Hackers are exploiting a new, undocumented vulnerability in the implementation of the cryptographic algorithm present in Gladinet’s CentreStack and Triofox products for secure remote file access and sharing. […] Read More
An unpatched zero-day vulnerability in Gogs, a popular self-hosted Git service, has enabled attackers to gain remote code execution on Internet-facing instances and compromise hundreds of servers. […] Read More
Cybersecurity researchers have disclosed details of a new fully-featured Windows backdoor called NANOREMOTE that uses the Google Drive API for command-and-control (C2) purposes. According to a report from Elastic Security Labs, the malware shares code similarities with another implant codenamed FINALDRAFT (aka Squidoor) that employs Microsoft Graph API for C2. FINALDRAFT is attributed to a Read […]
OpenAI Introduces GPT 5.2: A Long Context Workhorse For Agents, Coding And Knowledge WorkMarkTechPost OpenAI has just introduced GPT-5.2, its most advanced frontier model for professional work and long running agents, and is rolling it out across ChatGPT and the API. GPT-5.2 is a family of three variants. In ChatGPT, users see ChatGPT-5.2 Instant, Thinking and Pro. In the API, the corresponding models are gpt-5.2-chat-latest, gpt-5.2, and gpt-5.2-pro. Instant
The post OpenAI Introduces GPT 5.2: A Long Context Workhorse For Agents, Coding And Knowledge Work appeared first on MarkTechPost.
OpenAI has just introduced GPT-5.2, its most advanced frontier model for professional work and long running agents, and is rolling it out across ChatGPT and the API. GPT-5.2 is a family of three variants. In ChatGPT, users see ChatGPT-5.2 Instant, Thinking and Pro. In the API, the corresponding models are gpt-5.2-chat-latest, gpt-5.2, and gpt-5.2-pro. Instant
The post OpenAI Introduces GPT 5.2: A Long Context Workhorse For Agents, Coding And Knowledge Work appeared first on MarkTechPost. Read More
CopilotKit v1.50 Brings AG-UI Agents Directly Into Your App With the New useAgent HookMarkTechPost Agent frameworks are now good at reasoning and tools, but most teams still write custom code to turn agent graphs into robust user interfaces with shared state, streaming output and interrupts. CopilotKit targets this last mile. It is an open source framework for building AI copilots and in-app agents directly in your app, with real
The post CopilotKit v1.50 Brings AG-UI Agents Directly Into Your App With the New useAgent Hook appeared first on MarkTechPost.
Agent frameworks are now good at reasoning and tools, but most teams still write custom code to turn agent graphs into robust user interfaces with shared state, streaming output and interrupts. CopilotKit targets this last mile. It is an open source framework for building AI copilots and in-app agents directly in your app, with real
The post CopilotKit v1.50 Brings AG-UI Agents Directly Into Your App With the New useAgent Hook appeared first on MarkTechPost. Read More
Brave has introduced a new AI browsing feature that leverages Leo, its privacy-respecting AI assistant, to perform automated tasks for the user. […] Read More
Grounding the Ungrounded: A Spectral-Graph Framework for Quantifying Hallucinations in Multimodal LLMscs.AI updates on arXiv.org arXiv:2508.19366v4 Announce Type: replace-cross
Abstract: Hallucinations in LLMs–especially in multimodal settings–undermine reliability. We present a rigorous information-geometric framework, grounded in diffusion dynamics, to quantify hallucinations in MLLMs where model outputs are embedded via spectral decompositions of multimodal graph Laplacians, and their gaps to a truth manifold define a semantic distortion metric. We derive Courant-Fischer bounds on a temperature-dependent hallucination profile and use RKHS eigenmodes to obtain modality-aware, interpretable measures that track evolution over prompts and time. This reframes hallucination as quantifiable and bounded, providing a principled basis for evaluation and mitigation.
arXiv:2508.19366v4 Announce Type: replace-cross
Abstract: Hallucinations in LLMs–especially in multimodal settings–undermine reliability. We present a rigorous information-geometric framework, grounded in diffusion dynamics, to quantify hallucinations in MLLMs where model outputs are embedded via spectral decompositions of multimodal graph Laplacians, and their gaps to a truth manifold define a semantic distortion metric. We derive Courant-Fischer bounds on a temperature-dependent hallucination profile and use RKHS eigenmodes to obtain modality-aware, interpretable measures that track evolution over prompts and time. This reframes hallucination as quantifiable and bounded, providing a principled basis for evaluation and mitigation. Read More
Story of Two GPUs: Characterizing the Resilience of Hopper H100 and Ampere A100 GPUscs.AI updates on arXiv.org arXiv:2503.11901v4 Announce Type: replace-cross
Abstract: This study characterizes GPU resilience in Delta, a large-scale AI system that consists of 1,056 A100 and H100 GPUs, with over 1,300 petaflops of peak throughput. We used 2.5 years of operational data (11.7 million GPU hours) on GPU errors. Our major findings include: (i) H100 GPU memory resilience is worse than A100 GPU memory, with 3.2x lower per-GPU MTBE for memory errors, (ii) The GPU memory error-recovery mechanisms on H100 GPUs are insufficient to handle the increased memory capacity, (iii) H100 GPUs demonstrate significantly improved GPU hardware resilience over A100 GPUs with respect to critical hardware components, (iv) GPU errors on both A100 and H100 GPUs frequently result in job failures due to the lack of robust recovery mechanisms at the application level, and (v) We project the impact of GPU node availability on larger-scales and find that significant overprovisioning of 5% is necessary to handle GPU failures.
arXiv:2503.11901v4 Announce Type: replace-cross
Abstract: This study characterizes GPU resilience in Delta, a large-scale AI system that consists of 1,056 A100 and H100 GPUs, with over 1,300 petaflops of peak throughput. We used 2.5 years of operational data (11.7 million GPU hours) on GPU errors. Our major findings include: (i) H100 GPU memory resilience is worse than A100 GPU memory, with 3.2x lower per-GPU MTBE for memory errors, (ii) The GPU memory error-recovery mechanisms on H100 GPUs are insufficient to handle the increased memory capacity, (iii) H100 GPUs demonstrate significantly improved GPU hardware resilience over A100 GPUs with respect to critical hardware components, (iv) GPU errors on both A100 and H100 GPUs frequently result in job failures due to the lack of robust recovery mechanisms at the application level, and (v) We project the impact of GPU node availability on larger-scales and find that significant overprovisioning of 5% is necessary to handle GPU failures. Read More
SnapStream: Efficient Long Sequence Decoding on Dataflow Acceleratorscs.AI updates on arXiv.org arXiv:2511.03092v5 Announce Type: replace
Abstract: The proliferation of 100B+ parameter Large Language Models (LLMs) with 100k+ context length support have resulted in increasing demands for on-chip memory to support large KV caches. Techniques such as StreamingLLM and SnapKV demonstrate how to control KV cache size while maintaining model accuracy. Yet, these techniques are not commonly used within industrial deployments using frameworks like vLLM or SGLang. The reason is twofold: on one hand, the static graphs and continuous batching methodology employed by these frameworks make it difficult to admit modifications to the standard multi-head attention algorithm, while on the other hand, the accuracy implications of such techniques on modern instruction-following and reasoning models are not well understood, obfuscating the need for implementing these techniques. In this paper, we explore these accuracy implications on Llama-3.1-8B-Instruct and DeepSeek-R1, and develop SnapStream, a KV cache compression method that can be deployed at scale. We demonstrate the efficacy of SnapStream in a 16-way tensor-parallel deployment of DeepSeek-671B on SambaNova SN40L accelerators running at 128k context length and up to 1832 tokens per second in a real production setting. SnapStream enables $4times$ improved on-chip memory usage and introduces minimal accuracy degradation on LongBench-v2, AIME24 and LiveCodeBench. To the best of our knowledge, this is the first implementation of sparse KV attention techniques deployed in a production inference system with static graphs and continuous batching.
arXiv:2511.03092v5 Announce Type: replace
Abstract: The proliferation of 100B+ parameter Large Language Models (LLMs) with 100k+ context length support have resulted in increasing demands for on-chip memory to support large KV caches. Techniques such as StreamingLLM and SnapKV demonstrate how to control KV cache size while maintaining model accuracy. Yet, these techniques are not commonly used within industrial deployments using frameworks like vLLM or SGLang. The reason is twofold: on one hand, the static graphs and continuous batching methodology employed by these frameworks make it difficult to admit modifications to the standard multi-head attention algorithm, while on the other hand, the accuracy implications of such techniques on modern instruction-following and reasoning models are not well understood, obfuscating the need for implementing these techniques. In this paper, we explore these accuracy implications on Llama-3.1-8B-Instruct and DeepSeek-R1, and develop SnapStream, a KV cache compression method that can be deployed at scale. We demonstrate the efficacy of SnapStream in a 16-way tensor-parallel deployment of DeepSeek-671B on SambaNova SN40L accelerators running at 128k context length and up to 1832 tokens per second in a real production setting. SnapStream enables $4times$ improved on-chip memory usage and introduces minimal accuracy degradation on LongBench-v2, AIME24 and LiveCodeBench. To the best of our knowledge, this is the first implementation of sparse KV attention techniques deployed in a production inference system with static graphs and continuous batching. Read More
Enhanced Sentiment Interpretation via a Lexicon-Fuzzy-Transformer Frameworkcs.AI updates on arXiv.org arXiv:2510.15843v2 Announce Type: replace-cross
Abstract: Accurately detecting sentiment polarity and intensity in product reviews and social media posts remains challenging due to informal and domain-specific language. To address this, we propose a novel hybrid lexicon-fuzzy-transformer framework that combines rule-based heuristics, contextual deep learning, and fuzzy logic to generate continuous sentiment scores reflecting both polarity and strength. The pipeline begins with VADER-based initial sentiment estimations, which are refined through a two-stage adjustment process. This involves leveraging confidence scores from DistilBERT, a lightweight transformer and applying fuzzy logic principles to mitigate excessive neutrality bias and enhance granularity. A custom fuzzy inference system then maps the refined scores onto a 0 to 1 continuum, producing expert)like judgments. The framework is rigorously evaluated on four domain-specific datasets. food delivery, e-commerce, tourism, and fashion. Results show improved alignment with user ratings, better identification of sentiment extremes, and reduced misclassifications. Both quantitative metrics (distributional alignment, confusion matrices) and qualitative insights (case studies, runtime analysis) affirm the models robustness and efficiency. This work demonstrates the value of integrating symbolic reasoning with neural models for interpretable, finegrained sentiment analysis in linguistically dynamic domains.
arXiv:2510.15843v2 Announce Type: replace-cross
Abstract: Accurately detecting sentiment polarity and intensity in product reviews and social media posts remains challenging due to informal and domain-specific language. To address this, we propose a novel hybrid lexicon-fuzzy-transformer framework that combines rule-based heuristics, contextual deep learning, and fuzzy logic to generate continuous sentiment scores reflecting both polarity and strength. The pipeline begins with VADER-based initial sentiment estimations, which are refined through a two-stage adjustment process. This involves leveraging confidence scores from DistilBERT, a lightweight transformer and applying fuzzy logic principles to mitigate excessive neutrality bias and enhance granularity. A custom fuzzy inference system then maps the refined scores onto a 0 to 1 continuum, producing expert)like judgments. The framework is rigorously evaluated on four domain-specific datasets. food delivery, e-commerce, tourism, and fashion. Results show improved alignment with user ratings, better identification of sentiment extremes, and reduced misclassifications. Both quantitative metrics (distributional alignment, confusion matrices) and qualitative insights (case studies, runtime analysis) affirm the models robustness and efficiency. This work demonstrates the value of integrating symbolic reasoning with neural models for interpretable, finegrained sentiment analysis in linguistically dynamic domains. Read More
