arXiv subjects

Chunran Zhang

Publications and source records attributed to Chunran Zhang.

3 recordsLinked to original sources

arXiv2026

What a Model Refuses, a State Fears: How Authoritarian Information Control Reproduces in Language-Model Guardrails

As large language models become the front door to political information, what they refuse to discuss becomes a new instrument of information control. We argue that a model's guardrail encodes not a universal notion of harm but the political threat model of the state that governs its developer, and we derive the expected structure of that control from the comparative study of how authoritarian regimes censor. Across ten models and three languages, Chinese guardrails carry its signatures: they answer to the developer's own regime, refusing identical collective-action prompts far more when a prompt names China than a foreign state; within politics they target the capacity to coordinate rather than dissent, declining even to help organize pro-government mobilization; and their strictness is porous, collapsing under adversarial paraphrase, so that the models most resistant to attack are Western frontier systems, not the strictest refusers. Machine censorship thus reproduces the friction-based logic of prior-era information control while, lacking a censor's case-by-case judgment, proving blunter than the bureaucracy it resembles---so that audits which measure refusal directly overstate how controlled a model actually is.

cs.CY↗

arXiv2026

Query Expansion Should Be Coordinated: Dense Expands, Sparse Anchors

Retrieval-augmented generation (RAG) systems rely on retrieval modules to ground large language model (LLM) outputs. LLM-based query expansion enriches retrieval with document-like passages, but evaluations of hybrid retrieval often fuse fixed top-L prefixes of dense and sparse rankings. Because L controls cross-channel contributions and ranking access, it can alter measured expansion gains. We therefore evaluate complete-list effectiveness and record per-channel replay stopping depths required to certify the ordered top-K. This changes the design: because both rankings determine the fused result, their query constructions should be coordinated rather than designed independently. We present DESA (Dense Expansion and Sparse Anchoring), which shares generated references across channels but specializes their integration. Orthogonal residual expansion adds new semantic directions to the dense query, whereas score-product anchoring reorders the original sparse support without admitting expansion-only matches. The same references thus play complementary roles: Dense expands; Sparse anchors. Across seven BEIR datasets, DESA improves nDCG@10 and Recall@20 over the unexpanded query by 3.82% and 2.38%, while reducing dense and sparse replay stopping depths by 36.90% and 36.56%.

cs.IR↗

arXiv2026

Exact Adaptive Hybrid Retrieval Without Fixed Top-L Cutoffs

Modern retrieval-augmented generation (RAG) systems often fuse fixed Top-$L$ results from dense and sparse retrievers, treating later contributions as zero. The cutoff therefore determines both the ranking and its execution cost. Yet truncated fusion is not generally equivalent to complete-list fusion: unread cross-list ranks can change Top-$K$ membership or order even when the observed candidates contain every item in the complete-list Top-$K$. Because channel rankings vary across queries and corpus updates, a depth selected from historical queries may not transfer reliably. We propose Exact Adaptive Hybrid Retrieval (EAHR), which fixes the ordered Top-$K$ defined by complete-list weighted RRF as the retrieval target and treats channel depth as request-specific execution state. Per-Vector Scalar Quantization (PVS) and Posting Block-Max (PBM) produce resumable exact dense and sparse rankings. Fusion bounds unread contributions and requests further ranks only while they can change the Top-$K$. Every successful request therefore matches complete-list fusion without a preset Top-$L$; otherwise, execution continues safely to list exhaustion. Across five test collections and five temporal corpus snapshots, complete-list weighted RRF remained competitive, whereas fixed depths selected from historical queries did not transfer reliably. EAHR reproduced the complete-list ordered Top-20 in all 150 query-snapshot combinations. Under a warm-cache, interleaved, order-balanced protocol, the paired geometric-mean latency ratios of exhaustive batch execution to EAHR were 23.35 on TREC-DL 2019 and 30.28 on TREC-DL 2020. Anti-correlated rankings exhausted both lists, and some difficult queries were slower with EAHR. EAHR does not guarantee a speedup for every request; it fixes the exact result while adapting execution depth to the current rankings.

cs.IR↗