#推理

Anthropic's Claude Fable 5.2 doesn't generate images, it paints them

Anthropic's Claude Fable 5.2 creates images by writing Python programs of 1,000 to 2,000 lines that drive a simulated paint engine, placing individual brushstrokes on a virtual canvas rather than using diffusion. Developer Jake Eaton showed a photorealistic parking garage scene made with roughly 700,000 simulated brushstrokes, and engineer Michael Graczyk shared a 21-second video of a bird at sunrise built from exactly 111 strokes, with no undo and deliberate wobble in the lines. Anthropic reports 64.9 percent for Fable 5.2 on its internal RoboDojo Sim benchmark versus 29.8 percent for OpenAI's GPT-6 Astra, though researcher Aris Zhu's real robot-arm comparison found Astra painted faster and was the only model to stand a domino on end.

LeCun: If you're interested in human-level AI, don't work on LLMs

A Reddit post in r/TheMachineLearning links to a discussion titled "Don't work on LLMs — Y LeCun at CoRL," attributing to Yann LeCun the view that those interested in human-level AI should not work on LLMs. The submission links both the Reddit thread and a Hacker News comments page, where it had 6 points and 2 comments.

Show HN: Vantage-V-AI – 2-bit ternary KV-cache with attention sinks

Vantage-V-AI (v8.2.0) is an open-source, bounded-memory inference runtime that integrates with Hugging Face AutoModelForCausalLM via a one-line vantage.enable() call to cap the active attention window and retain sink tokens. It uses a 2-bit packed ternary KV-cache (4 elements per uint8 byte, 7.97x storage reduction over FP16) plus attention sinks, and reports benchmarks on 2x NVIDIA Tesla T4 GPUs with Qwen2.5-3B-Instruct, including 100.0% exact token parity between lossless and FP8 streaming modes and 0.988 average retrieval coherence from 512 to 4,096 tokens. The repository is at github.com/khoadev-vn/VANTAGE-V-AI under the MIT License.

Anthropic says outdated prompting habits are slowing down Claude Opus 5.5

Anthropic says users of Claude Opus 5.5, now the default on Pro, Max, Ultra, and Team plans, should drop phrases like "think hard" because the model always reasons on its own and scales depth to the task and effort level. The company recommends starting at the default "Medium" effort, which it says matched Opus 5 on "High" in internal tests, and describing the desired end result in a single message rather than giving instructions one at a time. Anthropic also advises attaching diagrams and tables as files, naming unwanted design styles specifically, and connecting files and systems via connectors.

Gemini 4 Carbon takes the MathArena crown but costs 15 times more than GPT-6.1 Sol

Google's Gemini 4 Carbon became the first non-OpenAI model in a while to top the MathArena math benchmark, displacing GPT-6.1 Sol, with Claude Fable 5.2 taking second place. Its most common failure mode is exceeding the time limit, and in one case it ran a script for more than eleven hours without checking its progress. With the reasoning settings used in testing, Carbon and Fable 5.2 cost roughly 15 and 40 times as much as GPT-6.1 Sol, respectively.

Preventing an AI catastrophe, a brief into to analysis by synthesis

Using the 1983 Petrov nuclear false-alarm incident and the film WarGames, this post argues that AI safety discussions often conflate alignment with basic epistemics, since the Soviet satellite was not misaligned but simply had no model of the world. It revisits Ulf Grenander's Pattern Theory, which holds that pattern analysis and pattern synthesis should share a common probabilistic model, and proposes that trustworthy agentic systems should reconstruct a generative explanation of reality and compare its predictions against observed sensor readings. The post notes that end-to-end neural network training displaced Pattern Theory, and suggests that today's strong synthesis capabilities make it worth revisiting Grenander's goal of understanding.

Anthropic can already retrain Mythos with "a fraction of the compute originally needed," says former researcher Coxon

Former OpenAI and Anthropic researcher Jacob Coxon says a slide from Anthropic's IPO roadshow was a major reason he left the company after about four months; the chart shows pre-training compute multiplier for code over time on a logarithmic y-axis, and Coxon says its steep climb points to accelerating algorithmic efficiency. He argues Anthropic's Mythos model was "not a one-time deviation, but a point on a superexponential," and that Anthropic could already retrain Mythos with a fraction of the compute originally needed. Coxon says algorithmic improvements are "on the verge of being automated" and wants frontier model development paused next year so competitors can catch up.

Advancing science & engineering with Antigravity’s /teamwork

Google describes how multi-agent workflows in Antigravity's /teamwork, built on Gemini 4 models, produced results on open problems including a full proof of the 2013 Courtade–Kumar conjecture formalized in Lean 4, a machine-checked formalization of the Weil conjectures spanning about 1.8 million lines of Lean, and 95% top-1 accuracy on molecular structure determination from NMR data. The post also reports engineering gains such as a memory-safe Rust rewrite of the Zircon microkernel adding about 800,000 lines, a libgav1 Rust port that became 20-30% faster than the original C++, and fleet-wide memory savings surpassing initial estimates of 1 PiB. Google says the tools are available to external researchers and developers in Antigravity today, with 10,000 academic researchers offered six months of free access to its highest usage tier.

AI models read the law to the right of their politics

Across 200 randomly selected split Supreme Court decisions, 13 AI models were asked whose legal reading was correct and, separately, whether the outcome was good for the country; when the two answers disagreed, it was almost always "the conservative reading of the law is right, but the liberal result is better," by 420 to 41. On the law alone, every model landed left of the Court's center, with the Claude models siding with the majority 85–87% of the time, like Roberts and Barrett. The author notes caveats including one answer per model per case and no direct comparison to how justices judge outcomes.

Simple Puzzle Reveals AI's Jagged Intelligence

An article describes training a GPT from scratch on a million solutions to the 15 puzzle, after which it solves 99.9 percent of randomly scrambled boards but fails on the easiest puzzles, including one that is already solved, while assigning its wrong moves over 97 percent probability. The author attributes this to AI emulating its training data through shortcuts rather than understanding, producing a "jagged" boundary where performance collapses outside the training distribution. The piece draws on Ilya Sutskever and Melanie Mitchell to argue that skill is not understanding and that AI can be confidently wrong.

The avalanche that hit math is coming for AI development

The author argues that AI is already accelerating frontier AI research—citing METR's finding that Anthropic's progress rate has doubled in a year, largely due to AI-assisted R&D—and that rapid gains in verifiable domains like mathematics suggest AIs could match the best human AI researchers within a couple of years, with a median estimate of 2028. He contends that AI research resembles the well-scoped, verifiable problems AIs excel at, so automating it could compress years of algorithmic progress into one, yielding either a "spark" or a "software-only singularity."

AI unlocks Gödel's notebooks, said to contain hundreds of proved theorems

A Helsinki University project led by logician Jan von Plato used the AI model Claude to digest 1,009 pages of Kurt Gödel's 1940s "workbooks" written in Gabelsberger shorthand, revealing hundreds of previously unknown proved theorems and 14 unpursued themes in logic. Claude found 663 theorems for which Gödel had a proof, sketch or idea; when checked in the Coq proof assistant, about 400 were verified as stated, about 200 were repaired and about 50 were false. Von Plato said Claude's reconstruction of a proof of the independence of the axiom of choice, which passed Coq, suggests Gödel had been telling the truth when he claimed in 1966 to have already proved the result.

When One GPU Isn’t Enough: Serving GLM-5.3 Across Two NVIDIA DGX Stations with vLLM

Running a frontier-scale model locally requires keeping all its weights in GPU memory, and the largest models no longer fit on one machine. Two NVIDIA DGX Stations connected over ConnectX-8 RDMA serve one model through a single endpoint, with tensor parallelism splitting the model weights and computation across both GPUs. The NVIDIA GLM-5.3 NVFP4 model is described as a quantized version of Z.ai's mixture-of-experts model.

Perplexity Releases Decider v1.3: Open-Weight Decision Models Add Locate

Perplexity released pplx-decider-v1.3 and pplx-decider-flash-v1.3, open-weight decision models that return probabilities for yes/no, multiple-choice and score questions rather than generated text, with weights under Apache 2.0 on Hugging Face and availability in the Perplexity Decisions API and on OpenRouter. The main addition is Locate, which extracts exact text spans by type or instruction for NER, PII detection and extractive QA. Perplexity reports 91.6% mean accuracy on 63 query-classification tasks and a 0.27% answer-flip rate, with all figures self-reported.

Rethinking Memory for AI Inference

Moving compute closer to memory can cut data movement, power consumption and latency, but different AI workloads need different approaches to memory architecture, interconnects and processing. Software support, standardization and thermal management remain major barriers to wider adoption, with processing-in-memory and near-memory processing still early despite efforts such as LPDDR6-PIM and compute on custom HBM base dies. The article surveys industry views on how edge inference, where models are known ahead of time, may benefit from ahead-of-time compilation and near-data compute.

llm-gemini 0.37

Google launched Gemini 4 Hydrogen, the lowest-cost member of the Gemini 4 family, priced at $1.50/M input, $0.075/M cached input and $7.50/M output, compared with Carbon and Argon at $2.00/$0.10/$10.00 through Nov 30, 2026, doubling from Dec 1, 2026. The post notes Carbon and Argon share the same price, that Hydrogen is only a 25% discount until December 1st (then 75%), and that it remains far more expensive than Claude Haiku 5.5 and GPT-6 Luna at $0.10/$0.50. Hydrogen is described as the new free tier model and the model powering AI features in Google Search.

DeepSeek-V4.1-Flash on Vera Rubin NVL72: 58x Throughput vs. GB300 NVL72 at 387 tokens/s

On a single NVIDIA Vera Rubin NVL72 rack, vLLM serves DeepSeek-V4.1-Flash at 1.7M tokens/s per GPU with 387 tokens/s per user, 58x GB300 NVL72, reaching 2.52M at peak throughput and 1,810 tokens/s for a single user. The gains come from kernels tuned for Rubin's split memory domains and FP4 tensor cores, specialized low-latency decode kernels, and a rack-wide KV cache and Engram tables shared over NVLink. The post details four optimization groups and reports a 96.5–97% prefix hit rate at high concurrency with workload-aware routing.

Google’s new Gemini model is better at understanding complicated questions

Google says its new Gemini 4 Hydrogen model can give more helpful answers to complex questions, including ones with multiple parts, hard-to-understand typos, or highly specific requests, by breaking them down and addressing each part. It is available starting today in Search, the Gemini app, all tiers of Google AI Studio, the Gemini API, Gemini for business, and Google Antigravity for AI Pro and Ultra subscribers, and is now the default model in AI Mode. Google says it delivers significant leaps in speed and intelligence compared to Gemini Flash 3.8 and matches or outperforms competing models like Claude Sonnet 5.5 and GPT-6.1 Terra.

Featured coverage of this story: 《Google Search gets a major upgrade with Gemini 4 Hydrogen》

Deploy frontier AI models on AWS Trainium with SGLang-Neuron

Amazon announced that SGLang-Neuron, released by RadixArk with Annapurna Labs, is publicly available through an AWS Deep Learning Container, letting users serve models and generate RL rollouts on Trainium via the same SGLang interface. The out-of-tree plugin runs on native PyTorch, is selected with SGLANG_PLATFORM=neuron, and supports PD disaggregation with KV-cache transfer via NIXL over EFA. The post includes a walkthrough on a Trainium3 EC2 instance using the pytorch-inference-sglang-neuronx container to serve openai/gpt-oss-120b.

Using Claude Opus 5.5 as your daily driver for knowledge work

Anthropic says Claude Opus 5.5 is now the default model on Pro, Max, Ultra, and Team plans and recommends it for daily knowledge work such as research, analysis, and producing memos, reports, spreadsheets, and decks, priced at $4/$20 per million input/output tokens. The post describes internal tests on research reports and a fictional merger analysis, plus guidance to start at Medium effort, drop "think hard" prompts, and attach charts rather than retyping them. It also walks through a demo project where Opus 5.5 tested four theories for slowing retail growth and proposed an alternative explanation involving new stores opening too close to existing ones.

Inkling 2 Small on vLLM: Day-0 Support

vLLM announces day-0 support for Thinking Machines' Inkling 2 Small, a multimodal text-and-vision MoE model with 594B total and 17B active parameters, up to 1M tokens of context, and a native DSpark speculator. The post details the architecture (48 layers, 384 experts with top-8 routing, block attention residuals, gated attention, a 23-layer ViT image encoder, and mixed NVFP4/MXFP8 expert precision) and vLLM's optimizations, including kernel fusion with PDL, fused MoE activation quantization, sequence parallelism for prefill, and a Gluon relative attention kernel, reaching up to 630 TPS/user at TP4 on GB200. It also covers LoRA and RL (via SkyRL) support, a serve command, agentic-workload benchmarks, and accuracy checks on NIAH up to 1M tokens, MMMU-Pro and HLE.

Four Amazon Bedrock inference choices that affect cost

The post explains how four request-level Amazon Bedrock settings—prompt cache duration, service tier, reasoning effort, and maxTokens—can change token processing and billing rates without altering the model or prompt. It reports tests showing that a 1-hour cache pays off from the second request per hour when gaps exceed 5 minutes, that Flex on Nova 2 Lite processed the same tokens at half the Standard rate in us-west-2, that lowering effort from high to low cut median output tokens by 169% on an agent test, and that a 155-token maxTokens fence avoided 1,893 output tokens in one test. It recommends confirming model support, changing one setting at a time, and verifying effects through response metadata, CloudWatch metrics, invocation logs, and billing data.

Google rolls out Gemini 4 Hydrogen, finally replacing 3.8 Flash

Google has released Gemini 4 Hydrogen, which replaces Gemini 3.8 Flash across its products with immediate access and more relaxed limits than earlier Argon and Carbon rollouts. Google says Hydrogen runs at four thinking levels (low, medium, high, and new xhigh), is 2.5 times faster than Flash 3.8 even in high mode, cuts "fabricated claims" by more than 5x, and more than triples 3.8 Flash's Terminal-Bench score. It scored 54 on the Artificial Analysis Intelligence Index in xhigh mode versus 50 for Argon.

Featured coverage of this story: 《Introducing Gemini 4 Hydrogen》

Air-LLM: Broadcasting AI Weights for Edge LLM Inference via RF Computing

The paper proposes AIR-LLM, an edge LLM inference architecture in which a central radio such as a 5G base station broadcasts LLM weights over the air and edge users perform the general matrix-vector multiplication of inference directly in the RF domain using RF mixers, avoiding weight storage and loading. It uses MIMO spatial multiplexing with an energy-efficient precoder-postcoder pair to shorten airtime, and because the central radio stays user-unaware, one broadcast can serve unlimited users in coverage. Evaluated on NVIDIA Sionna ray-traced channels of two urban scenes with a real RF mixer profile, AIR-LLM shows 4.0% WikiText-2 perplexity degradation on LLaMA-3.1-8B while saving 157.7x/40.4x energy versus FP16 and weight-only quantization baselines, and with 20 users its airtime is 104.1x/26.0x shorter.

OpenAI’s data suggests that frontier models are hiding their thoughts because developers optimize for capability and compute

OpenAI safety researchers traced the erosion of chain-of-thought monitorability across models from GPT-5 to GPT-6 Astra to four training factors: rising base-model capability, architecture choice, reinforcement learning, and inheritance from teacher models. They found no easy training fix, since Astra still learned to control its scratchpad even without direct penalties, and describe the loss of transparency as a tradeoff between safety, capability and compute rather than an inevitability. OpenAI says it urgently needs new monitoring methods beyond reading the scratchpad.

[AINews] D-Day (Decision Models Day)

Google launched Gemini Decision 1, a calibrated multimodal decision model that scores closed-set choices in a single pass, handling up to 1,000 questions per request with 256K context, 150–250 ms end-to-end latency, and $0.10 per 1M input tokens with no output charge. Google's own tests show it beating TypeSafe's Jev 1.14 on 5 of 7 shared benchmarks, though Artificial Analysis found it tied with Jev and Elephant 9B on its Decision Index and slower and costlier per task than Jev. The roundup also covers Fable 5.2 topping Agent Arena, AlphaGenome 2, Runway's Praxis-1, Gemini Robotics 2.5, and reports that Nvidia's board is preparing to remove Jensen Huang.

Semantic diffing of formal policies with anti-unification

The post describes an open source engine that computes semantic diffs of formal policies by anti-unification over an e-graph, using declared algebraic laws and domain rewrites so that shared structure appears once and each genuine disagreement is kept as a Variants node. Worked examples cover SMT-LIB-style rules, Cedar and Dogwood authorization policies, and OpenShell and CEL encodings, showing how presentational differences are absorbed while real bugs such as a weakened conjunction or a dropped guard remain visible. The engine builds from source with a pinned Rust toolchain and reads a single S-expression file.

AI Models Can Tell When Researchers Plant Concepts in Their Neural Networks

New research suggests large language models can sometimes detect when researchers artificially inject concept-related activity patterns into their neural networks, with Alibaba's Qwen3-235B flagging injections up to 53.9 percent of the time and Meta's Llama 3.1 405B up to 31.7 percent, while neither model claimed an injection in unaltered trials. However, the ability to name the injected concept appears distinct from detection and may stem from a side effect that makes models talk about the concept regardless of the question. Skeptics at New York University found models reported "injected thoughts" more often after a prompt than a genuine injection, raising doubts about whether the findings amount to introspection.

Google joins the decision-model wave with Gemini Decision 1, scoring 1,000 questions in a single pass

Google DeepMind is introducing the Gemini Decisions API, built for fast closed-ended decisions such as yes/no questions, multiple choice, and scale-based ratings, powered by the Gemini Decision 1 model that processes up to 1,000 independent questions in a single pass across text, images, audio, video, and PDFs. It scores options directly and returns probabilities with confidence values in 150 to 250 milliseconds, and Google says it already uses the API internally for dynamic model routing in Gemini Notebook, design prototyping in Stitch, and safety checks in Google Search. The API costs $0.10 per million input tokens with no output-token charge, about five times cheaper than Gemini 3.5 Flash-Lite according to Google, and is available now through Google AI Studio with integrations for OpenRouter, LangChain, Vercel, and other platforms rolling out.

Featured coverage of this story: 《Gemini Decisions API: Fast, calibrated multimodal decisions》

Weighted State Souping for Efficient Long-Context Retrieval

The paper proposes weighted state souping, a method that learns query-dependent weights for composing cached state encodings from an SSM, addressing the degradation of state souping and vanilla SSMs on long, crowded contexts. The authors report substantial long-context improvements across single- and multi-hop document QA, needle-in-a-haystack retrieval, variable tracking, and tool use, plus a sparse variant with near-constant encoding cost relative to context length. They also present theoretical arguments on why souping works and when it may fail.

On the Importance of Gating: Memorization vs. In-Context Learning in State Space Models

This paper argues that the gating mechanism in modern recurrent networks explains both the successes and failures of State Space Models (SSMs) on in-context learning and retrieval tasks: theory and experiments show gating causes SSMs to first learn an in-weights "memorization" solution while delaying or preventing convergence to a correct in-context learning solution, even when the architecture and memory capacity impose no fundamental limits. The authors also find that gating is often beneficial for improving generalization to long sequence lengths.

Doubling LLM token rates in ComfyUI

ComfyUI rewrote the hot path of local text generation for Qwen3.x LLMs, letting the same checkpoints on the same GPU generate tokens up to twice as fast with no change to model or output quality; on an RTX 5090, Qwen3.5 9B went from 106 to 219 tokens per second. The changes include a re-laid-out GEMV kernel, fused prologue operations, new attention and DeltaNet decode kernels, and an adaptive-tree MTP speculative decoding algorithm, all available in ComfyUI v0.45.0 with the Text Generate node's mtp option left on auto.

A lot of AI progress is just persistence

The author argues that much of the hype around LLMs stems not from superhuman intelligence but from their willingness to tirelessly attempt tedious tasks, citing examples like Claude recovering $400,000 in Bitcoin through brute-force search and optimizing ML architectures through sheer volume of attempts. While acknowledging genuinely impressive recent results like OpenAI's Millennium problem work, the piece contends there is no proof AI has special ability to optimize neural networks or reverse engineer programs, and that artificial diligence explains a large fraction of perceived capability.

The biggest AI cost reduction measure today is a cloud provider discount, lol

AI spend has moved from experiment to budget line in about two years, with 98% of FinOps teams now managing some of it, and the article argues that committed-spend discounts are today's biggest cut because they lower token price without reducing token volume. It details three engineering levers that do reduce volume: prompt caching (cutting the input side of the bill by about 85%), batch APIs at half price on Anthropic, Bedrock and OpenAI, and smaller models for routine work, noting Claude Haiku costs about a fortieth of Opus per token. It then frames self-hosted LLM versus API as a per-workload choice among rent, route or own, with self-hosting paying off only at high, steady GPU utilization.

Show HN: Tensward – Local AI inference optimisation

Tensward is an Apache-2.0 tool that measures how your LLM is served on your own machine by starting its own copy of vLLM or llama.cpp on a local port, sending a realistic workload, and reading engine and GPU metrics. It offers one flag change at a time, measures it, compares answers before and after, and keeps the change only if it is faster and the answers pass your check; it never stops or reconfigures your running server. It supports NVIDIA and Apple Silicon hardware, and the post includes two L4 case studies showing gains such as +40% requests/s and first word −47% on llama.cpp, and first word 19 s → 0.30 s on vLLM.

We don’t understand whether GDM intentionally trains on CoT

Redwood Research researchers note that the Gemini 4 Safety report appears to say GDM uses auxiliary rewards that observe chain-of-thought content, citing Kaufmann et al. on training on CoT, while a GDM blog post says they never penalize what the model writes in its CoT and GDM staff said they weren't training on CoT but couldn't explain details. The authors argue that "orthogonal" CoT rewards are a weak guarantee and that avoiding training against monitors is insufficient to preserve CoT integrity, and they present an appendix experiment in which five LLMs rated transcripts with hack acknowledgments removed 20-50 points higher than the originals. They conclude they suspect a misunderstanding but want clarification and greater transparency.

NaiveAI Promises 2,100 Tokens per Second. How Well Does It Code?

Latent Space benchmarks NaiveAI's open-weights Naive-N0.5-Flash, a MiMo-V2.5 derivative adapted to DeepSeek Sparse Attention with about 3.25T extra training tokens, against DeepSeek V4.1 Flash 1017 and GLM 5.3 Flash on speed, verbosity and three app-building prompts. It reports peak throughput near 2,100 tokens/s and a median response time of 3.1 seconds versus 5.3 and 10.1 seconds, but far higher thinking-token use on hard questions and mean times similar to DeepSeek. In the apps, Naive-N0.5-Flash produced the best solar system, tied DeepSeek on the tree model and trailed it on ray-tracer efficiency, while its no-thinking variant failed to build a usable tree app.

Gemini 4 Carbon is now available

Google released Gemini 4 Carbon to developers, enterprises, scientists, and consumers, describing it as its strongest model for cybersecurity capabilities and long-horizon agentic workflows in knowledge work, software engineering, and scientific reasoning. It ranks #1 on CursorBench 4.0, Perplexity's WANDR, and Zapier's AutomationBench at more than 2X lower cost per task than the next best external model, and is available via the API in Google AI Studio and Gemini for business at $2 per million input tokens and $10 per million output tokens. Google also previewed /swarm in Google Antigravity and reported safety results including a 0% prompt injection attack success rate on the Gray Swan IPI benchmark.

Running AV Reasoning Models Within Onboard Compute and Latency Limits

AV developers need reasoning models that fit onboard compute and latency constraints; larger models provide a foundation, but deploying them in a vehicle requires reducing model size, preserving useful reasoning, and validating performance on target hardware. The source mentions NVIDIA Alpamayo 2 Edge in this context, but the supplied material is truncated before detailing it.

How to make flash models even faster: optimizing DeepSeek V4.1 Flash 1017

Baseten describes using an internal harness to optimize 80+ kernels and bottlenecks for DeepSeek V4.1 Flash 1017, producing a Fast variant it reports at 991 output tokens/s on Artificial Analysis with the lowest time per task. The post details three hardware-level techniques: kernel fusion (including an improved fused router kernel cutting decode time by 2.7ms and a 30% faster mHC megakernel), Programmatic Dependent Launch to overlap kernel execution, and host loop minimization via breakable CUDA graphs in SGLang.