arxiv: 2604.22180 · v1 · submitted 2026-04-24 · 💻 cs.IR · cs.AI

Recognition: unknown

ResRank: Unifying Retrieval and Listwise Reranking via End-to-End Joint Training with Residual Passage Compression

Xiaojie Ke , Shuai Zhang , Liansheng Sun , Yongjin Wang , Hengjun Jiang , Xiangkun Liu , Cunxin Gu , Jian Xu

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Guanjun Jiang

Authors on Pith no claims yet

Pith reviewed 2026-05-08 10:25 UTC · model grok-4.3

classification 💻 cs.IR cs.AI

keywords information retrievalrerankinglarge language modelspassage compressionresidual connectionslistwise rankingend-to-end training

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The pith

ResRank achieves competitive ranking effectiveness by compressing each passage into a single embedding for listwise reranking without generating any tokens.

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

The paper proposes ResRank to unify retrieval and listwise reranking in a single end-to-end framework. It compresses passages using an Encoder-LLM into one embedding per passage, which is then used by a Reranker-LLM along with the query. Residual connections help align the compressed embeddings with the ranking task, and scoring is done via cosine similarity in one step instead of generating text. This setup is trained jointly in dual stages to align objectives. The approach delivers effectiveness on par with or better than full-text rerankers on standard benchmarks while drastically cutting latency and avoiding issues like lost in the middle.

Core claim

By projecting passages into compact single-token representations via an Encoder-LLM and integrating them through residual connections into the Reranker-LLM, ResRank enables listwise reranking with one-step cosine-similarity scoring, trained end-to-end jointly with the retrieval component, matching or exceeding the effectiveness of full-text LLM rerankers while using zero generated tokens and processing only one token per passage.

What carries the argument

The Encoder-LLM passage compression combined with residual connections to the Reranker-LLM, allowing listwise ranking on compressed inputs without autoregressive decoding.

If this is right

ResRank eliminates generation latency entirely by using direct similarity scoring.
The residual structure mitigates misalignment between compressed embeddings and ranking decisions.
Joint multi-task training unifies the retrieval and reranking stages into one optimization process.
Performance holds across TREC Deep Learning and multiple BEIR datasets with improved efficiency.

Where Pith is reading between the lines

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

The method could scale reranking to much larger candidate sets in production search systems where full-text processing is too slow.
Similar compression strategies might apply to other LLM tasks involving long contexts, such as summarization or question answering.
Further reductions in tokens per passage could be explored while maintaining ranking quality.

Load-bearing premise

Compressing each passage to a single embedding via the Encoder-LLM preserves sufficient information for accurate listwise ranking without significant loss compared to using full passage text.

What would settle it

A head-to-head comparison on a dataset where full-text listwise reranking outperforms ResRank by a large margin in NDCG or MAP, despite the efficiency gains.

Figures

Figures reproduced from arXiv: 2604.22180 by Cunxin Gu, Guanjun Jiang, Hengjun Jiang, Jian Xu, Liansheng Sun, Shuai Zhang, Xiangkun Liu, Xiaojie Ke, Yongjin Wang.

**Figure 1.** Figure 1: Architecture overview of ResRank. Each passage is independently compressed into a single embedding by view at source ↗

**Figure 3.** Figure 3: Ablation study summary. BEIR Avg. and TREC view at source ↗

**Figure 2.** Figure 2: Effectiveness vs. efficiency trade-off on TREC view at source ↗

read the original abstract

Large language model (LLM) based listwise reranking has emerged as the dominant paradigm for achieving state-of-the-art ranking effectiveness in information retrieval. However, its reliance on feeding full passage texts into the LLM introduces two critical bottlenecks: the "lost in the middle" phenomenon degrades ranking quality as input length grows, and the inference latency scales super-linearly with sequence length, rendering it impractical for industrial deployment. In this paper, we present ResRank, a unified retrieval-reranking framework that fundamentally addresses both challenges. Inspired by multimodal LLMs that project visual inputs into compact token representations, ResRank employs an Encoder-LLM to compress each candidate passage into a single embedding, which is then fed alongside the query text into a Reranker-LLM for listwise ranking. To alleviate the misalignment between the compressed representation space and the ranking space, we introduce a residual connection structure that combines encoder embeddings with contextualized hidden states from the reranker. Furthermore, we replace the conventional autoregressive decoding with a one-step cosine-similarity-based scoring mechanism, eliminating the generation bottleneck entirely. ResRank is trained through a carefully designed dual-stage, multi-task, end-to-end joint optimization strategy that simultaneously trains the encoder and reranker, achieving learning objective alignment between retrieval and reranking while substantially reducing training complexity. Extensive experiments on TREC Deep Learning and eight BEIR benchmark datasets demonstrate that ResRank achieves competitive or superior ranking effectiveness compared to existing approaches while requiring zero generated tokens and processing only one token per passage, yielding a fundamentally better balance between effectiveness and efficiency.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

ResRank compresses passages to single embeddings with residuals and cosine scoring to cut LLM reranking latency, but the abstract gives no numbers or ablations to show the compression keeps enough ranking signal.

read the letter

The key point is that ResRank unifies retrieval and listwise reranking by running an Encoder-LLM to turn each passage into one embedding, then feeding that plus the query into a Reranker-LLM that scores with cosine similarity instead of generating tokens. A residual connection mixes in hidden states to handle space misalignment, and the whole thing trains end-to-end in two stages to align the objectives. This directly targets the lost-in-the-middle problem and the super-linear latency that come with feeding full passages to LLMs.

Referee Report

2 major / 2 minor

Summary. The paper proposes ResRank, a unified retrieval-reranking framework that compresses each candidate passage to a single embedding via an Encoder-LLM, injects it into a Reranker-LLM alongside the query using residual connections to mitigate representation misalignment, and replaces autoregressive generation with one-step cosine-similarity scoring. It employs a dual-stage multi-task end-to-end joint training strategy and claims competitive or superior effectiveness on TREC Deep Learning and eight BEIR datasets while requiring zero generated tokens and only one token per passage.

Significance. If the compression-plus-residual mechanism preserves sufficient ranking-relevant information, the work would meaningfully advance efficient LLM-based reranking by eliminating generation latency and the lost-in-the-middle problem, offering a practical efficiency-effectiveness tradeoff for industrial IR systems. The end-to-end joint optimization and parameter-free inference path are potentially valuable contributions if empirically validated.

major comments (2)

[Method and Experiments] The central effectiveness claim rests on the assumption that single-embedding compression (plus residual hidden-state injection) retains enough query-specific and inter-passage distinction signals for listwise ranking. No direct measurement of information loss—such as passage reconstruction fidelity, ablation on ranking-feature preservation, or comparison of pre- vs. post-compression cosine similarities—is reported, leaving the weakest assumption untested.
[Abstract and §5] Abstract and §5: Competitive or superior results are asserted on TREC DL and eight BEIR datasets, yet the abstract supplies no numerical scores, baseline names, statistical significance tests, or ablation tables. Without these, the magnitude of improvement over full-text listwise rerankers and the contribution of the residual structure cannot be assessed.

minor comments (2)

[Method] Notation for the residual connection (e.g., how encoder embedding is added to which hidden states) could be clarified with an explicit equation in the method section.
[Training] The dual-stage multi-task training procedure would benefit from a diagram or pseudocode to show the exact loss weighting and stage transitions.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive and detailed feedback. We address each major comment point by point below, providing our responses and indicating the revisions planned for the next version of the manuscript.

read point-by-point responses

Referee: [Method and Experiments] The central effectiveness claim rests on the assumption that single-embedding compression (plus residual hidden-state injection) retains enough query-specific and inter-passage distinction signals for listwise ranking. No direct measurement of information loss—such as passage reconstruction fidelity, ablation on ranking-feature preservation, or comparison of pre- vs. post-compression cosine similarities—is reported, leaving the weakest assumption untested.

Authors: We agree that direct measurements of information retention would provide stronger support for the compression and residual mechanisms. While the end-to-end competitive results on TREC DL and BEIR datasets serve as indirect evidence that sufficient ranking-relevant signals are preserved, we acknowledge the value of explicit analysis. In the revised manuscript, we will add an ablation study isolating the residual connection's impact on ranking quality and a comparison of pre- versus post-compression cosine similarities across sampled queries. These will be included in Section 5 to directly test the assumption. revision: yes
Referee: [Abstract and §5] Abstract and §5: Competitive or superior results are asserted on TREC DL and eight BEIR datasets, yet the abstract supplies no numerical scores, baseline names, statistical significance tests, or ablation tables. Without these, the magnitude of improvement over full-text listwise rerankers and the contribution of the residual structure cannot be assessed.

Authors: We agree that incorporating specific numerical results into the abstract would improve the reader's ability to assess the improvements. Detailed tables, baseline comparisons (including full-text listwise rerankers such as RankGPT), and statistical significance tests are already present in Section 5. In the revised version, we will update the abstract to include key metrics such as nDCG@10 on TREC DL and average performance across the eight BEIR datasets, along with explicit mentions of the residual structure's contribution as shown in our ablations. revision: yes

Circularity Check

0 steps flagged

No significant circularity; claims rest on external benchmarks and architectural choices

full rationale

The paper presents ResRank as an architectural unification of retrieval and listwise reranking via Encoder-LLM compression, residual connections, and one-step cosine scoring, trained end-to-end on dual-stage multi-task objectives. All effectiveness claims are grounded in comparisons against existing methods on external TREC Deep Learning and BEIR datasets rather than self-referential metrics or fitted parameters renamed as predictions. No self-definitional equations, load-bearing self-citations, uniqueness theorems imported from prior author work, or ansatz smuggling appear in the derivation. The residual mechanism is introduced as an explicit design choice to address misalignment, not as a tautological fix. This is the common case of a self-contained empirical system whose central results do not reduce to their inputs by construction.

Axiom & Free-Parameter Ledger

1 free parameters · 1 axioms · 0 invented entities

The approach rests on standard LLM assumptions plus new design choices for compression and training; no explicit free parameters listed but joint optimization implies several tuned values.

free parameters (1)

training hyperparameters for dual-stage multi-task optimization
End-to-end joint training of encoder and reranker requires multiple learning rates, loss weights, and stage-specific schedules fitted to data.

axioms (1)

domain assumption Single-embedding compression of passages retains sufficient semantic information for listwise ranking when combined with residual connections
Invoked as the core mechanism to solve lost-in-the-middle and latency issues.

pith-pipeline@v0.9.0 · 5616 in / 1386 out tokens · 67247 ms · 2026-05-08T10:25:59.034643+00:00 · methodology

discussion (0)

Reference graph

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