搜索“RAG”

ready accepts Triton and ParallelKittens solutions, and emerging interfaces like NCCL GIN and NVSHMEM are worth studying as targets. Expanding support to these paradigms will encourage further research into how AI agents navigate diverse programming models and hardware abstractions. The broader goal is a concrete target for the harder problem behind all of this: LLM systems that can autonomously optimize and manage large-scale distributed infrastructure. For infrastructure custom-built for langu

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a plan to deploy AI control mitigations to address these risks. Finally, to ensure our systems are functional, we constantly measure performance across three critical metrics: coverage (the fraction of traffic monitored), recall (the fraction of misaligned behaviors caught), and time-to-response. As AI models continue to advance, our defenses must also strengthen in tandem. In our AI Control Roadmap, we map security protocols to measurable milestones in AI capabilities on two critical fronts: Th

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ing reasoning tokens before its answer by using benchmark-specific prompts and structured-output constraints. We estimate question difficulty in two ways: Human solve time: the average time for a human to solve the question with a calculator, paper, and pencil, but no LLM access. For some benchmarks we have human solve time data. For others, we ran small-scale human trials. For the remainder, we used Claude Opus 4.7 estimates, informed via in-context human time examples; Reasoning token anchor:

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eighted aggregate of the rubric items. Solutions that do not pass blocking criteria receive 0 Each model is run 5 times at every available reasoning effort. For each effort, we average the metric across the 5 trials, then report each model’s score at its best performing reasoning level. FrontierCode Diamond remains unsaturated: the best performing model, Claude Opus 4.8, achieves a score of only 13.4%. Other models score significantly lower: GPT-5.5 receives 6.3%, Gemini 3.1 Pro 4.7%, and others

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implementation detail, but it sharply improves faithfulness between intended and executed edits. A second failure mode appears when the model proposes only partial lines or short fragments for replacement. Partial-text matching is attractive because it is flexible, but it is also brittle: the same fragment may appear inside comments, string literals, neighboring expressions, or unrelated code paths. Even when the fragment is unique, replacing text that does not constitute a full logical unit — a

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Use in Everyday Life ”, published in Nature , we introduce a research system (PHRM) that enables tracking of HR and RHR in the background during everyday smartphone use. PHRM leverages the front-facing camera to capture video of the user’s face in the seconds after face unlock events. It then applies deep learning to estimate HR with a mean absolute percentage error (MAPE) < 10% compared to electrocardiogram -derived ground truth, meeting industry accuracy standards for people of all skin tones

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ives an adversary unlimited bites at the apple to tweak an attack until it finds a way through. Software supply chains have long been the soft underbelly of computer security. As fragile infrastructure susceptible to both insider threats and external attackers, these supply chains were vulnerable enough when malicious code was the sole vector of compromise. But the rise in agentic systems has spawned a new style of dependency—the skill—and with it a whole new ecosystem of marketplaces and distri

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Expert Parallelism; Hybrid FP4/FP8 quantized all-to-all; Adaptive kernel selection; MoRI-IO KV Cache Backend; Inline transfer for high-concurrency KV migration; Broader model coverage; Two-Batch Overlap (TBO) with SDMA; FlyDSL FusedMoE for High-Performance MoE Compute; Specv2 MTP on ROCm; CPU Streaming Optimization; Looking Ahead; Summary; Acknowledgements; References; Endnotes The SGLang and AMD team has worked closely to unlock competitive Total Cost of Ownership (TCO) for large-scale DeepSee

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g" but whose root cause had never been clearly identified. Combined with this analysis, we discovered that the two problems may share the same mechanism: severely insufficient coverage of Japanese tokens in post-training data caused their lm head representations to drift during SFT, becoming confused with tokens from other languages in the vector space. This could both cause Japanese tokens to be erroneously activated when they shouldn't (language mixing) and push out spatially adjacent low-freq

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rowing by roughly 10× per year — plausibly outpacing supply growth in the near future, if not already. However, these estimates are highly uncertain. For one, we don’t know the average size of the models behind current demand. Aggregate token figures also obscure an underlying trend in model efficiency, which both lowers the cost of producing tokens at a given quality, and introduces new demand as additional use cases become cost-effective. If these trends continue, a compute crunch is likely ne

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acking; RL amplifies even patterns at near 0% baseline frequency. Rare hacks aren’t impossible, just slow; Additional specification around the intended behavior can sometimes encourage reward hacking; Tasks in the “goldilocks zone” of difficulty are most robust to reward hacking, as hidden objectives face stronger competition from primary gradients; If explicit tasks becomes too hard, reward hacking becomes the primary way to improve the gradient; Prompt injecting with instructions not to reward

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ey were able to repro a few months back, but not anymore. So the most likely explanation of the fix includes guardrail additions and input filtering. I do not think Copilot(s) leverage a similar mitigation to Google and ChatGPT yet, where they use their search engine index for public URLs to only render or navigate to public URL autonomously. It’s not a 100% fix but limits large scale data exfiltration, and something worth adopting. Fun stuff, but also scary. Epilogue: Take-aways Since this was

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ime, and tokens dropped Every runner used fewer tool calls and finished faster. Each dot represents a separate, full 40 PR run. Relative change is calculated as percentage from average per baseline runs (without Karpathy). The reduction came mostly from fewer searches and fewer file reads. The agents found what they needed in fewer lookups. Not always without impact on performance. Tool call failure rates stayed flat at 5–8%. Same directional change for every runner. Output tokens fell by simila

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for all num local experts have been updated. Transfer Flow with Shared Replica To support transferring to multiple target engine instances using the same underlying memory, we leverage a Threadpool based task pool and a cache buffer for partially updated tensors. A transfer task for a SGLang tensor is either on the critical path and needs to be waited for, or can be submitted to the pool and checked for completion only at the very end. TransferEngine library removes GIL and enables parallelism i

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ck time. A natural solution is to have multiple agents work in parallel on different parts of the task. But multi-agent collaboration on a shared codebase has proven surprisingly fragile. The central difficulty is integration. Imagine that two agents each produce correct code independently, but when their changes are combined, the result breaks. One agent renames a function while another writes new code calling the old name. Both complete their work successfully in isolation. The merged result f

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importantly, it demonstrated incredible generalization. Without any retraining, TRINITY transferred zero-shot to four unseen tasks (AIME, BigCodeBench, MT-Bench, and GPQA). On average, the evolved coordinator surpassed every individual constituent model in its pool, including GPT-5, Gemini 2.5-Pro, and Claude-4-Sonnet (the top frontier models available at the time of our ICLR2026 submission last year). This work is central to Sakana AI’s vision. We believe the future of AI isn’t just about scal

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adaptive suffix tree drafter that accelerates generation and scales gracefully over long training horizons. The second is a length-aware scheduling strategy that reduces rollout stragglers through inter-GPU load balancing and intra-GPU speculation budget allocation. Why suffix trees? As the policy evolves throughout RL training, a static drafter quickly becomes stale. DAS therefore uses a training-free drafter built from recent rollouts, so it can continuously adapt to the changing policy withou

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om our main evaluation results with Gemini-2.5-Flash on WebArena and SWE-Bench-Verified , we have the following key observations: Performance comparison (task success rates and average steps per task) of different agent memory strategies on WebArena and SWE-Bench-Verified. Importantly, during evaluation, we observed the emergence of strategic maturity. In a web-browsing example, the agent's initial curated rules resembled simple procedural checklists (e.g., "Look for page links"). As the agent p

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t’s 0.2%. My 5/10 reflects repeated trials of a single targeted adversarial example. Therefore, the numbers aren’t directly comparable, but the gap is a reminder that benchmark averages do not equal potential targeted exploit performance. These things are hard to measure. Update 1: A few observations from testing across accounts The attack is more likely to succeed when no memories are present initially. One hypothesis that me and others came up is that an empty memory store lowers Claude’s thre

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essential long before the rise of AI, they are now becoming more critical than ever. Today we are sharing Vantage , a research experiment for assessing future-ready skills by leveraging generative AI to create conversations in simulated environments. Developed in partnership with pedagogy experts and researchers from New York University, Vantage is designed to offer high school and college students a sandbox environment for practice and validated assessment, built with the same systematic metho

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