Hy4

The Decoder·Nov 5, 2026

Tencent releases Hy4 with vision and computer use, claims second place among Chinese AI models

SiliconANGLE AI·Nov 4, 2026

Tencent releases Hy4, powerful frontier LLM introducing visual understanding and computer use

MarkTechPost·Oct 17, 2026

DeepSeek Just Released V4.1-Flash-1017, Scores 77.0 on DeepSWE, Opens Search API

DeepSeek has released DeepSeek-V4.1-Flash-1017, a new checkpoint of its V4.1-Flash model, available on its website, app, and API. The company states this version offers stronger scores, faster task completion, and shorter outputs compared to the September release, describing it as a Pareto improvement in performance and cost. Alongside the model update, DeepSeek has opened its Search and Fetch APIs to all developers. This provides agent builders with access to a more cost-effective coding model and a first-party web layer accessible through a single API key. DeepSeek-V4.1-Flash-1017 features a 552 billion backbone parameter count and 196 billion Engram parameters, activating 8 billion parameters per token during prefill and 16 billion during decode. It supports a context window of 1 million tokens with up to 384,000 output tokens. The model, served as deepseek-flash on the DeepSeek API, reportedly beats the previous V4.1-Flash on all 11 reported benchmarks, with improvements ranging from 2.0 to 11.1 points. Its best score is 77.0 on DeepSWE v1.1, surpassing Claude Opus 5's score of 74.0. DeepSeek-V4.1-Flash-1017 is an updated post-training checkpoint of DeepSeek-V4.1-Flash, utilizing reinforcement learning with self-feedback for end-to-end project work, including tasks like game creation and 3D modeling. It is tuned for DeepSeek Harness, Tencent CodeBuddy, and WorkBuddy.

MarkTechPost·Oct 8, 2026

Perplexity AI Releases pplx-embed-v2-late: A 0.6B Edge Model and a 9B Model Scoring 92.4% on MADQA

Perplexity AI released pplx-embed-v2-late, a new suite of ColBERT-style multimodal embedding models, on an unspecified date, offering two distinct sizes: a 0.6 billion parameter model optimized for speed and cost-efficiency, and a 9 billion parameter model designed for maximum quality. Both models are capable of retrieving text, images, and rendered PDF pages, and importantly, they share a unified embedding space, allowing for cross-modal understanding. These models are available for self-hosting under the MIT license, which permits commercial use, and are accessible on Hugging Face. Perplexity has indicated plans for a hosted API endpoint, though it is not yet live. The 0.6B model is engineered to function as a lightweight query encoder, suitable for deployment on edge devices or laptops, utilizing approximately 340 million active parameters for image processing. This smaller model aims to maintain performance close to larger, 8 billion parameter rivals. A key feature highlighted is the ability to search a 9B index using 0.6B queries, which recovers about half of the quality gap observed in text retrieval at the reduced query cost. The models generate 128-dimensional token vectors, significantly narrower than competitors' vectors which range from 2,048 to 4,096 dimensions, representing a 16x to 32x reduction in dimensionality. However, the models present certain limitations. The architecture stores one vector per token, leading to index sizes that scale directly with document length. In terms of performance benchmarks, pplx-embed-v2-late is not the top performer on the ViDoRe v3 image retrieval task; Tencent's EVIE model reportedly scores higher. Furthermore, a single input cannot simultaneously contain both text and images. Perplexity has stated that all performance scores are self-reported, and the accompanying technical report has not yet been published. Technical specifications reveal that the pplx-embed-v2-late-0.6b model comprises 594 million total parameters, with approximately 240 million active parameters for text and 340 million for images. The base model is derived from Qwen3.5-0.8B, pruned to 12 text layers. The 9B variant has 9 billion total parameters (though Hugging Face lists it as 8B) and utilizes 7.4 billion parameters. Both output 128 dimensions per token. Estimated memory requirements for weights in bf16 precision are around 1.2 GB for the 0.6B model and 16 to 18 GB for the 9B model. The models require sentence-transformers version 6.0.0 or later and transformers version 5.4.0 or later. The published checkpoints are distributed in F32 format, which doubles their download size.

The Hacker News·Sep 11, 2026

China-Linked UNC3569 Exploited Sogou Input Method Flaw to Deploy GRAYRABBIT Backdoor

A China-linked cyber-espionage group, identified as UNC3569, has successfully exploited a critical vulnerability within the Sogou Input Method, a widely adopted software for typing Chinese characters on Windows operating systems. This exploitation allowed the group to deploy a sophisticated backdoor, known as GRAYRABBIT, onto the compromised systems, according to research published by Gen Digital on Thursday. The initial vector for this attack involved a meticulously crafted link, which, upon interaction by the victim, initiated a chain of malicious actions. The ultimate outcome of the compromise granted the attackers the same level of access and control as the logged-in user, enabling them to perform any action within the user's privileges. This level of access is highly concerning as it can facilitate extensive data exfiltration, lateral movement within a network, and the deployment of further malicious payloads. Sogou Input Method is a prevalent tool used by millions of users, particularly in China, making this vulnerability a significant threat to a large user base. The company Tencent, which owns Sogou, has not yet issued a public statement regarding the exploit or the specific vulnerability. The GRAYRABBIT backdoor is described as a versatile tool that can be used for various malicious purposes, including surveillance, data theft, and maintaining persistent access to compromised environments. The group's modus operandi suggests a focus on espionage, aiming to gather intelligence from targeted individuals or organizations. The exploitation of input method editors (IMEs) like Sogou presents a unique attack surface, as these tools are deeply integrated into the operating system and often run with elevated privileges. Attackers leveraging such vulnerabilities can bypass traditional security measures more effectively. Gen Digital's research highlights the ongoing sophistication of nation-state-backed hacking groups and their ability to identify and exploit niche software vulnerabilities. The detailed analysis of the attack chain underscores the importance of timely patching and robust endpoint security solutions to mitigate such threats. The implications of this attack extend beyond individual users, potentially impacting businesses and government entities that rely on Sogou Input Method for their operations. The ability of UNC3569 to deploy a backdoor with such extensive privileges raises concerns about the potential for long-term surveillance and data compromise. Further investigation into the specific CVE or vulnerability identifier exploited is crucial for understanding the technical details of the attack and for developing effective countermeasures. The incident serves as a stark reminder of the persistent threat posed by advanced persistent threats (APTs) and the need for continuous vigilance in the cybersecurity landscape.