arXiv:2607. 24567v1 Announce Type: new Abstract: Semantic hashing methods for generating short binary hash codes that allow efficient approximate nearest neighbor search in high-dimensional data spaces have gained extensive consideration in recent years.
Semantic hashing methods for generating short binary hash codes that allow efficient approximate nearest neighbor search in high-dimensional data spaces have gained extensive consideration in recent years. Deep learning-based methods offer better semantic capturing capabilities than traditional approaches relying on manual feature engineering.
The paper introduces Hierarchical Hash Retrieval (HHR), a coarse‑to‑fine framework designed to improve hash‑based retrieval for large language models. HHR combines Geometry‑Aware Key Routing (GKR) to redistribute feature magnitudes and prune low‑logit keys, with Learned Hash Projection (LHP) to align Hamming distance with true query‑key relevance for fine‑grained retrieval. Experiments on diverse LLMs and benchmarks show that HHR outperforms existing methods, boosting LongBench scores by 1.10 points and achieving up to 3.30× decoding speedup at 128K context length for Llama‑3.1‑8B‑Instruct.
arXiv:2512. 04524v4 Announce Type: replace-cross Abstract: Domain adaptive retrieval aims to transfer knowledge from a labeled source domain to an unlabeled target domain, enabling effective retrieval while mitigating domain discrepancies.
Matryoshka Hash Representations (MHR) propose a two‑stage quantization approach for retrieval‑augmented generation. First, a long binary code is learned; then, frozen, additional zero‑initialized residual adaptors are trained to produce searchable prefixes of varying byte budgets. Evaluated on MS MARCO and transferred to seven BEIR datasets, MHR achieves higher NDCG@10 and Recall@100 at 32‑byte budgets than baselines, especially in low‑budget regimes, and can also improve candidate shortlisting and graph‑index pruning.
arXiv:2510. 04127v2 Announce Type: replace-cross Abstract: Approximate nearest neighbour (ANN) search underpins large-scale retrieval, increasingly within the retrieval-augmented generation pipelines that ground large language models, yet the methods that address it have multiplied across communities until they are seldom read as a single field.
arXiv:2609.37218v1 Announce Type: cross Abstract: Semantic watermarking improves robustness against watermark removal attacks by embedding detectable signals into sentence-level representations. Howe...
arXiv:2606. 00324v1 Announce Type: cross Abstract: Multimodal LLMs use dedicated encoders to bridge non-language modalities (vision encoders for images, depth models for audio codec tokens) because raw token embeddings alone cannot capture modality-specific structure.
arXiv:2608. 04405v1 Announce Type: cross Abstract: Long-context large language models (LLMs) are increasingly deployed in real-world applications, yet self-attention remains a major efficiency bottleneck -- especially during decoding -- due to the necessity of repeatedly processing ever-growing key-value (KV) caches.
arXiv:2608. 15438v1 Announce Type: cross Abstract: Building approximate nearest neighbor (ANN) indexes at billion scale is often dominated by expensive global clustering or graph construction, making time-to-index a first-order systems concern.
WIDE: Wildcard Inference with Dynamic Expansion for Cross-Modal Generative Retrieval proposes a new approach to address information asymmetry in cross-modal retrieval. The method introduces Adaptive Entropy Thresholding to calibrate uncertainty, Asymmetry-aware Wildcard Decoding to emit wildcards instead of forced identifiers, and Blind-Spot Re-ranking to evaluate expanded candidates. Experiments on the M-BEIR benchmark show that WIDE outperforms existing generative retrieval methods by reducing forced hallucination while keeping index structures compact.
arXiv:2606. 00514v1 Announce Type: new Abstract: Generative modeling and self-supervised representation learning (SSL) optimize structurally different objectives: generative training rewards distributional fidelity, while SSL rewards semantic coherence.
arXiv:2607. 25216v1 Announce Type: cross Abstract: Semantic ID-based generative recommendation tokenizes each item into a sequence of discrete semantic IDs and predicts the next item by generating semantic IDs.