Redundancy-Aware Context Selection

• Redundancy-aware context selection is a framework that optimizes subset selection by explicitly penalizing duplicate information to ensure diverse and informative outputs.
• It employs similarity-based metrics and adaptive strategies like greedy selection and MMR to balance relevance and coverage against redundancy penalties.
• Applications span domains such as QA, summarization, video analysis, and clinical NLP, leading to improved efficiency, coverage, and model generalization.

Redundancy-aware context selection encompasses a broad class of algorithms and frameworks designed to optimize the informativeness and efficiency of subset selection from a larger set of candidate items—such as text passages, images, audio segments, video frames, feature vectors, or tools—by explicitly penalizing redundancy among chosen elements while maintaining sufficient coverage or relevance for downstream objectives. Redundancy, operationalized via explicit similarity or overlap metrics, is recognized as a major bottleneck for modeling efficiency, information throughput, and model generalization in high-dimensional or overcomplete input regimes across domains including information retrieval, question answering, view synthesis, bioinformatics, dialogue, summarization, clinical NLP, and agent tool use.

1. Core Objectives and Formal Problem Statement

The canonical redundancy-aware selection problem is to choose a subset $S \subseteq \mathcal{X}$, $|S| = k$ (possibly subject to budget constraints), from a ground set $\mathcal{X}$ of candidates, so as to maximize a set-level objective trading off informativeness, coverage, or relevance against redundancy penalties. Redundancy is almost universally formalized as a function of pairwise similarity among items in $S$, such as

$$\mathrm{Red}(S) = \sum_{i<j\in S} \mathrm{sim}(x_i, x_j)$$

or, inverted, average pairwise dissimilarity for diversity,

$$\mathrm{Div}(S) = \frac{2}{k(k-1)}\sum_{i<j\in S} [1-\mathrm{sim}(x_i,x_j)]$$

The selection objective typically becomes

$$\max_{S \subseteq \mathcal{X}, |S|=k} ~ \alpha \,\mathrm{Rel}(S) - \beta\, \mathrm{Red}(S)$$

where $\mathrm{Rel}(S)$ is a relevance or coverage term, and $\beta$ is a redundancy-weight hyperparameter (sometimes adaptively tuned) (Peng et al., 31 Dec 2025, Wang et al., 2024, Balestra et al., 2023).

Constraints can include fixed-size subsets (cardinality constraint), token or memory budgets (knapsack constraint), or pointwise constraints on coverage or diversity.

2. Redundancy Quantification and Similarity Functions

Redundancy is domain-specific and quantification varies accordingly, but common frameworks include:

• Embedding Similarity: Cosine similarity between learned or pretrained embeddings, e.g., CLIP or MiniLM for images (Wang et al., 2024), Conan-v1 for retrieval chunks (Peng et al., 31 Dec 2025), or sentence embeddings for clinical notes (Dai et al., 23 Sep 2025).
• Spatial/Angular/Content Overlap (views, images): For view selection in graphics/vision, similarity is a convex combination of spatial position, camera orientation, and image content similarity (Wang et al., 2024).
• Jaccard Index / Set Overlap: For gene sets or passages, redundancy is captured via set intersection over union (Jaccard) (Balestra et al., 2023) or n-gram overlap (summarization) (Bi et al., 2020).
• Mutual Information / Conditional MI: In feature selection, redundancy and complementariness are quantified via mutual or conditional mutual information between context items conditioned on the query/label (Chen et al., 2015).
• Temporal Proximity or Kernelized Distance: For video frame selection, redundancy between frames is penalized by a temporal kernel (e.g., Gaussian in time) (Yang et al., 12 Dec 2025).
• Custom/Task-driven Influence: Contextual Influence Value (leave-one-out marginal utility) quantifies redundancy in the context for RAG via generator performance drops (Deng et al., 21 Sep 2025).

Adaptive or parameter-free selection rules often synthesize similarity signals from multiple modalities (semantic, structural, spatial, etc.), enabling fine-grained tuning of redundancy-awareness (Wang et al., 2024, Peng et al., 31 Dec 2025).

3. Algorithmic Strategies for Redundancy-Aware Subset Selection

The underlying combinatorial optimization problem is NP-hard in most settings (Wang et al., 2024, Peng et al., 31 Dec 2025); thus, practical systems rely on efficient approximation techniques:

• Greedy or Maximal Marginal Relevance (MMR) Selection: At each iteration, select the candidate with the greatest marginal gain in the objective, often focusing on maximizing minimal dissimilarity to the current set (farthest-point/similarity-greedy) (Wang et al., 2024).
• Adaptive Greedy under Constraints: For token/knapsack budgets (e.g., RAG), iteratively add items whose marginal utility (relevance minus pairwise redundancy accumulated over prior picks) is maximized, terminating when no feasible candidate increases the objective (Peng et al., 31 Dec 2025).
• Instance-Adaptive Hyperparameter Calibration: Closed-form adaptive solution for trade-off coefficients (e.g., $\beta^*$ in AdaGReS) based on empirical similarity statistics of the candidate pool and expected set size (Peng et al., 31 Dec 2025).
• Redundancy-Aware Ranking and Penalized Orderings: Greedy re-ranking with explicit penalties for overlap (e.g., Jaccard, set intersection) with already-selected items, possibly rescaled or accumulated artificially (Balestra et al., 2023).
• Set-Function Regularization and Soft Selection: Continuous optimization techniques (e.g., Gumbel-Softmax relaxation (Yang et al., 12 Dec 2025)) allow direct regularization of set-level diversity and redundancy during training of neural selectors.
• Hierarchical and Global Attention: In task-specific context selection (dialogue, video), architectural choices enforce multi-level attention to globally suppress redundancy at both fine and coarse granularity (Shen et al., 2021, Li et al., 2023).

Random baselines, simple frequency-based heuristics, or purely relevance-based (top-k) selectors serve as controls but generally overfit to salient but redundant candidates, underperforming redundancy-aware approaches.

4. Applications Across Modalities and Domains

Redundancy-aware context selection is critical in a wide range of technical domains:

Application Area	Context Atoms	Redundancy Metric	Primary Reference
RAG and QA	Passages, Chunks	Embedding sim, leave-one-out	(Peng et al., 31 Dec 2025, Deng et al., 21 Sep 2025)
View/Frame Subset Selection	Camera views, video frames	Multi-factor sim, temporal kernel	(Wang et al., 2024, Yang et al., 12 Dec 2025)
Extractive Summarization	Sentences	n-gram/semantic overlap	(Bi et al., 2020)
Feature Selection	Features	MI, conditional MI, dispersion	(Chen et al., 2015)
Bioinformatics	Gene sets (pathways)	Jaccard overlap, Shapley value	(Balestra et al., 2023)
Tool Use in Agents	Tool signatures/APIs	Dense sim., semantic graph merge	(Liu et al., 22 Oct 2025)
Clinical NLP	Discharge summaries, sections	Embedding sim., perplexity, gating	(Dai et al., 23 Sep 2025)
Dialogue/VQA	Utterances, frames, objects	Learned multi-level attention	(Shen et al., 2021, Li et al., 2023)

In RAG, attention-based and utility-based context pruning methods (AdaGReS, Contextual Influence) yield substantial improvements in Intersection-over-Union (IOU), EM, or human-judge answer quality, typically with token budget reductions of 30–80% (Peng et al., 31 Dec 2025, Deng et al., 21 Sep 2025, Fang et al., 13 Mar 2025).

In video understanding, joint optimization at the frame set level dramatically reduces selection of temporally clustered and visually redundant frames, improving VideoQA and reasoning accuracy, with empirical ablations isolating the value of explicit redundancy penalties (Yang et al., 12 Dec 2025, Li et al., 2023).

Structured redundancy-aware sampling in clinical pipelines reduces label noise and accelerates training, while priority-based section gating under fixed-token budget maintains clinical salience (Dai et al., 23 Sep 2025).

5. Set-Level Theoretical Properties and Guarantees

Redundancy-aware objectives commonly inherit challenging combinatorial properties:

• Monotonicity: Adding new, maximally dissimilar items to the set generally increases diversity or non-redundancy (Wang et al., 2024).
• (Approximate) Submodularity: While strict submodularity is rare due to supermodular (redundancy) terms, established bounds under $\varepsilon$-approximate submodularity yield provable near-optimality guarantees for greedy algorithms, e.g.:

$$F(S_{\mathrm{greedy}}) \geq (1-1/e)\,\mathrm{OPT} - k\varepsilon/e$$

with $\varepsilon$ controlled by the redundancy-weight and maximum pairwise similarity (Peng et al., 31 Dec 2025).

• Complexity: With careful use of dynamic programming, closed-form Shapley decomposition, or per-step pairwise updates, redundancy-aware selectors can attain polynomial or near-linear time per selection step, even in high-dimensional regimes (Balestra et al., 2023, Wang et al., 2024).
• Continuous Relaxations: Gumbel-Softmax and related relaxations allow for direct gradient-based optimization of discrete subsets in neural context pruning (Yang et al., 12 Dec 2025).

These properties guide hyperparameterization, scaling, and practical deployment.

6. Empirical Performance and Trade-Offs

Quantitative results across diverse benchmarks consistently demonstrate that explicit redundancy control yields higher informativeness-to-budget ratios, increased accuracy, or coverage, and superior sample/computation efficiency compared to baseline methods. Representative empirical findings include:

• Novel view synthesis: at 5% view sampling, redundancy-aware ILD selection improved PSNR by +0.66–1.99 dB over uniform or prior methods, matching/exceeding full-data performance at 10–20% sample rate (Wang et al., 2024).
• RAG/QA: AdaGReS improved IOU by 8–15 percentage points over top-k across open-domain and biomedical tasks with robust dynamic $\beta^*$ tuning (Peng et al., 31 Dec 2025); Contextual Influence selection improved EM by +17.94% to +26.04% over standard RAG (Deng et al., 21 Sep 2025).
• Summarization: AREDSUM-CTX attained statistically significant ROUGE-F1 gains and human-judged reductions in redundancy, outperforming both heuristic trigram-blocking and joint-sequence decoders (Bi et al., 2020).
• Tool selection: ToolScope achieved up to 38.6 percentage point gains in tool selection accuracy and reduced prompt tokens by 98.5–99.9% post-merging and redundancy-aware filtering (Liu et al., 22 Oct 2025).
• Clinical NLP: Redundancy-aware deduplication cut the training set by 15%, increased F1 by up to 0.022 (universal model), and improved external generalization by +0.062 F1 (Dai et al., 23 Sep 2025).

Most selectors show graceful performance degradation as budget tightens, with explicit redundancy penalties facilitating robust trade-off tuning.

7. Future Directions and Open Challenges

Open problems include:

• Automatic/Adaptive Hyperparameterization: Fully adaptive selection of redundancy weights, cutpoints, and budget-aware trade-off tuning remains an active research area, with AdaGReS’s $\beta^*$ calibration a recent solution (Peng et al., 31 Dec 2025).
• Differentiable/End-to-End Architectures: Integration of redundancy-aware objectives directly into neural selector modules (Gumbel-softmax, continuous set objectives) for reinforcement learning and joint training (Yang et al., 12 Dec 2025, Zhu et al., 16 Dec 2025).
• Generalization Across Domains/Tasks: Development of universal or transferable redundancy surrogates remains an unsolved problem (e.g., context selection in RAG, multi-modal VQA, or dialog) (Deng et al., 21 Sep 2025).
• Hybrid Metrics: Combining unsupervised (e.g., embedding sim., Jaccard) and supervised (influence, marginal loss) redundancy signals for effectiveness and efficiency, especially for model-in-the-loop settings (Deng et al., 21 Sep 2025, Zhu et al., 16 Dec 2025).
• Fine-Grained Redundancy Detection: Advances in local versus global redundancy detection, particularly for long/document-level or hierarchical context (multi-turn dialog, video, structured records) (Shen et al., 2021, Li et al., 2023).
• Computational Scalability and Distillation: Fast computation over artifact-rich or ultra-large candidate pools (e.g., tool libraries, gene sets) with efficient deduplication or surrogate scoring (Liu et al., 22 Oct 2025, Balestra et al., 2023).

A plausible implication is that further improvements in context selection, model interpretability, and data efficiency—especially for constrained-resource settings—will continue to depend on increasingly sophisticated, domain-adaptive redundancy-aware methodologies.

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References:

• (Wang et al., 2024) Diversity-Driven View Subset Selection for Indoor Novel View Synthesis
• (Peng et al., 31 Dec 2025) AdaGReS:Adaptive Greedy Context Selection via Redundancy-Aware Scoring for Token-Budgeted RAG
• (Deng et al., 21 Sep 2025) Influence Guided Context Selection for Effective Retrieval-Augmented Generation
• (Balestra et al., 2023) Redundancy-aware unsupervised rankings for collections of gene sets
• (Li et al., 2023) Compressing Context to Enhance Inference Efficiency of LLMs
• (Fang et al., 13 Mar 2025) AttentionRAG: Attention-Guided Context Pruning in Retrieval-Augmented Generation
• (Chen et al., 2015) Feature Selection with Redundancy-complementariness Dispersion
• (Liu et al., 22 Oct 2025) ToolScope: Enhancing LLM Agent Tool Use through Tool Merging and Context-Aware Filtering
• (Yang et al., 12 Dec 2025) HFS: Holistic Query-Aware Frame Selection for Efficient Video Reasoning
• (Zhu et al., 16 Dec 2025) Context-Picker: Dynamic context selection using multi-stage reinforcement learning
• (Dai et al., 23 Sep 2025) Model selection meets clinical semantics: Optimizing ICD-10-CM prediction via LLM-as-Judge evaluation, redundancy-aware sampling, and section-aware fine-tuning
• (Shen et al., 2021) Learning to Select Context in a Hierarchical and Global Perspective for Open-domain Dialogue Generation
• (Bi et al., 2020) AREDSUM: Adaptive Redundancy-Aware Iterative Sentence Ranking for Extractive Document Summarization
• (Li et al., 2023) Redundancy-aware Transformer for Video Question Answering