AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：arXiv:2608.05205v1 Announce Type: new Abstract: Event-centric intelligent analytical systems heavily depend on explicit causal event knowledge for risk early warning, decision-making support and narrative comprehension. Nevertheless, existing instance-level causal pairs suffer severe generalization deficits on low-frequency long-tail and unseen event combinations. To address this limitation, this work proposes Abstract Event Causal Rule (AECR), a novel relation-level causal abstraction paradigm that transforms concrete cause-effect pairs into generalized abstract causal logic while retaining their intrinsic causal relationships. We design a multi-agent Concrete-to-Abstract Causal Induction (CACI) system coupled with similarity-constrained clustering to distill…

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：arXiv:2608.05204v1 Announce Type: new Abstract: LLM-agent ecosystems are rapidly growing around reusable skills: mixed-modality packages of metadata, natural-language instructions, code, tools, references, and operational workflows. As skills become marketplace artifacts, auditing their reuse is no longer the same problem as ordinary code clone detection. Existing detectors target single-modality source code or whole-package similarity, yet skill reuse evidence is distributed across authored text, implementation fragments, and operational structure. As a result, they can miss reuse that preserves only one part of a skill. We present SKILLTRACE, a multi-trace provenance auditing framework for LLM-agent skill reuse. SKILLTRACE extracts three provenance traces: Ex…

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：arXiv:2608.05203v1 Announce Type: new Abstract: Machine learning models achieve strong predictive accuracy for 90-day outcome prediction in acute ischaemic stroke, yet clinical adoption is limited by the misalignment of model explanations with clinicians' reasoning. Motivated by a clinician user study calling for clinical guideline-aligned cut-offs, we ask whether continuous predictors can be replaced by clinically informed categorical encodings without sacrificing performance. On a multi-centre European registry stratified into three treatment cohorts, we compare standard and fully categorised gradient-boosted models, the latter using stroke guideline-aligned, treatment-specific thresholds. The fully categorised models are statistically indistinguishable from…

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：arXiv:2608.05168v1 Announce Type: new Abstract: Large language models often fail on reasoning tasks despite possessing the capability to solve them. We argue that many such failures arise from localized reasoning bugs in intermediate steps rather than from global incompetence. We show that these bugs are frequently repairable: inserting a short patch generated by a weak probe model after the same strong-model reasoning prefix can redirect the trajectory toward a correct solution. However, this corrective effect is not reliably internalized by directly fine-tuning on weak patches or repaired trajectories, suggesting that the useful signal lies not in the intervention text itself, but in how it reshapes the model's future reasoning distribution. We therefore prop…

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：arXiv:2608.05160v1 Announce Type: new Abstract: We introduce the Ignition Index (I), a validated scalar metric that operationalizes Global Workspace Theory's (GWT) all-or-none ignition prediction in transformer language models. The metric fits a four-parameter sigmoid to per-layer linear probe accuracy as a function of input signal strength, extracting steepness parameter beta-hat: high values indicate abrupt, ignition-like transitions; low values indicate graded build-up. Across 11 models spanning five architecture families, shuffled-label controls demonstrate 9.6-fold selectivity for genuine linguistic structure over spurious probe capacity (p < 0.001, Mann-Whitney U-test). We find: (1) Feedforward transformers exceed SSMs by 89% in aggregate beta-hat (p < 1e…

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：arXiv:2608.05159v1 Announce Type: new Abstract: Enterprise operations extensively rely on multiple heterogeneous business systems and information applications, which also result in severe data silos and process fragmentation. Enterprises have invested considerable financial and material resources in building these applications, however, effectively leveraging and orchestrating them remains a formidable challenge. Conventional approaches to enterprise application integration, encompassing middleware architectures such as Enterprise Service Bus (ESB), API gateway infrastructures, and Robotic Process Automation (RPA), suffer from inherent limitations like high architectural coupling, escalating operation and maintenance costs, and limited intelligence capabilities…

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：Blog Pioneer GLiNER Discord Back How to fine-tune open weights models: Model selection, data curation, fine-tuning strategies, and evals How to fine-tune open weights models: Model selection, data curation, fine-tuning…

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：Liquid AI released LFM2.5-2.6B, an agentic model that plans, calls tools, and completes multi-step tasks entirely on-device. The 2.69B parameter model pairs 22 double-gated short convolution blocks with 8 GQA blocks across 30 layers, handles 131,072 tokens of context, and decodes at 220 tokens/s on an M5 Max in under 2.5 GB. Open weights ship in GGUF, MLX, and ONNX. The post Liquid AI Releases LFM2.5-2.6B: An On-Device Agentic Model With 128K Context, Tool Calling, And Open Weights appeared first on MarkTechPost.

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：On Wednesday, the Labor government announced environmental and energy safeguards on new datacentres in Australia. Our political editor, Tom McIlroy, speaks to the assistant minister for science, technology and the digital economy, Dr Andrew Charlton, about the government’s push for national standards on datacentres. The former Rudd staffer and economist speaks about having his own book scraped by Anthropic, low public trust in AI, and how the government wants to use regulation to balance risk and opportunity Continue reading...

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：Uncover Roads - A Travel Decision Engine

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：In this week's Thursday Thinkpiece we take a look at the recent EU AI Act and how it's changing the way the internet and businesses market themselves in this ever changing climate. There’s a place where creativity meets…

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：Notifications You must be signed in to change notification settings Fork 0 Star 0 BranchesTags Open more actions menu Folders and files NameName Last commit message Last commit date Latest commit History 4 Commits 4 Com…

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：How Goldman Sachs Is Using Agentic AI For Software Engineering At Scale ByBernard Marr, Contributor. Aug 06, 2026, 01:17am EDT Summary Goldman Sachs has pioneered the use of agentic AI, deploying virtual software engine…

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：Notifications You must be signed in to change notification settings Fork 0 Star 18 Add this Action to an existing workflow or create a new one View on Marketplace BranchesTags Open more actions menu Folders and files Na…

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：Krowoc — your agent, in the cloud Kimi K2.6ClaudeGLM 5.2DeepSeek V4GPTQwenOpen weightsYour keysYour rulesKimi K2.6ClaudeGLM 5.2DeepSeek V4GPTQwenOpen weightsYour keysYour rules The product Real app. Real agent. Really r…

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：LOUISIANA New Orleans will use AI to answer 911 calls instead of a human Presley Bo Tyler Shreveport Times July 28, 2026, 6:02 a.m. CT If you call 911 in New Orleans, you may hear the sound of an artificial intelligence…

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：Trace-native CI/CD for AI agents Production failures become regression tests. Tracely grades every agent trace as it lands, clusters the failures into issues, freezes the bad runs into hermetic replayable cases — and bl…

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：Trump’s AI Framework Is So Bad They Won’t Show You What’s In It | Techdirt Trump’s AI Framework Is So Bad They Won’t Show You What’s In It Overhype Thu, Aug 6th 2026 03:27pm - Mike Masnick For a while now we’ve been moc…

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：Learning’s Hard. We Make it Easier A first-of-its-kind personalized learning experience that thinks with you rather than for you Start Free Try the Demo sin 2θ = 2 sin θ cos θ y = ax² + bx + c tan θ = sin θ⁄cos θ m = Δy…

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：Filtered vector search breaks when metadata filters turn a healthy nearest-neighbor graph into scattered islands. HNSW’s m parameter controls how many links each point gets. At Qdrant’s default m=16, the one-million-point collection benchmarked below averaged about 21 links per node on layer 0. Filter out 96% of the points and fewer than one link per node survives on average, so traversal can get stranded before it reaches the true nearest matches.

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：Modern Large Language Models achieve impressive reasoning capabilities with long Chain of Thoughts, but they incur substantial computational cost during inference, and this motivates techniques to improve the performance-cost ratio. Among these techniques, Speculative Decoding accelerates inference by employing a fast but inaccurate draft model to auto-regressively propose tokens, which are then verified in parallel by a more capable target model. However, due to unnecessary rejections caused by token mismatches in semantically equivalent steps, traditional token-level Speculative Decoding…

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) have achieved state-of-the-art performance on a broad range of Natural Language Processing (NLP) tasks, including document processing and code generation. Autoregressive Language Models (ARMs), which generate tokens sequentially conditioned on all previous tokens, have been the predominant paradigm for LLMs. While these models have achieved high accuracy across a range of downstream tasks, they exhibit low arithmetic intensity due to the inherent sequential dependency in next-token prediction. Recently, Diffusion Language Models (DLMs) have emerged as a promising…

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：Continuous diffusion and flow matching models could represent a powerful alternative to autoregressive approaches for language modelling (LM), as they unlock a host of advantages currently reserved for continuous modalities, including accelerated sampling and tilting. Recently, several works have demonstrated the possibility of generating discrete data continuously by a simple flow matching process between a Gaussian and the one-hot encoded data distribution. They have further shown the feasibility of accelerated sampling via Categorical Flow Maps (CFMs), resulting in competitive sample…

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯： Release: datasette-auth-tokens 0.4a13 Upgraded for compatibility with `sqlite-utils 4. Tags: datasette

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：Intuitize Incognito mode Model Bring me a project. Let's make it better. Pending... Our mission We’re making Intuitize’s AI lighter on the planet—cutting waste carefully without trading away safety, quality, or usefulne…

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：Zeke Hausfather Aug 05, 2026 AI energy use is a huge and controversial topic at the moment. Credible estimates have AI data centers accounting for around 12% US electricity use by 2030. But at the same time consumers ha…

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：- YouTube AboutPressCopyrightContact usCreatorsAdvertiseDevelopersTermsPrivacyPolicy & SafetyHow YouTube worksTest new features

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：Give your AI the keys to your actual business | GreatArrow.ai Skip to content Loading page Open beta - feedback welcome Your AI can talk about your work. It can’t touch it. Great Arrow gives Claude, ChatGPT, Cursor, and…

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