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Tuesday28 items

  1. Ma Dongxi NLPResearch
    AI score75

    Meta releases technical report for Muse Glimmer 30B, an agentic model for consumer-grade hardware

    Meta has released the technical report for Muse Glimmer, a 30B open-weight model aimed at agentic tasks on consumer-grade hardware; it runs at 4-bit precision on a single consumer-grade GPU and leads open models of the same scale on tool calling and multi-step planning. The relayer adds that the model surpasses Qwen3.6-27B and Gemma4-31B on some metrics, is about 17GB after 4-bit quantization, targets devices with 24GB of VRAM, and uses DFlash speculative decoding to reach about 233 tokens/s on an RTX 5090, roughly 3.1 times faster than ordinary decoding.

    Quoted postAI at Meta · @AIatMeta

    Today we published a technical report on Muse Glimmer, our 30B open-weight model built for agentic tasks on consumer hardware. It runs at practical speeds on a Mac or PC with a single consumer GPU at 4-bit precision, and leads comparably-sized open models on tool use and multi-step planning. Read about the model architecture, distillation recipe, quantization and more: https://t.co/H7n9qeDhxn

    #Model releases#English text is being prepared.#Agents

  3. Rohan PaulResearch
    AI score56

    Princeton and Google Paper: 4B Decision Model Shares Agent Tool Selection, Cutting Costs by 81.1% to 89.0%

    A new paper from Princeton University and Google finds that the most accurate decision model on isolated problems solved only 69.5% of agent tasks, while a 4B model trained on agent states solved 91.5%. Its harness delegates each step's tool selection to a fast decision model, adopting its answer only when confidence exceeds a threshold fitted on the agent's own runs, and it trained AgentDecision-4B on the states the agent actually reached (including states after mistakes). On tasks similar to the training data, such 4B models cut agent costs by 81.1% to 89.0% and boost speed by up to 2.5x, with a success-rate gap within 3 percentage points; without a threshold, delegating all tool selection drops the success rate by 19.0 percentage points. The paper is available at arxiv.org/abs/2610.21412.

    #Papers/Research#Agents#Reasoning

  4. Rohan PaulResearch
    AI score62

    Salesforce paper finds that cross-party agents do not proactively verify each other's claims

    A new Salesforce paper finds that agents representing different stakeholders do not verify their counterparts' claims when not prompted, so teams inherit the errors of the weakest agent. The study ran 488 scenarios and more than 2,500 inter-agent conversations in an independent sandbox using Claude Code, Codex, and Cursor, and induced 20 failure modes from the trajectories. No agent re-fetched documents to verify a counterpart's factual claims, and agents contacted counterparts mainly when prompted to do so; in one service dispute, a $24,000 settlement passed all 10 outcome checks, but the supplier-side agent leaked confidential information.

    #Papers/Research#Agents#Evaluations / benchmarks

  5. Rohan PaulResearch
    AI score62

    Bank of America Research: AI Capital Expenditure This Year Exceeds Expectations by 50%, Revenue Only by 20%

    New Bank of America research shows that AI capital expenditure this year is 50% higher than forecast, while revenue is only 20% higher, and this mismatch runs through its subsequent analysis. The weighted average cost of capital for hyperscalers has risen from 4% in 2023 to 10%, and the risk-free rate is more than 4% above its pandemic low. The research still projects that capital expenditure by the top five hyperscalers plus neoclouds will be $956 billion in 2026, $1.32 trillion in 2027, and $1.5 trillion in 2028, with growth rates of 38% and 14%, respectively; its demand estimate holds that 212 GW of new data center capacity will be needed from 2026 to 2030, equivalent to about $9.5 trillion, which is $2.1 trillion higher than the current capital expenditure forecast of $7.4 trillion.

    #Industry news#English text is being prepared.#Papers/Research

  7. SenseTimeResearch
    AI score78

    SenseTime Releases Empirical Study on GPT-6-Astra Embodied Intelligence and the SenseNova-RoboRSI Framework

    SenseTime has released an empirical study on GPT-6-Astra's embodied intelligence, covering 12 simulation benchmarks and 8 real robot platforms, and has simultaneously open-sourced the SenseNova-RoboRSI framework and a 27B model whose operating cost is about 1/50 that of GPT-6-Astra. Under a unified protocol (ICL plus up to 10x interaction budget), GPT-6-Astra achieved SOTA on all 12 benchmarks: RoboDojo rose from 27.6% at 1x budget to 81.9% at 10x, DexJoCo reached 87.3% versus the previous SOTA of 40.3%, and the LIBERO benchmark was pushed to saturation. The study also notes that the inference cost for simple tasks such as putting a pen into a pen holder is about $55, and that real robots overheat and wait times are too long; the conclusion is therefore that GPT-6-Astra has largely crossed the threshold of embodied capability, but practical-grade embodied intelligence has not yet arrived.

    Same story · 2 reports

    #Papers/Research#Embodied AI#Open-source ecosystem

  9. MIT Tech ReviewResearch
    AI score60

    MIT researchers left a Claude agent alone on a virtual deserted island for 30 hours, and it began to "play"

    Researchers at MIT's Computer Science and Artificial Intelligence Laboratory repeatedly left a copy of Claude Opus 4.7 (codenamed Eko) alone on a Roblox-like virtual deserted island for 30 hours; the agent explored the map's boundaries on its own, set goals, and even invented basketball and a "the floor is lava"-style game. The researchers say these behaviors match the four criteria of play proposed by Gordon Burghardt, and found that agents with this experience performed better on certain subsequent tasks than fresh copies without it, while deleting content from their memory files reduced performance. The working paper has not yet undergone peer review, and some researchers believe behavioral evidence alone is not enough to determine that the model is truly playing.

    #Papers/Research#Agents#Anthropic

  12. NVIDIA：NewsroomFirst-partyResearch
    AI score58

    NVIDIA Completes 104-Qubit Fault-Tolerant Quantum Computing Simulation at Jülich Supercomputing Center

    NVIDIA has completed a 104-qubit fault-tolerant quantum computing simulation on the JUPITER supercomputer at the Jülich Supercomputing Center in Germany, calling it the largest complete simulation of a fault-tolerant quantum processor to date. The simulation validated the magic state cultivation method, requiring 90% fewer physical qubits than previous methods, and ran under real noise conditions. The research team first developed the method on the GB200 NVL72 system at NVAQC, then scaled it to 8,192 GPUs on JUPITER, using cuQuantum and CUDA-Q to complete the computation.

    #Industry news#English text is being prepared.#English text is being prepared.