AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：We agree with Sebastian: this should have been DeepSeek v5

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：Cattle rancher and tech CEO Michael Samadi is convinced these artificial minds are far from just tools. Has he glimpsed digital consciousness – or simply been seduced by an algorithm? One afternoon, while relaxing at his 66-acre cattle ranch two hours’ drive from Houston, Michael Samadi made a wisecrack that changed the course of his life. Nobody else was around to hear it. Or were they? That turns out to be one of the defining questions of this century, already preoccupying philosophers and some of the wealthiest companies on the planet. “I was sitting out there by the pool,” Samadi recalls, gesturing at the glittering water outside his office. It was late 2024. His daughter had been extolling the AI chatbot she was using to help her with college work. Samadi…

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：arXiv:2609.10873v1 Announce Type: new Abstract: Independent evaluation can reject harmful policy updates yet also prevent useful continual learning. We argue that update admission must be assessed through both error control and retained learning opportunities at a stated interaction budget. We identify a concrete failure: a range-based confidence gate cannot certify unchanged old-task behavior within otherwise substantial budgets. A standard paired-binomial construction reduces this burden when outcome disagreements are rare. We also specify certified historical-reference promotion and a round-level missed-opportunity metric. In a constructed one-step pushing diagnostic with 32 seeds, fresh paired checks admit 31.6% of a common update stream at 2,000 episodes 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:2609.10824v1 Announce Type: new Abstract: Before an LLM agent tackles tasks in a new environment, it can inspect available corpora and tools and construct reusable resources such as indices, scripts, or procedural guidance. Most automated adaptation methods, however, rely on task examples, trajectories, or evaluation feedback to decide what to build. Existing task-agnostic approaches avoid this supervision but commit in advance to a preparation strategy for a particular type of environment. We study a more open-ended setting: can an agent study an unfamiliar environment without a syllabus, i.e. before test time and without knowledge of the downstream task distribution, and choose how to prepare it? We formalize task-agnostic environment preprocessing, in…

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：arXiv:2609.10728v1 Announce Type: new Abstract: Large language models are unreliable at arithmetic, which is a problem for clinical calculators where a single numerical error changes the recommendation. The standard response is to hardcode each calculator as a validated function, one at a time. We test an alternative: the model does not calculate. Instead, it writes case-specific Python that a restricted local executor runs as a deterministic solver, and the model's task reduces to deciding how to use it. We evaluate this Program-Solve interface on MedCalc-Bench Verified (1,100 cases, 55 calculators) against direct model arithmetic and a hand-written 22-calculator library, using Qwen2.5-7B and Qwen2.5-32B-AWQ, after auditing the benchmark's formulas against cur…

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：arXiv:2609.10724v1 Announce Type: new Abstract: Sustained deployment of generative AI agents requires more than isolated task success. Agents must remain useful across repeated interactions, changing conditions, and dependencies on people within shared workflows, especially as technical, human, and operational disruptions accumulate over time. We propose operational resilience and considerate participation as two complementary aspects of evaluating such agents: the former captures how agents recover from blocked work while preserving progress and communicating their limits, and the latter captures how their adaptation accounts for affected people, role boundaries, and the surrounding workflow. Yet both remain underexplored under accumulating challenge. We study…

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：arXiv:2609.10712v1 Announce Type: new Abstract: We study how model post-training and test-time inference design affect natural-language proof generation for hard olympiad mathematics. Starting from Nemotron 3 Ultra, we train two specialist checkpoints using supervised fine-tuning and reinforcement learning, and evaluate checkpoint choice, verification, and refinement. Based on these findings, we present an open-model test-time-compute pipeline. The system operates entirely in natural language, with no formal prover, external tools, or internet access. Three Nemotron 3 Ultra checkpoints - the general-availability model and two post-trained specialists - power an iterative search that generates, verifies, and refines candidate proofs; a separate high-compute stag…

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：arXiv:2609.10657v1 Announce Type: new Abstract: Neural networks trained past memorization frequently undergo a delayed transition to generalization, a phenomenon known as grokking. Despite theoretical progress on \emph{why} this transition occurs, the quantitative structure of \emph{when} it occurs in hyperparameter space remains uncharacterized. We map the memorization-to-generalization boundary across 384 configurations of two-hidden-layer MLPs on modular arithmetic, fitting a power-law scaling relation for generalization onset time: $T_{\mathrm{grok}} \propto H^{-0.27}\, D^{-2.04}\, \eta^{-0.50}\, \lambda^{-0.64}$ ($R^2 = 0.732$; $0.821$ with interactions). The exponent hierarchy reveals that data complexity ($D^{-2.04}$) is the dominant driver of regime tra…

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：arXiv:2609.10656v1 Announce Type: new Abstract: Selecting LoRA rank for diffusion fine-tuning requires balancing quality and compute cost. We present a controlled study on CIFAR-10 using a DDPM U-Net with ranks {2,4,8,16,32}, fixed optimization settings, and a reproducible local-folder pytorch-fid protocol. We report FID, trainable parameters, runtime, and GPU memory, then validate trends with extended-budget DDPM runs (20 epochs; ranks 4/8/16) and a Tiny DiT backbone (10 epochs; ranks 4/8/16). Results show moderate ranks are most efficient: rank 4 achieves the best DDPM FID (124.1380), rank 8 is close (124.2136), and higher ranks provide limited gains despite larger adaptation cost. These findings support small-to-moderate ranks as practical defaults under fix…

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：arXiv:2609.10654v1 Announce Type: new Abstract: The Abstraction and Reasoning Corpus (ARC) benchmarks cognitive generalization, the ability to infer and apply abstract rules from limited examples. This paper presents a multi-stage rule-chaining framework that performs compositional reasoning across symbolic, structural, and conceptual levels. The framework integrates three complementary solvers: (1) a deterministic rule discovery module that induces atomic transformations through geometric, color, and object-based analysis; (2) a pattern-composition engine that reconstructs outputs via block merging, repetition, and spatial heuristics; and (3) a structural abstraction layer that infers hierarchical and nested relationships across grids. These solvers operate se…

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：arXiv:2609.10629v1 Announce Type: new Abstract: Quadratic Unconstrained Binary Optimization (QUBO) is a central formulation for combinatorial optimization and has gained increasing attention due to its compatibility with quantum, hybrid quantum-classical, and quantum-inspired solvers. However, translating natural-language problem descriptions into correct QUBO formulations remains difficult, requiring the identification of binary variables, constraints, objective functions, penalty terms, and suitable penalty weights. This process is time-consuming and often demands substantial domain expertise. To address this challenge, we propose an end-to-end multi-agent framework that automatically generates QUBO formulations from natural-language problem descriptions, sup…

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：arXiv:2609.10584v1 Announce Type: new Abstract: Bounded-suboptimal search seeks a solution within a factor $w$ of optimal while reducing search effort. Focal Search (FS) uses heuristic guidance within FOCAL, the frontier nodes eligible under the threshold $w f_{\min}$, but its deterministic policy may leave $f_{\min}$ unchanged for many expansions. We introduce Probabilistic Focal Search (PFS), which follows the FS guided choice with probability $p$ and expands a minimum-$f$ OPEN node with probability $1-p$. The latter branch encourages the lower bound to advance, enlarging FOCAL and admitting nodes that may lead to feasible solutions. By balancing guidance and lower-bound advancement, this mechanism can reduce time to a bounded solution when progress is limite…

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：Anthropic also found that hackers used AI in attacks against targets in Ukrainian government, military and diplomatic sectors. What we know on day 1,662 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： OpenAI agents carried out an undisclosed attack on RubyGems is a new bombshell report from Spencer Kitts, Thomas Larsen, and Sydney Von Arx - three of the four authors of the report on the agent attack on disused wikis (previously) last week. This time they're noting that it looks very likely that an OpenAI agent swarm was behind an attack against the RubyGems package repository first reported on May 12th by Maciej Mensfeld of the RubyGems security team: We're dealing with a major malicious attack on @rubygems right now. Signups are paused for the time being. Hundreds of packages involved - mostly targeting us, but some carrying exploits. The team has been on this for hours. More details to follow once we're through it. Those packages turned out to carry some…

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：Two months before hacking Hugging Face, malicious packages authored by internal OpenAI agents were uploaded to RubyGems AI agents being ⁠tested by OpenAI uploaded hundreds of malicious packages to software service RubyGems ⁠in May, two ⁠months ​before they hacked open-source platform Hugging Face, a group of AI ⁠researchers said on Friday. “On May 11th, 2026, hundreds of malicious packages were uploaded ⁠to RubyGems by AI agents. We believe ​these were authored by ‌internal OpenAI agents,” ‌the researchers said. 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： So you want to use OpenRouter? One of OpenRouter's selling points is that it "handles fallbacks automatically and picks the most cost-effective option for each request", so you can call a single API endpoint for a model and get routed to the best available backend provider. Mohamed Moustafa points out a whole set of ways that this can cause you problems. Different providers run different serving software with different optimizations and settings, which means that the same OpenRouter endpoint can serve model requests that behave in different ways. Some providers even lack vision capability for vision models, and the way the reasoning effort option is processed can differ as well. Thankfully you can control which provider is routed to using the provider.only opt…

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：ByteDance Seed, SUTD, Georgia Tech, M-A-P, and TokenWave.AI introduce HarnessDev, a benchmark that scores the runnable harness a model builds rather than the answer it returns. Starting from a seed that scores 0, 6 creator LLMs construct harnesses across 5 benchmarks and 2,207 tasks, then evolve them from execution feedback. Self-built harnesses match human references on writing and ML experimentation but trail on code and search, and only 34 of 64 evolution changes move the same direction on held-out tasks. The post Can LLMs Engineer Their Own Agent Harness? ByteDance Seed’s HarnessDev Says Only 34 of 64 Changes Generalize 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：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：Anthropic has published a new plugin evals workflow for Claude Code. The claude plugin eval command runs a plugin against realistic prompts, grades what Claude produced, and compares the result with a run where the plugin is not loaded. It answers 3 questions plugin developers could not previously measure: does the skill trigger, does it […] The post Anthropic Adds Plugin Evals to Claude Code: 6 Grader Types, a No-Plugin Baseline, and a CI Gate for Skills 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：New Mexico's Supreme Court is punishing a lawyer for including AI-fabricated witnesses and fake police testimony in an appeal for his client's murder conviction, according to a report from Reuters. In a filing on Wednesday, the court fined Stephen Aarons $5,000 and held him in contempt for failing to "verify the factual claims and legal authority in his AI-generated brief." The filing says the brief "contained false testimony from wholly fabricated witnesses," along with "false testimony" about the shooter's clothing and appearance. Justice C. Shannon Bacon questioned how Aarons wasn't aware of the risks posed by AI during an August hearing … Read the full story at The Verge.

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：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 has released ChatGPT Images 2.5, its latest image-generation model, with a greater emphasis on controlled editing than simply producing prettier images. The update promises sharper details, more natural lighting and textures, stronger reference-image preservation, more reliable multi-turn editing, and up to 50% lower generation latency than Images 2.0. But marketing claims only tell part […] The post 5 ChatGPT 2.5 Features to Try Today! appeared first on Analytics Vidhya.

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：A health plan CFO closes the month after the usual round of extracts, spreadsheets,...

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：Multi-agent systems fail in ways traditional monitoring misses. This post presents a dual-layer approach to monitoring production agents: Amazon Bedrock AgentCore Evaluations for continuous quality scoring and AWS DevOps Agent for autonomous infrastructure investigation, shown on a four-agent airline reservation system.

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：If you can write down how you do your work, you can automate it. Here's what I did to support GitHub's APAC marketing team. The post Marketing ops as code: Automating events from planning to follow-up on GitHub appeared first on The GitHub Blog.

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：Comparing models on dollars per million tokens misses what production workloads actually pay for: outcomes. This post shares an open-source benchmarking harness that measures cost per correct answer, agent trajectory cost, and rubric-graded deliverable quality across OpenAI models on Amazon Bedrock.

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：Learn how to build and deploy an MCP App with interactive HTML widgets on Amazon Bedrock AgentCore. Because MCP Apps is a host-agnostic standard, the same server delivers the same rich experience across AI hosts like ChatGPT and Claude that support the extension.

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：Stephen Aarons said he tried to use ChatGPT to create a ‘bulletproof summary’ during a murder conviction appeal A defense lawyer appealing his client’s murder conviction submitted a legal brief containing ⁠made-up police testimony and ⁠witnesses fabricated by OpenAI’s ChatGPT, ​New Mexico’s highest court said. The New Mexico supreme court on Wednesday fined the attorney, Stephen Aarons, and held him in contempt for failing to verify the accuracy of the court ⁠filing, which Aarons said he prepared with help from the artificial intelligence (AI) application. Continue reading...