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Headlines · 45
- Voyage-affiliated test favors supervised classifiers over Jev when labels are available
An author who works at Voyage reported testing a supervised workflow that embeds inputs with an open-source or inexpensive embedding model and fits an sklearn classifier using labeled data. The author said the approach was cheaper and delivered a 37% error reduction against Jev. They clarified that the gain came from using training labels: Jev does not use them, and the supervised approach performed worse when labels were unavailable. The report linked a write-up and code.
r/MachineLearning · 🔥 0 - Mattt releases Swift packages for on-device speech and decisions
Mattt announced AnySpeechRecognizer and AnyDecisionModel, two Swift packages for Apple platforms. AnySpeechRecognizer mirrors Apple’s speech-to-text API, supports selectable engines such as NVIDIA Parakeet and Alibaba Qwen3-ASR running locally through Core ML, and adds speaker diarization. AnyDecisionModel returns typed answers and probabilities for yes-or-no, multiple-choice, and rating-scale questions, using on-device models through MLX, Core AI, or llama.cpp, or decision models from Jev or Ollama. The packages can download open models from Hugging Face. Reported speed, memory, and word-error-rate results came from one machine and a few datasets; testing was limited to macOS 27, with no iPhone results.
Hugging Face · 🔥 0 - Mica 4B demo breaks a Minecraft request into chained decisions
In a creator-provided demonstration, Mica v0.5 4B interpreted a request for at least three porkchops and a netherite ingot by decomposing it into multiple-choice questions. The creator says the model identified the goals and quantities, then chose among currently valid actions while a bot performed the walking, mining and crafting. The video displays the questions, options and probabilities assigned to each answer. The creator also says the Minecraft agent is unfinished and has not been released.
r/LocalLLaMA · 🔥 0 - Oumi Investigates Jev’s Likely Architecture and Training
Oumi researchers used behavioral probes, benchmark results and serving profiles to estimate which model TypeSafe’s Jev may be based on and how it may have been trained and served. Without insider knowledge, they say Jev is likely built on Qwen 3.5 or 3.6 in a 35B-A3B configuration, with a tokenizer merging Qwen and GPT-OSS. They also speculate that its RLCD method uses soft-label cross-entropy on synthetic data rather than PPO or GRPO. For serving, they propose tree batching and either a form of sparse attention or deployment on two GPUs with the prefill split between them.
r/LocalLLaMA · 🔥 0 - lieflat less ai tone v2 expands its evaluation corpus
The author released version 2 of the open-source lieflat less ai tone skill on GitHub, expanding the testing methodology and corpora. The first round used 629 articles, while the second used 270 topic-, fact- and length-matched pairs totaling about 5.2 million Chinese characters. According to the author, the evaluation combined 90 scripted indicators, about 430,000 semantic judgments by Jev, and human review of metaphor appropriateness, identifying Europeanized antithesis, dense parallelism and mismatched metaphors as prominent AI-like traits.
即刻 · AI探索站 · 🔥 0 - Google Introduces A2UI Express and Swappable Inference Formats
Google’s A2UI Team introduced A2UI Express as the new default inference format for generative UI. A2UI Agent SDKs compile model output into validated A2UI JSON messages, and developers can substitute other DSLs with their own compilers. The release also demonstrates TypeSafe’s Jev decision model selecting among predefined layouts. Google reports that Express can reduce latency by up to threefold and use up to threefold fewer tokens than Direct JSON, while Jev-based layout decisions can reduce latency by up to 80-fold.
Google Developers Blog · 🔥 0 - Creator launches JEV arena where AI models fight with prompt attacks
A creator announced a JEV-style arena where user-configured AI models select actions including moving, blocking, punching, kicking and using fireballs. Users write each fighter’s strategy, choose its model and can add a JAILBREAK move that inserts a user-written message into the opponent’s next prompt without changing the game state. OpenJev is one available fighter, while replays record decisions and each submitted configuration receives a separate rating.
r/LocalLLaMA · 🔥 0 - Integrating the Jev judgment model with Codex
Juejin author 二哥呀 demonstrates how to install TypeSafe AI’s Jev Skill in Codex, configure an API key, and require the agent to call Jev’s Choice API before taking an action. In the shown example, Jev 1.13.0 selected web search for an Nvidia stock-price question with 0.99 confidence, after which Codex executed the selected action. The author also says Jev works with Claude Code and PI, while speed and cost comparisons come from TypeSafe AI rather than independent testing.
掘金 · 人工智能 · 🔥 0 - Mica v0.5 open-weight decision model posts fast local benchmarks
A Reddit author introduced Mica v0.5, an open-weight 4B decision model built on Qwen3.5-4B with LoRA that assigns probabilities to options in one forward pass. In the author's tests on one RTX 6000 Ada, median decision times were 11–18 ms, compared with 133–157 ms for PPLX Decider 1.2 Flash and 31–33 ms for Laya; on synthetic security logs, Mica reportedly caught 21 of 22 planted intrusions. These measurements and the author’s broader accuracy claims have not been independently corroborated in the supplied report.
r/LocalLLaMA · 🔥 0 - Modem launches Jev-powered Smart Triggers
Modem Blog says Modem now evaluates incoming events with TypeSafe’s Jev decision model against customizable plain-English conditions before starting its agent. The agent runs only when an event matches, enabling targeted workflows across customer support, calls, Slack, email, pull requests, issues and feedback. Modem says the feature is available to all users, with evaluations taking under one second and costing 1/100th of a cent.
Modem Blog · 🔥 0 - OpenAI Makes Decisions API Generally Available
OpenAI has made Decisions API generally available to all customers, adding voice and visual modalities for classification, routing and agent-action tasks based on predefined questions and answer choices. TechCrunch reported pricing of $0.10 per million input tokens, with no output-token charges. API product lead Nikunj Handa said the service reduced some response times from one to three seconds to 100, 120 or 180 milliseconds; these figures were not independently verified in the supplied reports. OpenAI reconfigured an existing Luna model for faster, lower-cost inference rather than training a separate decision model.
TechCrunch AI · 🔥 0 - BroadLink says Huansi Hub cuts AI appliance cost to about RMB1 per device per year
BroadLink founder and CEO Liu Zongru told Geek Park's Hardwire that the company's Huansi Hub is built from a set of very small expert models — each possibly under 10B parameters — with a large model moved to the background as a "grader." He said the system-1 layer only judges and executes rather than generating text, with a claimed hit rate above 95% and a 99% target, and that DeepSeek's latest model grades the previous day's interaction logs each night and distills its judgments back into the small models. Liu said the hub has already been deployed with B2B customers across several appliance categories, that AI cost can be compressed to roughly RMB1 per device per year, and that existing Wi-Fi plus Bluetooth appliances need only a module OTA upgrade so users can talk to them through a WeChat mini-program assistant; all figures are the company's own claims.
极客公园 · 🔥 0 - Agnes AI demos self-evolving agents: Honor of Kings team and test-time-training robot arm
At the 2026 Physical AI and World Models conference in Nanjing, Agnes AI (爱思科技) livestreamed a 5v5 Honor of Kings match in which an AI coach rewrote the team's tactical playbook after each game, lifting its win rate against built-in bots from 0% to 100% over five revisions, according to the company's booth demo reported by 新智元. No model parameters were trained in that experiment; the coach, described as the forthcoming Agnes 3.0 Pro, only edited prompts, action choices and code, with each version required to pass researcher-set tests before play. A separate demo showed a Franka Panda arm in MuJoCo simulation updating its own weights during work via Agnes Embed TTT, raising task success from roughly 30% to about 90% while cutting iteration time to as little as 30 minutes to an hour. Agnes frames both routes as part of its open-source Agnes Harness agent infrastructure and its "万物共脑" (one brain for everything) pitch, and says a version of the harness also coordinated simulated Mars-base devices and booth robots.
新智元 · 🔥 0 - Perplexity's open pplx-decider-v1.2 claimed 10x cheaper than GPT 6 Luna
A Reddit post in r/LocalLLaMA says Perplexity's pplx-decider-v1.2 costs one tenth of GPT 6 Luna with low reasoning while producing identical-quality decisions, and that pplx-decider-flash-v1.2 does the same within the Decisions API. The poster, /u/Balance-, also says v1.2 is 2.5x cheaper than Jev, links to an Artificial Analysis decision index page, and states that both pplx-decider models are open source under Apache 2.0 with repositories on Hugging Face. The supplied material includes no official Perplexity announcement and no independent verification of the pricing or quality claims.
r/LocalLLaMA · 🔥 0 - Cloudflare launches Clairic extraction model preview
Cloudflare launched Clairic in preview on Workers AI, a System One-style model designed to extract specified values from unstructured documents in a single pass, with confidence scores and null values for missing fields. Cloudflare says it can also perform simple calculations, estimates, and forecasts, while open weights are planned for a later release. In a Cloudflare-published customer-service test, the full Clairic-Clef-GLM-5.3 chain took 19.3 seconds, compared with 61.7 seconds for GLM-5.3 alone.
Techmeme · 🔥 0 - China Telecom's Xingchen lab open-sources Xing4.0-29B-Intent intent-parsing model
China Telecom AI's Xingchen AGI lab released and open-sourced Xing4.0-29B-Intent on October 19, a 29B model that combines intent classification with parameter extraction and returns results as schema-constrained JSON. Alongside the weights it published 2.753 million supervised fine-tuning examples rebuilt from 61 public datasets covering 19 domains and 52 languages, plus an inference SDK and official images under Apache 2.0. Zixun ran 11 hands-on tests across smart spaces, smart cockpits, office collaboration and personal-agent scenarios and reported generally accurate multi-object parameter binding; the headline benchmark numbers, including an overall score of 58 against 49 for Gemini-3.6-Flash across 48 public datasets, come from the model team's own evaluation, which the reports note does not represent all tasks or deployments.
智东西 · 🔥 0 - Google Research open-sources Harness², an agent harness that rewrites itself
Google Research has open-sourced Harness² (Recursive Agent Harnessing for an Open World) on GitHub under Apache 2.0, a framework that lets agents recursively rewrite their own harness — prompts, memory, tools, skills, subagents and other components — without changing model weights. Task-level recursion runs a Propose–Probe–Compose loop that keeps only edits supported by observed execution behavior and requires no ground truth, while a harnessing-level reflector saves adopted edits and lessons into an experience library for later tasks. The release supports OpenCode and Codex executors (OpenCode by default), and Google Research says it remains a research project rather than an officially supported product; Google's own benchmarks report one-step gains of up to 11.5 points on WorkBuddy-Bench, 4.5 on JobBench and 11.8 percentage points on LAB with GLM-5 and Gemini 3.5 Flash. DoNews noted that default network access is unrestricted and the Codex executor disables the sandbox, so formal evaluation needs network restrictions.
InfoQ 中文 AI&大模型 · 🔥 0 - StartLux-Decision-v1.1 tops Hugging Face decision-model leaderboard
Reports from 36Kr, TMTPost and InfoQ Chinese say StartLux's StartLux-Decision-v1.1-27B Instant ranked first on Hugging Face's updated Decision Index 0.3.2 with 69.1 points, which InfoQ gives as 69.12; the reports differ on whether 173 or 174 entries were listed. The new generation offers Instant, which returns option probabilities in a single pass, and AutoThink, which generates extra reasoning only when initial confidence is insufficient; the team also introduced the RLCD² training method and the DecisionPivot benchmark, and says 27B AutoThink scored 73.37 on its own Decision Index 0.3 evaluation versus 57.96 for hosted Jev. Model weights and inference code are published, but the weights use a CC BY-NC 4.0 license limited to non-commercial use, with commercial deployment requiring separate authorization.
InfoQ 中文 AI&大模型 · 🔥 0 - Jev becomes Vercel's fastest-adopted AI model ever
Vercel data published on Thursday shows that TypeSafe AI's lightweight decision model Jev was used by 35% of paid teams within two weeks of its limited release, nearly double the reach of the second most popular model, Sonnet 5.5, making it the fastest-adopted model in its first two weeks on Vercel's platform. Released in limited form on September 15, Jev restricts outputs to a few predetermined options to run cheaper and faster, making it suited to classifying large volumes of data; Vercel has used it to replace GPT-6 Luna for classifying harmful commands. The figures come from Vercel's AI gateway, a unified API service similar to OpenRouter, and may undercount labs such as OpenAI and Anthropic that offer their own APIs; the same report says open-source models account for more than 70% of tokens processed through the gateway and nearly 25% of total spend.
TechCrunch AI · 🔥 0 - Tian Keyu AI lab's $200M valuation sparks founder-integrity debate
According to a 36Kr AI commentary, an AI lab founded by Tian Keyu has raised nearly $30 million at a $200 million post-money valuation despite having a 10-person team, no product and no public name; the article, citing Bloomberg, names 5Y Capital and IDG Capital as investors. Tian is the first author of a NeurIPS 2024 Best Paper and a central figure in ByteDance's so-called model-sabotage incident; a first-instance court ordered him to pay 500,000 yuan and return all internship pay, and ByteDance has appealed, so the case remains under second-instance review. The commentary argues that the market should not treat controversy-driven attention as a valuation asset, while noting that the competing accounts of the 2024 incident are both one-sided.
36氪 人工智能 · 🔥 0 - OpenRouter launches Armor beta to block out-of-policy tool calls
OpenRouter announced the beta launch of OpenRouter Armor, which uses TypeSafeAI's Jev model to guard against out-of-policy tool calls before an agent executes them; OpenRouter says regex-based filtering often isn't enough to detect unwanted tool calls. The company describes Armor as an "auto" permissions mode similar to Claude Code's Auto that works with any model or agent, and says that on an internal test set it generated, Jev 1.13 had the highest accuracy and lowest cost compared with other decision models and Claude Code's use of Sonnet 5. Those performance and cost comparisons are vendor claims and have not been independently replicated.
OpenRouter (X) · 🔥 0 - Tessl ships Verifiers for repo rules; says 75% of its PRs merge without human review
Tessl announced general availability of Tessl Verifiers, rules stored in a repo as JSON files with an instruction and a yes-or-no checklist; running tessl change verify has an LLM judge score each rule pass or fail, and they can run locally, in pre-commit or as a CI gate. Tessl says the default judge is openai/gpt-6-luna, with Jev available as an alternative. In a separate post, Tessl self-reports that about 75% of its pull requests merged without human correction after adopting Tessl Code Review, across more than 1,200 agent-written PRs a week, with 91% of its findings being real defects versus 68% for its previous AI reviewer and about 35% lower review cost per PR; all figures are vendor-reported.
Tessl Blog · 🔥 0 - TypeSafe ships Jev 1.14.0 with image input, Box grounding and 262k context
TypeSafe AI released Jev 1.14.0, which the company describes as an unflashy, small update with broad but subtle changes and the clearest expected gains in multilingual performance; it warns that non-English languages, Chinese included, still lag English. The release also opens three experimental features grouped under a "Danger Zone" program: image input, a new Box question type that returns normalized bounding-box coordinates, and long context that raises per-request and per-question limits to 262k tokens. All three are off by default and require manual API switches, and TypeSafe says they do not meet production standards and are used at the developer's own risk. Pricing is unchanged at $0.042 per million input tokens with free output, and TypeSafe's internal evaluation put Jev 1.14.0 first among eight models at 60.0 while cautioning the benchmark may favor its own model and scores may change. DoNews also reported that Liquid AI launched its d1-ultra decision model the next day, claiming it beats Jev 1.14 across eight task types, a vendor claim.
智东西 · 🔥 0 - Intern-Decision paper details 0.8B–4B multimodal decision models
A new arXiv paper by Tong Zhu and co-authors presents Intern-Decision, a family of 0.8B, 2B and 4B multimodal decision models that return structured choices and candidate probabilities from text and optional images. The method places one decision placeholder per field in a complete assistant JSON skeleton and evaluates all fields in a single causal forward pass, with ground-truth symbols used only in supervision. The authors report that the 4B model reaches 90.02% unweighted average accuracy across seven evaluation suites versus 88.74% for Jev v1.13.0, and a mean latency of 44.16 ms for a fixed 289-token, three-field request on one RTX 4090; they also describe a confidence-based handoff study in which routing 42 of 111 Jevbench-Hard items to Atria-Dawn-Preview raised accuracy from 82/111 to 97/111.
Hugging Face · Papers · 🔥 0 - JevAgentHarness delegates agent tool choices to small decision models
An arXiv paper submitted 15 Oct 2026 by Kunlun Zhu and co-authors introduces JevAgentHarness, which delegates the multiple-choice decisions LLM agents make at each step — such as which tool to call next — to decision models like Jev, falling back to the large LLM when a per-choice confidence threshold fitted on the agent's own trajectories is not met. The authors built AgentDecisionBench and report that the decision model most accurate on isolated questions solved only 69.5% of agent tasks, and that delegating tool choices without a fitted confidence threshold lowered success by 19.0 points. They also trained AgentDecision-4B, an open 4B decision model, on post-mistake agent states labeled with Jev's answers; per the authors, on tasks unlike its training data the agent solved 91.5% with AgentDecision-4B and 82.0% with Jev, while on tasks like the training data such models made agents 81.1–89.0% cheaper and up to 2.5 times faster without losing 3 or more points of task success. The models, data and harness are said to be released.
Hugging Face · Papers · 🔥 0 - Pi 1.0 ships native MCP and Code Mode, plus the Pi Durable agent harness
Pi has upgraded to version 1.0, with Code Mode and native MCP reaching stable release, and the team launched a new harness called Pi Durable the same day. According to co-founder Mario Zechner, Pi Durable uses an intent-execute-result "effect sandwich" and a versioned document store to persist user messages, model replies, tool results and task state so an agent can resume exactly where a crashed process left off. MCP, which Pi's founders had publicly criticized, is now built in with tool search and progressive disclosure; some users worry Pi is becoming bloated, while Armin Ronacher says the default context load has not increased.
36氪 人工智能 · 🔥 0 - DecisionRobust benchmark tests JEV-style decision models against text and image attacks
A new arXiv paper introduces DecisionRobust, a benchmark for systematically evaluating how robust JEV-style decision models are to textual and visual inputs. It covers practical decision-making scenarios with semantics-preserving text perturbations, image perturbations and prompt injection in both modalities. The authors evaluated 30 model configurations on text tasks and 17 on vision tasks, including 24 and 12 held-out models respectively; they report that attacks expose vulnerabilities beyond the models used for construction and selection, that optimized image perturbations transfer weakly to many targets, and that resistance to input perturbations does not necessarily imply resistance to prompt injection.
Hugging Face · Papers · 🔥 0 - vLLM Semantic Router open-sources Decision 3.0 decision models, tops decision leaderboard
The vLLM community project vLLM Semantic Router released and open-sourced the Decision 3.0 family of multimodal decision models: d3, d3-flash, d3-mini, d3-nano, d3-lite and d3-edge, spanning roughly 0.6B to 27B parameters, with weights published on Hugging Face. The flagship d3 is built on Qwen3.8-27B and accepts multiple images and video, while the other five are based on Qwen3.5 at different sizes; unlike Jev, which is served mainly via cloud API, the series is open-weight and locally deployable. According to team-provided figures, on the Jev Decision Index 0.3.1 leaderboard d3 ranks first with a text decision index of 64.7 and a vision score of 71.6, ahead of Jev's 60.1 and Microsoft Decision-1's 53.2 (9B, fine-tuned from Qwen3.5-9B); the team also reports median latency on a single AMD Instinct MI325X GPU of 55 ms for text, 273 ms with images and 553 ms for 10-second video, while one developer ran d3-lite via Core ML on an M5 Pro Mac to classify and route 1,000 pull requests in about two minutes.
智东西 · 🔥 0 - Hands-on with Jev: triaging 8 vulnerability reports in one second
A hands-on piece from OSChina uses TypeSafe Jev's three judgment primitives — Choice, Noul and Score — to auto-triage vulnerability reports submitted to a low-code platform, reporting that it screens 8 reports in about one second and that the judgment logic can be wired into Claude Code and Codex. The article also notes Jev is now fully open with no waitlist and a console at console.typesafe.ai, though the earlier free credit for new users has been dropped: sign-up is free but requesting an API key requires binding a payment method.
开源中国 · 🔥 0 - Microsoft launches Decision-1, a Qwen-based model that scores fixed choices
Microsoft released Microsoft-Decision-1, a small model that does not write open-ended text but returns a calibrated probability for each of a fixed set of options, aimed at routing, classification, prioritization, verification, workflow and agent control. According to the Foundry model card, it was built by post-training Alibaba's open-weight Qwen3.5-9B; Microsoft software engineering VP Achint Srivastava announced it in a 9 October blog post and said the company will soon rebase it on other models, including its own MAI models and OpenAI's. It is available in Microsoft Foundry and due on OpenRouter, priced at $0.042 per million input tokens with output tokens free. Microsoft says it posted the highest accuracy, 83.5%, across a 36-benchmark comparison covering nearly 150,000 questions held out from training, and claims it is 2.5 times faster than runner-up H2O-Lightning-4B, 35 times faster than GPT-6 Sol and 2.8 times faster than Jev — all vendor-run comparisons.
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