CAG achieved performance comparable to LC, but yields a 2.5x reduction in latency (15.41 seconds for LC vs. 4.31 seconds for CAG per inference on average).
16

---

Published as a conference paper at ICLR 2026
D ADDITIONAL EXPERIMENT RESULTS
D.1 TRAINING LDAR ON HALLUCINATION TASK
Performance
Passage Usage
1.00
32K
0.8
128K
0.95
Score
Ratio
0.6
0.90
0.4
0.85
0.2
20
40
60
80
100
20
40
60
80
100
Gradient Step
Gradient Step
Figure 6: Performance and passage-usage ratio of LDAR trained with the Hallucination task signal. Results shown for 1ama-3.11885 under 32K and 128K context-length settings.
We visualize the training curve of LDAR when trained with the evaluation signal from the Halluci- nation task in the LaRA benchmark. The Hallucination task measures whether an LLM can correctly refuse to answer when the provided context lacks the required information (which is always the case in this task). As shown in Figure 6 LDAR quickly learns retrieve almost passages, thereby forcing the LLM to refuse answering. This behavior results in degenerate strategies that do not reflect realistic retrieval requirements. Consequently, evaluation benchmark rather than training objective. We employed models trained on the Location task-the most widely adopted retrieval strategy in practice-to evaluate and report the performance of LDAR in Hallucination task at Table
treat the Hallucination task purely an
17

---

Published as a conference paper at ICLR 2026
D.2 VISUALIZATION OF RETRIEVAL STRATEGIES LEARNED BY LDAR
Figure 7 shows additional visualizations of retrieval strategies learned by LDAR. LDAR adaptively determines both the lower and upper bounds of the similarity distribution band used for passage retrieval, thereby maximizing LLM prediction accuracy by balancing information coverage against the risk of distraction.
3.5
3.6
0.09
0.40
24-
4.9
Possagt Kark
Passage fark
Plasage Nare
F355ago Rank
3.60
0.75
3.60
0.55
0.50
Pacouge Rank
Pyorage Rank
Parcage Fank
Pacouge Ra1s
0.70
3.63
3.09
3.30
Parsage Fanc
Facsage Sank
Paroutt Rank
Parcage
0.02
Seitutty
3.5
30
1/0
17e
342
331
150
Psurage
Figure 7: Visualization of passages retrieved by LDAR based on the similarity distribution, with retrieved passages marked in green if they contain the correct answer and in red otherwise.
18

---

Published as a conference paper at ICLR 2026
Case study of LDAR's Retrieval Strategy Figure 8 shows case-study analyzing LDAR's learned retrieval strategies. When the similarity distribution shows a clear high-similarity region, LDAR tends to focus narrowly that region. In these cases, the useful passage tends to be located within the top rank passages as illustrated in Figure (top).
Conversely, when the overall similarity distribution is low in value and relatively flat in shape, LDAR expands its retrieval interval to ensure broader coverage, even at the risk of including more distract- ing passages. In these cases, the useful passage often not located within the top rank passages as illustrated in Figure g (bottom), making a wider retrieval band necessary.
Although LDAR does not receive any textual information, this case study demonstrates that the shape of the similarity distribution is indicative of where useful passages tend to appear. LDAR learns these patterns and adapts its retrieval strategy accordingly.
Case Study Distribution containing clear high-similarity region
Query: How does the Chain of thought (CoT) tochmique improve faimess in LLMs according?
Similanty
Answer: The CoT technique enhanocs the hopeful and perforsance of L.M.Ms towards Gaimess by
Rankl: The CoT technique cobances the hopeful and performance LLMs towards fairmess
RankS: Language modeling bias, often defined as "bias that resalts hamn to various social groups"
Rank50: This vision integrates the cutting edge technological innovation with an unwavering...
100
Rassage
200
31e
Rank100: Fairness Amidst Noo- Gmahh Dato: Current Achievements and Future Directions, arXiv
Case Study B: Distribution that is magnitude and relutively flat shape
Query: What are the symbolic keywords highlighted Table 2 in paper
Smilarty
0.00
Answer: The symbuls Edit Area," "Edit Punction, 'No Training, 'Datch Edit and 'Bdited #Params!
Coverge
Rankl: Tabll 2| Phemomenon Paradigms Example] beed
Anapho slund of 2>
0.55
RankS: Tabll shows resalt of our evaluntion on these newly assembled datasets. Although....
Rank10: M. Brockschmidh, "Codesearchne: challenge: Evaluating state semantic code
108
Passage Bank
Rank21: Table 2:... L.Mss. 'No Training" refers ... 'Batch Edit means ... 'Editor aParams' indicates.
Figure 8: A case study illustrating LDAR's learned retrieval behavior. When the similarity distribu- tion contains clear high-similarity region, LDAR tends focus narrowly on that region. When the overall similarity distribution is low in value and relatively flat in shape. LDAR expands its retrieval interval to ensure broader coverage, even at the risk of including more distracting passages.
19

---

Published as a conference paper at ICLR 2026
D.3 ANALYSIS OF LDAR'S 'S TRAINING/ INFERENCE EFFICIENCY
LDAR introduces a small additional computation during inference because it performs a forward pass of the learned LDAR retrieval strategy me. To quantify this overhead, measured the end-to- end inference time per example in Location task. Specifically, the inference time includes the time required to (1) compute the query and passage embeddings. (2) run the bridge model (e.g., reranker or LDAR retriever), and (3) process the selected passages through the prediction LLM to generate the final answer. Note as baseline methods distribute their computation differently across these three stages, their total inference cost varies accordingly.
Table summarizes the average per-example inference time across all methods using both open- source LLMs and closed-source LLMs. With open-source models, LC exhibits the highest la- tency because it forwards all retrieved passages to the prediction LLM. Methods such as RAG, RankZephyr, and BGM (which rely on text-based rerankers) also incur substantial overhead due to heavy cross-encoder computation. In contrast, LDAR employs a lightweight, text-freee adaptive re- trieval mechanism, selecting passages minimize potential interference from distracting passages. As a result, LDAR achieves the fastest inference time among LC and all reranker baselines, while simultaneously achieving the best overall performance.
In the -lourd- setting, the prediction LLM is highly optimized for fast inference, making total inference time less sensitive to input length. Even under these conditions, LDAR remains faster than LC and all reranker baselines while also achieving better performance. These results demonstrate that LDAR provides distraction-awant and ally efficient manner at inference time.
retrieval in a computation-
Table 6: Comparison of retrieval strategies on LaRA Location task. Each cell reports the average per-example inference time or performance with standard error, computed using open-source and closed-source LLMs. Numbers in parentheses denote standard error for inference time and the token- usage ratio for performance metrics, respectively.
Metric
Top-1
Top-5
Top-10
Top-25
Top-50
RAG
Self- Route
Adap- tive-):
BGM
Rank Zephyr
LDAR
Time (Open-source)
1.3
1.5
1.7
2.5
4.0
15.4
18.4
4.5
8.6
13.3
10.2
3.9
(Closed-source)
0239
3.9
1196
3.2
5.8
0.30
5.9
8.2
1159
(4.0
9.0
(270
1750
Score (Open-souree)
31
60.1
66.9
71.3
67.9
56.2
71.6
65.8
47.7
68.9
56.1
77,3
Scure (Closed source)
31,5
(856
70.8
80.0
80.3
00200
88.0
0.30
75.2
80.0
1C30
64,4
00.00
72.2
62.7
90.5
(030)
Based on the analysis of LDAR's s inference-time efficiency, we now illustrate how the cost benefits accumulate over time when deploying the learned LDAR strategy. For each LDAR training epoch, compute (1) the cumulative training cost up to that epoch and (2) the inference cost when apply- ing LDAR strategy learned until that epoch with a certain number of inference calls (10K, 100K, and 500K inference calls). We then plot the cumulative cost on the x-axis and the resulting performance on the y-axis. Figure 9 (top, bottom) shows the results for using open-source and closed-source LLMs are prediction models, respectively. Because L.ARR has lower inference-time overhead com- pared to LC, its cost advantage becomes increasingly pronounced as the number of deployments grows.
Importantly, as LDAR trains, it learns to identify the most effective passage set that minimizes distraction. This not only improves performance but also reduces token usage during both train- ing and inference. As shown in the center plot of Figure 9 (bottom). LDAR progressively selects fewer tokens over training epochs, thereby lowering the per-epoch training cost as well. CAG (Chan) et al. 2025) is cache-augmented generation method that speeds up inference by preloading docu- ments and reusing a precomputed K KV-ccche. We included CAG as an additional baseline to compare dnference-time efficiency with LDAR.
Additionally, to provide a fair comparison in terms of API usage, we converted all training and inference costs into USD and visualized the results in Figure 10 GPU hours are converted into USD based on the pricing of the cloud computing services we used, and API costs are computed according to the official pricing of OpenAl and Google. Under this USD-based metric, LDAR demonstrates greater cost efficiency than LC, particularly when closed-source LLMs are used as the prediction model, the cost of processing long-context inputs is extremely high.
20

---

Published as a conference paper at ICLR 2026
Open-source LLMS 10K Inferences
Open-source LLMS 100K Inferences
Open-source LLMS 500K Inferences
0.80
LDAR
Petormance
0.70
0.05
0.60
0.55
0.45
0.LKK
0.225
0abor
0R40K
100e
0.30K
Dzggrr
0.2600
0.300m
0.600
0 7200
L000k
L2t50
7500
2 3.00
Training Inference Hours
Training Inference GPU Hours
Training Inference Hours
Closed source LLMs 10K Inferences
Clesed-scurce LLMs 100K Inferences
Closed-source LLMS 500K Inferences
0.90
Performance
0.88
0.86
0.82
LOAR
0.78
LC
0.22K
0.038K
0,88K
0.00
0.33K
0:224K
0.228
0.338
0.200K
2222
0,238
1.1kk
1.2KKK
1.125K
1.330K
Training Inference Hours
Training Inference GPU Hours
Training Inference Hours
Figure 9: Average performance plotted against the total cumulative computational cost (GPU hours)
for both training and inference. Each figure shows total cost incurred when using the LDAR retriever different training epochs and running certain number of inference calls (10K, 100K, and 500K). The top row presents results using open-source LLMs, while the bottom row presents results using closed-source LLMs.
Open-source LLMs 10K Inferences
Open-source LLMs 100K Inferences
Open-source LLMs
500K Inferences
LDAR
LG
Pemomance
0.70
CAG
0.60
0.55
0.45
$0.0K
50.0k
$e.k
40.1
saJk
10.26
50.5
k1.0k
nK
P2.K
Training Inference Cost (USD)
Training Inference Cost (USD)
Training
Inference Cost (USD)
Closed-soure source LLMS 10K inferences
Clused-soure LLMs 100K Inferences
Closed Clesed-source source LLMs 500K Inferences
0.90
Performance
0.88
0.84
0.80
LDAR
$0.8k
$2.0k
11
11.2K
$1.4
$7.0K
$6.0
19.0
$10.0k
$12 0K
$12.K
$14.K
$3.00
$40.0K
545.0 18.0K
150.0k
463 ok
465. dk
$78.0
$12.0
$33.0
Training
Inference Cost (USD)
Traning Inference Cost (USD)
Traning Inference Cost (USD)
Figure 10: Average performance plotted against the total cumulative computational cost (USD) for both training and inference. We converted GPU hours into USD based on the pricng cloud com- puting service we used, and API costs are computed according to the official pricing of OpenAl and Google. Each figure shows total cost incurred when using the LDAR retriever at different training epochs and running certain number of inference calls (10K. 100K, and 500K). The top presents results using open- spen-source LLMs, while the bottom row presents results using closed-soure source LLMs.
21

---

Published as a conference paper at ICLR 2026
D.4 CROSS-EMBEDDER GENERALIZATION
For an LDAR strategy trained with one embedding model to generalize to another, we found that certain degree of scale alignment and rank alignment between the two embedders is necessary.
To illustrate this, Figure 11 (left) compares the mean cosine-similarity values produced by bge- large-en-vI.5 (Xiao al. 2024) and gte-large-en-vl. (Zhang al. (024b) over 100 randomly sampled query-passage pairs from the Location task. Figure I1 (right) further shows how passages ranked by the bge-large-cn--1.5 map to their corresponding ranks in the gte-large-en-v1.5. These plots demonstrate that the two embedding models exhibit some amount of scale alignment (their similarity ranges are comparable) and rank aligmment (higher-rankes passages in one model tend to remain relatively high-ranked in the other. Correlation: 0.7715). Because of this alignment, LDAR strategy trained with bge-large-en-vl. can generalize to gie-large-en-v1.5 1.5 to a reasonable extent, and vice versa, as shown in Table 7
Cosine Similarity Distribution
Rank Agreement
bge-large-en-1155
gte-large-en-11.
Cosine similarity
gte-large-en-v1.5
20
40
80
Perfect Agreement
100
1.0
0.8
0.6
0.4
0.2
0.0
100
80
60
40
20
Rank quantile (high + low)
Rank of bge-large-nn-w1.5
Figure 11: Comparison of cosine-similarity and rank agreement between bge-large-en-v1.5 and gte- large-en-v1.5 on the LaRA Location task. (Left) Average sorted cosine-similarity curve with stan- dard deviation across all examples. (Right) Passage ranks under bge-large-en-vl.. plotted against their ranks under gte-large-en-vl.5 the dashed line indicates perfect rank agreement between the two embedding models.
Table 7: Performance comparison across Location, Reasoning, Comparison, and Hallucination tasks.
(a) Location
(b) Reasoning
Embedding Model
L.C
RAG
LDAR
Embedding Model
L.C
RAG
LDDAR
gee-large-en-v155
(1.0)
72.4 (0.03)
77.6 (0.22)
bge-large-en-11.
50.6 (1.0)
(0.02
59.6 (0.36)
gic-largceen-v1.5
(1.0)
70.4 (0.02)
74.4 (0.44)
gic-large-en-v1.5 1.55
50.0(
51.9 (0.02)
57.1 (0.31)
hge ga--large-n--1.5
(1.0)
(0.02)
(0.30)
hge 1e-large-en-v1.5
51.9 (0.02)
bge large en v1.5
(1.0
72.4 (0.03)
75.5 (0.34)
gle bge lurge en v1.5
50.6 (1.0)
45.4 (0.02)
54.5 (0.39)
(c) Comparison
(d) Hallucination
Eimhedding Model
LCC
RAG
LDAR
Eibbeddiigg Model
LCC
RAG
LDAR
bgo-large-co-v1.5
34.1 (1.0)
19.5 (0.03)
41.5 (0.50)
bgo-largo-cn-vi. gte-large-en-v1.
(8.00
78.3 (0.03)
(0.22)
gie-large-en-vl5.
34.1
(0.02)
39.0 (0.54)
58.4
77.9 (0.02)
70.1 (0.44)
bge - gie large en-v1.5
34.1 (1.0)
24.3 (0.02)
26.8 (0.62)
bge + glz large en v1.5
58.4
77.9 (0.02)
71.4 (0.31)
lag--e-go-----1.55
34.1 (1.0)
19.5 (0.03)
(0.47)
gie bgo-lungeee--v1.5
58.4(1.)
78.3 (0.03)
(0.36)
(e) Average
Embedling Model
LC
LDAR
Ige-large-en-vl.5
53.1 (1.0)
53.9 (0.03)
63.0 (0.32)
gle larglare en
(1.0)
(0.02)
(0.43)
bge - sle-
53.1 (1.0)
56.1 (0.02)
$7.7 (0.37)
gle bge large
53.1 (1.0)
53.9 (0.03)
55.9 (0.39)
22

---

Published as a conference paper at ICLR 2026
D.5 TRAINING LDAR WITH COST-REGULARIZED OBJECTIVE
Figure 12 the optimization of LDAR if we included a penalty term proportional to the number of retrieved tokens during training. Introducing such cost caused the optimization collapse into a local optimum, where the LDAR strategy retrieved too few passages and suffered a drop in accuracy.
Including cost term would have been necessary if performance increases monotonically with the number of retrieved passages. However, both prior work (e.g.. the inverted-U findings in (Jin al. 2024 Leng et al. 2024)) and our own experiments show that performance peaks at an intermediate retrieval size and then decreases, forming an inverted-U relationship due distraction from addi- tional passages. Because the objective is not a linear trade-off but rather identifying the peak of this inverted-U, we found penalizing token usage during training often pushes the model away from the optimal region.
Therefore, instead of imposing an explicit cost term, we focused on letting LDAR learn to retrieve passages the performance peak of the inverted-U. Notably, even without a cost term, LDAR naturally avoids the high- dighhdisttaation region and achieves both higher accuracy and lower token usage compared to the long-contin context baseline (Table
Performance
Passage Usage
0.6
Score
Ratio
0.2
0.2
0.0
LDAR
Regullegde
10
20
Gradient Step
Gradient Step
Figure 12: Performance and passage-usage ratio of LDAR with and without the cost-regularization term, evaluated on the LaRA Reasoning task using Qwen-2.5-7B.
23

---

Published as a conference paper at ICLR 2026
D.6 VISUALIZATION OF LDAR'S S LEARNED RETRIEVAL STRATEGY IN CLUSTERS
To better understand LDAR's retrieval behavior, we performed a clustering analysis over LDAR's retrieval on both an open-source model (Qwen-2.5-7B) and closed-source model (Gemini-2.5- pro) (Figure [3] and [4. We collected 100 similarity distributions and clustered them based on LDAR's retrieval band value, resulting in two primary clusters (Cluster O and Cluster 1). The figure summarizes the mean similarity distribution for cach cluster along with LDAR's average retrieval band (gz. qc))
The visualization demonstrates that when the similarity distribution contains a relatively high- similarity region, LDAR concentrates its retrieval on that narrow interval (Cluster 0). Conversely, when similarity values are relatively low, LDAR expands the retrieval range to increase informa- tion coverage (Cluster 1). To specifically examine cases where LDAR avoids the top interval, we additionally isolated all instances where the learned upper quantile satisfies qu < 1.0. These cases typically fall within Cluster I, but we regrouped them separately to analyze this behavior in finer detail. As illustrated in the third plot, these distributions exhibit the lowest overall similarity values.
Cluster (n=53)
Cluster (n=47)
Group: qu 1.0 (n-6) Qu-0.78, q: Q.
Qu-1.00, q 90-0.7
0,.0. 0.30.37
mac-p.727
Cocina simitartt
0.7
5577
0.6
235
22-0,355
2 205
0.5
21-6 502
mini
0.4
mmib 413
1.00
0.75
0.25
0.00
1.00
0:75
0.25
0.00
1.00
0.75
0.50
0.00
Rank quantiee (high- - lowl
Rank quantiic (high... - low)
Rank quanttle (high - low)
Figure 13: Visualization of clustering analysis over LDAR's s retrieval behavior on Qwen-2.5-77B in Location task. We collected 100 similarity distributions and clustered them based on LDAR's s retrieval band value, resulting in two primary clusters (Cluster 0 and Cluster 1). We additionally isolated all instances where the learned upper quantile satisfies qu < 1.0. The figure shows the mean similarity distribution for each cluster along with LDAR's average retrieval band (qr, qv) n. indicates the number of samples in each cluster and group.
Cluster 0 37)
Cluster (n=63) qu=1.00, 00.66
qu=1.00, q 00.52
max
maceo. 71D
Cosine similarity
0.7
0.-0.011 011
0.6
580
a1-0 550
02-0.352
21-5-50
0.5
mln-0449
sin4p 429
1.00
0.75
0.50
0.25
0.00
1.00
0.75
0.50
0.25
0.00
Rank quantile (high low)
Rank quantile (high low)
Figure 14: Visualization of clustering analysis over LDAR'ss s retrieval behavior on Gemini-2.5-pro in Location task. We collected 100 similarity distributions and clustered them based on LDAR's retrieval band value, resulting in two primary clusters (Cluster O and Cluster 1). In this case, there is no instance where the learned upper quantile satisfies qu < 1.0. The figure shows the mean similar- ity distribution for each cluster along with LDAR's s average retrieval band (qL, qu). n indicates the number of samples in each cluster and group.
To better understand why LDAR band shifts downward in such cases, we performed a fixed- width sliding-window experiment (Figure (8). For each cluster, we sweep windows of size w e (20, 50, 100} across the similarity-ranked passages and measured accuracy using each window as the retrieved set. The sliding-wing window experiment demonstrates that when the similarity distribu- tion contains relatively high-similarity region (Cluster 0), narrower interval (w 20, 50) shows a better performance peak than wider interval (w = 100). Conversely, when similarity values are relatively low (Cluster 1), wider interval (w 100) shows better performance peak. This behavior
24

---

Published as a conference paper at ICLR 2026
aligns with LDAR's ' learned retrieval bands. Interestingly, for Cluster we observe a secondary per- formance peak when a wide window (w = 100) covers the mid-quantile region. This suggests that, in low-similarity scenarios, informative passages than concentrated at the very top. We believe such occasional peaks in the mid-quantile region made LDAR occasionally shifts its retrieval band downward select mid-similarity passages when the overall similarity scale is low. The slidingwwiddw experiment for the group qu < 1.0 (Figure [8] (bottom)) further shows that the highest-ranked passage can sometimes act as a distractor when the overall similarity scale is low.
often dispersed across the middle ranks rather
By comparing LDAR's behavior across open-source and closed-source LLMs (Figure 13 and 14]. LDAR generally uses wider quantile bands when interacting with closed-soure, models, reflecting the fact that these models possess stronger long-context processing capabilities. Additionally, the frequency of non-top-interral retrieval (i.e., cases where qu < 1.0) decreases notably for closed- source models. This indicates that closed-source LLMs are inherently more resilient to noisy or misleading passages, reducing the need for LDAR to shift its retrieval band downward to avoid distraction. Together, these observations highlight that LDAR internalizes and adapts to the long- context characteristics of the underlying LLM, producing retrieval behaviors that both model- aware and capability-alignee
25

---

Published as a conference paper at ICLR 2026
VISUALIZATION OF LDAR's S ZERO-SHO SHOT RETRIEVAL ON HELMET AND ADA- D.7
Cluster 0 (n=100) qu01.00, 9u.0.62
0.900
0.6
8.00
Cosine similarity
0.7-
0.6
42-1
2585
4200520
0.5
41-0450
0.4
ma. 32929
1.00
0.75
0.50
0.25
0.00
Rank quantile (high low)
Figure 15: Visualization of clustering analysis of LDAR's zero-shot retrieval behavior on GPT-40- mini for the BestAnswer task the Ada-LEva benchmark (Wang et al. (024a).. We used the LDAR checkpoint pretrained with the Location task for zero-shot evaluation. We collected 100 similarity distributions and clustered them based on LDAR's s retrieval band value, In this setting, the analysis yields single coherent cluster, indicating that LDAR consistently applies a similar retrieval strategy across examples.
Cluster (4=54)
Cluster (n=46) 1,00 Q.=0.22
Group: du 1.0 (n=1)
du=1 00, 00553
Qu=0. 98, 0000.77
mac-4
mace
0.7
Cocine similarity
0.6
02-0382
oten ine
0.5
0.4
1.00
0.75
0.50
0.25
0.00
1.00
2:75
0:50
0.25
0.00
1.00
0.25
0.50
0.25
0.00
Rank quantile (high-. - low)
Rank quantile (high - low)
Rank quantile (high low)
Figure 16: Visualization of clustering analysis of LDAR's zero-shot retrieval behavior on GPT- 4o-mini for the HotpotQA task. We used the LDAR checkpoint pretrained with the Comparison task for zero-shot evaluation. We collected 100 similarity distributions and clustered them based on LDAR's s retrieval band value, resulting in two primary clusters (Cluster and Cluster 1). We additionally isolated all instances where the learned upper quantile satisfies qu < 1.0. The figure shows the mean similarity distribution for each cluster along with LDAR'ss s average retrieval band (gL, (u). indicates the number of samples in each cluster and group.
Cluster (n=57)
Cluster (n=43)
Group: au (n=1) Qu=0.999, a: =0.41
que11000 9. -0.71
1,001.0,000 Q -0004
maxug.711 771
mac- 73A
Cosine sinitrrty
2233.23
0.7
22- GA2
-2 455
0.6-
minis 923
1.00
0.75
0.50
0.25
3.00
1.00
0.75
0.25
0.00
1.00
0.75
0.50
0.25
0.00
Rank quantie thigh - low!
Rank quanttio (high- - low)
Rank quantile (high - low)
Figure 17: Visualization of clustering analysis of LDAR's zero-shot retrieval behavior on GPT-40- mini for the NQ task. We used the LDAR checkpoint pretrained with the Location task for zero-shot evaluation. We collected 100 similarity distributions and clustered them based on LDAR's 's retrieval band value, resulting in two primary clusters (Cluster 0 and Cluster 1). We additionally isolated all instances where the learned upper quantile satisfies qu < 1.0. The figure shows the mean similarity distribution for each cluster along with LDAR's average retrieval band (qL, (T). indicates the number of samples in each cluster and group.
26

---

Published as a conference paper at ICLR 2026
To show that LDAR's learned retrieval behavior is not just corpus-specific distributional quirks, we additionally conducted a zero-shot evaluation on long-contex benchmark constructed from StackOverflow. In this task, each question is paired with many candidate answers, and the LLM must identify the single answer originally marked as the most helpful (Wang et al. 2024a).
LDAR can zero-shot generalize to tasks with other corpus (i.e., exhibit semantic robustness) long as the overall similarity distribution similar in scale. Figure T3] T7 show that similarity distribu- tion of tasks with different semantics (LaRA (novel, paper, finance), Ada-LEval (StackOverfloww) and HELMET (Wikipedia)) are in similar scale, and LDAR succeeds in showing similar behaviors. This also leads meaningful zero-shot performance on tasks with different semantics (Table 2 and Table 8). Thus, as long as the embedder provides a stable and comparable similarity scale between queries and passages across different corpus, the learned L.DRR strategy can zero-shot generalize to corpus with different semantics.
Table 8: Zero-shot evaluation on long-context benchmark constructed from StackOverflow ques- tions paired with many candidate answers, where the LLM must identify the single answer originally marked as the most helpful (Wang al. 2024a).
Ada-LEval
LC
RAG
LDAR
Qwen-2.5-7B
24.0 (1.0)
63.0 (0.016)
65.0 (0.273)
Gemini-2.5-pr
87.5 (1.0)
80.0 (0.016)
89.5 (0.669)
27

---

Published as a conference paper at ICLR 2026
D.8 ANALYZING LDAR's s LEARNED RETRIEVAL STRATEGY VIA SLIDING WINDOW EXPERIMENTS
Cluster (n=53) Qu-1.00, 9L-0.75
Cluster (n=47)
Group: qu (n-6)
qu-0.97, Quw0.37
Qu-0.78, q: -0.23
Cosine similarty
mace ALL
o 500
0-4555
g2-b330
21-4302
0.4
mini
1,00
0.75
0.50
0.25
0.00
1.00
0.75
0.50
0.25
0.00
1.00
0.75
0.50
0.25
0.00
Rank quantle (high - low!
Rank quantile (high - low)
Rank quantlle (high - low)
Cluster O: w0
Cluster D: w-50
Cluster w=100
Mean Per reffrmancc
#-
0.6
6.8
1.00
0.75
0.50
0.25
0.00
1.00
0.5
0.50
0.15
0.20
1.00
0.75
050
0.25
0.00
Rank quantic thigh lew)
Rank quantile (high - low)
Rank quantile (high low)
Cluster w=
Cluster w=50
Cluster w-100
Mean Performance
=-20
5-50
0.4
9:23
0.73
0.30
0.25
1.50
0.75
0.02
Rank quantile thigh low)
quantile (high low)
Rank quantile (high low)
9,
Group: a,
au 1.0:
Meen Performence
0.
0.4-
w=100
Figure 18: Visualization of clustering analysis over LDAR's retrieval behavior Qwen- -B Location task, along with the corresponding sliding-windas experiments for each cluster. We collected 100 similarity distributions and clustered them based LDAR's 's retrieval band value, sulting in two primary clusters (Cluster and Cluster 1). We additionally isolated all instances where the learned upper quantile satisfies qu < 1.0. The figure shows the mean similarity distribution for each cluster along with LDAR's s average retrieval band (qr, qu n indicates the number of samples in each cluster and group.
28

---

Published as a conference paper at ICLR 2026
D.9 ABLATION STUDIES ON ARCHITECTURAL CHOICES
As the number of passages associated with each query varies (Section 4.1 it produces similarity vectors of different lengths. We used Transformer encoder it can handle variable-length sequences and is able to model relationships within the similarity distribution. To demonstrate the effectiveness of our architectural design choices, we performed an ablation study with different design variants of LDAR. For our MLP variant, we followed the common practice by summarizing each similarity vector into fixed-size representation using simple pooling (i.e., taking the mean over similarity scores across passages), and feed this pooled representation into a stack of MLP layers (Zaheer it al. 2017). Table 9 shows that LDAR with the transformer encoder shows significantly better performance compared to LDAR with MLP.
Also, we used periodic embeddings because they are highly effective for capturing fine-grained variation in continuous inputs Gorishniy al. (2022). As shown in Table 9 LDAR with periodic embeddings achieves significantly better performance than LDAR using a standard learnable em- bedding.
LLM
Architecture
Location
Reasoning
Comp
Hallu
Average
Llamu-3.J 3.1
Transformer with periodic embeddding (LDAR)
77.6 (0.216)
59.6 (0.361)
41,5 (0.501)
73.3 (0.217)
63.9 (0.324)
Llama-3.1-88
Transformer with learaale embedding
(0.359)
55.8 (0.158)
(0.353)
67.5 (0.359)
(0.307)
Llama-3. 1-8B
MLP
71.4 (0.319)
54,5 (0.286)
34.1 (0.424)
70.1 (0.323)
57.5 (0.338)
GPT-to-mini
Transformer with periodic cmbbedding (LDAR)
88.8 (0.397)
80.5 (0.254)
63.4 (0.613)
51.8 (0.397)
71.1 (0.415)
GPT-4o-mini
Transfomer with learmalle emhedding
86.7 (0.520)
79.2 (0.285)
58.5 (0.600)
35.0 (0.521)
64.9 (0.481)
GPT-4o mini
MLP
84.6 (0.332)
77.9 (0.307)
56.0 (0.497)
29.8 (0.335)
63.3 (0.367)
Table 9: Ablation of architecture choices for LDAR.
D.10 ABLATION STUDY ON TASK ALIGNMENT
Most tasks in LaRA (such as Location) are single-hop QA tasks, where the answer can be derived from single relevant passage. In contrast, the Comparison task in LaRA and HotporQA in HEL- MET both require multi-hop reasoming, where multiple passages must be retrieved and combined to produce the correct answer (Section 5.1p.
Our analysis in Section 5.3 shows that for multi-hop tasks, LDAR optimizes to retrieve a larger number of passages compared to other single-hop tasks (e.g.. Location task). Based on this observed behavior, we want to highlight that our zero-shot mapping is structurally motivated.
To further validate that the positive zero-shot results are not coincidental, we conducted an additional misaligned transfer experiment. Specifically, we evaluate (1) Location-trained L.DAR (single-hop) transferred to HotpotQA (multi-hop). and (2) Comparison-trained LDAR (multi-hop) transferred to NQ (single-hop). As shown in Table 10 misaligned transfers lead to degraded performance: Loca- tion + HotpotQA retrieves too few passages to support multi-hop reasoning, while Comparison + NQ retrieves too many passages, introducing additional distraction that harms performance. These results demonstrate that LDAR's zero-shot transfer benefits arise from alignment between task struc- ture, rather than coincidence.
Table 10: Ablation study on task alignment. The top table shows the aligned task setting (Compari- son - HotpotAQ, Location + NQ). The bottom table shows the misaligned task setting (Location - HotpotAQ, Comparison + NQ).
Task aligned
HotpotQA
NQ
1.C
RAG
LDAR
LC
RAG
LDAR
Qwen-3-4B
0.56 (1.0)
0.51 (0.019)
0.62 (0.536)
0.51 (1.0)
0.41 (0.021)
0.53 (0.486)
GPT-4o-mini
0.64 (1.0)
0.65 (0.019)
0.76 (0.629)
0.59 (1.0)
0.52 (0.021)
0.59 (0.374)
Task misaligned
HotpotQA
NQ
LCC
RAG
LDAR
LC
RAG
LDAR
Qwen-3-4B GPT-4o-mini
0.56 (1.0)
0.51 (0.019)
0.60 (0.417)
0.51 (1.0)
0.41 (0.021)
0.50 (0.575)
0.64 (1.0)
0.65 (0.019)
0.74 (0.493)
0.59 (1.0)
0.52 (0.021)
0.58 (0.424)
29

---

Published as a conference paper at ICLR 2026
D.11 FULL COMPARISON RESULTS RETRIEVAL STRATEGIES
In this subsection, we provide more detailed result of Table by showing the performance table for each LLM. Table II [9 compares the performance of retrieval strategies across context lengths and tasks for each LLM. In this subsection, the columns are grouped by context length. Within each group, Loc, Reas, Comp, and Hallu denote the Location, Reasoning, Comparison, and Hallucination tasks in LaRA benchmark, respectively.
Table 11: Comparison of retrieval strategies under context length settings of 32k and 128k for LLaMA-3.1-8B. Each cell reports accuracy, with the value in parentheses denoting the token-usage ratio relative to LC. The best-performing strategy for each task is highlighted in bold.
Context Length 32k
Context Length 128k
Method
Loc
Reas
Comp
Hallu
Overall
Loc
Reas
Comp
Hallu
Overall
Top-1
51.7
17.0
19.3
87.3
43.8
33,6
18.1 (0004)
2.4 (00055
88.8
35.7
(0.01:)
(0.018)
(0.191
(0019)
(0025)
(000))
Top-5
64.2
40.4
45
84
58,5
64.2
41.5
24.3
77.7
51.9
(0.95)
(0,071)
(0261)
(0.085)
(0.85)
10025
(0024)
(0.23)
(0.02))
(0025)
Top-10
71.4
34.0
61.2
83.0
62.4
68.3
50.6
24.3
76.7
55.0
Top-25
76.7
42.5
58.0
76.0
63.3
68,3
54.5
24.3
75.9
55.8
(0.96)
0..8b)
(.451)
(0470)
(0:1)))
00.13
(.120
(..126)
(0.131)
(.129)
Top-50
82.1
27.6
51.6
79.5
60.2
67.3
55.8
31.7
72.4
56,8
L.C
82.1
40.4
51.6
78,8
63.
69.3
50.6
34.1
58.4
53.1
(1.00)
11.000
(1.00)
(1703)
(1 1100
(1000)
(1.000))
(1000)
(1000)
RAG
80.3
46.8
58.0
83
67.3 (0095)
72.4
45.4
19.5
78.3
53.9
(A06))
101911
(0:85)
(0026)
(0034)
(0025)
(0028)
(002S)
Rclf-Route
78.5
44.6
48.3
75.2
61.7
67.3
53.2
24.3
63.2
52.0
(0396)
(0266)
(0.00)
00935
Adaptive-k
58.9
31.9
41.9
84.3
54
56,1
37.6
19.5
69.8
45,8
(0.395)
10.3621
(0335)
(0419)
10
10.3455
(UAIS)
(0.63))
(0.02)
(0L95))
BGM
78.6
46.8
$4.8
77.4
64.4
49.4
21.9
77.8
54.6
RankZephyr
73.2
42.5
51.6
81.3
62,2
57.1
48.0
9.7
76.7
47.9
(00M5)
(0,077)
(0,01))
(0085)
(0/05)
(0//20)
(0.224)
(0025)
(0026)
(0025)
LDAR
91.0
55.3
67.7
82,6
74,2
77.6
59,6
41.5
73.3
63.0
(0.534)
(0,545)
(0,224)
(0,571)
10.3791
(0.216)
(0.361)
(0.3013
(0217)
(0.324)
Table 12: Comparison of retrieval strategies under context length settings of 32k and 128k for LLaMA-3.2-3B. Each cell reports accuracy, with the value in parentheses denoting the token-usage ratio relative to LC. The best-performing strategy for each task is highlighted in bold.
Context Length 32k
Context Length 128k Comp
Method
Loc
Reas
Comp
Hallu
Overall
Loc
Reas
Hallu
Overall
Top-1
53.5
19.3
86.5
43.5
27.5
2.4
88.0
35.3
Top-5
66.0
23.4
35.4
83.0
51.9
56.1
38.9
14.6
77.2
46.7
(AIN)
(0201:
(0205
(0150)
(1025)
00026
(0025)
Top-10
71.4
31.9
29.0
83.0
44.1
65,3
42,8
19.5
78.5
51.5
(0.05)
(0n51)
Top-25
78.5
27.6
45.1
83.4
58.7
66.3
41.5
17.0
71.9
49,2
(0474)
(8.86)
(0.45))
(0476)
(0.47)
(0.133)
(0.13)
(8.136)
(6.311)
(.
Top-50
76.7
31.9
38.7
80.8
57.0
66.3
42.8
21.9
63.7
48.7
(1.66)
(03661
10
(1.56)
LC
80.3
25.5
32.2
65.
50.8
33.7
19.5
$2.0
43.4
030000
(1.000)
(1,00)
(1.000)
(1.00)
(1.000
RAG
78.5
31.9
22.5
79.7
53.2
70.4
41.5
14,6
69.4
49.0
(0.M5)
10,971
(0.01)
(0:05)
10005;
(8,26)
(1033))
(6.25)
(002))
(0025)
Relf-Route
78.5
21,2
32,2
35.0
24.3
56.0
44.6
(0.36))
Adaptive-k
58.9
14,8
32.2
78.6
46.1
47.9
24.6
12.1
67.4
38.0
(0.95)
10.1681
(0385)
(0:45)
(1-405)
(0. 10.306
(0.05)
(0.75)
(1500)
(0895)
BGM
78.4
31.2
45.2
74.8
57.6
66.3
48.9
17.0
75.4
51.9
RankZephyr
69.6 (0.055)
[9_1 (0.077)
35,4
84.7
$2.2
50.0
48,0
12.1
76.4
46.6
(0:00))
(0,05)
(0,065)
10/226
(0.53)
(0.05))
(0.26)
(0012)
LDAR
85.7
34,0
58.0
76.5
63.6
73.5
55.5
34,2
69.1
58.1
(1669)
(0.407)
(0.776)
(0.313)
(0.996]
(0
(0.155
(0.2113
(015))
(L174)
30

---

Published as a conference paper at ICLR 2026
Table 13: Comparison of retrieval strategies under context length settings of 32k and 128k for Qwen- 2.5-7B. Each cell reports accuracy, with the value parentheses denoting the token-usage ratio relative to LC. The best-performing strategy for each task is highlighted in bold.
Context Length 32k
Context Length 128k Comp
Method
Loc
Reas
Comp
Hallu
Overall
Loc
Reas
Hallu
Overall
Top-1
53.5
31.9
19.3
88.2
48
30.6
24.6
2.4 (1103)
85.7
35.8
(0.019)
(0.19)
(0019)
m0019
(0006)
Top-5
71.4
31.9
48.3
83.0
58.7
58,1
45
21.9
75.6
50.2
(UMM)
10.097
(0.01)
(0:05)
(0305)
(1834)
(0n25)
(0.12))
(0.12)
Top-10
78.5
42.5
58.0
83.0
66.3
19.5
71.6
53.9
Top-25
85.7
46.8
58.0
81.3
68.0
74.4
58.4
29.2
22:37
58.5
(0.47)
(.486)
(0,55)
(0.75)
(0,43)
(0.13))
(L134)
(a.13))
(0.12))
Top-50
82.1
46.8
$4.8
80.8
66.
74.4
53.2
34.1
64.8
56.6
(0.66)
LCC
83.9
46.8
$4.8
76.4
65,5
35.7
41.5
12.2
42.5
33.0
(1000)
(1.000)
(1.00)
(1,03)
(1,00))
(1,00))
(1
(1.000
(1000)
RAG
78.5
44.6
45_1
84.3
63.
71.4
59.7
24.9
71.0
56.8
(0.05)
(0,097)
(0001))
(0,055)
(0,0A5)
10/26
(U024)
(0.055)
(0826)
(QU125)
Self-Route
85.7
42.5
62,2
24.3
67.7
51.6
(0.36))
(1.27))
(0:259)
(0416)
Adaptive-k:
64.2
23.4
48.3
83,4
$4.8
37.7
23.3
17.0
70.8
37.2
(0.355)
00.3221
(0.35)
(0A47)
(0.055)
10 3965
(0A45)
(0.635)
(0.00)
(0485)
BGM
77.0
49,2
48.4
63.0
72.4
39.0
75.9
62.4
(0007)
RankZephyr
75.0
40.4
51.6
82.6
62.4
62,2
55.8
26.8
73.2
54.5
(0.095)
(0,097)
(0,011)
(0,055)
(0,045)
(0,025)
(I024))
(0.023)
(0.226
(0.125)
LDAR
91.0
59,5
61.2
83.4
73.8
76.5
64.9
46.3
69,1
64,2
(0.474)
(0.39)
(0,404)
(0.489)
(0.499]
(0.129)
(0.273)
(0.37)
(0.12)
(0.213
Table 14: Comparison of retrieval strategies under context length settings of 32k and 128k for Qwen- 3-4B. Each cell reports accuracy, with the value in parentheses denoting the token-usage ratio rela- tive to LC. The best-performing strategy for each task is highlighted in bold.
Context Length 32k
Context Length 128k Hallu
Method
Loc
Reas
Comp
Hallu
Overall
Loc
Reas
Comp
Overall
Top-1
53.5
25.5
29.0
93.4
50.4
30.6
31.1
7.3
94.1
40.8
(0.019)
(0.01)
(0.09)
(0.09)
(U004)
(0005)
00005)
(0.05)
Top-5
66.0
48.9
51.6
91.7
64.5
61.2
57.
31.7
90.4
60.1
(0.95)
(0.97)
(0.00)
10,055
m0065
(u024)
(8025)
(1826)
(0025)
Top-10
78.5
53.1
$1.6
68.3
39.0
64.9
(1145)
(1051
Top-25
82.1
48.9
64.5
90.4
71.5
80.6
68.8
29.2
87.0
66.4
(0.47g)
(..86)
(0,45))
(0.4%)
(0,43)
(0.33)
(8.124)
(0.126)
(..31))
(0.19)
Top-50
82_1
46,8
61.2
89.5
69.9
77.5
43.9
88.0
68
LC
82.1
42.5
61.2
88,8
68,8
79.5
67.3
41.4
82.0
67.5
(1,000
(1.60)
(1,00)
11 (000))
il (00%)
i1 A80
(000
(1000)
(1000)
(1.000)
RAG
71.4
44.6
$4.8
91.3
65.5 (0005)
76.5 (0025))
57.1
36.5
87.6
64.4
(0.95)
(0,87)
(0,91)
(0:055
(0024))
(0025)
(0026)
(0022)
Self-Route
83.9
$1.0
58.0
70.3
63,6
31.7
63.0
(0233))
(0.17))
(0345)
(L15()
(0.45))
(0917)
Adaptive-k
67.8
38.2
45
90.4
60.4
60,2
$3.2
29.2
88.0
57.6
(0.395)
(0.362)
(1355)
(0.479
(0,405)
10.396;
(1AR5)
(0.73)
(0,00)
(0A85)
BGM
82.3
49.8
88.7
5
72.4
41.7
86.5
66.0
RankZephyr
69.4
46,8
51,6
90.0
64,5
58,1
66.2
21.9
88.6
58.7
(0.995
(0,287)
(0,001)
(0,955
(0,05)
(0/266)
(0025)
(0026)
(0U12)
LDAR
87.5
57.4
70.9
90.0
76.4
85,7
68.8
46.3
86.0
71.7
(0.339)
(0.486)
(0:344)
(0,493)
(0:33
(0.088)
(0.433)
(0.33))
(0.48))
(0.011)
31

---

Published as a conference paper at ICLR 2026
Table 15: Comparison of retrieval strategies under context length settings of 32k and 128k for Mistral-Nemo- NemB-12B. Each cell reports accuracy, with the value parentheses denoting the token- usage ratio relative to LC. The best-performing strategy for each task is highlighted in bold.
Context Length 32k
Context Length 128k Comp
Method
Loc
Reas
Comp
Hallu
Overall
Loc
Reas
Hallu
Overall
Top-1
51.7
27.6
16.1
74.7
42.5
33.6
33.7
9.7 (u105)
67.9
36.2
(0.19)
(1019)
m001;
(1004)
(0833)
(.066)
Top-5
66.0
46.8
45.1
70.8
57.2
61.2
54.5
29.2
50.0
48.7
(006))
(0.071
(1201)
(0/955)
(0:05;
(1633)
(0023)
(0026)
(0025)
Top-10
76.7
46.8
55.6
59.3
66.3
43.9
49.9
00053
Top-25
67.8
34.0
25.8
41.3
42.2
67.3
48.0
24.3
18.5
39.5
(0.474)
(0486)
(0.45)
(.475)
(0.43))
(0.133)
(0.14)
(b.16)
(0.131)
(..19)
Top-50
67.8
34.0
41.9
30,0
43, 4
54.0
45.4
36.5
17.4
38.3
(0.66)
10363)
LC
73.2
29.7
38.7
39.5
45.5
28.6
32.5
12.2
12.7
21.5
(1000)
(1,000)
(1.000)
(1000))
(1.000)
(1000)
RAG
73.2
$1.0
$4.8
68.6
61.3
67.3
45.4
36.5
48.4
49.4
(0.055)
(0,097)
(0,00)
(0,055)
(0,055)
(0024)
(0023)
(UU12))
(00025
Self-Route
76_7
42.5
$4.8
39.5
21.9
14.0
37.5
(0252)
(0.00)
(0,24)
(1214)
(0:45))
(1977)
(0-43)
Adaptive-k
55.3
19.1
48,3
50.0
43.9 43.2
36.7
22.0
14.6
34.1
26.9
(0.35)
(0.362)
(0.35)
(.499)
(0,455)
(0.346)
(0.108)
(0.675)
(0502)
(0,95)
BGM
78.5
54.6
51.9
59.1
61.0
64.3
58
31.7
50.0
51.1
RankZephyr
75.0
36.1
32.2
63.9
5
53.0
54.5
34
$1.8
73.3
(0.955)
(0,097)
(0,001]
(0,055)
(0,05)
(0,02))
(1024)
(0.025)
(0022)
(0.123)
LDAR
83.9
57.4
58.0
50.0
62.3
73.5
59.7
46.3
24,1
50.9
(0226)
(0.172)
(0.445
(0.208)
(0.315)
(0.1002)
(0.04)
(0.006)
(0.102)
(0.165)
Table 16: Comparison of retrieval strategies under context length settings of 32k and 128k for GPT- 4o. Each cell reports accuracy, with the value in parentheses denoting the token-usage ratio relative to LC. The best best-performing strategy for each task is highlighted in bold.
Context Length 32k
Context Length 128k Hallu
Method
Loc
Reas
Comp
Hallu
Overall
Loc
Reas
Comp
Overall
Top-1
58.9
42.5
35.6
90.4
56.8 10.019
33.6
32.4 (U304)
17.0 (0.05)
87.8
42.7
(0.099)
(0.09)
(0015)
(0019
(0105)
Top-5
78.5
70.2
58.0
83.0
72.4
65.3
70.1
31.7
74.8
60.5
(0.955
(0.07)
(0.001
10005)
10005;
(9025)
(1824)
(025)
(0826)
(0025)
Top-10
83.9
67.7
78.6
73.4
74.0
43.9
64.3
(0.189
(U0))
Top-25
91.0
61.7
77.4
75.2
76.3
82.6
79,2
51.2
63.4
69.
(0.474)
(.486)
(0.45))
(0,475)
(0.473)
(0.133)
(0.13)
(0.126)
(0.111)
(0.19)
Top-50
92.8
70,2
77.4
72.1
78,
79.5
77.9
65,8
61.9
71.3
LC
87.5
70.2
80.6
72.3
77.6
90.8
80.5
65.8
56.3
73.4
(1000)
11,0001
(1/00)
(1,00))
(000))
(100
(100))
(1000)
(1000)
(1.00)
RAG
83.9
55.3
67.7
78.3
71.3
80.6
63.6
46,3
67.3
64.5
(0085)
10,871
(0:05)
10
B0026
(0024)
(025)
(0028)
(UUR))
Self-Route
91.0
70,2
70.9
75.6
76,9
80.5
63.4
60.5
71.2
(0.302)
(0384)
(0,47))
(0762))
(1936)
00.83)
Adaptive-k
75.0
59.5
64.5
81.3
70.1
64,2
57.1
48.7
67.4
59.4
(0.99)
10.162
(0.05))
(0-005)
(0. 10.306
(0.75)
(0500)
(0498)
BGM
80.4
61.7
61.3
75.5
66.2
31.7
70.4
60.9
RankZephyr
83.9 (0.95)
61.7
64,5
82
73,
64,2
75.3
36.5
71.9
62.0
(0,057)
(0,081)
(00455
(0,055)
10/226)
(0025)
(0.26))
(0023)
LDAR
94.6
72.3
87.0
76,9
82.7
91.8
84,4
68.3
60.3
76.2
(0.597)
(1.33)
(0.514)
(0.565)
(0.61)
(0.376)
(0.35)
(0.58)
(0.376)
(0.43)
32

---

Published as a conference paper at ICLR 2026
Table 17: Comparison of retrieval strategies under context length settings of 32k and 128k for GPT- 40-mini. Each cell reports accuracy, with the value in parentheses denoting the token-usage ratio relative to LC. The best- pest-performing strategy for each task is highlighted in bold.
Context Length 32k
Context Length 128k
Method
Loc
Reas
Comp
Hallu
Overall
Loc
Reas
Comp
Hallu
Overall
Top-1
55.3
36.1
41.9
81.3
53.6
32.6
40.2
21.9
76.4
42.8
(0.01:
(0018)
(0019)
(1004)
(a005)
60006
Top-5
78.5
48.9
74
75.6
69.9
63.2
66.2
41.4
62.6
58.4
(UM8)
(0.077
(0.001)
(0305)
(0005)
(1:34)
(0023)
(0012)
Top-10
80.3
74.1
70.0
69.3
67.5
51.2
53.1
60.3
(0.011)
Top-25
85.7
61.7
77.4
64.3
72.3
77.5
75.3
43.9
45.2
60.5
(0.474)
(..86)
(0,457)
(0.475)
(1A13))
(0.133)
(1114)
(0.126)
(0.11)
(.129)
Top-50
89.2
$7.4
77.4
62.1
71,5
79.5
74.0
48.7
40.7
60.7
(0.66))
LCC
87.5
55.3
77.4
$2.8
68,
81.6
71.4
58.3
32.0
60.8
(1000)
(12000)
11.000
(1700)
(1,00)
(1000)
(1000)
(1000)
(1000)
RAG
82.1
59.5
70.9
67.1
69.9
70.4
72.7
46.3
59.3
62.2
(0.055)
(0,09T)
(0.01))
(0,055)
(0,A5))
(0U226)
(U024)
(0.025)
(0026)
(0.025)
Self-Route
87.5
68.6
72.77
43.9
29.3
54.3
(0.336)
19365)
(0:324)
(1946)
(0U544)
Adaptive-k
75.0
55.3
64.5
70.4
66.3
59
50.6
41,4
49.4
50.1
(0.305)
(0.362)
(0.385)
(0A7))
(0A0S))
10 396
(BARS)
(0.73)
(.500)
(1-05)
BGM
82.1
$7.4
80.3
64.3
71.0
78.6
36.6
57.9
60.1
RankZephyr
83.9 (0.095)
5
74.1
70.0
70.3
62.2
66.2
31.7
58.9
$4.8
(02097)
(0,001)
(0,055)
(0,245)
10,026)
(0.02))
(0012))
(0.125)
LDAR
89.2
70.2
80.6
60.4
75,1
88,8
80.5
63.4
$1.8
71.1
(0.66))
(0.435)
(6.71)
(0,684)
(0/631)
(0.397)
(0.254)
(0.63))
(0.97)
(0-15)
Table 18: Comparison of retrieval strategies under context length settings of 32k and 128k for Gemini-2.5-pro Each cell reports accuracy, with the value in parentheses denoting the token-usage ratio relative to LC. The best best-performing strategy for each task is highlighted in bold.
Context Length 32k Comp
Context Length 128k Comp
Method
Loc
Reas
Hallu
Overall
Loc
Reas
Hallu
Overall
Top-1
57.1
31.9
29.0
93.4
$2.8 10.01)
29.5
4,8 (000))
93.3
40.3
(0.099)
(0.09)
(0018)
10019
(1004)
(0105)
c0u15)
Top-5
76.7
59.5
58.0
90.8
71.2
61.2
58.4
29.2
85
58.6
(0.855
(0.07)
(0.00)
(0.05)
(0005)
18025
(1824)
(0025)
(1026)
(0.02)
Top-10
83.9
63.8
58.0
89.5
75.5
36.5
82.5
64.8
Top-25
85.7
63.8
64.5
86.5
75.1
81.6
76.6
53.6
78.0
72.4
(0.47g)
(1440)
(0.45))
(0.47)
(0,433)
(0.133)
(1.124)
(0.126)
(0.131)
(0.12)
Top-50
83.9
63,8
67.7
84.7
75.0
81.6
76.6
43.9
71.9
68.5
LC
89.2
59.5
62.1
86.5
74.3
91.8
80.5
46.3
76.6
73.8
(1.000
(1.60)
(1,000
(1 (000)
[1 (000))
(1A03)
(1000)
(1000)
(1000)
(10000
RAG
82.1 (0L85)
61.7
61.2
87.5 (0:055
73.
75.5
64.9
36.5
84.4
65.3
(0,057)
(0,01)
(0105)
(80025)
(1024))
(0023)
(0026)
(0022)
Self-Route
89
63,8
64.5
9 (UARE)
86.7
71.4
43,9
70.1
68
(0.255)
(0.144
10:285)
(0.259)
(0.56)
(1935)
Adaptive-k
69.6
44.6
61.2
90.0
66,3
70.4 (0 19K6
53.2
41.4
78.3
60,8
(0.99)
(0.162)
(0:355)
(0479)
(0-005)
(0:005)
(0.783
605005
(0.95)
BGM
83.9
61.7
54,8
80.3
70.2
68.3
29.3
81.3
60.9
RankZephyr
82,1
65.9
58,0
90.4
74,
64, 2
71.4
21.9
87.8
61.3
(00055
(0,257)
(0,001)
(0,355)
(0,055)
10/226)
(1024))
(0.25)
(0026)
(0025)
LDAR
92.8
68.0
67.7
86.9
78.8
91.8
83.1
63.4
80.7
79,8
(0.641)
(0.665)
(0,665)
(0.441
(0,633)
(0.73k)
(0.52)
(0.62))
(0.73))
(0.65))
33

---

Published as a conference paper at ICLR 2026
Table 19: Comparison of retrieval strategies under context length settings of 32k and 128k for Gemini-2.5-flash. Each cell reports accuracy, with the value in parentheses denoting the token- usage ratio relative to LCC The best-performing strategy for each task is highlighted in bold.
Context Length 32k
Context Length 128k Comp
Method
Loc
Reas
Comp
Hallu
Overall
Loc
Reas
Hallu
Overall
Top-1
55.3
38.2
25.8
92.6
53.0
30.6
28.55
4,8 (0005
93_3 (0035)
39.3 3 (00)5)
(0.019)
(0.19)
(0.19)
10019)
(0:05)
(U004)
Top-5
78.5
68.0
61.2
87 87.8
73.9
57.1
55.8
34.1
85.4
58.1
(U08)
m0.07)
(0/00))
(0705)
(0095)
(1634)
(0025)
(0016)
(0015)
Top-10
85.7
59.5
61.2
85.2
65.3
63.6
39.0
82.5
62.6
Top-25
87.5
59.5
64.5
82,
73.4
78.5
70.1
43,9
78.0
67.6
(0.474
(0486)
(0,55))
(0.476)
(0,73)
(0.133
(0.124)
(b.126)
(a.111)
(0.129)
Top-50
82.1
63.8
$4.8
80.0
70,2
80.6
68.8
$1.2
71.9
68.1
(0.66)
(0.971
(0,60))
L.C
85.7
63.8
74_1
80.4
76,0
87,8
75..3
56.1
68.8
72.0
(1000)
(1.00)
(1:00)
(1000)
1000
(1000)
(1000)
(1930)
(1000)
RAG
82.1
59.5
45
86.5
68.3
74.5
62.3
41.4
85.7
66.0
(0065)
10,071
(018))
(0:05)
(0022))
(002))
(0025)
(0026)
(U02S)
Self-Route
91.0
65.9
61.2
80.8
74.7
81,6
74.0
58.5
70.1
71.0
Adaptive-k
66.0
46.8
48.3
87
62,
64.2
55.8
46.3
78.3
61.2
(0.99)
00.631
(0:35)
IIANI
(1,055)
(0.109
(0:0R)
(0.73)
(130))
BGM
83.9
55.3
68.0
66.3
39.0
84.1
64.5
RankZephyr
78.5 (0.095)
57.4
51,6
88.1
68.9 9
60.2 2
72.7
21.9
84.4
59.8
(0,077)
(0,001)
(0,055)
(0,055)
(0,02))
(B.244)
(Q.05)
(0.12))
(0.025)
LDAR
91.0
70.2
80.6
83.0
81.2
89.8
83.1
68.3
71,1
78.1
(0.611)
(0:716)
(6:556)
(0.1)
(0,633)
(0.49k)
(0.654)
(0.00)
(0.65)
(0.55)
34


Reviews:
[{"review_id": "QU7GvrcYqU", "reviewer": "Reviewer_8eo8", "summary": "This work investigates the noisy information in the context of RAG, and introduces LDAR (learning distraction-aware retrieval) that learns to select passages to minimize interference from distracting passages. The training framework invites the target LLM in-loop to refine the retrieval model. The approach shows effective on the LaRA benchmark compared with several baseline settings on Llama 3.1 8B, Llama 3.2 3B, Qwen 2.5 7B, Qwen 3 4B, Mistral-Nemo-12B, GPT-4o, GPT-4o-mini, Gemini-2.5-pro, and Gemini-2.5-flash. Some interesting findings are shown, including the more capable models (e.g., GPT-4o and Gemini 2.5) perform well on the long-context setting.", "questions": "* What is the basic retrieval model (Top-k) in the experiments? \n\n* What is the exact long-context setting in the experiments? The entire dataset or top-k with a very large k?\n\n* From Table 2, LC looks powerful when the LLMs are capable. LC can be speed up with prompt cache (Cache-Augmented Generation). Could you compare the method with CAG in terms of accuracy and efficiency?", "limitations": "", "rating": 2, "confidence": 5, "soundness": 1, "presentation": 2, "contribution": 2, "strengths": "* This work is well-motivated and easy to follow.\n\n* The proposed method can improve the performances for a range of LLMs. \n\n* The proposed method, which does not require the internal information such as attention values of the target LLM, is model-agnostic and can be applied with any LLMs including the closed ones such as GPT and Gemini.", "weaknesses": "* This work should be compared with other reranking methods, which are important related work to address. However, in the current manuscript, none of the reranking methods such as RankRAG is mentioned and compared. \n\n* The experimental setting of the baseline methods is not comprehensive. The number of passages in RAG, LC, BMG, and Adaptive-k are not clear. Number of passages greatly impact the end performance. Without an analysis on the passage size, the contribution of the proposed method is hard to clarify. \n\n* The proposed method adds computation cost during inference. An analysis of the runtime could be added.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "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": "", "extra_scores": {}}, {"review_id": "5nCT1ebQci", "reviewer": "Reviewer_771K", "summary": "The paper Beyond RAG vs. Long-Context: Learning Distraction-Aware Retrieval for Grounding LLMs studies the trade-off between retrieval-augmented generation (RAG), which may miss relevant information, and long-context models, which can include too much irrelevant content. It introduces LDAR (Learning Distraction-Aware Retrieval), a lightweight retrieval method that adaptively selects a contiguous range of passages from the ranked candidates based only on their similarity scores, achieving a balance between information coverage and distraction. LDAR uses a small transformer trained with policy gradients to predict how wide the retrieval range should be for each query, optimizing task performance according to the LLM’s context capacity. Experiments on multiple benchmarks and models show that LDAR achieves higher accuracy than both RAG and long-context approaches while using fewer tokens. The main contributions are a learning-based distraction-aware retrieval framework, the banded retrieval design that improves stability and generalization, and extensive validation demonstrating efficient grounding for LLMs.", "questions": "1) Do different retrievers induce different cosine-similarity distributions, and would this shift the behavior/performance of the adaptive retriever?\n\n2) Using only accuracy as the reward seems limited. Since the token usage ratio is also reported, can the optimization include a cost term (e.g., inference/compute budget)?\n\n3) In the visualization of retrieval intervals (Fig. 6), most learned bands overlap with the top-k region. For bands outside the top-k, can you provide concrete examples to intuitively explain why passages outside the top interval are selected/beneficial (or why the top interval is insufficient)?\n\n4) Is selecting a continuous interval (band) inherently reasonable? Does it implicitly assume that high-quality passages are locally clustered in the cosine-sorted order? If possible, please add a fixed-length sliding-window experiment: slide a window of length (w) over the cosine-sorted sequence, evaluate accuracy for each window as the retrieved set, examine whether stable peak regions exist, and compare their alignment with the LDAR-learned band.", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 2, "contribution": 3, "strengths": "1) The paper proposes a dynamic interval (band) method to select high-quality context from a passage sequence sorted by cosine similarity.\n\n2) The motivation section analyzes in depth how different retrieval strategies, when applied over a cosine-sorted list, affect answer correctness, and it systematically frames the problem of adaptively balancing recall versus distraction.\n\n3) The evaluation covers a reasonably broad set of settings on four LaRA tasks (location/reasoning/comparison/hallucination) and the long-context variants of HotpotQA/NQ from HELMET.", "weaknesses": "1) The analysis experiments for the method itself are not sufficient (see Questions).\n\n2) The method appears sensitive to LLM size, which affects cross-model generalization and thus reduces transferability and robustness across heterogeneous systems.\n\n3) There is a lack of concrete case studies: results are mainly aggregate metrics and curves, without step-by-step comparisons on representative examples.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "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": "", "extra_scores": {}}, {"review_id": "zt7b9e7ga6", "reviewer": "Reviewer_brC4", "summary": "This paper presents LDAR(Learning Distraction-Aware Retrieval), a method for adaptive retrieval that compellingly addresses the performance-cost tradeoff between RAG and full long-context. The core contribution which involves identifying the problem of LLMs getting incorrect results given both gold passage and distraction passage, and a lightweight, band-based retriever that is sound and justified well againts the \"distraction problem\"(Figure 3 , Section 4.1). The experimental evaluation is thorough, demonstrating LDAR's effectiveness and adaptability across a wide range of modern LLMs and challenging tasks. However, the method's design, which feeds raw similarity scores to a Transformer , raises significant concerns about overfitting to task-specific patterns, a limitation the authors themselves acknowledge. This casts doubt on the true generalizability claimed in the zero-shot experiments (see Section 5.5 ). Other key weaknesses include lack of ablation studies and justification for key architectural choices the complete omission of the method's training cost.", "questions": "Please list up and carefuly describe any questins and suggestions for the authors.Think ofthe things where a response from the author can change youropinion, clarify a confusion or address a limitation. This is important for a productive rebuttal and discussion phase with the authors.\n\n- Can you extend Sec. 5.5 with broader zero-shot tests? Add more tasks (train/test task cross-matrix) and more corpus database.\n- Have you done any ablation on the necessity of choosing transformers as the backbone of the retriever?\n- Could you provide a clear training cost-benifit analysis?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "## Originality\nThe paper's primary originality lies in its precise and insightful problem formulation. It moves beyond the simplistic \"RAG vs. Long-Context\" debate to investigate why RAG systems fail. The core insight, clearly demonstrated in Section 3 and Figure 2, is that performance degradation is not just from irrelevant passages but that even a set of exclusively gold passages can collectively confuse an LLM and lead to incorrect answers. Identifying this \"distraction\" effect, even among relevant documents, is a novel and important contribution.\n\nThe proposed solution, LDAR, is also original. Instead of relying on complex textual rerankers or fine-tuning the LLM, it introduces a lightweight, \"score-only\" adaptive retriever. The \"band-based\" retrieval strategy (Section 4.1) is a creative mechanism to make the reinforcement learning problem tractable, cleverly reducing the search space from a complex combinatorial problem (as in Bernoulli sampling) to a low-dimensional, continuous one.\n\n## Quality\nThe empirical quality of this work is a major strength. The authors validate LDAR with a thorough and rigorous experimental setup (Section 5).\n\n1. Comprehensive Benchmarking: The method is tested across a diverse and modern set of both open-source (Llama-3.1, Qwen-2.5, Mistral-Nemo) and closed-source (GPT-4o, Gemini-2.5) models, demonstrating broad applicability.\n\n2. Challenging Tasks: The evaluation spans six knowledge-intensive benchmarks, including the difficult LaRA tasks (Location, Reasoning, Comparison) and HELMET's long-context adaptations of HotpotQA and NQ.\n\n3. Strong Results: The results in Table 1 are compelling. LDAR consistently achieves a superior performance-to-cost ratio, significantly outperforming the full long-context (LC) baseline in overall accuracy while using substantially fewer tokens (often achieving ~2x token efficiency).\n\n4. Well-Justified Method: The core design choice of \"band-based\" retrieval is well-justified empirically by the comparison in Figure 3, which clearly shows it finds a more stable and effective trade-off than the Bernoulli-based alternative.\n\n## Clarity\nThe paper is exceptionally well-written, organized, and easy to follow, earning a top score for presentation. The authors use visualizations effectively to build a clear and logical narrative.\n\n## Significance\nThis paper addresses a highly significant and practical problem for the LLM community. As context windows expand to 1M tokens, the question of how to best utilize this vast context for optimal cost-performance is of paramount importance. This work provides a practical and effective solution that moves \"beyond\" the binary choice of RAG vs. full context.", "weaknesses": "## Serious Concerns Regarding Generalization and Overfitting\n- The model's score-only policy may overfit similarity-shape statistics of a corpus. LDAR uses only the cosine-similarity distribution (no text), and explicitly restricts access to textual content for scalability. While elegant, this design risks learning corpus-specific distributional quirks rather than semantic robustness, limiting the proposed method's contribution.\n\n- Zero-shot section (Sec. 5.5) lacks mechanism analysis. Authors transfer LaRA-trained policies to HELMET (HotpotQA/NQ) and report modest gains, but do not explain why a Comparison-trained policy improves HotpotQA or why a Location-trained policy improves NQ. Given that different tasks have vastly different retrieval needs, this positive result may be a coincidence.\n\n- The \"score-only\" architecture is also vulnerable to overfitting to the statistical properties of the passage database (e.g., LaRA's mix of novels and finance reports). The paper provides no evidence that a policy learned on this database distribution would generalize to a database with a different profile (e.g., social media, legal documents). The test on HELMET's Wikipedia database (Section 5.5) is not a sufficient stress test and lacks a deep analysis of the distribution shift.\n\n## Missing Ablation Studies for Key Architectural Choices\n- The paper employs a Transformer Encoder to process the sequence of similarity scores. This is a non-trivial architectural choice, implying that the relationships between scores are important. However, this is not justified with a baseline, such as a simpler Multi-Layer Perceptron (MLP), to prove this complexity is necessary.\n- The model uses \"Periodic Embedding\" to encode the raw scalar similarity scores. This is a specific and non-obvious design choice. The paper provides no justification or ablation study comparing this to simpler, standard alternatives (such as learnable embedding with truncation of sorted scores).\n\n## Omission of Training Cost and Practicality\n- The paper's central claim is improved inference efficiency (i.e., lower token usage). However, it provides no information whatsoever about the training cost. The RL training loop, as formulated in Eq. (3), requires a forward pass of the (potentially massive) pretrained LLM $F_{\\psi}$ to obtain the reward signal $r_{\\psi}$ for every gradient step. This suggests the training process may be prohibitively expensive.\n- The paper provides no data on the number of LLM calls required for convergence or the total wall-clock training time. This omission makes it impossible to assess the method's practical viability or cost-benefit trade-off.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "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": "", "extra_scores": {}}, {"review_id": "w3IxamLgCU", "reviewer": "Reviewer_VnKc", "summary": "This paper address the distracting issue caused by RAG. They propose a method called LDAR which learns a lightweight retriever that learns to select passages to minimize potential interference from distracting passage. Their retriver operates on the cosine similarity distribution and selects a dynamic set of passages from a contiguous quantile interval to balance coverage and distraction. For example, if the risk of having distraction is higher, retrieves passages from a narrow quantile interval, minimizing the risk of having distraction. The retriever is trained with the reward signal of whether the downstream LLM gives a correct answer using the retrieved passages. Their expeirments on various benchmarks show significantly better results than the baseline methods.", "questions": "1. what is the passages ratios compared with the initial top-N because LDAR quantile cutting step.\n2. For more capable or larger LLMs, is the quantile cutting still useful compare with using long-context.", "limitations": "", "rating": 8, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The motivation of this paper is solid, learning to balance coverage and distraction for RAG is an important question.\n2. The proposed method is lightweight by only finetuning a retriever model.\n3. The experimental setups are comprehensive, the zero-shot experiment is persuasive and the performance is good.", "weaknesses": "1. An analysis of the proposed method is needed. For example, it would be interesting to compare with a baseline that learns to rerank the retrieved passages alone with the same optimization objective.\n2. The interpretability of the addressed distaction issue is limited. It would be interesting to see how the proposed retriever balance coverage and distraction, some case studies would be helpful.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "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": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)