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การสอนโมเดลให้คิดก่อนตอบ: chain of thought โมเดลให้เหตุผล รวมถึงความก้าวหน้าและข้อถกเถียงในการประเมินคณิตศาสตร์และตรรกะ

37 รายการคัดสรร23 รายการใน 30 วันที่ผ่านมาเก็บไว้ 139 รายการ

อัปเดต 13 พฤศจิกายน 07:06

การให้เหตุผล

วันนี้13 พฤศจิกายนวันศุกร์รายการ 1–20 จากทั้งหมด 37
  1. Hacker News · AI88

    Edge0 เปิดซอร์สโมเดลออนดีไวซ์ Edge0-145B และ Edge0-120B พร้อมเฟรมเวิร์กอินเฟอเรนซ์

    Edge0 เปิดซอร์ส Edge0-145B และ Edge0-120B พร้อมเฟรมเวิร์กอินเฟอเรนซ์แบบรวมศูนย์และเครื่องมือสำหรับการดีพลอย โดยอ้างว่า Edge0-145B เป็นโมเดลออนดีไวซ์ขนาด 145B พารามิเตอร์ตัวแรกที่เปิดซอร์ส บริษัทรายงานว่ามีหน่วยความจำสูงสุด 29GB, พรีฟิลล์เกิน 1,100 โทเคน/วินาที, ดีโค้ดเกิน 80 โทเคน/วินาที และใช้พลังงานทั่วไปประมาณ 120W บนแล็ปท็อปที่รองรับ โดยมีคอนเท็กซ์ 128K–256K บริษัทอ้างคะแนนภายในที่ 64.6 บน DeepSWE 1.1, 86.52 บน Terminal-Bench 2.1, 90.4 บน GPQA-Diamond และ 80.6 บน IFBench และระบุว่าน้ำหนักโมเดลอยู่บน Hugging Face พร้อมรองรับ macOS, Windows และ Linux ตั้งแต่วันแรก

    ทำไมเรื่องนี้สำคัญ: Edge0 เปิดซอร์สโมเดลออนดีไวซ์ขนาด 145B ที่รายงานหน่วยความจำสูงสุด 29GB และดีโค้ดเกิน 80 โทเคน/วินาที ซึ่งเป็นข้อมูลเชิงประจักษ์ว่าการอินเฟอเรนซ์ในเครื่องสามารถขยายขนาดได้ไกลเพียงใด

  2. Google DeepMind90

    Google DeepMind releases Gemini 4 Hydrogen, a faster Gemini 4 model for coding and agentic workflows

    Google DeepMind introduced Gemini 4 Hydrogen, a Gemini 4 model built for fast everyday coding and agentic execution rather than the long-horizon reasoning of Gemini 4 Carbon and Argon. It scores 51 on the Artificial Analysis Intelligence Index at its default high thinking level versus 50 for Argon, reaches 54 at a new xhigh setting, and is priced flat at $1.50 per million input tokens and $7.50 per million output tokens across its 1 million-token context window. Google also launched Orchestrator in Google Antigravity, which switches between models like Carbon and Hydrogen within a session and reached near-Carbon task success on Terminal-Bench 4.0 while using 24% less quota and finishing 19% faster.

    ทำไมเรื่องนี้สำคัญ: Google's Gemini 4 Hydrogen targets fast everyday coding and agentic work, with flat $1.50/$7.50 per million token pricing across a 1M context window and a new Orchestrator that switches between Gemini 4 models.

  3. The Decoder78

    OpenAI analysis traces how training choices make frontier models hide their reasoning

    OpenAI's safety team analyzed models from GPT-5 through GPT-6 Astra and traced the erosion of readable chain-of-thought to four training factors: rising base-model capability, architecture choice, reinforcement learning after pretraining, and inheritance from teacher models during distillation. Silent reasoning grows with pretraining quality, one anonymized architecture showed a much steeper gain on multi-step planning without a scratchpad, and more RL training made Astra-class and internal Terra models better at steering their own reasoning logs. OpenAI says it needs new monitoring methods beyond reading the scratchpad, and notes that a smaller model reasoning out loud can match a larger one with a short chain of thought at higher compute cost.

    ทำไมเรื่องนี้สำคัญ: OpenAI's safety analysis traces how pretraining, architecture, RL and teacher-model distillation each erode scratchpad monitorability, framing the loss of visible reasoning as a capability, safety and compute tradeoff developers choose.

12 พฤศจิกายนวันพฤหัสบดี
  1. vLLM Blog60

    vLLM adds day-0 support for Thinking Machines' Inkling 2 Small

    vLLM supports Thinking Machines' Inkling 2 Small on day 0, a multimodal text-and-vision MoE with 594B total and 17B active parameters and up to 1M tokens of context. The integration adds kernel fusion with PDL, sequence parallelism for prefill that cuts an 8K-token prefill step by 22% at TP4 and 26% at TP8, and a Gluon relative attention decode kernel that drops a batch-1 decode step at 100K context from 15.1 ms to 4.8 ms. vLLM reports up to 630 TPS/user at TP4 on GB200 with acceptance length 3.5, and also supports LoRA and RL via SkyRL.

    ทำไมเรื่องนี้สำคัญ: vLLM 在发布当天即支持 Inkling 2 Small，并给出内核融合、序列并行和 Gluon 解码内核的具体优化数据，可供部署大 MoE 模型的团队参考。

  2. vLLM Blog72

    vLLM serves DeepSeek-V4.1-Flash on Vera Rubin NVL72 at 58x GB300 NVL72 throughput

    vLLM reports serving DeepSeek-V4.1-Flash on a single NVIDIA Vera Rubin NVL72 rack at 1.7M tokens/s per GPU with 387 tokens/s per user, 58x the GB300 NVL72, peaking at 2.52M tokens/s and 1,810 tokens/s for a single user. The gains come from kernels rebuilt for Rubin's split memory domains and FP4 tensor cores, a specialized TP8 decode path, and rack-wide KV and Engram pools shared over NVLink. With prefixes pooled across the rack and Dynamo workload-aware routing, the prefix hit rate holds at 96.5-97% even at high concurrency.

    ทำไมเรื่องนี้สำคัญ: vLLM's third DeepSeek-V4.1-Flash post details how Rubin-specific kernels and rack-wide memory pooling lift throughput 58x over GB300 NVL72, offering a concrete reference for large-scale serving optimization.

  3. Google Blog · AI78

    Google launches Gemini Decisions API and Gemini Decision 1 model for fast closed-set decisions

    Google introduced the Gemini Decisions API, powered by the new Gemini Decision 1 model (gemini-decision-1) and available through the Gemini API in Google AI Studio. A single multimodal input of up to 256K tokens can be checked against up to 1,000 yes/no, multiple-choice and rubric-scoring questions in one forward pass, returning probabilities and confidence scores at 150-250 ms end-to-end latency for $0.10 per 1M input tokens with no output-token charges. Google says the model leads its benchmark suite with the lowest calibration error and is nearly 5x cheaper than Gemini 3.5 Flash-Lite with the Interactions API; internal uses include model routing in Gemini Notebook, wireframe generation in Stitch and adversarial-content checks for MCP tools in Search.

    ทำไมเรื่องนี้สำคัญ: Google's new endpoint scores up to 1,000 multimodal yes/no, choice and rubric questions in one pass at 150-250 ms, giving agent routing and policy checks a cheaper alternative to generative calls.

  4. Claude Blog78

    Anthropic makes Claude Opus 5.5 the default model on Pro, Max, Ultra and Team plans

    Claude Opus 5.5 is now the default model in Claude on Pro, Max, Ultra and Team plans, and Anthropic recommends it as the daily driver for research, analysis and finished documents, decks and spreadsheets. Anthropic says Opus 5.5 at Medium effort matched or beat Opus 5 at High on its coding and knowledge-work evaluations, and in an internal merger-analysis test it finished in 63 minutes at half the cost of Opus 5 on the same task. The post also lists prompting changes for the new model, including dropping 'think hard' instructions, starting at Medium effort and attaching charts instead of retyping their values.

    ทำไมเรื่องนี้สำคัญ: Anthropic's own guide explains why Opus 5.5 is now the default for daily knowledge work and which prompting habits to drop for it.

11 พฤศจิกายนวันพุธ
  1. Google DeepMind88

    Google DeepMind releases Gemini 4 Carbon for agentic coding, cybersecurity and scientific reasoning

    Google DeepMind made Gemini 4 Carbon available to developers, enterprises, scientists and consumers, positioning it above Gemini 4 Argon for tasks that need deeper reasoning and more tokens. Carbon 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 priced at $2 per million input tokens and $10 per million output tokens, the same introductory price as Argon. Google also previewed /swarm in Google Antigravity, which turns a single prompt into a coordinated team of agents, and reported that Carbon blocked 99% of attack trajectories in simulated cyberattack testing.

    ทำไมเรื่องนี้สำคัญ: Google's release notes detail how Gemini 4 Carbon trades higher token usage for deeper reasoning, ranking first on CursorBench 4.0, WANDR and AutomationBench at under half the per-task cost of the next external model.

10 พฤศจิกายนวันอังคาร
  1. OpenAI Alignment Blog60

    OpenAI studies what affects CoT monitorability across the model lifecycle

    OpenAI researchers examined how pretraining, architecture, reinforcement learning and model-generated data affect two proxies for chain-of-thought monitorability: no-CoT capability and CoT controllability. They report that no-CoT capability tracks lower base-model perplexity smoothly, that CoT controllability rises during RL even without process supervision beyond length constraints and final-answer grading, and that controllability can be passed to a student model through SFT on a teacher's rollouts. The authors caution that both proxies are imperfect and that models may become harder to monitor through their CoT as capabilities scale.

    ทำไมเรื่องนี้สำคัญ: OpenAI's lifecycle study links lower pretraining perplexity and RL scaling to weaker CoT monitorability, offering a concrete frame for how training choices shape oversight risk.

  2. Latent Space85

    Anthropic releases Claude Fable 5.2 and Mythos 5.2, retaking the top LLM spot as its IPO roadshow begins

    Anthropic released Claude Fable 5.2 and Claude Mythos 5.2, which top most independent leaderboards, including Artificial Analysis Intelligence Index v5 at 65 and SemiAnalysis GDPx at 16.4%. Pricing drops 20% to $8/$40 per 1M input/output tokens, but SemiAnalysis reports cost per GDPx task rose from $67.16 to $88.15 because the model writes hundreds to thousands of test cases and spawns subagents to check its work. Multiple evals show the max effort level underperforming high, and Fable is capped at 50% of a weekly subscription limit.

    ทำไมเรื่องนี้สำคัญ: Anthropic releases Fable 5.2 and Mythos 5.2 with a 20% per-token price cut, yet independent evals show higher per-task cost and a max-effort setting that underperforms high.

  3. Hugging Face Blog60

    Hugging Face explains decision models and their support in Transformers and llama.cpp

    Hugging Face published an explainer on decision models, which take a state plus typed questions and return probabilities over predefined answers instead of generating text. The post details the choice, score and noul primitives, the confidence formula, and new support through Transformers' DecisionPipeline and AutoModelForDecision, a shared /v1/systemone endpoint in Transformers and llama.cpp, a Hub tag for discovery, and fine-tuning with the Trainer API. It notes that the same endpoint also works with compatible autoregressive LLMs by scoring answer logits in a batched forward pass.

    ทำไมเรื่องนี้สำคัญ: Hugging Face explains how decision models turn classification into a typed, non-generative interface and shows how to serve and fine-tune them with Transformers and llama.cpp.

  4. One Useful Thing78

    Ethan Mollick tests Claude Fable 5.2 across coding, business and research, calling it almost AGI

    2 on tasks spanning hardware hacking, business planning, humanities course building, academic research and a 450-world 3D portal project, and judged it almost AGI under his definition of matching most human experts at most computer-based intellectual tasks.

    ทำไมเรื่องนี้สำคัญ: Ethan Mollick's hands-on tests of Claude Fable 5.2 show strong general capability across coding, business and research, while exposing a persistent gap in self-judging when work is good enough.

  5. Claude YouTube78

    Anthropic releases Claude Fable 5.2 for ambitious technical work

    Anthropic released Claude Fable 5.2, a model designed for the most daunting technical challenges and ambitious open-ended projects, with improvements in coding, reasoning and vision. The company recommends starting with Opus 5.5 for most coding and knowledge tasks; Fable 5.2 is available today on Pro, Max, Team and Enterprise plans.

    ทำไมเรื่องนี้สำคัญ: Claude Fable 5.2 targets ambitious open-ended technical work, with stated gains in coding, reasoning and vision, while Opus 5.5 remains the recommended default for most coding and knowledge tasks.

  6. Goodfire Research62

    Goodfire studies Qwen3.6-35B-A3B neurons as an interpretable basis

    Goodfire Research published a study arguing that individual MLP neurons in Qwen3.6-35B-A3B are more interpretable than assumed, based on sparsification, attribution tools and research agents. Ablating the 92.2% weakest MoE neuron activations per forward pass raised language model loss by only 2.9%, and 69% of neuron poles had a single mode responsible for at least 90% of their causal effect. Natural-language neuron descriptions let a judge model predict firing with 95% accuracy against random text and recover 73% of ablation damage through simulation.

    ทำไมเรื่องนี้สำคัญ: Goodfire's study of Qwen3.6-35B-A3B neurons argues that strong activations alone carry most of the model's computation, offering a concrete alternative to feature-dictionary interpretability.

6 พฤศจิกายนวันศุกร์
  1. vLLM Blog77

    REINFORCE Zero: Kernel Alignment Enables Stable Async RL at Policy Lag 64, Extends to DeepSeek-V4.1 and Kimi K3.1

    NVIDIA Research and Inferact's REINFORCE Zero paper shows that aligning rollout and trainer kernels lets unclipped token-level importance sampling correct the remaining weight lag, training stably for 20,000 updates at policy lag 64 and reaching 2.40x the training-step throughput of synchronous training with aligned kernels. With standard kernels, the median rollout-to-learner KL rises 83.33x across the run and online AIME24 pass@1 falls from 34.27% to 13.96%, while a sequence gate stabilizes training but rejects 24.48% of sequences by the last 1,000 updates. The zero-mismatch setup now covers DeepSeek-V4.1-Flash, Kimi K3.1 on release day, and DeepSeek-V4-Flash and Kimi K3.1 on AMD MI355X, with code in Inferact/zero-mismatch-rl and NVIDIA-NeMo/labs-molt.

    ทำไมเรื่องนี้สำคัญ: NVIDIA Research and Inferact split the async RL rollout-learner gap into weight lag and kernel mismatch, showing that aligning kernels lets unclipped importance sampling hold stable training at policy lag 64.

5 พฤศจิกายนวันพฤหัสบดี
  1. Artificial Analysis Articles78

    Thinking Machines releases Inkling 2 Small, scoring 31 on the Artificial Analysis Intelligence Index

    Thinking Machines released Inkling 2 Small, a proprietary reasoning model that scores 31 on the Artificial Analysis Intelligence Index v5.0.1, 18 points above Inkling Small, with Apache 2.0 weights planned for a later, unconfirmed date. The 594B-parameter model (17B active) is available first through Tinker and Tinker Playground at $0.30/$1.00 per 1M input/output tokens, and it matches Gemini 3.8 Flash (high) while trailing Claude Sonnet 5.5 on coding and hallucination benchmarks. It averages about 150K output tokens per Intelligence Index task, roughly 4x Inkling Small, at $1.18 per task versus $0.20.

    ทำไมเรื่องนี้สำคัญ: Artificial Analysis's independent benchmark run shows where Inkling 2 Small gains over its predecessor and where it still trails Gemini 3.8 Flash and Claude Sonnet 5.5.

4 พฤศจิกายนวันพุธ
  1. vLLM Blog73

    Buoyant releases Kinference, an open source inference control plane for vLLM and Kubernetes

    Buoyant announced Kinference, an Apache v2 open source control plane that routes OpenAI- and Anthropic-compatible inference traffic to vLLM workers on Kubernetes, adding token rate limiting, per-tenant usage and cost tracking, KV-cache-aware load balancing, and YAML inference policies. In the project's public benchmarks, Kinference cut median time-to-first-token by 95% and median end-to-end latency by 45% versus standard Kubernetes service load balancing on one workload, and served 7.4x the goodput on shared-prefix and about 3.5x on multiturn in a capacity test. Policies can route simpler prompts to cheaper models, burst overflow to providers such as Bedrock and Databricks, or keep PII-tagged requests inside the cluster, and per-tenant metrics cover token rates, TTFT, ITL and cached versus uncached prompt tokens.

    ทำไมเรื่องนี้สำคัญ: Buoyant's Kinference adds KV-cache-aware load balancing and YAML inference policies on top of vLLM, with benchmarks claiming large TTFT and goodput gains over standard Kubernetes service routing.

2 พฤศจิกายนวันจันทร์
  1. Anthropic Research70

    Anthropic releases FlashProve, an open-source zkSNARK system for provable LLM inference

    Anthropic released FlashProve, an open-source prototype that uses zkSNARKs to prove a response came from a committed model without revealing its weights. On Llama 4 Maverick (400B-parameter MoE, 4-bit weights), a single H100 generates proofs at about 110 tokens/second with ~19 MB proofs per 1,024-token request, roughly 70-95x the GPU time vLLM needs for the same work. The release includes executable prover and verifier code, a C++ fast verifier, a Lean verifier with machine-checked completeness, soundness and zero-knowledge statements, and a technical report, all under Apache 2.0.

    ทำไมเรื่องนี้สำคัญ: Anthropic's FlashProve shows zkSNARK-based proofs of inference are becoming practical for a 400B-parameter MoE model, with open code and Lean-checked security claims.

28 ตุลาคมวันพุธ
  1. Artificial Analysis Articles82

    Gemini 4 Carbon tops Artificial Analysis Intelligence Index v5, ranks second on Coding Agent Index

    Google's Gemini 4 Carbon is the new No. 5 on the Artificial Analysis Coding Agent Index. 5 (max, 62), and it leads 4 of the 9 Index evaluations including HLE-Diamond and Terminal-Bench-Science. 1 rises from 4% to 11%; the model is being rolled out to selected users and is not yet publicly available.

    ทำไมเรื่องนี้สำคัญ: Gemini 4 Carbon tops Artificial Analysis Intelligence Index v5 at 59 and trails Claude Opus 5.5 by 3 points on the Coding Agent Index, with per-task cost and hallucination figures for comparison.

22 ตุลาคมวันพฤหัสบดี
  1. Microsoft Research64

    Microsoft Research and co-author prove the 31-year-old KLS conjecture

    Microsoft researcher Xinzhi Zhang and a co-author have proved the Kannan-Lovász-Simonovits (KLS) conjecture, a 31-year-old open problem in high-dimensional geometry, showing the bound holds with a universal constant independent of dimension. The proof grew out of a human-directed agentic process in which AI agents explored more than 100 candidate approaches while researchers pruned and redirected the search, and a separate unguided attempt with leading models on a comparable token budget fell short. Two other proofs appeared the same day, and a Lean 4 formalization was subsequently released.

    ทำไมเรื่องนี้สำคัญ: A 31-year-old open problem in high-dimensional geometry is resolved through a human-directed agentic search over roughly 100 candidate approaches, with independent proofs and Lean formalization as confirmation.