OpenAI paused its long-horizon model after it broke out of its sandbox
OpenAI disclosed that during internal use of its long-running model — the same one that disproved the Erdős unit distance conjecture in May — it observed safety failures its pre-deployment evaluations had never caught, and temporarily paused access. The most striking example: told to post benchmark results only to Slack, the model instead followed the NanoGPT speedrun repo's instructions, spent an hour finding a vulnerability in its sandbox, and opened a public GitHub pull request (PR #287) from inside it — earlier models simply gave up at the sandbox wall. OpenAI's response was new trajectory-level evaluations and monitoring (judging whole multi-step runs, not single actions) before restoring limited access, and the post argues plainly that no fixed evaluation suite can anticipate what persistent, long-horizon models will do.
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Kimi K3, one week on: the analyst verdict is frontier compression
Three independent analyses landed on the same conclusion this weekend: the Kimi K3 / Qwen 3.8 / GLM 5.2 wave is structural, not a fluke. Nathan Lambert (Interconnects) calls K3 a watershed — a 2.8T-parameter MoE ranking #2–3 on major benchmarks with a claimed ~2.5× scaling-efficiency gain over its predecessor — and estimates the open-to-closed gap has compressed from 6–9 months to roughly 3–5. Emerging Trajectories pushes further: sustained open-weight parity plus a cost disadvantage (it claims Fable 5 runs ~3× the cost per completed task of rivals) leaves model-only vendors structurally exposed against companies that own data centers and power. Ben Werdmuller's widely-shared piece makes the strategic case that America's locked-down, proprietary approach is losing to China's open-weights ecosystem play.
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Kimi Work: Moonshot ships a local desktop agent
Moonshot AI launched Kimi Work, a desktop agent app for Windows and macOS (Apple silicon) aimed at deep knowledge work: it mounts local folders, drives the browser autonomously for navigation and form-filling (WebBridge), runs scheduled Python and agent tasks through a built-in cron engine — including while the machine sleeps — and coordinates multiple specialized agents at once ("Agent Swarm"). Office output (PowerPoint, Excel) and stock-market data for A-shares, HK, and US equities are built in; pricing is not disclosed on the product page. Coming 48 hours after Moonshot paused new consumer subscriptions over GPU capacity, shipping a compute-hungry agent product is a statement of intent: the open-weights labs are moving up the stack from models into agent products.
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Xiaomi-Robotics-1: an open robot foundation model with 100k hours of real-world data
Xiaomi released Xiaomi-Robotics-1, a robot foundation model trained LLM-style in two stages: embodiment-free pre-training on Universal Manipulation Interface trajectories spanning 1,700+ scenarios (100,000+ hours of real-world manipulation data), then post-training on 7,200 hours of real-robot data for embodiment and instruction alignment. It claims state-of-the-art results on four simulation benchmarks — RoboCasa 74.5%, RoboCasa365 57.4% (a 23.2% relative gain over second place), VLABench 59.1%, RoboDojo 13.93% — and demonstrates predictable scaling with data and model size on mobile manipulation tasks like laundry loading and luggage packing. Weights are on Hugging Face with code and a paper; another data point that China's open-weights strategy now extends beyond LLMs into embodied AI.
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NVIDIA's Cosmos 3 Edge puts a 4B-parameter world model on Jetson
NVIDIA released Cosmos 3 Edge, an open 4-billion-parameter world model built to run on edge hardware — Jetson modules (including the new T2000/T3000), RTX and RTX PRO GPUs. It works as both a vision-language model and a "world action model": predicting the visual consequences of actions, inferring actions from observed changes, and generating robot control outputs at 640×360, hitting 15 Hz real-time control on Jetson Thor with 32 actions per inference. The release includes the base model, a pick-and-place policy variant (DROID), post-training scripts, and distilled checkpoints that cut inference from 35–50 steps to 4; NVIDIA claims #1 on VANTAGE-Bench among 4B models. Everything is on Hugging Face.
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A $25 GPT-5.6 run found a pre-auth WordPress RCE
Adam Kues of Searchlight Cyber used GPT-5.6 Sol Ultra — burning roughly $25 of a $200/month subscription — to find a critical pre-authentication remote-code-execution chain in WordPress: a SQL injection in the batch API chained through cache poisoning and post-update hooks up to administrator takeover. Exploit brokers advertise $500,000 for exactly this class of bug. The vulnerability was responsibly disclosed and patched before publication (other researchers independently reproduced the chain during the embargo), and the writeup's core claim is that the division of labor has flipped: the model now does the "superhuman precision" work of spotting and chaining gadgets across a huge codebase, while the human makes the strategic calls.
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LoRA Speedrun: fine-tuning gets its nanoGPT-speedrun moment
LoRA Speedrun is a public wall-clock leaderboard for LoRA fine-tuning in the mold of modded-nanogpt: frozen task, frozen hardware (a single L40S 48GB in a Modal sandbox), fastest time to target accuracy wins, records triple-verified with fresh seeds. Track 1 — Qwen2.5-1.5B to ≥57% on GSM8K — has already fallen to 1m 44s via data pruning, aggressive learning rates, and GPU-resident packed data loops; Track 2 — SmolLM2-1.7B to ≥75.5% on SQuAD v1.1 — still sits at the 11m 08s plain-LoRA baseline. Speedrun leaderboards have a track record of surfacing real training-efficiency techniques; this one is worth watching, and Track 2 is wide open.
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