AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：arXiv:2608.13660v1 Announce Type: new Abstract: Medical image segmentation models are typically trained under the assumption that all data are available simultaneously. However, in clinical practice, datasets often arrive sequentially, requiring models to adapt continuously to evolving data distributions. We study this problem in gynecological image segmentation, where substantial heterogeneity across imaging modalities, anatomical structures, and annotation protocols creates a particularly challenging continual learning setting. Under these large distribution shifts, existing continual learning methods struggle to preserve previously learned knowledge, leading to catastrophic forgetting. To better understand forgetting in this setting, we investigate how diffe…

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：arXiv:2608.13708v1 Announce Type: new Abstract: Automatically generating textbook-grounded assessment items can reduce science teachers' workload, but existing retrieval-augmented generation (RAG) systems rely on flat retrieval, support only single-question generation, lack safeguards against weak evidence, and are ill-suited to low-resource, board-exam-structured curricula. We address these limitations with TeachMateGPT, a multi-agent system contributing four advances to curriculum-grounded science-assessment authoring. (i) COPE, a hierarchical knowledge base replacing token-window chunking with a multi-resolution index that segments documents along syllabus structure and links them at three granularities via a traversable graph-based lineage, matching evidenc…

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：arXiv:2608.13706v1 Announce Type: new Abstract: Existing defenses against hallucination in retrieval-augmented and multi-agent pipelines remain partial: evidence is trusted despite modality disagreement, debate verifies an aggregate report rather than individual claims, and such verification occurs only after drafting, leaving inter-agent errors undetected until the final text. To close this gap, we present CLAIR-Fin, a nine-agent framework that decomposes each question into atomic claims maintained in a typed Financial Claim Ledger. Each claim is resolved through Asymmetric Evidence Authority, which conditions evidence trust on claim type rather than treating all modalities as equally reliable; Chain-of-Custody Verification, which checks grounding at the hand-…

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：arXiv:2608.13698v1 Announce Type: new Abstract: Reinforcement Learning with Verifiable Rewards (RLVR), often optimized with Group Relative Policy Optimization (GRPO), has become a central recipe for improving the reasoning capabilities of pretrained language models but current studies remain heavily English-centric. We conduct a large-scale empirical study of multilingual and non-English GRPO across a wide range of base models, training languages, and different reasoning language rewards. We find that training to reason in the native language often leaves only a small gap to training for English reasoning. We further observe strong crosslingual transfer: training in one language often improves performance in many others. However, specific trends are highly mode…

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：arXiv:2608.13624v1 Announce Type: new Abstract: Large Audio Language Models (LALMs) have seen increasing use for audio understanding tasks such as speech recognition and audio question answering, raising concerns about fairness across demographic subgroups. Fairness evaluation in spoken-input settings is challenging due to confounding factors, including semantic variation in spoken content and speaker-specific characteristics. Ignoring these factors can result in misleading conclusions about model bias. We propose a semantic-aware mixed-effects regression framework for fairness evaluation in LALMs that explicitly accounts for these confounders. Our approach incorporates sentence-level semantic embeddings of reference text as covariates and models speaker identi…

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：arXiv:2608.13588v1 Announce Type: new Abstract: Multi-hop question answering requires complex reasoning across multiple evidence segments, which often overwhelms retrieval-augmented generation systems with lengthy and noisy contexts, thereby undermining both efficiency and accuracy. While existing prompt compression methods attempt to address this issue, they are typically designed for single-turn queries and fail to capture interdependent reasoning steps. We propose IterCOMP, a unified, training-free prompt compression framework that incorporates multi-hop reasoning within an iterative compression loop. IterCOMP decomposes documents into evidence segments, evaluates question answerability, and generates targeted follow-up questions to iteratively integrate ess…

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：arXiv:2608.13580v1 Announce Type: new Abstract: Jais 2 is a family of Arabic-centric large language models developed jointly by MBZUAI, Cerebras, and Inception, designed to advance Arabic-centric language modeling, with strong performance across the Arabic and culturally grounded benchmarks evaluated in this report. The family includes, to our knowledge, the largest open Arabic-centric LLM trained from scratch at 70B parameters, and a competitive 8B-parameter variant among the evaluated open models. A custom Arabic-centric vocabulary enables efficient training and inference. In addition, an optimized architecture and training recipe yield highly compute-efficient training. With a substantially smaller token budget than comparable models, Jais 2 achieves strong…

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：arXiv:2608.13578v1 Announce Type: new Abstract: Transformer architectures rely on dense self-attention to model long-range dependencies, but this mechanism exhibits quadratic complexity with respect to sequence length. We introduce BCMT (Blockwise Causal Memory Transformer), an architecture for long-context language modeling that decouples local token interactions from global context propagation. Dense causal self-attention is applied independently within local blocks, while each block produces an adaptive summary aggregated through an exponential causal memory. This memory is subsequently injected back into the token representations, enabling efficient propagation of long-range contextual information without relying on explicit global attention. Unlike standar…

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：arXiv:2608.13571v1 Announce Type: new Abstract: When a language model fails to answer a query on the first attempt, an agentic system retries, consuming additional tokens each time. This retry overhead creates a gap between what a model's per-token price implies and what a full workflow actually costs. We call this gap \emph{token inflation} and define it as the ratio of true workflow cost to single-call cost. Systems like FrugalGPT route based on the latter, which can underestimate real cost by more than $2\times$ on difficult tasks. We address this with InflationAgent, a four-stage router that (1) measures token inflation systematically across model tiers and task types, finding inflation as high as $4.25\times$ for a 7B model on multi-hop question answering;…

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：arXiv:2608.13570v1 Announce Type: new Abstract: Latent reasoning has emerged as a powerful alternative to text-based Chain-of-Thought (CoT), offering significant gains in computational efficiency by compressing verbose reasoning into compact embeddings. However, compressing reasoning into the latent space renders the thinking opaque, hindering its interpretability. Current methods present a stark trade-off: they either function as unexplainable ''black boxes'' (e.g., Coconut), where the latent reasoning is not human-readable, or rely on separate post-hoc decoders for explainability (e.g., Heima), introducing architectural overhead and decoupling the explanation from the actual reasoning process. In this work, we present a unified framework for Self-Explainable…

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：arXiv:2608.13568v1 Announce Type: new Abstract: Coding agents spend most of their context budget on retrieval. Lexical retrieval (grep) is universal, instant, and zero-setup, but noisy: it cannot tell a definition from a call from a comment. Semantic retrieval via the Language Server Protocol (LSP) is precise and typed, but needs a running, indexed server and pays a per-symbol round-trip. The claim that semantic retrieval is more token-efficient is, we find, asserted almost everywhere and measured almost nowhere: no public source isolates the LSP-vs-lexical token delta for an agent at equal task-success. This paper formalizes the question with one metric (tokens-to-success), specifies a five-arm ablation isolating semantic retrieval from confounds, maps three p…

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：arXiv:2608.13676v1 Announce Type: new Abstract: Objective: Foundation models represent the next advancement in AI for EEG analysis; however current explainable AI techniques provide attribution scores in the time-channel input space, which is mismatched to clinical intuition about EEG. Thus, there is a critical need for a universal method that can extend the interpretability of any foundation model to alternative and physiologically relevant domains without modifying or retraining the underlying model. Methods: EEG-PRISM leverages linear transformations and established backpropagation rules to map time-channel attribution scores into alternative domains. We derive mappings to the frequency domain via an invertible DFT and to the source domain via an approximate…

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：arXiv:2608.13675v1 Announce Type: new Abstract: Between October 2018 and July 2026 AI models progressed from simple systems like BERT to massive agents that solve complex math and write software. The ability to resolve real coding issues improved by nearly six times per year since late 2024. During this time costs dropped sharply with OpenAIs budget model GPT 5 point 6 Luna matching flagship capabilities for just one to six dollars per million tokens beating older versions at a fraction of the price. Top performance is now split across specialized models as Claude Opus 5 leads in frontend coding Claude Fable 5 excels at repository level coding and GPT 5 point 6 Sol dominates terminal tasks. In a grade school math test using the Qwen 2 point 5 model basic method…

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：arXiv:2608.13668v1 Announce Type: new Abstract: Linear attention keeps a state of fixed size. At long context, many stored items share this state, and interference between them degrades retrieval. Gated DeltaNet-2 (GDN-2), like every delta-rule model before it, derives its erase vector from the key of the current token. However, the interference in its reads is measured through the query, and the erase step cannot reach it. We introduce the Query-derived Erase Direction (QED). QED adds a second erase direction derived from the query and orthogonal to the key. In the fast-weight view, a key-directed delta edit cannot change the key-orthogonal part of a read. It uses the editable part to cancel old-state content measured along the query. It also improves retrieva…

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：arXiv:2608.13652v1 Announce Type: new Abstract: Generic event-level anomaly detection for collider physics has two recurring problems: anomaly scores are hard to interpret, and they correlate strongly with energy scale and object multiplicity. We present Organized Representation via Contrastive learning for Anomaly detection (ORCA), a two-stage framework that first learns an embedding space via supervised contrastive learning across a diverse set of physics processes, then runs a standard autoencoder in that space to generate event-level anomaly scores. On a simulated dataset consistent with conditions at the High-Luminosity Large Hadron Collider, ORCA delivers significant gains in both breadth and depth of sensitivity to new physics signals with respect to a b…

這項 arXiv 研究檢驗了主動學習中不確定性採樣在標籤噪聲下失效的原因，究竟是獲取了更多錯誤標籤，還是錯誤集中在困難區域本身帶來額外危害。實驗對比了邊緣不確定性採樣與隨機採樣，在三個公開二分類表格數據集上發現：不確定性採樣在乾淨標籤下能提升學習效率，但其穩健性高度依賴數據集、預算、噪聲結構和評估指標。

L-FNO是一種新型隨機神經算子，將FNO風格的協變量路徑、洛倫茲譜核和基於似然的訓練目標相結合，用於建模罕見、突發且自激的事件動態。在八個合成點過程基準和三個真實世界數據集上，L-FNO在事件似然、校準診斷和罕見事件檢測方面均優於現有神經算子基線。

這項arXiv研究提供了來自Chutes的為期一年的LLM服務生產軌跡，覆蓋了多個模型和用户的完整生產行為。研究從聚合、時間、模型和用户四個視角揭示了通常被聚合視圖隱藏的工作負載演變和用户-模型結構，並計劃隨論文發佈完整軌跡。

一項新的arXiv研究以XLCoST代碼翻譯基準為基礎，利用基於幅度的專家掩碼，系統分析了Qwen3.6-35B-A3B混合專家模型在層級上的敏感性。研究發現，後期MoE層（尤其是35-39層）對激進掩碼的容忍度遠高於早期和中期層，可以在相對較小的質量損失下大幅減少專家數量。這些結果支持深度感知的壓縮策略，併為物理權重手術、基於激活的專家評分和基於訓練恢復等技術指明瞭實用路徑。

一篇新的arXiv論文提出RubricForge方法，通過從少量帶真值標籤的軌跡中自動生成可讀的評判標準，在不訪問環境獎勵的情況下評估語言模型智能體。與G-Eval等通用評判器相比，RubricForge將失敗軌跡被誤判為成功的比例降低約一半，並提升排序忠實性，同時保持判定可歸因。

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：Discussion | Link

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：OpenAI funds 14 independent projects exploring new AI policy ideas to expand economic opportunity and strengthen societal resilience in the Intelligence Age.

Level1Techs論壇帖子指控谷歌非法保留客户數據，違反GDPR，並稱已採取法律行動。討論未能反駁這一説法。

微軟表示，由於AI漏洞挖掘工具產生大量需要驗證、修復和測試的問題，Exchange Server Subscription Edition（SE）的累積更新CU1將推遲發佈，且目前無法給出具體日期。

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：Discussion | Link

BlockArena 是一款由 Puter AI Builder 生成的霓虹體素風第一人稱射擊遊戲，支持桌面端鼠標鍵盤操作、P2P 多人對戰、人機練習和完整的 FPS 戰鬥機制。

okf 是一個面向 Open Knowledge Format 的 Go 命令行工具，採用 agentic-first 設計，輸出 JSON，支持信任層級、來源追溯、生命週期與認證計算。它是 Google 參考實現的輕量級、供應商中立替代品。

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：We ran 904 DeepSWE rollouts on DeepSeek V4 Pro 0813 and Claude Fable 5. Fable leads pass@1 at 90x the cost; Pro wins pass@4, and a Pro-first cascade hits 82.7%.

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：Shadow traffic proves a candidate is operationally sound. It can't tell you if users like it better. Run the split at the endpoint instead of in your app code.

據彭博社報道，Stripe 已同意以超過 70 億美元收購 AI 推理網關 OpenRouter。這個價格只有在把 OpenRouter 視為 AI 推理市場而非簡單 API 代理時才合理。Stripe 押注的是：通過控制請求路由、提供商關係和 800 萬開發者，在 AI 成本產生的時刻銷售支付、計量、額度和計費服務。