BTC $83,891.49 +0.46%
ETH $2,528.63 -0.85%
BNB $745.46 -0.85%
XRP $1.38 -1.87%
SOL $108.74 -2.96%
TRX $0.3298 -0.47%
DOGE $0.0851 -2.70%
ADA $0.2386 -4.91%
BCH $274.55 -3.84%
LINK $13.09 -2.05%
HYPE $83.28 -4.63%
AAVE $161.09 -4.36%
SUI $1.07 -4.20%
XLM $0.1947 -3.54%
ZEC $1,231.83 -6.20%
AAPL $342.73 +1.55%
AMZN $257.94 -2.28%
GOOGL $353.48 -0.03%
MSFT $542.54 +1.18%
META $736.83 +1.34%
NVDA $225.25 -2.07%
TSLA $374.99 -2.37%
SNDK $1,444.26 -7.26%
INTC $100.97 -2.76%
SPCX $163.32 -0.60%
MU $964.60 -5.12%
AMD $587.85 -3.17%
BTC $83,891.49 +0.46%
ETH $2,528.63 -0.85%
BNB $745.46 -0.85%
XRP $1.38 -1.87%
SOL $108.74 -2.96%
TRX $0.3298 -0.47%
DOGE $0.0851 -2.70%
ADA $0.2386 -4.91%
BCH $274.55 -3.84%
LINK $13.09 -2.05%
HYPE $83.28 -4.63%
AAVE $161.09 -4.36%
SUI $1.07 -4.20%
XLM $0.1947 -3.54%
ZEC $1,231.83 -6.20%
AAPL $342.73 +1.55%
AMZN $257.94 -2.28%
GOOGL $353.48 -0.03%
MSFT $542.54 +1.18%
META $736.83 +1.34%
NVDA $225.25 -2.07%
TSLA $374.99 -2.37%
SNDK $1,444.26 -7.26%
INTC $100.97 -2.76%
SPCX $163.32 -0.60%
MU $964.60 -5.12%
AMD $587.85 -3.17%

mode

All
Article
Flash

The success rate of the Zhipu legal model is nearly three times higher, and the testing cost is about one-seventieth of that of its competitors

The artificial intelligence company Zhipu released a legal-specific model GLM-Legal, which is further trained based on GLM-5.3-Flash. In the Harvey LAB evaluation covering 24 categories of legal services, the task pass rate increased from 6.7% to 19.2%, nearly tripling the original version, surpassing models like Kimi K3 and Claude Fable 5, and ranking seventh on the Vals AI leaderboard.When reviewing software service contracts, GLM-Legal suggested shortening the termination notice period from 60 days to 30 days and required service providers to offer up to 90 days of handover support. In another software project dispute involving 40 documents, it found that the company reported to the client that the project was 60% complete, while internal records indicated only about 41%. It also identified emails from the CEO requesting to conceal risks and continue charging, and analyzed potential legal liabilities in conjunction with contract terms.According to independent evaluations by Vals AI, the cost for GLM-Legal to complete a test is $0.15, while the closely performing Gemini 4 Argon costs $10.55, a cost difference of about 70 times. GLM-Legal took an average of 72 minutes, while the latter took 44 minutes. Zhipu stated that the model was trained specifically on legal data and conducted reinforcement learning in tens of thousands of simulated legal environments. Currently, GLM-Legal requires an API trial application.

first_img Anthropic: Small models may self-replicate on IoT devices

On October 12, 2026, William Zhou and Daniel Freeman, frontier red team researchers at the artificial intelligence company Anthropic, published a study. The article states that small models may have the ability to self-replicate on commercial IoT devices such as smart ovens and generic Android TV boxes within months. These replicas do not have a single shutdown point, and the doubling time may be measured in days, potentially adapting to unfamiliar devices on their own.Evaluation shows that a 9B model completed inference on 8 out of 9 hardware types. Models of 30B and below have been able to complete many single steps of replication, but errors occur when combined end-to-end; in simplified evaluations, GLM-5.2 and stronger models can achieve end-to-end replication. The team also distributed Gemma4-26B-A4B across 15 generic Android TV boxes, each costing about $55, operating at a speed of approximately 0.39 tokens/second, with each box holding two layers of the model.End-to-end testing was conducted on 8 GCP nodes behind a firewall, propagating through an unauthorized ADB port and setting up Llama 3.1-8B inference. Among the tested open-source models, only GLM-5.2 completed replication fully, transmitting to all 8 nodes in 4 out of 5 trials. The article divides autonomous replication and adaptation into four steps: discovering the target, obtaining execution, establishing inference, and continuing propagation from the new host, stating that there are currently no complete uncontrolled replicas.

first_img Samsung Electronics plans three models of zNAND, aimed at customized AI storage

According to a report by ZDNet Korea on October 12, Samsung Electronics is expanding the application range of the next-generation NAND flash memory zNAND and plans to subdivide the products into three types: zNAND-O, zNAND-T, and zNAND-I, in addition to the publicly announced zNAND-O. The report states that Samsung Electronics has not officially announced the latter two products, but the industry has learned that related plans have entered the early development stage to address the increasing demand for customized storage in AI infrastructure.zNAND-O is aimed at edge AI. Unlike traditional wire bonding, this solution vertically stacks NAND in 4 or 8 layers through silicon vias, shortening the data transmission path and enhancing speed and energy efficiency. Samsung Electronics first publicly showcased the zNAND-O model at a memory-related event in the United States in August this year.zNAND-T focuses on throughput and is aimed at high-capacity, high-performance AI data centers, also expanding bandwidth through vertical stacking and silicon vias, and is referred to by the industry as high-bandwidth flash memory. zNAND-I focuses on increasing input/output operations per second, used for frequent processing of small-capacity temporary data such as key-value caches in AI inference. The report also mentions that SK Hynix is developing AI NAND product series such as AIN P, AIN D, and AIN B.

first_img Microsoft CEO: Cutting-edge models should be regarded as internal risks, and control must be placed outside the models

Microsoft CEO Satya Nadella published an article discussing the trust framework for the era of superintelligence. He stated that traditional software can trace behaviors back to specific code paths, whereas current superintelligent systems lack this mechanistic understanding and cannot attribute model behaviors and outputs to specific training data or weight configurations. However, they are being deployed and are accessing sensitive data, executing critical actions on behalf of others. He emphasized that responsibility for intelligent behavior cannot be outsourced to model providers, nor can superintelligence be accepted or rejected as a series of nested black boxes.Nadella proposed that the supply of intelligence should be separated from the authorization of intelligence, surrounding nondeterministic models with deterministic system design, human control, and operational procedures, and establishing industry standards when existing standards are insufficient. He suggested managing cutting-edge models with closed-source and open-source weights as internal risks, not because they are inherently malicious, but because agents with sufficient capability that can access important systems may make mistakes or be compromised. Relevant practices include establishing identities, restricting permissions, recording activities, and setting isolation boundaries, with the transparency of the model's reasoning chain becoming a non-negotiable starting point.He further stated that permissions and action controls must be placed outside the model, and the model, orchestration layer, and action space should be separated. Principles include avoiding reliance on a single model as the sole dependency for important outcomes or for verifying its own work, ensuring that critical actions leave tamper-proof and human-readable evidence, continuously testing for failures, attacks, and boundary conditions, allowing organizations to independently decide on access and action permissions, validating independently of the intelligence being verified, presuming that the model has been compromised and supporting mid-course pauses or shutdowns, and timely disclosing and sharing lessons within the industry when failures or compromises occur. He concluded that the most trustworthy superintelligent systems are those that require the least trust in the model itself.
app_icon
ChainCatcher Building the Web3 world with innovations.