A Coding Implementation for an Agentic AI Framework that Performs Literature Analysis, Hypothesis Generation, Experimental Planning, Simulation, and Scientific ReportingMarkTechPost In this tutorial, we build a complete scientific discovery agent step by step and experience how each component works together to form a coherent research workflow. We begin by loading our literature corpus, constructing retrieval and LLM modules, and then assembling agents that search papers, generate hypotheses, design experiments, and produce structured reports. Through snippets
The post A Coding Implementation for an Agentic AI Framework that Performs Literature Analysis, Hypothesis Generation, Experimental Planning, Simulation, and Scientific Reporting appeared first on MarkTechPost.
In this tutorial, we build a complete scientific discovery agent step by step and experience how each component works together to form a coherent research workflow. We begin by loading our literature corpus, constructing retrieval and LLM modules, and then assembling agents that search papers, generate hypotheses, design experiments, and produce structured reports. Through snippets
The post A Coding Implementation for an Agentic AI Framework that Performs Literature Analysis, Hypothesis Generation, Experimental Planning, Simulation, and Scientific Reporting appeared first on MarkTechPost. Read More
How the MCP spec update boosts security as infrastructure scalesAI News The latest MCP spec update fortifies enterprise infrastructure with tighter security, moving AI agents from pilot to production. Marking its first year, the Anthropic-created open-source project released a revised spec this week aimed at the operational headaches keeping generative AI agents stuck in pilot mode. Backed by Amazon Web Services (AWS), Microsoft, and Google Cloud,
The post How the MCP spec update boosts security as infrastructure scales appeared first on AI News.
The latest MCP spec update fortifies enterprise infrastructure with tighter security, moving AI agents from pilot to production. Marking its first year, the Anthropic-created open-source project released a revised spec this week aimed at the operational headaches keeping generative AI agents stuck in pilot mode. Backed by Amazon Web Services (AWS), Microsoft, and Google Cloud,
The post How the MCP spec update boosts security as infrastructure scales appeared first on AI News. Read More
LightMem: Lightweight and Efficient Memory-Augmented Generationcs.AI updates on arXiv.org arXiv:2510.18866v3 Announce Type: replace-cross
Abstract: Despite their remarkable capabilities, Large Language Models (LLMs) struggle to effectively leverage historical interaction information in dynamic and complex environments. Memory systems enable LLMs to move beyond stateless interactions by introducing persistent information storage, retrieval, and utilization mechanisms. However, existing memory systems often introduce substantial time and computational overhead. To this end, we introduce a new memory system called LightMem, which strikes a balance between the performance and efficiency of memory systems. Inspired by the Atkinson-Shiffrin model of human memory, LightMem organizes memory into three complementary stages. First, cognition-inspired sensory memory rapidly filters irrelevant information through lightweight compression and groups information according to their topics. Next, topic-aware short-term memory consolidates these topic-based groups, organizing and summarizing content for more structured access. Finally, long-term memory with sleep-time update employs an offline procedure that decouples consolidation from online inference. On LongMemEval and LoCoMo, using GPT and Qwen backbones, LightMem consistently surpasses strong baselines, improving QA accuracy by up to 7.7% / 29.3%, reducing total token usage by up to 38x / 20.9x and API calls by up to 30x / 55.5x, while purely online test-time costs are even lower, achieving up to 106x / 117x token reduction and 159x / 310x fewer API calls. The code is available at https://github.com/zjunlp/LightMem.
arXiv:2510.18866v3 Announce Type: replace-cross
Abstract: Despite their remarkable capabilities, Large Language Models (LLMs) struggle to effectively leverage historical interaction information in dynamic and complex environments. Memory systems enable LLMs to move beyond stateless interactions by introducing persistent information storage, retrieval, and utilization mechanisms. However, existing memory systems often introduce substantial time and computational overhead. To this end, we introduce a new memory system called LightMem, which strikes a balance between the performance and efficiency of memory systems. Inspired by the Atkinson-Shiffrin model of human memory, LightMem organizes memory into three complementary stages. First, cognition-inspired sensory memory rapidly filters irrelevant information through lightweight compression and groups information according to their topics. Next, topic-aware short-term memory consolidates these topic-based groups, organizing and summarizing content for more structured access. Finally, long-term memory with sleep-time update employs an offline procedure that decouples consolidation from online inference. On LongMemEval and LoCoMo, using GPT and Qwen backbones, LightMem consistently surpasses strong baselines, improving QA accuracy by up to 7.7% / 29.3%, reducing total token usage by up to 38x / 20.9x and API calls by up to 30x / 55.5x, while purely online test-time costs are even lower, achieving up to 106x / 117x token reduction and 159x / 310x fewer API calls. The code is available at https://github.com/zjunlp/LightMem. Read More
Staying Ahead of AI in Your CareerKDnuggets The point is this: those who learn to collaborate with AI rather than fear it will hold the keys to tomorrow’s job market.
The point is this: those who learn to collaborate with AI rather than fear it will hold the keys to tomorrow’s job market. Read More
SAP outlines new approach to European AI and cloud sovereigntyAI News SAP is moving its sovereignty plans forward with EU AI Cloud, a setup meant to bring its past efforts under one approach. The goal is simple: give organisations in Europe more choice and more control over how they run AI and cloud services. Some may prefer SAP’s own data centres, some may use trusted European
The post SAP outlines new approach to European AI and cloud sovereignty appeared first on AI News.
SAP is moving its sovereignty plans forward with EU AI Cloud, a setup meant to bring its past efforts under one approach. The goal is simple: give organisations in Europe more choice and more control over how they run AI and cloud services. Some may prefer SAP’s own data centres, some may use trusted European
The post SAP outlines new approach to European AI and cloud sovereignty appeared first on AI News. Read More
Everyday Decisions are Noisier Than You Think — Here’s How AI Can Help Fix That Towards Data Science
Everyday Decisions are Noisier Than You Think — Here’s How AI Can Help Fix ThatTowards Data Science From insurance premiums to courtrooms: the impact of noise
The post Everyday Decisions are Noisier Than You Think — Here’s How AI Can Help Fix That appeared first on Towards Data Science.
From insurance premiums to courtrooms: the impact of noise
The post Everyday Decisions are Noisier Than You Think — Here’s How AI Can Help Fix That appeared first on Towards Data Science. Read More
Neural Networks Are Blurry, Symbolic Systems Are Fragmented. Sparse Autoencoders Help Us Combine Them.Towards Data Science Neural and symbolic models compress the world in fundamentally different ways, and Sparse Autoencoders (SAEs) offer a bridge to connect them.
The post Neural Networks Are Blurry, Symbolic Systems Are Fragmented. Sparse Autoencoders Help Us Combine Them. appeared first on Towards Data Science.
Neural and symbolic models compress the world in fundamentally different ways, and Sparse Autoencoders (SAEs) offer a bridge to connect them.
The post Neural Networks Are Blurry, Symbolic Systems Are Fragmented. Sparse Autoencoders Help Us Combine Them. appeared first on Towards Data Science. Read More
As IT environments become increasingly distributed and organizations adopt hybrid and remote work at scale, traditional perimeter-based security models and on-premises Privileged Access Management (PAM) solutions no longer suffice. IT administrators, contractors and third-party vendors now require secure access to critical systems from any location and on any device, without compromising Read More
Semantic Anchors in In-Context Learning: Why Small LLMs Cannot Flip Their Labelscs.AI updates on arXiv.org arXiv:2511.21038v1 Announce Type: cross
Abstract: Can in-context learning (ICL) override pre-trained label semantics, or does it merely refine an existing semantic backbone? We address this question by treating LLMs as prompt-induced classifiers and contrasting their behavior under emph{natural} demonstrations (with correct labels) and emph{inverted} demonstrations (systematically flipping label meanings). We decompose ICL behavior into three alignment metrics (truth, prior, and prompt alignment) and introduce a semantic override rate, defined as correctness under flipped semantics. Across eight classification tasks and eight open-source LLMs (1–12B parameters), we find consistent evidence for a semantic anchor view. With natural demonstrations, ICL improves accuracy while maintaining strong prior alignment; most correct predictions coincide with zero-shot behavior, even when the prior is weak. With inverted demonstrations, models cannot learn coherent anti-semantic classifiers: prompt alignment increases only by sacrificing accuracy, and semantic override rates remain exactly zero in our few-shot 1–12B setting. Rather than flexibly remapping label meanings, ICL primarily adjusts how inputs project onto stable semantic directions learned during pre-training, clarifying fundamental limits of few-shot prompting and suggesting that overriding label semantics at these scales requires interventions beyond ICL. All code is available at: https://github.com/AnanthaPadmanaban-KrishnaKumar/semantic-anchors-icl.
arXiv:2511.21038v1 Announce Type: cross
Abstract: Can in-context learning (ICL) override pre-trained label semantics, or does it merely refine an existing semantic backbone? We address this question by treating LLMs as prompt-induced classifiers and contrasting their behavior under emph{natural} demonstrations (with correct labels) and emph{inverted} demonstrations (systematically flipping label meanings). We decompose ICL behavior into three alignment metrics (truth, prior, and prompt alignment) and introduce a semantic override rate, defined as correctness under flipped semantics. Across eight classification tasks and eight open-source LLMs (1–12B parameters), we find consistent evidence for a semantic anchor view. With natural demonstrations, ICL improves accuracy while maintaining strong prior alignment; most correct predictions coincide with zero-shot behavior, even when the prior is weak. With inverted demonstrations, models cannot learn coherent anti-semantic classifiers: prompt alignment increases only by sacrificing accuracy, and semantic override rates remain exactly zero in our few-shot 1–12B setting. Rather than flexibly remapping label meanings, ICL primarily adjusts how inputs project onto stable semantic directions learned during pre-training, clarifying fundamental limits of few-shot prompting and suggesting that overriding label semantics at these scales requires interventions beyond ICL. All code is available at: https://github.com/AnanthaPadmanaban-KrishnaKumar/semantic-anchors-icl. Read More
AI4X Roadmap: Artificial Intelligence for the advancement of scientific pursuit and its future directionscs.AI updates on arXiv.org arXiv:2511.20976v1 Announce Type: cross
Abstract: Artificial intelligence and machine learning are reshaping how we approach scientific discovery, not by replacing established methods but by extending what researchers can probe, predict, and design. In this roadmap we provide a forward-looking view of AI-enabled science across biology, chemistry, climate science, mathematics, materials science, physics, self-driving laboratories and unconventional computing. Several shared themes emerge: the need for diverse and trustworthy data, transferable electronic-structure and interatomic models, AI systems integrated into end-to-end scientific workflows that connect simulations to experiments and generative systems grounded in synthesisability rather than purely idealised phases. Across domains, we highlight how large foundation models, active learning and self-driving laboratories can close loops between prediction and validation while maintaining reproducibility and physical interpretability. Taken together, these perspectives outline where AI-enabled science stands today, identify bottlenecks in data, methods and infrastructure, and chart concrete directions for building AI systems that are not only more powerful but also more transparent and capable of accelerating discovery in complex real-world environments.
arXiv:2511.20976v1 Announce Type: cross
Abstract: Artificial intelligence and machine learning are reshaping how we approach scientific discovery, not by replacing established methods but by extending what researchers can probe, predict, and design. In this roadmap we provide a forward-looking view of AI-enabled science across biology, chemistry, climate science, mathematics, materials science, physics, self-driving laboratories and unconventional computing. Several shared themes emerge: the need for diverse and trustworthy data, transferable electronic-structure and interatomic models, AI systems integrated into end-to-end scientific workflows that connect simulations to experiments and generative systems grounded in synthesisability rather than purely idealised phases. Across domains, we highlight how large foundation models, active learning and self-driving laboratories can close loops between prediction and validation while maintaining reproducibility and physical interpretability. Taken together, these perspectives outline where AI-enabled science stands today, identify bottlenecks in data, methods and infrastructure, and chart concrete directions for building AI systems that are not only more powerful but also more transparent and capable of accelerating discovery in complex real-world environments. Read More
