Researchers from the People's Public Security University of China have developed an AI algorithm to track Bitcoin money laundering, with an overall accuracy rate of approximately 90%.

According to Reuters, Binance is collaborating with the U.S. Federal Bureau of Investigation (FBI) and the U.S. Secret Service to probe a $6.3 billion cryptocurrency theft case.

Binance said in a statement that it has begun recovering affected assets for its users, and continues to collaborate with law enforcement agencies, asset issuers, blockchain intelligence firms, and industry partners to track, freeze, and recover additional funds. Binance added that the company has set aside over $6.34 billion to cover losses from the incident, with $5 billion already held in tokens identical to the affected assets. The remaining amount is being purchased via market channels and over-the-counter (OTC) platforms to match the token types of the remaining affected assets and fill the asset gap. Due to low liquidity of some tokens and the time required for OTC settlements, the related purchases may take an extended period. The above sum does not include assets that have already been frozen or protected, which are still under ongoing recovery and verification. To date, $3 billion in assets have been frozen, and Binance is working with partners and law enforcement to recover these funds. Binance stated that it will continue to deploy its corporate resources to cover confirmed funding gaps, and will release more verified information as recovery efforts advance. Ultimately, all user funds will be verified through a public proof-of-reserves mechanism.

He Yi released a statement addressing the status of AAVE-related collateral funds, clarifying: The 60 million USDT collateralized in AAVE is not fully frozen; hackers only stole approximately $46 million worth of ETH. Once these assets are unfrozen through law enforcement processes, an additional roughly $14 million in assets will be recoverable.

Beating AI News Flash: French AI company Mistral has officially open-sourced the weights of its flagship model Large 4 (codenamed Le Chonk). The model has a total of 1.05 trillion parameters, with approximately 49 billion activated per inference. After opening API previews on October 6, Mistral conducted three additional weeks of reinforcement learning training, and the version released now is an improved iteration. Mistral reported its Terminal-Bench 4.0 programming score rose from 28% to 41%; FrontierCode 2.0 scored 19.7%, ranking first among open-source models. In Artificial Analysis' programming agent index, Large 4 scored 48%, close to GLM-5.3's 49%, while consuming 47% fewer tokens per average question. However, Artificial Analysis only recorded a 25% score for Terminal-Bench 4.0 under its own test configuration, failing to replicate the official 41% result. In other tests, financial table task Finch's score increased from 67.4% to 73.3%; the agent's success rate in resisting prompt injection attacks rose from 93.3% to 97.1%. Its cybersecurity index stands at 51%, still lower than DeepSeek V4.1 Flash's 57%, with more modest overall capability improvements. Artificial Analysis has just upgraded its intelligence index to v5. On the same metric, Large 4's score rose from 25 in the preview version to 26, below GLM-5.3 and DeepSeek V4.1 Flash's 32 points. But inference efficiency improved significantly: output tokens per question dropped by 39%, and hallucination rate fell from 41% to 28%. API prices were also nearly halved. Based on October 29 evaluation pricing and usage estimates, per-question cost fell from $5.54 to $2.06, a 63% reduction. The model weights have been uploaded to Hugging Face, offering FP8 and NVFP4 versions, supporting self-deployment, fine-tuning, and general commercial use. Separate authorization from Mistral is required to operate a commercial model hosting service.

According to TradingBeats monitoring, a whale has concentrated on adding short positions in US stock index futures over the past half hour. The combined notional value of new short positions opened in Nasdaq 100 and S&P 500 contracts totals approximately $9.51 million, with the S&P 500 position switching from long to short. The account currently holds around $7.29 million in XYZ100 short positions at an average price of $28,796.94, plus roughly $2.4 million in S&P 500 short positions at an average price of $7,646.54, bringing the total value of the two short positions to about $9.7 million.

According to TradingBeats' monitoring, a whale has been steadily adding short positions on Intel (INTC) today, lifting its short position count from around 79,900 contracts to 146,000, an 82.7% increase. Since the start of today, the account has added a net total of roughly 66,100 INTC short positions, with a net sell turnover of approximately $4.58 million. During US pre-market trading hours, the whale further net sold around $2.37 million at an average execution price of $68.07, a 31.3% rise. The account currently holds about 146,000 INTC short positions, valued at roughly $10.058 million, with an average entry price of $79.52, generating an unrealized profit of around $1.549 million. Over the same period, INTC contracts trade at $68.91, some 13.3% lower than the position's overall holding cost. The position uses 10x isolated margin leverage, with a liquidation price of approximately $82.46.