Reasoning

QQbitAI15:31

AI score76

PSI claims its AI system has cracked 5 open quantum problems

Physical Superintelligence (PSI), founded less than two months ago, claims that its self-developed AI research system Emmy has achieved results on 5 open problems in quantum information theory, with the paper, Lean formal proofs, and verification code published on GitHub for review. These 5 problems come from the open quantum problems catalog established by German physicist Reinhard Werner in 1999, spanning five directions: entanglement purification, quantum field theory, high-dimensional quantum communication, quantum channel capacity, and quantum causality, among which OQP 2, OQP 12, and OQP 27 were included as early as the beginning of this century. Emmy decomposes difficult problems into verifiable mathematical sub-hypotheses, then hands them to the Lean formal proof assistant, symbolic computation engines, and high-precision verification code for screening; a result is only deemed established once compilation passes. PSI emerged in September this year and has completed a $58 million seed round led by Breakthrough Energy Ventures.

Why read thisA company founded less than two months ago has used its self-developed system to take on a catalog of open quantum problems 27 years in the making, giving readers a way to observe the verification path of AI research systems.

AARC Prize12:53First-partyAnalysis

AI score62

ARC Prize publishes Meta Muse Spark Open's evaluation results on ARC-AGI

ARC Prize published the validation results of Meta's Muse Spark Open on ARC-AGI: ARC-AGI-1 at 89.0% with $0.29 per task, ARC-AGI-2 at 57.5% with $0.73 per task, and ARC-AGI-3 at 0.1% under the standard harness with $3.9K. ARC Prize states that its v1/2 scores are close to those of Haiku 5.5 and GPT-6 Luna, but the cost is about 9 times that of the latter; the model is priced on OpenRouter at $1.25 per million input tokens and $4.25 per million output tokens. As the reasoning tier increases, ARC-AGI-2 rises from 29.4% at medium (averaging 55k tokens/task) to 57.2% at xhigh (180k tokens/task).

Why read thisARC Prize gives Meta's new model's scores on three versions of ARC-AGI along with per-task cost, directly comparable with competitors scoring the same.

BBaseten Engineering Blog12:51First-partyModel releases

AI score68

Baseten releases decision model joey-1, with P50 latency of 20ms and $0.01 per million tokens

Baseten's Base Labs has released joey-1, a 2B decision model that directly outputs a single-token label and calibrated confidence, with a P50 end-to-end latency of 20ms, about 2.5 times faster than elephant-1 (49ms), at a price of $0.01 per million tokens, one-tenth that of mammoth-1. joey-1 is post-trained from Qwen3.5-2B and shares the same data recipe and API with elephant-1 and mammoth-1, so switching requires changing only one line. The model is now available on Baseten Model APIs, with zero data retention by default, and its weights are open-sourced on Hugging Face; Model APIs supports a 16k context window, while dedicated deployments can unlock a 262k context window and support fine-tuned weights.

Why read thisThe original text provides joey-1's latency, pricing, and the division of labor among the three model tiers, so readers can judge whether decision-making tasks should switch to a smaller model.

MMistral AI12:45First-partyModel releases

AI score87

Mistral Releases Open Weights for Mistral Large 4 (Le Chonk)

Mistral has released the open weights for Mistral Large 4 (Le Chonk), a 1-trillion-parameter natively multimodal model that continues reinforcement learning on top of the preview version, with improved coding and defensive cybersecurity capabilities. It scores 19.7% on FrontierCode 2.0 and 48% on the Artificial Analysis Coding Agent Index, roughly on par with GLM-5.3, but uses an average of 47% fewer tokens per task; on Terminal-Bench 4.0 it improves by 13 percentage points to 41%. The model supports three reasoning modes, uses a modified MIT-based Mistral Ecosystem Open-Weight license, and is priced via API at $0.70 per million input tokens, $0.07 for cached input, and $2.10 for output.

Why read thisThe original text provides open weights, three reasoning modes, and comparisons across multiple benchmarks, allowing readers to judge the trade-offs for self-hosted coding and security scenarios.

AArtificial Analysis10:36Analysis

AI score65

Artificial Analysis releases Intelligence Index v5, with 60% of evaluations based on private datasets

Artificial Analysis releases Intelligence Index v5, adding two new benchmarks, FrontierCode 2.0 and Terminal-Bench-Science, upgrading HLE-Diamond, AA-Omniscience v1.1 and CritPt v2, removing GDPval-AA v2.1, SciCode and AA-LCR v1.1, and changing 60% of the index to evaluations based on private test sets or held-out answers. It also launches Progress Index v1, which uses a unified scale to track changes in model capabilities over time. The results show that Claude Opus 5.5 (max) ranks first with 58 points, followed by GPT-6 Astra (max, 57) and GPT-6.1 Sol (max, 55), while Gemini 4 Argon (high) scores 50; the gap between open-weight models and the leaders has widened from 3 points to 12 points, and among released weights, DeepSeek V4.1 Flash 1017 (max) and GLM-5.3 (max) lead with 32 points. The per-task cost of the top ten models differs by more than 13 times, with GPT-6.1 Terra (max) lowest at $0.96 and Claude Fable 5.1 (max) highest at $13.04.

Why read thisThe original text lists the benchmarks added and upgraded in the v5 index as well as the items removed, and provides the scores of leading models and the per-task cost gap, which can be used to compare the capabilities and costs of each model.

AAi2 Blog09:25First-partyResearch

AI score65

Ai2 Releases "Old Ideas, Novel Problems" Study: LLMs Are Unreliable at Judging the Novelty of Research Ideas

Ai2 released the "Old Ideas, Novel Problems" study and a companion benchmark, testing the reliability of six models—GPT-5.1, GPT-5.2, GPT-5.4, Claude Sonnet 4.5, Claude Opus 4.5, and Claude Opus 4.6—at judging the novelty of research ideas. The results show that small changes to the evaluation setup produce drastic differences: after switching GPT-5.4 to a simpler prompt on the Human+Generated data, its accuracy dropped from 93.5% to 40.9%, below chance level. The study also found that retrieval and additional reasoning do not reliably improve judgment quality; two purpose-built novelty evaluation systems performed worse than a simple Claude Opus 4.6 prompt baseline and cost more.

Why read thisAi2 built a benchmark using ICLR 2026 review signals, quantifying the instability whereby minor prompt tweaks can dramatically flip novelty-judgment accuracy.

GGoogle Research09:25First-partyResearch

AI score67

Google Research proposes the sleep framework, enabling LLMs to retain knowledge during continual learning

Google Research proposes the sleep framework, which introduces periodic sleep stages to LLMs to consolidate short-term context into long-term parameters, thereby mitigating catastrophic forgetting and static knowledge cutoff. The framework consists of two stages: memory consolidation and dreaming. The former uses MoE parameter expansion and knowledge seeding based on GKD and reinforcement learning imitation, while the latter achieves self-improvement through random expert activation, filtering synthetic samples, and RL refinement. On BABILong, this method scales nearly perfectly within 10M tokens, and achieves accuracies of 79.2%, 69.0%, and 46.1% on AIME 24/25 and HMMT 25, respectively.

Why read thisGoogle proposes splitting continual learning into two stages—awake interaction and offline consolidation—and presents comparison results on 10M-token long context and mathematical reasoning.

FFull Artificial Analysis article08:00First-partyAnalysis

AI score82

Gemini 4 Carbon Tops the Artificial Analysis Intelligence Index v5

Google's Gemini 4 Carbon ranks first on the Artificial Analysis Intelligence Index v5 with a score of 59, leading in four of the 9 evaluations: HLE-Diamond, Terminal-Bench-Science, AA-Omniscience, and AutomationBench-AA. On the Coding Agent Index, Gemini 4 Carbon (high) paired with the Antigravity CLI scores 59, trailing Claude Code paired with Claude Opus 5.5 (max, 62 points) by 3 points. The model has a 1M-token context window, supports text, image, video, and speech input, and is priced at a standard rate of $4/$20 per million input/output tokens; it is currently available to select users with a 50% discount.

Why read thisArtificial Analysis's independent evaluation places Gemini 4 Carbon at the top of the Intelligence Index v5 and provides a breakdown comparison with Claude Opus 5.5.

OOpenRouter：Announcements08:00First-partyProduct release

AI score62

OpenRouter Rebuilds Provider Routing Algorithm, Reducing TTFT by 16.9%

OpenRouter announced that it has rebuilt its provider routing algorithm, shifting from allocating traffic mainly based on input token price to a real-time routing cost that comprehensively factors in actual price, time to first token (TTFT), throughput, error rate, and output quality. The company says the new algorithm reduces TTFT by 16.9%, increases throughput by 16.3%, and cuts 429 errors by 46%, while cost per request rises by 6.0%. Users do not need to change any code; explicitly setting provider.sort or provider.order can still bypass the new logic.

Why read thisThe original article presents experimental data on switching between the old and new routing algorithms, so readers can use it to judge the trade-off between inference cost and latency.

UUnite.AI07:23

AI score94

OpenAI Says Its Model Produced Results in Three Research Areas: Mathematics, Computer Science, and Physics

On October 26, 2026, OpenAI announced that its internal model had produced three research results in the fields of mathematics, computer science, and physics, including a counterexample to the long-standing Exponential Time Hypothesis (ETH) in computational complexity theory. In mathematics, the model provided a proof of the local smoothing conjecture in arbitrary dimensions, and thereby resolved the Kakeya conjecture and the Stein restriction conjecture; the output was obtained on October 18 and Lean formalization was completed the next day. The computer science result was verified by a group led by Russell Impagliazzo, Ramamohan Paturi, and Ryan Williams, and OpenAI agreed to disclose all raw data and fund related conferences; the physics result provided a rigorous existence proof for a four-dimensional quantum field theory with gauge fields and interacting fermions, and also included a pure Yang–Mills existence proof, with the group led by David Gross deciding to temporarily not disclose the AI-produced materials and to write up the results themselves.

Why read thisThe original text fully presents OpenAI's tiered disclosure strategy for the three AI-produced results, as well as the verification and authorship arrangements of the external expert panels.

Ggihyo.jp02:42Model releases

AI score90

OpenAI releases GPT-6.1 Terra, with API pricing at half that of GPT-5.6 Terra

On October 26, 2026, OpenAI released and made available GPT-6.1 Terra, strengthening visual understanding, computer operation, and writing capabilities. The standard API price is half that of GPT-5.6 Terra, namely $1 per million tokens for input, $0.05 for cached input, and $5 for output. The model has a context window of 1.05 million tokens, a maximum input of 922,000 tokens, and a maximum output of 128,000 tokens. Reasoning effort can be selected from none to max, with medium as the default. On Agents' Last Exam, the medium setting scores 15 points higher than GPT-5.6 Terra while costing about one-third as much; under the max setting, the average speed for completing tasks is more than 3 times that of GPT-6 Astra and GPT-6.1 Sol. The model is already available to Plus, Pro, Enterprise, and Edu users of ChatGPT Work and Codex, and the API model ID is gpt-6.1-terra.

Why read thisThe original article lists the new model's benchmark performance on visual understanding and computer operation as well as changes in API pricing, which can be used to judge cost trade-offs for everyday automation tasks.

QQbitAI16:31Model releases

AI score79

RockAI Technology, together with HKUST and CMU, open-sources EdgeRock-4B, reducing KV Cache by 99.1%

Yanxin Matrix, under RockAI Technology, together with universities including HKUST and CMU, has released and open-sourced EdgeRock-4B. At a 256K context, its KV Cache is fixed at 72MiB, a 99.1% reduction compared with the source model Qwen3.5-4B. A single 80GB GPU can run 512 concurrent long-inference streams, with an output throughput of 12,124 tokens/s, 6.42 times that of the source model; the average pass@1 on AIME 2024–2026 is 82.9%. The model is derived from Qwen3.5-4B through staged conversion, requiring no pretraining from scratch; the weights and inference code have been open-sourced and are available for commercial use, supporting text and image inference.

Why read thisThe original text provides measured comparisons of fixed memory and throughput, based on which readers can assess changes in memory cost for long-inference scenarios.

TThe Elec08:23Model releases

AI score82

Moonshot AI Releases Kimi K3.1 Pro, AA Intelligence Index Score of 55 Surpasses GPT-6 Astra

Moonshot AI has released Kimi K3.1 Pro, an upgraded version of Kimi K3, featuring a MoE architecture with 2.8 trillion total parameters and 104 billion activated parameters, activating 16 of 896 experts at a time. According to Artificial Analysis benchmarks, the model ranks fourth on the Intelligence Index with a score of 55, higher than GPT-6 Astra's 53, while the previous generation Kimi K3 scored 44. It ranks first on Terminal Bench 4.0 with 67%, compared with 13% for the previous generation and 64% for Claude Sonnet 5.5; pricing is $2 per million input tokens and $10 per million output tokens, but its output speed of 73 tokens/s is below the average of 81 tokens/s.

MMoonshot AI · Kimi Blog08:13First-partyModel releases

AI score87

Moonshot AI releases Kimi K3.1 Pro: a 3T-class open-weight model with a 1 million token context

Moonshot AI releases Kimi K3.1 Pro, the first model in the K3.1 series and its strongest 3T-class open-weight model to date, with a 1 million token context window, alongside Kimi K3.1 Pro Highspeed, which is 2-3 times faster. The model is now available on Kimi.com, Kimi Work, Kimi Code and the Kimi API, with full model weights to be released on November 4. On Terminal-Bench 4.0, K3.1 Pro scores 65.7%, higher than GPT-6 Astra's 58.2% and slightly below Claude Opus 5.5's 66.4%; on the internal benchmark Kimi Code Bench 3.0 Long-horizon it scores 61.2%, higher than K3's 55.95%.

Why read thisThe official disclosure covered K3.1 Pro's benchmark performance on coding, 3D and financial tasks, as well as the trade-offs made on cybersecurity data before open-sourcing the weights.

IIT Home · Artificial Intelligence09:34Product release

AI score79

OpenAI fully opens its decision API, based on GPT-6 Luna, at $0.1 per million input tokens

OpenAI announced the full opening of the Decisions API, which is built on GPT-6 Luna and is aimed at questions with bounded answers, helping applications and agents make near-real-time decisions based on text, images, and structured inputs, with response speeds up to 10 times faster than calling GPT-6 Luna through the Responses API. On pricing, input is charged at $0.1 per million tokens, cached input hits are charged at $0.01 per million tokens, and billing is based only on input tokens, with output tokens free. Since the public beta, native GPT-Live integration, Prompt caching, and inference data residency support in the United States and the European Union have been added, and up to 400 questions can be submitted per request.

Why read thisThe original text gives the pricing, latency improvements, and input limit changes of the decision API, so readers can use this to judge which near-real-time decision scenarios it suits.

Ffal05:27Model releases

AI score75

fal Releases FLUX 3 Max Video Model, Generating a 10-Second Video Takes 12.45 Seconds

fal, in partnership with bfl_ai, released the post-trained video model FLUX 3 Max, which takes 12.45 seconds to generate a 10-second video, compared with 277.8 seconds for Seedance 2.5; the two share an Elo score of 1,139 on third-party benchmark evaluations. The model is tied for first place in image-to-video quality on an independent third-party benchmark, and can generate 10-second, 720/768p videos in about 12 seconds. fal said FLUX 3 Max will be 75% off for the next three weeks.

Why read thisThe original text provides comparison data on generation speed and third-party Elo, allowing readers to judge the trade-off between speed and quality for the video model.

AARC Prize01:14First-partyAnalysis

AI score73

DeepSeek V4.1 Flash 1017 scores 78.1% on ARC-AGI-2

ARC Prize announces the results of DeepSeek V4.1 Flash 1017 on the ARC-AGI validation set: ARC-AGI-2 score 78.1%, cost per task $0.16; ARC-AGI-1 score 94.5%, cost per task $0.06. The best score of the DeepSeek Flash series on ARC-AGI-2 rose from 61.4% for V4 Flash 0731 to 72.9% for V4.1 Flash, and then to 78.1% for V4.1 Flash 1017.

Why read thisARC Prize provides per-version scores and per-task costs for the DeepSeek Flash series on ARC-AGI, enabling horizontal comparison of model iteration magnitude.

TTomasz Tunguz blog01:46Opinion

AI score67

GPU revenue ladder: from DeepSeek's $38 to SpaceX's $2.44

Tomasz Tunguz converts the inference data disclosed by DeepSeek and SpaceX's third-quarter financial report into the same basis, yielding four price points for revenue per GPU card-hour, from DeepSeek's theoretical maximum of $38 to SpaceX's actual $2.44. The B200 spot price is $8.77, the SpaceX contract price is $5.27, and the payback period for its new compute deployment is less than one year. The author also points out reverse pressure: GPU rent rose from $4.40 to $8.08 within half a year, while the cost of the equivalent benchmark threshold fell 377 times in 18 months, and JPMorgan says more than 60% of the data center capacity planned for 2027 has not yet started construction.

Why read thisThe author converts DeepSeek's and SpaceX's data into revenue per card-hour on the same basis, so readers can understand the price space and cost pressure of the inference business.

BBaseten Base Labs: Model Research01:45First-partyModel releases

AI score68

Baseten Base Labs releases decision models mammoth-1 and elephant-1

Baseten's Base Labs releases two decision models, elephant-1 (9B) and mammoth-1 (27B), both fully fine-tuned from Qwen3.5, with model weights publicly released. mammoth-1 ranks first among 208 models on the Jev Decision Index 0.4 with a score of 71.24, leading the second-place Perplexity Decider v1.2 by 1.8 points; elephant-1 scores 62.65 on that leaderboard and ties for first with d1-ultra on the Artificial Analysis Decision Index with a score of 65. Training used the eighth version of the data mixture with 6.21M samples, and replaced the full-attention layers with a block bidirectional attention mask; under fixed data, this change raised the public 10k-sample score from 44.8 to 48.6.

Why read thisThe original text discloses the data recipe and attention mask changes for the decision models, showing the specific impact of data scale and fixes on benchmark scores.

MMicrosoft Research00:43First-partyResearch

AI score78

Microsoft Research proves the 31-year-old unsolved KLS conjecture

Microsoft Research researcher Xinzhi Zhang and co-authors proved the KLS conjecture proposed in 1995, giving a dimension-independent fixed bound; the previous best result only allowed the bound to grow slowly with dimension. The proof came from a human-guided agent search process, in which the team explored more than 100 candidate paths, with researchers evaluating, pruning, and redirecting them; an independent team also gave a complete proof at the same time, followed by Lean 4 formal verification. The authors say earlier unguided end-to-end attempts with multiple frontier models failed to achieve the goal.

Why read thisMicrosoft Research uses human-guided agent search to prove the KLS conjecture, allowing readers to see how AI is directed in mathematical discovery.