The controlled version of MV-GTA uses deterministic, verifiable text segments; isolated text encoders; view-specific graph heads; and relevance derived from external labels or RDKit descriptors, but does not establish free-form routing, consistent three-view specialization, statistical equivalence to specialists, or superior downstream prediction.
Abstract
Graph-text retrieval typically maps a graph and its description to a single embedding, even when a query concerns only one semantic aspect, such as a class label or molecular property. Multiple heads can separate these aspects, but a change in the query head may alter retrieval even when the wrong text is sent to that head. Such behavior demonstrates architectural channelization, not necessarily semantic routing. We examine the conditions under which this distinction can be resolved. Our controlled version of MV-GTA uses deterministic, verifiable text segments; isolated text encoders; view-specific graph heads; and relevance derived from external labels or RDKit descriptors. Correct routing and per-sample derangements form a causal test of whether retrieval depends on content. On BBBP and BACE, correct routing improves label and property nDCG by 0.305 to 0.685 over deranged training. The expected graph head exceeds the best wrong head by 0.303 to 0.453. Topology does not specialize consistently across the two datasets. In a matched three-seed comparison, one joint model obtains mean topology, label, and property nDCG of 0.720/1.000/0.877; three separately trained Single specialists obtain 0.633/0.976/0.859. Property paraphrase augmentation also improves unseen-template nDCG by 0.140 and 0.147 over a matched-exposure canonical control. Consistency and hard-template extensions, however, reduce canonical retrieval in some settings. The evidence is therefore limited to explicit, externally grounded label and property routing and observed multi-interface consolidation. It does not establish free-form routing, consistent three-view specialization, statistical equivalence to specialists, or superior downstream prediction.
Graph-based retrieval-augmented generation connects facts that no single passage states, but current implementations pay for that three times: in infrastructure, requiring a vector store, graph database and document store to be kept consistent; in graph quality, because an extraction pipeline that never refuses output fills the graph with edges that assert nothing; and over time, because a graph that only accumulates treats superseded and current facts alike. post-graph-rag is an open-source engine addressing all three. Text chunks with embeddings, a canonical entity graph and community summaries live in one PostgreSQL database, with pgvector for search and edge tables for traversal. Extraction-time invariants run before anything is written: vague predicates, pronominal names and bare quantities are rejected; predicates are normalised onto an optional vocabulary; entities resolve to one vertex per canonical name via model-supplied aliases; and denied relations keep the positive predicate under a negation flag. A temporal layer lets relations carry a validity period from the prose, lets a later document supersede an earlier incompatible assertion from document order alone, and answers as-of queries. Against LightRAG on three corpora under identical extraction and embedding models, post-graph-rag builds a denser graph everywhere, up to $2.4\times$ the relations per entity, and a more queryable one: distinct edge labels run at 0.46 to 0.58 per relation, 0.11 under a controlled vocabulary, against 0.77 to 1.33. It answers comparably with lower query latency, and supports temporal evolution the baseline lacks: 13 and 8 relationships superseded on a novel sequence and a decade of filings, against zero. These are engineering measurements, not a benchmark result. Code: https://github.com/crajah/post-graph-rag, https://github.com/crajah/post-graph
A Spectral-Aware Feature Alignment module to unify feature dimensionality and align cross-domain semantics in a community-aware manner and a Graph Diffusion Tokenized Transformer that constructs hybrid token sequences from local and global structural contexts for Transformer encoding, and applies diffusion-based refinement to mitigate distribution shifts on unseen graphs.
Mo Li, Zhaosong Zhao, Linlin Ding et al.· Annual International ACM SIG...· 0 citations
Existing retrieval-augmented generation (RAG) systems treat web pages as flat text, losing the structural and semantic signals encoded in HTML. We present PolyUQuest, a verifiable, structure-aware web RAG framework built on a heterogeneous graph that unifies hyperlink topology between pages, DOM hierarchy within pages, and entity-relation knowledge across pages. A two-tier router dispatches each query to one of three retrieval modes matched to its structural need, including direct block retrieval, cross-page graph traversal, and multi-hop entity reasoning. Each answer carries traceable provenance: every cited block records its source page, heading path, and entity links, so users can inspect the structural evidence behind a claim. We evaluate on the official websites of the Hong Kong Polytechnic University (PolyU), comprising 4,240 pages, 31,086 DOM blocks, 29,119 entities, and 37,680 relations, together with a multi-type evaluation benchmark. PolyUQuest improves correctness, coverage, and faithfulness over the evaluated baselines while maintaining query-time token consumption comparable to ChunkRAG and substantially below the graph-based RAG baselines. The demonstration provides an interactive interface for inspecting cited answers, comparing retrieval traces across routing modes, and exploring evidence graph paths. PolyUQuest is being prepared for deployment as a student-facing QA service at PolyU.
Ying Liu, Yingzi Ye, Quan Feng et al.· arXiv.org· 0 citations
Independent agents that reason in latent space can share computed state as key-value cache fragments rather than text. Merged by a conflict-free replicated data type, these fragments form a store that converges under any delivery order or duplication. Yet a later query, unknown at encode time, cannot reliably read the merged cache: colocated fragments interfere, so colocation is not addressability. MaSRead addresses the read to content. It routes through opaque keyed tag sets derived from fragment words and decodes each selected fragment under a hard attention mask that hides the rest. Under lexical connectivity, a graph walk reaches the fragments required by a multi-hop query. Across chain, pipeline, symmetric, hub, and natural-language stores, MaSRead recovers visited fragments in isolation, remains effective as unrelated fragments accumulate, and transfers to another model family. After routing, materialized decoding depends on fragment length rather than total store size; end-to-end work still includes store-dependent routing and one read per visited fragment. The limits are explicit: lexical routing can miss disconnected evidence, and answer composition remains bounded by the frozen reader. Thus a replicated latent store becomes selectively readable for later queries when the needed fragments connect to the query through content.
Carlos Baquero, Lu'is Brito, João Resende· 0 citations
This work presents S$^2$GE as an instance showing that diagnosis-driven interface design can improve native decoder usability and introduces an intervention triangle with three matched conditions: readable graph evidence, shuffled graph evidence, and no-graph input that separates evidence inclusion, structural readability, and decoder-usable topology.
Xiao-Yu Guo, Peng-Cheng Chen, Jiong Yu et al.· 0 citations
GraphSynthQA, a knowledge-graph)—guided synthesis framework in an open-web setting, which iteratively retrieves and verifies evidence from the internet to expand a KG, then synthesizes complex, answer-verifiable queries grounded in multi-evidence dependencies.
Chiwei Zhu, Mingxuan Du, Benfeng Xu et al.· Annual International ACM SIG...· 0 citations
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