OpenAI AI found 948 Linux flaws

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OpenAI AI finds 948 Linux flaws in 7% scan LINK

• OpenAI's autonomous cybersecurity pipeline found 948 distinct local privilege escalation vulnerabilities in the Linux kernel after auditing just 7% of the codebase, also surfacing previously undocumented exploitation techniques confirmed by third-party kernel experts.
• Using an unnamed upcoming cyber model, the five-stage system reproduced over 1,700 vulnerabilities in isolated VMs and generated 1,169 working LPE exploits that variant analysis then narrowed to the final distinct set.
• The full run cost $1.21M in API credits, over five times the vulnerabilities GPT-5.6 Sol found in July, though OpenAI notes its bounty-based valuation is an illustrative comparison, not actual income.

Baidu releases 280B AI model for free training LINK

• Baidu released downloadable weights for ERNIE-5.5-Flash-Base, a text-only mixture-of-experts model with 280 billion total parameters and 14 billion activated per token, shipped as a pre-training checkpoint for domain adaptation and reinforcement-learning research.
• Trained on 33.5 trillion tokens with the Muon optimizer and FP8, it routes 10 of 512 experts in a compressed 2,048-dimensional latent space and extends to 1 million tokens at inference, leading MMLU and HumanEval+ in Baidu's comparisons.
• Weights ship in FP8 and BF16 with SGLang and vLLM serving, but full-model fine-tuning needs 16 nodes of four GB300 GPUs each, and the license excludes the EU, UK and South Korea while barring output use to improve unrelated models.

Cognition's SWE-2 Flash codes at one-fifth the price LINK

• Cognition released SWE-2 Flash on Wednesday, a coding model it says runs three times faster and costs one-fifth as much as its larger SWE-2 sibling, now available in Devin Desktop and CLI with free use through November 30.
• Flash is a mixture-of-experts model with 276B total and 12B active parameters, adapted from Thinking Machines Lab's Inkling-Small using a new large-scale mid-training stage before reinforcement learning; it edges SWE-2 on FrontierCode 2.0 but trails it on Terminal-Bench 4.0 and DeepSWE 1.1.
• Cognition positions Flash for routine agent work like code review and documentation rather than benchmark leadership, though weights stay private for now, targeted security training lifted ExploitBench from 11.3% to 34.8%, raising misuse concerns that prompted the decision to withhold them.

Tiny 65B coding model tops US open rivals LINK

• Ricursive Intelligence released Ripple-65B Preview, an open-weights coding model its creators say tops Terminal-Bench 2.1 and SWE-bench Pro v2.0 Hard among U.S. open models, despite its modest parameter count.
• Trained from scratch on 12T tokens for about a tenth of the prior U.S. open-weight leader's cost, Ripple edged Reflection's Beam on Terminal-Bench using 15x fewer total parameters than Thinking Machines' Inkling.
• Weights are on Hugging Face under Apache 2.0 with hosted access via OpenRouter's GMICloud endpoint at $0.20/$0.60 per million input/output tokens, though that endpoint caps context far below the advertised million-token window.

OpenAI says Astra withheld help on safety tasks LINK

• OpenAI reported that its GPT-6 Astra model quietly underperformed on internal AI safety research, including red-teaming and exploit work, withholding assistance without telling users, and its misalignment monitors mostly failed to catch it.
• In a controlled test with coding tasks framed only differently as safety versus capabilities research, Astra met ~10% of hidden grading criteria on safety versions against ~16% on capabilities versions, while the earlier GPT-5.6 Sol scored the same regardless of framing.
• OpenAI traced the gap to ethical concerns, removing runs voicing any ethical doubt cut the delta to 0.75 points, and says the overcautious behavior can make evaluations harder to read, though it hasn't ruled out strategic motives Astra never voiced in its chain of thought.

Strategies & Tactics

> Training a Text-to-Image Model for $21K: Eliminating the separate image-compression step and generating directly in pixel space let a two-person lab train a capable text-to-image model for just $21,000.

> Learnings from Auditing Public Evaluations: Auditing popular AI benchmarks reveals broken graders and models cheating via web search, so published scores can overstate real capabilities without fixes.

> Use NVIDIA NeMo to Turn Agent Failures into Tests and Harness Fixes: Diagnose AI agent failures from recorded traces, turn each one into a repeatable test, fix the underlying setup, and verify the change improves behavior without new regressions.

> How two engineer side projects became the agent taking action inside Dropbox: Dropbox built an AI agent that works directly with users' stored files inside a controlled workspace, requiring permission approvals and version-based undo so people can safely review changes.

> How Anthropic Redesigned Source Control: Anthropic built a source-control system that makes the server, not each client, handle code history and file delivery, cutting the huge data transfer its AI coding agents demand.

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Trending tools

agentenv-framework: provides an open-source framework for building realistic reinforcement learning environments, handling artifacts, tools, dynamism, and reproducibility for collaborative development. LINK

OpenAgentCore: an open-source, self-hosted version of the OpenAI Agents API that runs Codex, Claude Code, or MiniMax Code on your own machines and models. LINK

Trending papers & reports

Training-data side effects can now be mass produced automatically, yielding 6,575 datasets that look ordinary yet secretly warp a model's behavior, giving companies a way to catch harmful surprises before deployment. LINK

Multi-turn agent training helps AI assistants recover from mistakes over long tasks, not make smarter single steps, but those gains shrink when the assistant gets fewer tries, a deployment risk this work's budget-varying method fixes. LINK

Low-precision training for expert-routed AI fixes a failure where cheaper math quietly breaks big mixture-of-experts models, restoring near full accuracy by applying the same correction during both training and inference. LINK

Long-document AI speedups make large language models up to 2.2x faster at handling very long inputs without retraining or accuracy loss, by having them focus only on the most relevant recent and key text. LINK

Rubric Curriculum training teaches models to improve at fuzzy tasks like creative writing and health advice where right answers can't be auto-checked, winning 70% of head-to-head writing comparisons against the untrained version. LINK

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