AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯： For a while, I must admit, it looked as if software developer roles like mine were done for. How could we fight against tireless robots? But our industry is slowly realizing that making truly cutting-edge software still requires humans to think and work together, to maximize their skill sets and to practice their respective crafts. A.I. can write very good software, but it also makes it easy to do someone else’s job badly, which is part of why all those projects fail. Now that everyone can code, it’s become clearer why many shouldn’t. — Paul Ford, A.I. Was Supposed to Give Us New Killer Apps. What Happened? Tags: paul-ford, generative-ai, deep-blue, ai, llms

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 CEO Dario Amodei says the time has come to slow down AI development and will give third-party evaluators like METR access to its models to help ensure its "adherence to safety practices and commitments." In a winding essay, Amodei proposed a three-step plan to "pace the frontier" - jargon that simply means to slow the pace of training and development to give companies time to build safeguards and regulators to evaluate models. Amodei says that giving external evaluators wide-ranging access is just the first step, and one it is taking now unilaterally. Step two would involve the industry coming together as a whole, likely with gove … 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：In a social media post, Dario Amodei proposed a plan including third-party evaluations of AI systems The CEO of the artificial intelligence company Anthropic issued a new appeal on Saturday for the AI industry to “slow down” and offered a three-part plan for doing so, saying that his company would “unilaterally” commit to the first of the steps. In a post on social media, Dario Amodei shared a link to an essay titled We Must Pace the Frontier in which he lays out how Anthropic would provide “third-party evaluators with permanent, employee-level access to our systems, so that they can verify adherence to our safety measures, report on incidents, and assess models’ alignment during training”. 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：Before co-founding Kepler, Vinoo Ganesh led Spark at Palantir and built Project Frontline — a pioneering program for Forward Deployed Engineers. He takes us through the best practices of FDEs.

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 hired the founders of Git AI, an open-source tool that tracks how much code is written by AI The post “Same mission, bigger stage”: OpenAI hires Git AI founders to help Codex prove its ROI appeared first on The New Stack.

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：President Donald Trump is weakening environmental regulations in the name of speeding up the construction of AI data centers, raising health risks for Americans, a cadre of former EPA officials said this week in a briefing and new report. They are urging - perhaps futilely - the president to adopt a "Data Center Health Protection Pledge" to signal that the administration is taking the environmental threat seriously. At the start of his term, Trump's EPA administrator, Lee Zeldin, proclaimed his goal of making America "the AI capital of the world" through deregulation. Since then, Zeldin and the White House have rolled back dozens of rules … 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：Influencers aren’t just battling competitors for brand deals. They’re now battling AI versions of themselves Earlier this year, Emily Schuman, the creator of the lifestyle blog Cupcakes and Cashmere, saw a photo of herself she didn’t recognize on Instagram. In a sponsored ad, Schuman, who has more than 500,000 Instagram followers, is holding up a vial of GLP-1 drugs, advertising the telehealth company Gala. There was just one problem: Schuman had never heard of Gala and had nothing to do with the ad. The ad was based on a digitally altered version of a selfie Schuman had taken in her car and posted on Instagram years earlier. Once her followers got wind, they flooded her inbox with questions about the sponsorship as it didn’t seem consistent with Schuman’s pers…

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 spent the last few years planting flags across the increasingly difficult terrain in mathematics. This week, it claimed one of its biggest prizes yet: a solution to a legendary Millennium Prize problem. In normal circumstances, this would have been celebrated as a historic achievement. Instead, many mathematicians have watched OpenAI's relentless advance with growing unease. To them, the company appears less like an enthusiastic newcomer than an impossibly well-resourced interloper, charging into problems they have dedicated their lives to studying with little apparent regard for long-standing norms or the consequences for those … 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：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：As OpenAI model cracks Millennium Prize Problem that puzzled experts for decades, many feel shocked at pace of change It was a week that left mathematicians reeling. Hot on the heels of a flurry of cases of artificial intelligence furthering the field, OpenAI declared a major scalp: its latest AI model had cracked a Millennium Prize Problem, a puzzle with a $1m reward that had defied human brains for decades. The achievement bore little resemblance to how mathematical problems normally fall. A near-trillion dollar private company had unleashed 10,000 agents – AI systems that carry out tasks autonomously – on the problem. The bill was estimated at $15m. 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：Economics graduates also appear to be affected as demand for them falls in well-paid roles in finance AI may be warping the job prospects for students in the previously high-demand subjects of computer science and economics, according to a detailed look into the careers of recent UK graduates. Data obtained for the 2027 Guardian University Guide published on Saturday shows that coding and software development were the fastest-falling occupations for graduates last year, while employer demand for graduates in well-paid roles in financial categories such as economists and management consultants also declined. 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：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...