AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：arXiv:2608.16927v1 Announce Type: new Abstract: As supervised fine-tuning data continues to scale, selecting high-value subsets from large candidate pools is crucial for reducing training cost and improving model performance. Existing methods often measure diversity directly in the original embedding space, where geometric metrics entangle dominant semantic directions, fine-grained supervision differences, and local noise. We address this limitation by formulating data selection as a coarse-to-fine hierarchical coverage problem and propose MASS. MASS learns low-dimensional principal manifold coordinates with a dense autoencoder for coarse semantic grouping, and then performs quality-aware sparse feature coverage within each group using a TopK sparse autoencoder…

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：arXiv:2608.16926v1 Announce Type: new Abstract: Data selection in supervised fine-tuning aims to select a small set of effective samples from large-scale candidate data, reducing training cost while preserving model performance. However, existing methods usually treat data value as a relatively static property, and pay limited attention to the compatibility between data and the capability distribution of the target model. To address this issue, we propose Data-DPO, a target model-oriented SFT data selection method. Data-DPO observes the local training feedback of the target model on different samples through one-step probing, transforms activation differences among samples into pairwise data preferences, and trains a lightweight reward model to learn target-mod…

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：arXiv:2608.16925v1 Announce Type: new Abstract: We build an instrument that reads, from a single fit and with no oracle, whether the operator a hybrid PDE-parameter estimator postulates is wrong-and separates that from a merely unidentifiable parameter. On one self-adjoint parabolic inverse problem, an information-matrix statistic with plug-in scale and per-seed parameter has median 0.19 under correct specification, rejection rate $0.033$ against a pre-registered ceiling of $0.10$, and rises to $224$ and $85$ under two misspecifications, firing in every replicate. On a correctly specified but non-identifiable design it stays mute-$0.050$ at $n=200$, Clopper-Pearson $[0.024, 0.090]$-while a rank statistic collapses to zero at a pre-registered boundary $c_5^*=2.1…

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：arXiv:2608.16913v1 Announce Type: new Abstract: Road safety monitoring has historically been reactive, relying on crash-record analysis after fatalities and injuries have already occurred. Proactive identification of high-risk locations and dangerous driving behaviour before incidents occur is a critical but underexplored challenge. This paper addresses this gap using connected vehicle telemetry data from Greater Sydney, Australia, to detect and forecast near-miss risky driving events at the Local Government Area (LGA) level. Risky driving is quantified through g-force thresholds (hard braking >0.6g, harsh cornering >0.47g, harsh acceleration >0.5g), and spatio-temporal heatmaps are constructed to identify high-risk zones. Eight predictive models are benchmarke…

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：arXiv:2608.17124v1 Announce Type: new Abstract: Combining the answers a large language model (LLM) samples for a question into one decision is a test-time information fusion problem, usually solved by majority voting. Voting is unreliable on difficult questions, where the sampled answers share correlated errors, so the wrong answer can win and drawing more samples makes the decision worse. Selecting a candidate by reading a correctness signal from the model's hidden states is a promising alternative, but its accuracy varies across models and tasks, and no measure indicates when it can be trusted. In this paper, we propose CASE (Correctness-Axis SElection), a dynamic selection combiner that trains a linear gate on the answer-token hidden state and selects the hi…

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：arXiv:2608.17071v1 Announce Type: new Abstract: We present KernelArc, a multi-agent framework for autonomous GPU kernel optimization across heterogeneous workloads. Strategy-specialized agents run in parallel and coordinate through conclusions-only shared memory, a deterministic benchmark guard, and read-only cross-agent state with plateau-triggered drafting. We evaluate \kernelarc{} on NVIDIA H100 and B200 GPUs using category-representative SOL-ExecBench workloads. The resulting implementations span custom BF16 GEMM, static cuBLASLt Expert-API configuration tables, fused mixture-of-experts backward, shape-gated decoder-layer fusion, native NVFP4 grouped-query attention, and paged prefill attention. At the public SOL-ExecBench leaderboard snapshot recorded on J…

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：arXiv:2608.17067v1 Announce Type: new Abstract: As text-to-image generative models advance, they raise critical safety concerns, particularly the generation of Not-Safe-For-Work (NSFW) content such as violence and nudity, further exacerbated by red-teaming adversarial attacks. Existing defenses predominantly operate under white-box assumptions, relying on text encoder optimization, weight editing, or inference-time intervention, and fundamentally cannot scale to proprietary models. Black-box alternatives based on LLM prompt rewriting offer broader applicability, yet fail in a critical regime we identify as the \textit{benign adversarial} problem: prompts that are linguistically safe but still trigger harmful generation due to the model's learned data distributi…

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：arXiv:2608.17053v1 Announce Type: new Abstract: A bounded agent may obtain information for a decision from its own past, from peers, or from both sources. Retaining task-relevant history can reduce later communication, while a peer message can supply what memory lacks. Under limits on both resources, how should an agent allocate its information budget? Given a fixed task and decision rule, the memory and message rate pairs attaining a performance threshold form an achievable region under specified rules for using history and peer observations. We call its efficient boundary the remembering--signaling frontier. Across conditions where history permits the same maximum reduction in task loss, we hypothesize that a bounded agent will need less peer communication wh…

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：arXiv:2608.17007v1 Announce Type: new Abstract: Agent Skills can specify procedural and resource obligations for tool use, and language models instantiate them as concrete programs. However, when models turn this guidance into code for existing tool interfaces, even a semantically correct program may load an entire input and exceed the memory available to one tool call. We present SkillEffect, a checked-lowering runtime for computations with a recoverable source relation, an audited bounded implementation, and a registered output postcondition. Before granting execution authority, an independent checker rebuilds each proposed lowering from the submitted program and immutable input. Every relation plugin supplies a source recognizer, input-fact extractor, bounde…

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：arXiv:2608.16977v1 Announce Type: new Abstract: AI systems are increasingly capable of contributing to mathematical research. In research practice, frontier-model reasoning is a limited resource, and expert mathematical review is even more sharply constrained. Allocating these scarce resources well is therefore central to making AI-assisted mathematical discovery efficient. In most current AI-for-math workflows, human effort is concentrated at the beginning and end, in selecting suitable research problems and later reviewing the resulting artifacts. These two stages are becoming bottlenecks for research-level mathematics. We address them by proposing a new human-AI discovery paradigm. The human input is no longer a single problem selected in advance, but a rese…

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：arXiv:2608.16971v1 Announce Type: new Abstract: Radiology reports describe findings and locations in free text, but downstream search and analysis require these relations in a fixed schema. Learning this extraction requires labels that are unevenly distributed across institutions: smaller hospitals have less local evidence, and pooling data may be infeasible. We introduce FedPref: frozen public language models propose alternative JSON extractions, local annotations rank them, and sites collaboratively train compact Qwen3-8B adapters while sharing only model updates. A heterogeneous teacher pool provides cross-model contrast when repeated single-model samples collapse. On development data from six simulated hospitals with unequal data volume and disease prevalen…

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：arXiv:2608.16956v1 Announce Type: new Abstract: API buyers purchase a dated contract, not a model name alone: the contract includes the requested and served model, reasoning-effort term or its omission, output rail, service product, prompt, and price schedule. We study the reasoning-effort term through a registered paired contrast of Sonnet 5 with explicit high effort against the same model with effort omitted, using 30 AIME 2026 items and five calls per item. Every paid attempt was assigned one frozen terminal category, and inference resampled items while retaining their repeated calls. Mean delivered cost was \$0.01031 per call higher under the explicit-high contract than under the omitted contract [+\$0.00204, +\$0.01974]. The corresponding accuracy contrast…

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：arXiv:2608.16891v1 Announce Type: new Abstract: Agentic AI systems request tool actions that can modify files, send messages, launch jobs, or change workflow state. This shifts the safety problem from harmful text generation to harmful operational side effects. Prompt-level governance can shape model behavior, but it does not create an execution boundary. We introduce Aegis, a runtime governance system that treats model outputs as action proposals and mediates them through a trusted decision layer before tool execution. The model proposes; the trusted runtime decides. Aegis evaluates proposals against active policy state, resolves provenance server-side, fails closed under uncertainty, and routes selected cases through Senate-style settlement, a quorum- based n…

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：arXiv:2608.16890v1 Announce Type: new Abstract: Clinical trial programming -- transforming study protocols into analysis-ready datasets under CDISC standards -- is a bottleneck in regulatory submissions, yet LLM-based code generation fails catastrophically on this task: across 11 single-shot attempts with five frontier models, none produces a valid subject-level analysis dataset. We introduce GxP-Agent, a multi-agent system that encodes regulatory process ordering as a directed acyclic graph (DAG), decomposing monolithic dataset generation into 15 domain-specific nodes executed by worker agents with pharmaverse skill context, validation gates, and conditional retry. On CDISC-Bench, a new execution-based benchmark built from the FDA pilot submission CDISCPilot01…

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：The workspace for SEOs and their agents Purpose-built for the work of search. Ready for what comes next. Choose a plan “The most unique day-to-day SEO analysis tool in the market.” Aleyda Solis International SEO Consult…

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：Explaining AI agents is like explaining 3D printers Explaining AI agents is like explaining 3D printers by Alex Yumashev · Aug 17 2026 Have you ever tried explaining why somebody needs a 3D printer? "It prints stuff" so…

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：Ruth Hale had been hired to find the words. That was not what the offer letter said. The offer letter said Staff Researcher, Model Organisms, $485,000, equity that would buy a house in Iowa and a closet in San Francisco…

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：OpenAI Group PBC today announced it’s rolling out a stricter version of its ChatGPT chatbot created for younger users. The announcement of “ChatGPT for Teens” follows scrutiny over how generative artificial intelligence, whether the products of OpenAI or other AI firms, can be misused by younger users. OpenAI itself is currently fighting a number of […] The post OpenAI’s junior version of ChatGPT with guardrails has launched appeared first on SiliconANGLE.

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：AI agent systems are becoming increasingly common. They can dramatically improve productivity, but once an agent workflow starts making repeated calls across planners, workers, reviewers, tools, and retries, model cost…

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：OpenAI Group PBC is falling further behind its rival Anthropic PBC, if its latest financials are any indication. The artificial intelligence model maker told investors that its revenue rose 18% on a sequential basis, from the first to the second quarter, but it also grew its net losses. The numbers are likely to be hugely […] The post OpenAI falls further behind Anthropic, with disappointing revenue growth and mounting losses appeared first on SiliconANGLE.

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：OpenAI Group PBC recently paused some of its artificial intelligence training workloads over concerns that they could cause cybersecurity issues. The ChatGPT developer disclosed the move in a blog post published today. According to the company, the pause is part of a broader initiative designed to improve its cybersecurity guardrails. The project will also see […] The post OpenAI paused some AI training runs over cybersecurity concerns appeared first on SiliconANGLE.

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：Daring Fireball: Follow-Up Thoughts on Watermarking Schemes for AI-Generated Text Follow-Up Thoughts on Watermarking Schemes for AI-Generated Text Monday, 17 August 2026 Some follow-up to this weekend’s stemwinder “Anth…

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：The Fastest AI Just Got Faster. Introducing the all new Cerebras CS-4, a revolutionary rack-scale solution that delivers upto 30x faster inference compared to GPUs, enhanced economics, and a simple path todeploy hypersc…

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

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：Inference is the bottleneck of the agent era. Agents read, plan, and call tools, often for hours or days. They consume tokens at a rate chat never approached. Every one of those tokens takes a full forward pass over the…

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：OpenAI's overhead will rise 20 percent for some workloads as it hardens security Expanded multistage chain of thought monitoring makes frontier model work more expensive Thomas Claburn Thomas Claburn AI AND SOFTWARE REP…

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：Modern AI agents increasingly rely on search infrastructure to execute complex, neuro-symbolic reasoning workflows. These workflows often compile into deeply nested, non-monotonic Boolean queries over text fields. However, standard query evaluation strategies over inverted indices face severe theoretical limits when handling these structures. Stateful iterator models (Document-at-a-Time) are structurally bounded by NC^1 formula evaluation, suffering a worst-case O(2^|Q|) exponential blowup in query complexity when unrolling re-convergent logic. Conversely, recursive materialization models…

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：Large language models (LLMs) exhibit a wide range of human-like behaviors, from expressing thoughts and emotions, to engaging in relationship-building with users, to refusing requests and maintaining boundaries. Despite their prevalence, researchers and practitioners lack methods and empirical insights to make informed decisions about when and what types of human-like behaviors LLMs should exhibit. To fill this gap, we present a multi-dimensional analysis of the prevalence, potential effects, and controllability of these behaviors using LLM-as-a-judge and human evaluation. Across 21,000…

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