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版本更新 · v3arXiv:2509.23219

WirelessMathBench-XL: An Auditable Benchmark for Wireless Mathematical Reasoning

Xin Li, Mengbing Liu, Yiyang Zhu, Wenhe Zhang, Li Wei, Jiancheng An, Chau Yuen

原文摘要

Benchmarks built from arXiv papers can overlap the arXiv text used in LLM pretraining, yet technical-domain releases rarely ship the per-item evidence needed to check this. We introduce a reverse-probe n-gram audit that indexes the benchmark side and streams the corpus past it, so memory scales with the benchmark rather than the corpus and flags on the distributed problems can be inspected and recomputed. We instantiate it in WirelessMathBench-XL, a 4,027-problem benchmark for wireless mathematical reasoning built from 836 arXiv papers across 20 subfields, with multiple-choice, progressive fill-in, and full-equation-completion formats. Streaming 12.9 B RedPajama-arXiv 13-grams yields a strict zero-hit view $\mathcal{S}_0$ covering 95.7% of the problems, and shows that the Dolma and ProofPile-2 arXiv slices repackage the same RedPajama dump. Filtering to $\mathcal{S}_0$ moves every evaluated model by less than 1 pp. With 30/800 flagged test items, this comparison bounds detected-overlap inflation at 0.31-0.51 pp for frontier models rather than testing for contamination, and it does not cover paraphrase, target-answer, alternate-version, post-training, or closed-corpus exposure. Frontier models reach 86.5-91.3%, and GRPO on the released training split lifts a 7B model 25.1 pp above a size-matched general-math control. We release source-paper identifiers, per-problem audit and threshold metadata, license-aware data files, audit and bootstrap scripts, Croissant metadata, and a Datasheet.

cs.LG

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2609.35279

Measuring Collapse and Correction in Homogeneous-Panel LLM Debate

Xin Li, Mengbing Liu, Chau Yuen

原文摘要

Multi-agent large language model (LLM) debate is often evaluated by whether final answers improve, but a changed answer is not necessarily a better one: the same discussion can correct an initially wrong majority or collapse an initially correct one. Standard final-accuracy evaluations conflate these opposing transitions. We introduce an auditable protocol for homogeneous debate on multiple-choice questions (MCQs) that records each run as a transition ledger over collapse, correction, onset, and signed intervention utility. On 6,925 MMLU-Pro debates, the protocol records 253 collapses together with the corrections that offset them, and the correction side changes how interventions should be judged. Replay experiments reveal the central tradeoff: freezing every debate prevents 251 collapses but loses 804 corrections under equal weights, and a leave-one-model-out probe-gated freeze that is tuned for net utility never fires, so collapse prevention alone can recommend the wrong policy. A compact pre-debate 8-probe screen is a triage signal: its unadjusted family-level association with conditional-collapse risk is high (G=7, Spearman $ρ$ = 0.893, exact two-sided p = 0.0123), but initial-majority accuracy is a close comparator ($ρ$ = -0.821; family partial $ρ$ = 0.767, p = 0.0877), so we do not treat it as calibrated or capability-adjusted prediction. Round-level traces localize many collapses to the first debate round, where early disagreement can precede both collapses and corrections. Reasoning-mode models, including one model at two reasoning-effort levels, also show both collapses and corrections. We release replayable schemas, coders, audits, cost cards, and zero-API rebuild scripts so future model rows can be compared under the same denominators and signed utility ledger.

cs.CL

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2509.26157

Entropy Guided Dynamic Patch Segmentation for Time Series Transformers

Sachith Abeywickrama, Emadeldeen Eldele, Min Wu, Xiaoli Li, Chau Yuen

原文摘要

Patch-based transformers have emerged as efficient and improved long-horizon modeling architectures for time series modeling. Yet, existing approaches rely on temporally-agnostic patch construction, where arbitrary starting positions and fixed lengths fracture temporal coherence by splitting natural transitions across boundaries. This naive segmentation often disrupts short-term dependencies and weakens representation learning. We propose a novel Entropy-Guided Dynamic Patch Encoder (EntroPE), as a temporally informed framework that dynamically detects transition points via conditional entropy and dynamically places patch boundaries. This preserves temporal structure while retaining the computational benefits of patching. EntroPE consists of two key modules, namely an Entropy-based Dynamic Patcher (EDP) that applies information-theoretic criteria to locate natural temporal shifts and determine patch boundaries, and an Adaptive Patch Encoder (APE) that employs pooling and cross-attention to capture intra-patch dependencies and produce fixed-size latent representations. Extensive experiments on long-term forecasting, classification, and anomaly detection demonstrate that the proposed method improves both accuracy and efficiency, establishing entropy-guided dynamic patching as a promising new paradigm for time series modeling. Code is available at https://github.com/Sachithx/EntroPE.

cs.CV · cs.AI · cs.LG

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arXiv ↗PDF ↗