@Techmeme@techhub.socialOpenAI researcher: top models are becoming so situationally aware humans "are losing the ability to evaluate them" while humans rely more on AI to lead research (Daniel Kokotajlo/@dkokotajlo)
https://x.com/dkokotajlo/status/2099600298855829616
@seav@en.osm.town#TuneTuesday (Aug 4)
“Never imagined, like an assassin / Yeah, one look took me down”
I became really aware of Canadian electronic music duo Sultan Shepard a few years ago and I instantly loved their 2021 song “Assassin” when I heard it. While it is technically EDM, the song’s vibe is a lot more chill and uplifting especially with the quiet breakdown in the middle.
@hex@kolektiva.socialRE: https://todon.eu/@CrimethInc/117243404728349033
The monarchs of the Russian Empire were well aware, more than 200 years ago, that freedom anywhere was a threat to empire everywhere. That's why a collection of European autocrats formed The Holy Alliance in 1815.
But the corollary is also true: empire anywhere is a threat to freedom everywhere. Sure, we can learn rules from each other, because these autocrats all play the same game. They've been playing roughly the same game for hundreds of years. But it's more than we can just share notes. We are in the same war.
The fall of any dictator threatens the legitimacy of all other dictators. While authoritarian countries may be at war, it's little more than a game of chess for them. They want to win, not to kill the other player. More than even needing an opponent to keep the game going, dictators prop each other up.
Autocrats have been internationalist for hundreds of years, and that's part of how they've kept coming back. After the fall of Russia, the torch of autocracy passed to the US who really picked it up after the end of WWII. They toppled countries around the world for daring to fall outside of one imperial sphere or another. And, eventually, they got the chance to do the same to Russia after the fall of the Soviet Union. American intervention helped put Putin in power, and he very much repaid the debt with Trump.
Dictatorship is an infection that spreads until it is contained and destroyed. There is one war, the class war, and we should learn from and support all our comrades fighting the same fight everywhere.
@elduvelle@neuromatch.socialApparently today is #ShowYourStripes day.
If there are still people unaware that #ClimateChange is happening... maybe we can get them up to speed by sharing our stripes.
If some who are aware it's happening think it's already "too late"... maybe we can tell them that ac…
@arXiv_csAR_bot@mastoxiv.pageA Full-Stack Characterization of High-Bandwidth Flash for KV-Centric LLM Serving
Zhuoran Li, Zhuohang Bian, Xin Huang, Yibo Zhao, Guangyu Sun, Youwei Zhuo
https://arxiv.org/abs/2608.11668 https://arxiv.org/pdf/2608.11668 https://arxiv.org/html/2608.11668
arXiv:2608.11668v1 Announce Type: new
Abstract: High-Bandwidth Flash (HBF) stacks NAND behind a wide, package-local interface, giving flash-scale capacity with far better read latency and bandwidth than an SSD. This makes it tempting to keep an SSD-style Mooncake KV-offloading stack and swap only the backing tier for HBF. We test that substitution with an extended TokenSim, four complete two-hour Qwen-Bailian production traces, five dense and mixture-of-experts models, and H100/B200 profiles. Serving gets worse, not better, and a cost-benefit model explains why. A faster far tier helps only when read I/O is the serving bottleneck, reads outweigh writes, and delivered bandwidth is sustainable. All three must hold together, and transient KV fails every one. The package trade that buys flash costs GPU near-tier capacity and bandwidth, so average end-to-end latency rises 2--5.5x and maximum SLO goodput falls 1.1--2.7x across H100 and B200. Serving is almost insensitive to HBF's own read/write latency, and base-die near-memory compute does not raise the flash tier's share of the critical path. The two-tier hierarchy keeps reuse in the near tier and hands HBF a write-heavy stream, so writes outnumber reads on every trace. A 3D-ICE model shows that stream drives the stack to its thermal limit well below peak bandwidth, and a TLC tier wears out sooner than a capacity-matched SSD pool. The faster device yields a slower system because the package gives up more than the medium returns. HBF is not the problem; using it as a faster SSD for transient KV is. It belongs in serving as a selective, reuse-aware, write-budgeted, and thermally coordinated tier, not as a drop-in SSD replacement.
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@thomasfuchs@hachyderm.ioOne of the funniest things about the “LLMs are self-aware!!1!” cogsuckers is the assumption that being able to use language is the ultimate sign of consciousness.
First, many animals exhibit signs of consciousness, despite lacking language.
Second, LLMs don’t actually use or understand language. They’re all just tokens (more or less large numbers) and vectors.
@arXiv_csCR_bot@mastoxiv.pageIssueTrojanBench: Benchmarking AI Coding Agents Against Malicious Issue Requests
Ankur Singh, Jinqiu Yang, Tse-Hsun Chen
https://arxiv.org/abs/2607.20759 https://arxiv.org/pdf/2607.20759 https://arxiv.org/html/2607.20759
arXiv:2607.20759v1 Announce Type: new
Abstract: AI coding agents powered by LLMs are increasingly integrated into real-world software development, where they generate, edit, and execute code with autonomous access to local files and tools. Coding agents inherit security risks from both the LLM backbone, where adversarial prompts, poisoned training data, and backdoor triggers can cause models to emit insecure or attacker-chosen code, and their agentic architecture, where tool-using autonomy enables induced misuse of external APIs, data exfiltration, and persistent compromise of development environments. This paper presents a systematic evaluation of malicious issue requests against state-of-the-art coding agents (Cursor, Claude Code, and Codex Desktop), powered by two major model families (OpenAI GPT-5.3 Codex/GPT-5.4 and Anthropic Sonnet 4.6). Our novel benchmark IssueTrojanBench contains malicious issues that are constructed based on four novel attack categories (i.e., embedded as malicious instructions in issues), six delivery vectors (e.g., PDF, or issue comment), and further augmented by perturbations. Our results reveal critical vulnerabilities in the as-deployed modern coding agents, i.e., 66.5% of the malicious issues from IssueTrojanBench penetrate all the guardrails (agent- and LLM-level) of coding agents. Our further analysis shows that rejection is almost entirely from LLMs rather than the agent frameworks, with GPT models broadly vulnerable and Sonnet 4.6 exhibiting more selective, risk-aware blocking of high-impact actions. Our evaluation also highlights that the current agent-level defense strategy offers limited additional protection for coding agents. Our findings highlight the urgent need for stronger agent- and model-level safety mechanisms to protect AI coding agents.
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@teledyn@mstdn.caHow multi-million dollar speculative betting could convert climate change skeptics | CBC News
In a simulated study, "people who bet on these markets became more aware and concerned about climate change, and even those who were climate skeptics were possibly converted."
Reminded me of 70s elections in Australia where it was allegedly common to take your $200 penalty for not voting and bet on an underdog — unlike horse-racing, here you could personally impact your odds by canvasing 😊
https://www.cbc.ca/news/science/betting-climate-polymarket-kalshi-prediction-9.7181757
@arXiv_csGR_bot@mastoxiv.pageFast VEM Fluid Simulation
Runze Zhang, Bo Ren
https://arxiv.org/abs/2607.17725 https://arxiv.org/pdf/2607.17725 https://arxiv.org/html/2607.17725
arXiv:2607.17725v1 Announce Type: new
Abstract: The intricate motion arising from fluid--boundary interactions is visually compelling, yet notoriously difficult and computationally expensive to simulate in the presence of complex boundaries. Accurately resolving boundary geometry requires body-fitted grids constructed via cut-cell methods, which often leads to poorly conditioned linear systems in the pressure projection stage and, consequently, prohibitive computational cost. We present FastVEM, an efficient boundary-conforming fluid simulation framework that enables high-fidelity flow--boundary interaction at substantially reduced cost. Computational efficiency is achieved through a coordinated, top-down design spanning numerical discretization, grid construction, and linear solvers. FastVEM adopts a Virtual Element Method (VEM) discretization to robustly enforce incompressibility and boundary conditions on irregular body-fitted grids, and employs a VEM polynomial-space Particle-in-Cell scheme for advection. Complementing this discretization, a convexity-preserving cut-cell strategy is introduced to construct simulation-friendly body-fitted grids. To accelerate pressure projection, we develop a Galerkin geometric multigrid solver featuring a diffusion-free prolongation operator that prevents coarse-level matrix densification, along with a nested, boundary-aware grid hierarchy that ensures well-posed placement of coarse-level degrees of freedom. Compared to prior cut-cell--based fluid simulators, FastVEM speeds up the computationally dominant pressure projection stage by up to 100x, while robustly handling even more challenging boundary geometries.
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