Apple turns to Google to power AI upgrade for Siri

Summary: Analysts say the deal is likely to be welcomed by consumers - but reflects Apple's failure to develop its own AI tools.


X could face UK ban over deepfakes, minister says

Summary: It comes after government urged Ofcom to use all its powers – up to and including an effective ban – against X.


Inside the sub-zero lair of the world's most powerful computer

Summary: Faisal Islam gets rare access to Willow - Google's quantum computer.


Nvidia unveils self-driving car tech as it seeks to power more products with AI

Summary: The chip giant is looking for ways to move the AI revolution beyond software and into physical products.


'It's amazing' – the wonder material very few can make

Summary: Just a handful of companies can make cadmium zinc telluride, a material with powerful properties.


The End of Reward Engineering: How LLMs Are Redefining Multi-Agent Coordination

Summary: arXiv:2601.08237v1 Announce Type: new Abstract: Reward engineering, the manual specification of reward functions to induce desired agent behavior, remains a fundamental challenge in multi-agent reinforcement learning. This difficulty is amplified by credit assignment ambiguity, environmental non-stationarity, and the combinatorial growth of interaction complexity. We argue that recent advances in large language models (LLMs) point toward a shift from hand-crafted numerical rewards to language-based objective specifications. Prior work has shown that LLMs can synthesize reward functions directl


ToolACE-MCP: Generalizing History-Aware Routing from MCP Tools to the Agent Web

Summary: arXiv:2601.08276v1 Announce Type: new Abstract: With the rise of the Agent Web and Model Context Protocol (MCP), the agent ecosystem is evolving into an open collaborative network, exponentially increasing accessible tools. However, current architectures face severe scalability and generality bottlenecks. To address this, we propose ToolACE-MCP, a pipeline for training history-aware routers to empower precise navigation in large-scale ecosystems. By leveraging a dependency-rich candidate Graph to synthesize multi-turn trajectories, we effectively train routers with dynamic context understandin


Thematic Working Group 5 -- Artificial Intelligence (AI) literacy for teaching and learning: design and implementation

Summary: arXiv:2601.08380v1 Announce Type: new Abstract: TWG 5 focused on developing and implementing effective strategies for enhancing AI literacy and agency of teachers, equipping them with the knowledge and skills necessary to integrate AI into their teaching practices. Explorations covered curriculum design, professional development programs, practical classroom applications, and policy guidelines aiming to empower educators to confidently utilize AI tools and foster a deeper understanding of AI concepts among students.


Advancing ESG Intelligence: An Expert-level Agent and Comprehensive Benchmark for Sustainable Finance

Summary: arXiv:2601.08676v2 Announce Type: new Abstract: Environmental, social, and governance (ESG) criteria are essential for evaluating corporate sustainability and ethical performance. However, professional ESG analysis is hindered by data fragmentation across unstructured sources, and existing large language models (LLMs) often struggle with the complex, multi-step workflows required for rigorous auditing. To address these limitations, we introduce ESGAgent, a hierarchical multi-agent system empowered by a specialized toolset, including retrieval augmentation, web search and domain-specific functi


FinVault: Benchmarking Financial Agent Safety in Execution-Grounded Environments

Summary: arXiv:2601.07853v1 Announce Type: cross Abstract: Financial agents powered by large language models (LLMs) are increasingly deployed for investment analysis, risk assessment, and automated decision-making, where their abilities to plan, invoke tools, and manipulate mutable state introduce new security risks in high-stakes and highly regulated financial environments. However, existing safety evaluations largely focus on language-model-level content compliance or abstract agent settings, failing to capture execution-grounded risks arising from real operational workflows and state-changing action


Tackling Heterogeneity in Quantum Federated Learning: An Integrated Sporadic-Personalized Approach

Summary: arXiv:2601.07882v1 Announce Type: cross Abstract: Quantum federated learning (QFL) emerges as a powerful technique that combines quantum computing with federated learning to efficiently process complex data across distributed quantum devices while ensuring data privacy in quantum networks. Despite recent research efforts, existing QFL frameworks struggle to achieve optimal model training performance primarily due to inherent heterogeneity in terms of (i) quantum noise where current quantum devices are subject to varying levels of noise due to varying device quality and susceptibility to quantu


Explainable Autoencoder-Based Anomaly Detection in IEC 61850 GOOSE Networks

Summary: arXiv:2601.09287v1 Announce Type: new Abstract: The IEC 61850 Generic Object-Oriented Substation Event (GOOSE) protocol plays a critical role in real-time protection and automation of digital substations, yet its lack of native security mechanisms can expose power systems to sophisticated cyberattacks. Traditional rule-based and supervised intrusion detection techniques struggle to detect protocol-compliant and zero-day attacks under significant class imbalance and limited availability of labeled data. This paper proposes an explainable, unsupervised multi-view anomaly detection framework for


SpatialJB: How Text Distribution Art Becomes the "Jailbreak Key" for LLM Guardrails

Summary: arXiv:2601.09321v1 Announce Type: new Abstract: While Large Language Models (LLMs) have powerful capabilities, they remain vulnerable to jailbreak attacks, which is a critical barrier to their safe web real-time application. Current commercial LLM providers deploy output guardrails to filter harmful outputs, yet these defenses are not impenetrable. Due to LLMs' reliance on autoregressive, token-by-token inference, their semantic representations lack robustness to spatially structured perturbations, such as redistributing tokens across different rows, columns, or diagonals. Exploiting the Trans


zkSTAR: A zero knowledge system for time series attack detection enforcing regulatory compliance in critical infrastructure networks

Summary: arXiv:2510.23060v3 Announce Type: replace Abstract: Industrial control systems (ICS) form the operational backbone of critical infrastructure networks (CIN) such as power grids, water supply systems, and gas pipelines. As cyber threats to these systems escalate, regulatory agencies are imposing stricter compliance requirements to ensure system-wide security and reliability. A central challenge, however, is enabling regulators to verify the effectiveness of detection mechanisms without requiring utilities to disclose sensitive operational data. In this paper, we introduce zkSTAR, a cyberattack


QueryIPI: Query-agnostic Indirect Prompt Injection on Coding Agents

Summary: arXiv:2510.23675v3 Announce Type: replace Abstract: Modern coding agents integrated into IDEs orchestrate powerful tools and high-privilege system access, creating a high-stakes attack surface. Prior work on Indirect Prompt Injection (IPI) is mainly query-specific, requiring particular user queries as triggers and leading to poor generalizability. We propose query-agnostic IPI, a new attack paradigm that reliably executes malicious payloads under arbitrary user queries. Our key insight is that malicious payloads should leverage the invariant prompt context (i.e., system prompt and tool descrip


Red Teaming Large Reasoning Models

Summary: arXiv:2512.00412v3 Announce Type: replace Abstract: Large Reasoning Models (LRMs) have emerged as a powerful advancement in multi-step reasoning tasks, offering enhanced transparency and logical consistency through explicit chains of thought (CoT). However, these models introduce novel safety and reliability risks, such as CoT-hijacking and prompt-induced inefficiencies, which are not fully captured by existing evaluation methods. To address this gap, we propose RT-LRM, a unified benchmark designed to assess the trustworthiness of LRMs. RT-LRM evaluates three core dimensions: truthfulness, saf


Bridging the Gap: Empowering Small Models in Reliable OpenACC-based Parallelization via GEPA-Optimized Prompting

Summary: arXiv:2601.08884v1 Announce Type: new Abstract: OpenACC lowers the barrier to GPU offloading, but writing high-performing pragma remains complex, requiring deep domain expertise in memory hierarchies, data movement, and parallelization strategies. Large Language Models (LLMs) present a promising potential solution for automated parallel code generation, but naive prompting often results in syntactically incorrect directives, uncompilable code, or performance that fails to exceed CPU baselines. We present a systematic prompt optimization approach to enhance OpenACC pragma generation without the


AI-NativeBench: An Open-Source White-Box Agentic Benchmark Suite for AI-Native Systems

Summary: arXiv:2601.09393v1 Announce Type: new Abstract: The transition from Cloud-Native to AI-Native architectures is fundamentally reshaping software engineering, replacing deterministic microservices with probabilistic agentic services. However, this shift renders traditional black-box evaluation paradigms insufficient: existing benchmarks measure raw model capabilities while remaining blind to system-level execution dynamics. To bridge this gap, we introduce AI-NativeBench, the first application-centric and white-box AI-Native benchmark suite grounded in Model Context Protocol (MCP) and Agent-to-A


How well LLM-based test generation techniques perform with newer LLM versions?

Summary: arXiv:2601.09695v1 Announce Type: new Abstract: The rapid evolution of Large Language Models (LLMs) has strongly impacted software engineering, leading to a growing number of studies on automated unit test generation. However, the standalone use of LLMs without post-processing has proven insufficient, often producing tests that fail to compile or achieve high coverage. Several techniques have been proposed to address these issues, reporting improvements in test compilation and coverage. While important, LLM-based test generation techniques have been evaluated against relatively weak baselines


Enabling Global, Human-Centered Explanations for LLMs:From Tokens to Interpretable Code and Test Generation

Summary: arXiv:2503.16771v2 Announce Type: replace Abstract: As Large Language Models for Code (LM4Code) become integral to software engineering, establishing trust in their output becomes critical. However, standard accuracy metrics obscure the underlying reasoning of generative models, offering little insight into how decisions are made. Although post-hoc interpretability methods attempt to fill this gap, they often restrict explanations to local, token-level insights, which fail to provide a developer-understandable global analysis. Our work highlights the urgent need for \textbf{global, code-based}


Apple turns to Google to power AI upgrade for Siri

Summary: Analysts say the deal is likely to be welcomed by consumers - but reflects Apple's failure to develop its own AI tools.


X could face UK ban over deepfakes, minister says

Summary: It comes after government urged Ofcom to use all its powers – up to and including an effective ban – against X.


Inside the sub-zero lair of the world's most powerful computer

Summary: Faisal Islam gets rare access to Willow - Google's quantum computer.


Nvidia unveils self-driving car tech as it seeks to power more products with AI

Summary: The chip giant is looking for ways to move the AI revolution beyond software and into physical products.


'It's amazing' – the wonder material very few can make

Summary: Just a handful of companies can make cadmium zinc telluride, a material with powerful properties.


AI Survival Stories: a Taxonomic Analysis of AI Existential Risk

Summary: arXiv:2601.09765v1 Announce Type: new Abstract: Since the release of ChatGPT, there has been a lot of debate about whether AI systems pose an existential risk to humanity. This paper develops a general framework for thinking about the existential risk of AI systems. We analyze a two premise argument that AI systems pose a threat to humanity. Premise one: AI systems will become extremely powerful. Premise two: if AI systems become extremely powerful, they will destroy humanity. We use these two premises to construct a taxonomy of survival stories, in which humanity survives into the far future.


DecisionLLM: Large Language Models for Long Sequence Decision Exploration

Summary: arXiv:2601.10148v1 Announce Type: new Abstract: Long-sequence decision-making, which is usually addressed through reinforcement learning (RL), is a critical component for optimizing strategic operations in dynamic environments, such as real-time bidding in computational advertising. The Decision Transformer (DT) introduced a powerful paradigm by framing RL as an autoregressive sequence modeling problem. Concurrently, Large Language Models (LLMs) have demonstrated remarkable success in complex reasoning and planning tasks. This inspires us whether LLMs, which share the same Transformer foundati


Introducing Axlerod: An LLM-based Chatbot for Assisting Independent Insurance Agents

Summary: arXiv:2601.09715v1 Announce Type: cross Abstract: The insurance industry is undergoing a paradigm shift through the adoption of artificial intelligence (AI) technologies, particularly in the realm of intelligent conversational agents. Chatbots have evolved into sophisticated AI-driven systems capable of automating complex workflows, including policy recommendation and claims triage, while simultaneously enabling dynamic, context-aware user engagement. This paper presents the design, implementation, and empirical evaluation of Axlerod, an AI-powered conversational interface designed to improve


Reinforced Linear Genetic Programming

Summary: arXiv:2601.09736v1 Announce Type: cross Abstract: Linear Genetic Programming (LGP) is a powerful technique that allows for a variety of problems to be solved using a linear representation of programs. However, there still exists some limitations to the technique, such as the need for humans to explicitly map registers to actions. This thesis proposes a novel approach that uses Q-Learning on top of LGP, Reinforced Linear Genetic Programming (RLGP) to learn the optimal register-action assignments. In doing so, we introduce a new framework "linear-gp" written in memory-safe Rust that allows for e


AmbShield: Enhancing Physical Layer Security with Ambient Backscatter Devices against Eavesdroppers

Summary: arXiv:2601.09867v1 Announce Type: new Abstract: Passive eavesdropping compromises confidentiality in wireless networks, especially in resource-constrained environments where heavyweight cryptography is impractical. Physical layer security (PLS) exploits channel randomness and spatial selectivity to confine information to an intended receiver with modest overhead. However, typical PLS techniques, such as using beamforming, artificial noise, and reconfigurable intelligent surfaces, often involve added active power or specialized deployment, and, in many designs, rely on precise time synchronizat


ReasAlign: Reasoning Enhanced Safety Alignment against Prompt Injection Attack

Summary: arXiv:2601.10173v1 Announce Type: new Abstract: Large Language Models (LLMs) have enabled the development of powerful agentic systems capable of automating complex workflows across various fields. However, these systems are highly vulnerable to indirect prompt injection attacks, where malicious instructions embedded in external data can hijack agent behavior. In this work, we present ReasAlign, a model-level solution to improve safety alignment against indirect prompt injection attacks. The core idea of ReasAlign is to incorporate structured reasoning steps to analyze user queries, detect conf


Agent Skills in the Wild: An Empirical Study of Security Vulnerabilities at Scale

Summary: arXiv:2601.10338v1 Announce Type: new Abstract: The rise of AI agent frameworks has introduced agent skills, modular packages containing instructions and executable code that dynamically extend agent capabilities. While this architecture enables powerful customization, skills execute with implicit trust and minimal vetting, creating a significant yet uncharacterized attack surface. We conduct the first large-scale empirical security analysis of this emerging ecosystem, collecting 42,447 skills from two major marketplaces and systematically analyzing 31,132 using SkillScan, a multi-stage detect


Be Your Own Red Teamer: Safety Alignment via Self-Play and Reflective Experience Replay

Summary: arXiv:2601.10589v1 Announce Type: new Abstract: Large Language Models (LLMs) have achieved remarkable capabilities but remain vulnerable to adversarial ``jailbreak'' attacks designed to bypass safety guardrails. Current safety alignment methods depend heavily on static external red teaming, utilizing fixed defense prompts or pre-collected adversarial datasets. This leads to a rigid defense that overfits known patterns and fails to generalize to novel, sophisticated threats. To address this critical limitation, we propose empowering the model to be its own red teamer, capable of achieving auton


Principal ideal problem and ideal shortest vector over rational primes in power-of-two cyclotomic fields

Summary: arXiv:2601.07511v2 Announce Type: replace Abstract: The shortest vector problem (SVP) over ideal lattices is closely related to the Ring-LWE problem, which is widely used to build post-quantum cryptosystems. Power-of-two cyclotomic fields are frequently adopted to instantiate Ring-LWE. Pan et al. (EUROCRYPT~2021) explored the SVP over ideal lattices via the decomposition fields and, in particular determined the length of the shortest vector in prime ideals lying over rational primes $p\equiv3,5\pmod{8}$ in power-of-two cyclotomic fields via explicit construction of reduced lattice bases. In th


Investigating Tool-Memory Conflicts in Tool-Augmented LLMs

Summary: arXiv:2601.09760v1 Announce Type: new Abstract: Tool-augmented large language models (LLMs) have powered many applications. However, they are likely to suffer from knowledge conflict. In this paper, we propose a new type of knowledge conflict -- Tool-Memory Conflict (TMC), where the internal parametric knowledge contradicts with the external tool knowledge for tool-augmented LLMs. We find that existing LLMs, though powerful, suffer from TMC, especially on STEM-related tasks. We also uncover that under different conditions, tool knowledge and parametric knowledge may be prioritized differently.


Agentic Pipelines in Embedded Software Engineering: Emerging Practices and Challenges

Summary: arXiv:2601.10220v1 Announce Type: new Abstract: A new transformation is underway in software engineering, driven by the rapid adoption of generative AI in development workflows. Similar to how version control systems once automated manual coordination, AI tools are now beginning to automate many aspects of programming. For embedded software engineering organizations, however, this marks their first experience integrating AI into safety-critical and resource-constrained environments. The strict demands for determinism, reliability, and traceability pose unique challenges for adopting generative


Evolving with AI: A Longitudinal Analysis of Developer Logs

Summary: arXiv:2601.10258v1 Announce Type: new Abstract: AI-powered coding assistants are rapidly becoming fixtures in professional IDEs, yet their sustained influence on everyday development remains poorly understood. Prior research has focused on short-term use or self-reported perceptions, leaving open questions about how sustained AI use reshapes actual daily coding practices in the long term. We address this gap with a mixed-method study of AI adoption in IDEs, combining longitudinal two-year fine-grained telemetry from 800 developers with a survey of 62 professionals. We analyze five dimensions o


Summary: arXiv:2601.10338v1 Announce Type: cross Abstract: The rise of AI agent frameworks has introduced agent skills, modular packages containing instructions and executable code that dynamically extend agent capabilities. While this architecture enables powerful customization, skills execute with implicit trust and minimal vetting, creating a significant yet uncharacterized attack surface. We conduct the first large-scale empirical security analysis of this emerging ecosystem, collecting 42,447 skills from two major marketplaces and systematically analyzing 31,132 using SkillScan, a multi-stage dete


CodeAssistBench (CAB): Dataset & Benchmarking for Multi-turn Chat-Based Code Assistance

Summary: arXiv:2507.10646v5 Announce Type: replace Abstract: Programming assistants powered by large language models have improved dramatically, yet existing benchmarks still evaluate them in narrow code-generation settings. Recent efforts such as InfiBench and StackEval rely on Stack Overflow questions and remain limited to single-turn interactions, manually curated data, and isolated snippets rather than full project environments. We introduce CodeAssistBench (CAB), the first benchmark for evaluating multi-turn, project-grounded programming assistance at scale. CAB automatically constructs datasets f


Apple turns to Google to power AI upgrade for Siri

Summary: Analysts say the deal is likely to be welcomed by consumers - but reflects Apple's failure to develop its own AI tools.


X could face UK ban over deepfakes, minister says

Summary: It comes after government urged Ofcom to use all its powers – up to and including an effective ban – against X.


Inside the sub-zero lair of the world's most powerful computer

Summary: Faisal Islam gets rare access to Willow - Google's quantum computer.


Nvidia unveils self-driving car tech as it seeks to power more products with AI

Summary: The chip giant is looking for ways to move the AI revolution beyond software and into physical products.


'It's amazing' – the wonder material very few can make

Summary: Just a handful of companies can make cadmium zinc telluride, a material with powerful properties.


AI Survival Stories: a Taxonomic Analysis of AI Existential Risk

Summary: arXiv:2601.09765v1 Announce Type: new Abstract: Since the release of ChatGPT, there has been a lot of debate about whether AI systems pose an existential risk to humanity. This paper develops a general framework for thinking about the existential risk of AI systems. We analyze a two premise argument that AI systems pose a threat to humanity. Premise one: AI systems will become extremely powerful. Premise two: if AI systems become extremely powerful, they will destroy humanity. We use these two premises to construct a taxonomy of survival stories, in which humanity survives into the far future.


DecisionLLM: Large Language Models for Long Sequence Decision Exploration

Summary: arXiv:2601.10148v1 Announce Type: new Abstract: Long-sequence decision-making, which is usually addressed through reinforcement learning (RL), is a critical component for optimizing strategic operations in dynamic environments, such as real-time bidding in computational advertising. The Decision Transformer (DT) introduced a powerful paradigm by framing RL as an autoregressive sequence modeling problem. Concurrently, Large Language Models (LLMs) have demonstrated remarkable success in complex reasoning and planning tasks. This inspires us whether LLMs, which share the same Transformer foundati


Introducing Axlerod: An LLM-based Chatbot for Assisting Independent Insurance Agents

Summary: arXiv:2601.09715v1 Announce Type: cross Abstract: The insurance industry is undergoing a paradigm shift through the adoption of artificial intelligence (AI) technologies, particularly in the realm of intelligent conversational agents. Chatbots have evolved into sophisticated AI-driven systems capable of automating complex workflows, including policy recommendation and claims triage, while simultaneously enabling dynamic, context-aware user engagement. This paper presents the design, implementation, and empirical evaluation of Axlerod, an AI-powered conversational interface designed to improve


Reinforced Linear Genetic Programming

Summary: arXiv:2601.09736v1 Announce Type: cross Abstract: Linear Genetic Programming (LGP) is a powerful technique that allows for a variety of problems to be solved using a linear representation of programs. However, there still exists some limitations to the technique, such as the need for humans to explicitly map registers to actions. This thesis proposes a novel approach that uses Q-Learning on top of LGP, Reinforced Linear Genetic Programming (RLGP) to learn the optimal register-action assignments. In doing so, we introduce a new framework "linear-gp" written in memory-safe Rust that allows for e


Reinforced Linear Genetic Programming


Zep AI (Agent Context Engineering, YC W24) Is Hiring Forward Deployed Engineers

Summary:
Article URL: https://www.ycombinator.com/companies/zep-ai/jobs/

Comments URL: https://news.ycombinator.com/item?id=46648714

Points: 0

# Comments: 0


Millimeter-Wave Gesture Recognition in ISAC: Does Reducing Sensing Airtime Hamper Accuracy?

Summary: arXiv:2601.10733v1 Announce Type: cross Abstract: Most Integrated Sensing and Communications (ISAC) systems require dividing airtime across their two modes. However, the specific impact of this decision on sensing performance remains unclear and underexplored. In this paper, we therefore investigate the impact on a gesture recognition system using a Millimeter-Wave (mmWave) ISAC system. With our dataset of power per beam pair gathered with two mmWave devices performing constant beam sweeps while test subjects performed distinct gestures, we train a gesture classifier using Convolutional Neural


Towards Reliable ML Feature Engineering via Planning in Constrained-Topology of LLM Agents

Summary: arXiv:2601.10820v1 Announce Type: cross Abstract: Recent advances in code generation models have unlocked unprecedented opportunities for automating feature engineering, yet their adoption in real-world ML teams remains constrained by critical challenges: (i) the scarcity of datasets capturing the iterative and complex coding processes of production-level feature engineering, (ii) limited integration and personalization of widely used coding agents, such as CoPilot and Devin, with a team's unique tools, codebases, workflows, and practices, and (iii) suboptimal human-AI collaboration due to poo


ABC-Bench: Benchmarking Agentic Backend Coding in Real-World Development

Summary: arXiv:2601.11077v1 Announce Type: cross Abstract: The evolution of Large Language Models (LLMs) into autonomous agents has expanded the scope of AI coding from localized code generation to complex, repository-level, and execution-driven problem solving. However, current benchmarks predominantly evaluate code logic in static contexts, neglecting the dynamic, full-process requirements of real-world engineering, particularly in backend development which demands rigorous environment configuration and service deployment. To address this gap, we introduce ABC-Bench, a benchmark explicitly designed t


X-Distill: Cross-Architecture Vision Distillation for Visuomotor Learning

Summary: arXiv:2601.11269v1 Announce Type: cross Abstract: Visuomotor policies often leverage large pre-trained Vision Transformers (ViTs) for their powerful generalization capabilities. However, their significant data requirements present a major challenge in the data-scarce context of most robotic learning settings, where compact CNNs with strong inductive biases can be more easily optimized. To address this trade-off, we introduce X-Distill, a simple yet highly effective method that synergizes the strengths of both architectures. Our approach involves an offline, cross-architecture knowledge distill


VidLeaks: Membership Inference Attacks Against Text-to-Video Models

Summary: arXiv:2601.11210v1 Announce Type: new Abstract: The proliferation of powerful Text-to-Video (T2V) models, trained on massive web-scale datasets, raises urgent concerns about copyright and privacy violations. Membership inference attacks (MIAs) provide a principled tool for auditing such risks, yet existing techniques - designed for static data like images or text - fail to capture the spatio-temporal complexities of video generation. In particular, they overlook the sparsity of memorization signals in keyframes and the instability introduced by stochastic temporal dynamics. In this paper, we c


Enforcing Control Flow Integrity on DeFi Smart Contracts

Summary: arXiv:2504.05509v2 Announce Type: replace Abstract: Smart contracts power decentralized financial (DeFi) services but are vulnerable to security exploits that can lead to significant financial losses. Existing security measures often fail to adequately protect these contracts due to the composability of DeFi protocols and the increasing sophistication of attacks. Through a large-scale empirical study of historical transactions from the 37 hacked DeFi protocols, we discovered that while benign transactions typically exhibit a limited number of unique control flows, in stark contrast, attack tra


Power to the Clients: Federated Learning in a Dictatorship Setting

Summary: arXiv:2510.22149v2 Announce Type: replace-cross Abstract: Federated learning (FL) has emerged as a promising paradigm for decentralized model training, enabling multiple clients to collaboratively learn a shared model without exchanging their local data. However, the decentralized nature of FL also introduces vulnerabilities, as malicious clients can compromise or manipulate the training process. In this work, we introduce dictator clients, a novel, well-defined, and analytically tractable class of malicious participants capable of entirely erasing the contributions of all other clients from t


Summary: arXiv:2601.11077v1 Announce Type: new Abstract: The evolution of Large Language Models (LLMs) into autonomous agents has expanded the scope of AI coding from localized code generation to complex, repository-level, and execution-driven problem solving. However, current benchmarks predominantly evaluate code logic in static contexts, neglecting the dynamic, full-process requirements of real-world engineering, particularly in backend development which demands rigorous environment configuration and service deployment. To address this gap, we introduce ABC-Bench, a benchmark explicitly designed to


RITA: A Tool for Automated Requirements Classification and Specification from Online User Feedback

Summary: arXiv:2601.11362v1 Announce Type: new Abstract: Context and motivation. Online user feedback is a valuable resource for requirements engineering, but its volume and noise make analysis difficult. Existing tools support individual feedback analysis tasks, but their capabilities are rarely integrated into end-to-end support. Problem. The lack of end-to-end integration limits the practical adoption of existing RE tools and makes it difficult to assess their real-world usefulness. Solution. To address this challenge, we present RITA, a tool that integrates lightweight open-source large language mo


Empathy Guidelines for Improving Practitioner Well-being & Software Engineering Practices

Summary: arXiv:2508.03846v2 Announce Type: replace Abstract: Empathy is a powerful yet often overlooked element in software engineering (SE), supporting better teamwork, smoother communication, and effective decision-making.This paper introduces 17 actionable empathy guidelines designed to support practitioners, teams, and organisations. We also explore how these guidelines can be implemented in practice by examining real-world applications, challenges, and strategies to overcome them shared by software practitioners. To support adoption, we present a visual prioritisation framework that categorises th


Summary: arXiv:2504.05509v2 Announce Type: replace-cross Abstract: Smart contracts power decentralized financial (DeFi) services but are vulnerable to security exploits that can lead to significant financial losses. Existing security measures often fail to adequately protect these contracts due to the composability of DeFi protocols and the increasing sophistication of attacks. Through a large-scale empirical study of historical transactions from the 37 hacked DeFi protocols, we discovered that while benign transactions typically exhibit a limited number of unique control flows, in stark contrast, atta


Apple turns to Google to power AI upgrade for Siri

Summary: Analysts say the deal is likely to be welcomed by consumers - but reflects Apple's failure to develop its own AI tools.


Inside the sub-zero lair of the world's most powerful computer

Summary: Faisal Islam gets rare access to Willow - Google's quantum computer.


Nvidia unveils self-driving car tech as it seeks to power more products with AI

Summary: The chip giant is looking for ways to move the AI revolution beyond software and into physical products.


'It's amazing' – the wonder material very few can make

Summary: Just a handful of companies can make cadmium zinc telluride, a material with powerful properties.


Google appeals landmark antitrust verdict over search monopoly

Summary: The tech giant is seeking a pause on court-ordered fixes aimed at limiting its monopoly power.


US believes its power matters more than international law, UN chief

Summary:
Article URL: https://www.bbc.co.uk/news/articles/c4g0zx0llpzo

Comments URL: https://news.ycombinator.com/item?id=46676843

Points: 25

# Comments: 9


Greenpeace pilot brings heat pumps and solar to Ukrainian community

Summary:
Article URL: https://www.pveurope.eu/power2heat/greenpeace-pilot-brings-heat-pumps-and-solar-ukrainian-community

Comments URL: https://news.ycombinator.com/item?id=46676824

Points: 5

# Comments: 0


Summary:
Article URL: https://www.pveurope.eu/power2heat/greenpeace-pilot-brings-heat-pumps-and-solar-ukrainian-community

Comments URL: https://news.ycombinator.com/item?id=46676824

Points: 11

# Comments: 5


Summary:
Article URL: https://www.pveurope.eu/power2heat/greenpeace-pilot-brings-heat-pumps-and-solar-ukrainian-community

Comments URL: https://news.ycombinator.com/item?id=46676824

Points: 16

# Comments: 11


Summary:
Article URL: https://www.pveurope.eu/power2heat/greenpeace-pilot-brings-heat-pumps-and-solar-ukrainian-community

Comments URL: https://news.ycombinator.com/item?id=46676824

Points: 21

# Comments: 15


Summary:
Article URL: https://www.pveurope.eu/power2heat/greenpeace-pilot-brings-heat-pumps-and-solar-ukrainian-community

Comments URL: https://news.ycombinator.com/item?id=46676824

Points: 26

# Comments: 21


Summary:
Article URL: https://www.pveurope.eu/power2heat/greenpeace-pilot-brings-heat-pumps-and-solar-ukrainian-community

Comments URL: https://news.ycombinator.com/item?id=46676824

Points: 36

# Comments: 25


Summary:
Article URL: https://www.pveurope.eu/power2heat/greenpeace-pilot-brings-heat-pumps-and-solar-ukrainian-community

Comments URL: https://news.ycombinator.com/item?id=46676824

Points: 38

# Comments: 27


Porsche Sold More Electrified Cars in Europe in 2025 Than Pure Gas-Powered Cars

Summary:
Article URL: https://newsroom.porsche.com/en/2026/company/porsche-deliveries-2025-41516.html

Comments URL: https://news.ycombinator.com/item?id=46686640

Points: 9

# Comments: 0


Summary:
Article URL: https://newsroom.porsche.com/en/2026/company/porsche-deliveries-2025-41516.html

Comments URL: https://news.ycombinator.com/item?id=46686640

Points: 84

# Comments: 47


Summary:
Article URL: https://newsroom.porsche.com/en/2026/company/porsche-deliveries-2025-41516.html

Comments URL: https://news.ycombinator.com/item?id=46686640

Points: 138

# Comments: 125


Summary:
Article URL: https://newsroom.porsche.com/en/2026/company/porsche-deliveries-2025-41516.html

Comments URL: https://news.ycombinator.com/item?id=46686640

Points: 161

# Comments: 155


Summary: Analysts say the deal is likely to be welcomed by consumers - but reflects Apple's failure to develop its own AI tools.


Summary: Faisal Islam gets rare access to Willow - Google's quantum computer.


Summary: The chip giant is looking for ways to move the AI revolution beyond software and into physical products.


Summary: Just a handful of companies can make cadmium zinc telluride, a material with powerful properties.


Summary: The tech giant is seeking a pause on court-ordered fixes aimed at limiting its monopoly power.


Porsche sold more electrified cars in Europe in 2025 than pure gas-powered cars

Summary:
Article URL: https://newsroom.porsche.com/en/2026/company/porsche-deliveries-2025-41516.html

Comments URL: https://news.ycombinator.com/item?id=46686640

Points: 208

# Comments: 230


Summary:
Article URL: https://newsroom.porsche.com/en/2026/company/porsche-deliveries-2025-41516.html

Comments URL: https://news.ycombinator.com/item?id=46686640

Points: 229

# Comments: 251


Summary:
Article URL: https://newsroom.porsche.com/en/2026/company/porsche-deliveries-2025-41516.html

Comments URL: https://news.ycombinator.com/item?id=46686640

Points: 230

# Comments: 256


Summary:
Article URL: https://newsroom.porsche.com/en/2026/company/porsche-deliveries-2025-41516.html

Comments URL: https://news.ycombinator.com/item?id=46686640

Points: 241

# Comments: 265


Summary:
Article URL: https://newsroom.porsche.com/en/2026/company/porsche-deliveries-2025-41516.html

Comments URL: https://news.ycombinator.com/item?id=46686640

Points: 249

# Comments: 273


Summary:
Article URL: https://newsroom.porsche.com/en/2026/company/porsche-deliveries-2025-41516.html

Comments URL: https://news.ycombinator.com/item?id=46686640

Points: 256

# Comments: 283


Summary:
Article URL: https://newsroom.porsche.com/en/2026/company/porsche-deliveries-2025-41516.html

Comments URL: https://news.ycombinator.com/item?id=46686640

Points: 268

# Comments: 300


Summary:
Article URL: https://newsroom.porsche.com/en/2026/company/porsche-deliveries-2025-41516.html

Comments URL: https://news.ycombinator.com/item?id=46686640

Points: 280

# Comments: 321


Summary:
Article URL: https://newsroom.porsche.com/en/2026/company/porsche-deliveries-2025-41516.html

Comments URL: https://news.ycombinator.com/item?id=46686640

Points: 294

# Comments: 348


Summary:
Article URL: https://newsroom.porsche.com/en/2026/company/porsche-deliveries-2025-41516.html

Comments URL: https://news.ycombinator.com/item?id=46686640

Points: 320

# Comments: 382


Summary:
Article URL: https://newsroom.porsche.com/en/2026/company/porsche-deliveries-2025-41516.html

Comments URL: https://news.ycombinator.com/item?id=46686640

Points: 330

# Comments: 399


Summary:
Article URL: https://newsroom.porsche.com/en/2026/company/porsche-deliveries-2025-41516.html

Comments URL: https://news.ycombinator.com/item?id=46686640

Points: 338

# Comments: 423


Summary: Analysts say the deal is likely to be welcomed by consumers - but reflects Apple's failure to develop its own AI tools.


Summary: Faisal Islam gets rare access to Willow - Google's quantum computer.


Summary: The chip giant is looking for ways to move the AI revolution beyond software and into physical products.


Summary: Just a handful of companies can make cadmium zinc telluride, a material with powerful properties.


Summary: The tech giant is seeking a pause on court-ordered fixes aimed at limiting its monopoly power.


Summary:
Article URL: https://newsroom.porsche.com/en/2026/company/porsche-deliveries-2025-41516.html

Comments URL: https://news.ycombinator.com/item?id=46686640

Points: 362

# Comments: 485


Summary:
Article URL: https://newsroom.porsche.com/en/2026/company/porsche-deliveries-2025-41516.html

Comments URL: https://news.ycombinator.com/item?id=46686640

Points: 369

# Comments: 499


Summary:
Article URL: https://newsroom.porsche.com/en/2026/company/porsche-deliveries-2025-41516.html

Comments URL: https://news.ycombinator.com/item?id=46686640

Points: 385

# Comments: 538


Summary:
Article URL: https://newsroom.porsche.com/en/2026/company/porsche-deliveries-2025-41516.html

Comments URL: https://news.ycombinator.com/item?id=46686640

Points: 411

# Comments: 586


Show HN: On-device browser agent (Qwen) running locally in Chrome

Summary:
Demo of LOCAL Browser agent (powered by Web GPU Liquid LFM & Alibaba Qwen models) opening the All in Podcast on Youtube running as a chrome extension.
Source: https://github.com/RunanywhereAI/on-device-browser-agent
Post: https://www.reddit.com/r/LocalLLaMA/comments/1qh10q9/comment...
Geting in the support for web sdk soon, meanwhile have full support for mobile sdks : <a href="https://github.com/Runany


Optimal Power Allocation and Sub-Optimal Channel Assignment for Downlink NOMA Systems Using Deep Reinforcement Learning

Summary: arXiv:2601.12242v1 Announce Type: new Abstract: In recent years, Non-Orthogonal Multiple Access (NOMA) system has emerged as a promising candidate for multiple access frameworks due to the evolution of deep machine learning, trying to incorporate deep machine learning into the NOMA system. The main motivation for such active studies is the growing need to optimize the utilization of network resources as the expansion of the internet of things (IoT) caused a scarcity of network resources. The NOMA addresses this need by power multiplexing, allowing multiple users to access the network simultane


Improving Large Molecular Language Model via Relation-aware Multimodal Collaboration

Summary: arXiv:2601.12256v1 Announce Type: new Abstract: Large language models (LLMs) have demonstrated their instruction-following capabilities and achieved powerful performance on various tasks. Inspired by their success, recent works in the molecular domain have led to the development of large molecular language models (LMLMs) that integrate 1D molecular strings or 2D molecular graphs into the language models. However, existing LMLMs often suffer from hallucination and limited robustness, largely due to inadequate integration of diverse molecular modalities such as 1D sequences, 2D molecular graphs,


Agentic Artificial Intelligence (AI): Architectures, Taxonomies, and Evaluation of Large Language Model Agents

Summary: arXiv:2601.12560v1 Announce Type: new Abstract: Artificial Intelligence is moving from models that only generate text to Agentic AI, where systems behave as autonomous entities that can perceive, reason, plan, and act. Large Language Models (LLMs) are no longer used only as passive knowledge engines but as cognitive controllers that combine memory, tool use, and feedback from their environment to pursue extended goals. This shift already supports the automation of complex workflows in software engineering, scientific discovery, and web navigation, yet the variety of emerging designs, from simp


Empowering All-in-Loop Health Management of Spacecraft Power System in the Mega-Constellation Era via Human-AI Collaboration

Summary: arXiv:2601.12667v1 Announce Type: new Abstract: It is foreseeable that the number of spacecraft will increase exponentially, ushering in an era dominated by satellite mega-constellations (SMC). This necessitates a focus on energy in space: spacecraft power systems (SPS), especially their health management (HM), given their role in power supply and high failure rates. Providing health management for dozens of SPS and for thousands of SPS represents two fundamentally different paradigms. Therefore, to adapt the health management in the SMC era, this work proposes a principle of aligning underlyi


Vision Language Models for Optimization-Driven Intent Processing in Autonomous Networks

Summary: arXiv:2601.12744v1 Announce Type: new Abstract: Intent-Based Networking (IBN) allows operators to specify high-level network goals rather than low-level configurations. While recent work demonstrates that large language models can automate configuration tasks, a distinct class of intents requires generating optimization code to compute provably optimal solutions for traffic engineering, routing, and resource allocation. Current systems assume text-based intent expression, requiring operators to enumerate topologies and parameters in prose. Network practitioners naturally reason about structure


SimFuzz: Similarity-guided Block-level Mutation for RISC-V Processor Fuzzing

Summary: arXiv:2601.11838v1 Announce Type: new Abstract: The Instruction Set Architecture (ISA) defines processor operations and serves as the interface between hardware and software. As an open ISA, RISC-V lowers the barriers to processor design and encourages widespread adoption, but also exposes processors to security risks such as functional bugs. Processor fuzzing is a powerful technique for automatically detecting these bugs. However, existing fuzzing methods suffer from two main limitations. First, their emphasis on redundant test case generation causes them to overlook cross-processor corner ca


Zero-Permission Manipulation: Can We Trust Large Multimodal Model Powered GUI Agents?

Summary: arXiv:2601.12349v1 Announce Type: new Abstract: Large multimodal model powered GUI agents are emerging as high-privilege operators on mobile platforms, entrusted with perceiving screen content and injecting inputs. However, their design operates under the implicit assumption of Visual Atomicity: that the UI state remains invariant between observation and action. We demonstrate that this assumption is fundamentally invalid in Android, creating a critical attack surface. We present Action Rebinding, a novel attack that allows a seemingly-benign app with zero dangerous permissions to rebind an ag


PINA: Prompt Injection Attack against Navigation Agents

Summary: arXiv:2601.13612v1 Announce Type: new Abstract: Navigation agents powered by large language models (LLMs) convert natural language instructions into executable plans and actions. Compared to text-based applications, their security is far more critical: a successful prompt injection attack does not just alter outputs but can directly misguide physical navigation, leading to unsafe routes, mission failure, or real-world harm. Despite this high-stakes setting, the vulnerability of navigation agents to prompt injection remains largely unexplored. In this paper, we propose PINA, an adaptive prompt


MirageNet:A Secure, Efficient, and Scalable On-Device Model Protection in Heterogeneous TEE and GPU System

Summary: arXiv:2601.13826v1 Announce Type: new Abstract: As edge devices gain stronger computing power, deploying high-performance DNN models on untrusted hardware has become a practical approach to cut inference latency and protect user data privacy. Given high model training costs and user experience requirements, balancing model privacy and low runtime overhead is critical. TEEs offer a viable defense, and prior work has proposed heterogeneous GPU-TEE inference frameworks via parameter obfuscation to balance efficiency and confidentiality. However, recent studies find partial obfuscation defenses in


A Security Framework for Chemical Functions

Summary: arXiv:2601.14019v1 Announce Type: new Abstract: In this paper, we introduce chemical functions, a unified framework that models chemical systems as noisy challenge--response primitives, and formalize the associated chemical function infrastructure. Building on the theory of physical functions, we rigorously define robustness, unclonability, and unpredictability for chemical functions in both finite and asymptotic regimes, and specify security games that capture the adversary's power and the security goals. We instantiate the framework with two existing DNA-based constructions (operable random


OAMAC: Origin-Aware Mandatory Access Control for Practical Post-Compromise Attack Surface Reduction

Summary: arXiv:2601.14021v1 Announce Type: new Abstract: Modern operating systems provide powerful mandatory access control mechanisms, yet they largely reason about who executes code rather than how execution originates. As a result, processes launched remotely, locally, or by background services are often treated equivalently once privileges are obtained, complicating security reasoning and enabling post-compromise abuse of sensitive system interfaces. We introduce origin-aware mandatory access control (OAMAC), a kernel-level enforcement model that treats execution origin -- such as physical user pre


Powerful Training-Free Membership Inference Against Autoregressive Language Models

Summary: arXiv:2601.12104v1 Announce Type: cross Abstract: Fine-tuned language models pose significant privacy risks, as they may memorize and expose sensitive information from their training data. Membership inference attacks (MIAs) provide a principled framework for auditing these risks, yet existing methods achieve limited detection rates, particularly at the low false-positive thresholds required for practical privacy auditing. We present EZ-MIA, a membership inference attack that exploits a key observation: memorization manifests most strongly at error positions, specifically tokens where the mode


Cross-reality Location Privacy Protection in 6G-enabled Vehicular Metaverses: An LLM-enhanced Hybrid Generative Diffusion Model-based Approach

Summary: arXiv:2601.12311v1 Announce Type: cross Abstract: The emergence of 6G-enabled vehicular metaverses enables Autonomous Vehicles (AVs) to operate across physical and virtual spaces through space-air-ground-sea integrated networks. The AVs can deploy AI agents powered by large AI models as personalized assistants, on edge servers to support intelligent driving decision making and enhanced on-board experiences. However, such cross-reality interactions may cause serious location privacy risks, as adversaries can infer AV trajectories by correlating the location reported when AVs request LBS in real


SpecMap: Hierarchical LLM Agent for Datasheet-to-Code Traceability Link Recovery in Systems Engineering

Summary: arXiv:2601.11688v1 Announce Type: new Abstract: Establishing precise traceability between embedded systems datasheets and their corresponding code implementations remains a fundamental challenge in systems engineering, particularly for low-level software where manual mapping between specification documents and large code repositories is infeasible. Existing Traceability Link Recovery approaches primarily rely on lexical similarity and information retrieval techniques, which struggle to capture the semantic, structural, and symbol level relationships prevalent in embedded systems software. We p


Hybrid Concolic Testing with Large Language Models for Guided Path Exploration

Summary: arXiv:2601.12274v1 Announce Type: new Abstract: Concolic testing, a powerful hybrid software testing technique, has historically been plagued by fundamental limitations such as path explosion and the high cost of constraint solving, which hinder its practical application in large-scale, real-world software systems. This paper introduces a novel algorithmic framework that synergistically integrates concolic execution with Large Language Models (LLMs) to overcome these challenges. Our hybrid approach leverages the semantic reasoning capabilities of LLMs to guide path exploration, prioritize inte


OpenAI for OpenAPI: Automated generation of REST API specification via LLMs

Summary: arXiv:2601.12735v1 Announce Type: new Abstract: REST APIs, based on the REpresentational State Transfer (REST) architecture, are the primary type of Web API. The OpenAPI Specification (OAS) serves as the de facto standard for describing REST APIs and is crucial for multiple software engineering tasks. However, developers face challenges in writing and maintaining OAS. Although static analysis shows potential for OAS generation, it is limited to specific programming languages and development frameworks. The powerful code understanding capabilities of LLMs offer new opportunities for OAS generat


Docker Does Not Guarantee Reproducibility

Summary: arXiv:2601.12811v1 Announce Type: new Abstract: The reproducibility of software environments is a critical concern in modern software engineering, with ramifications ranging from the effectiveness of collaboration workflows to software supply chain security and scientific reproducibility. Containerization technologies like Docker address this problem by encapsulating software environments into shareable filesystem snapshots known as images. While Docker is frequently cited in the literature as a tool that enables reproducibility in theory, the extent of its guarantees and limitations in practi


Efficient Code Analysis via Graph-Guided Large Language Models

Summary: arXiv:2601.12890v1 Announce Type: new Abstract: Malicious behavior is often hidden in small, easily overlooked code fragments, especially within large and complex codebases. The cross-file dependencies of these fragments make it difficult for even powerful large language models (LLMs) to detect them reliably. We propose a graph-centric attention acquisition pipeline that enhances LLMs' ability to localize malicious behavior. The approach parses a project into a code graph, uses an LLM to encode nodes with semantic and structural signals, and trains a Graph Neural Network (GNN) under sparse sup


ArchAgent: Scalable Legacy Software Architecture Recovery with LLMs

Summary: arXiv:2601.13007v1 Announce Type: new Abstract: Recovering accurate architecture from large-scale legacy software is hindered by architectural drift, missing relations, and the limited context of Large Language Models (LLMs). We present ArchAgent, a scalable agent-based framework that combines static analysis, adaptive code segmentation, and LLM-powered synthesis to reconstruct multiview, business-aligned architectures from cross-repository codebases. ArchAgent introduces scalable diagram generation with contextual pruning and integrates cross-repository data to identify business-critical modu


Earth Embeddings as Products: Taxonomy, Ecosystem, and Standardized Access

Summary: arXiv:2601.13134v1 Announce Type: new Abstract: Geospatial Foundation Models (GFMs) provide powerful representations, but high compute costs hinder their widespread use. Pre-computed embedding data products offer a practical "frozen" alternative, yet they currently exist in a fragmented ecosystem of incompatible formats and resolutions. This lack of standardization creates an engineering bottleneck that prevents meaningful model comparison and reproducibility. We formalize this landscape through a three-layer taxonomy: Data, Tools, and Value. We survey existing products to identify interoperab


FlipFlop: A Static Analysis-based Energy Optimization Framework for GPU Kernels

Summary: arXiv:2601.13345v1 Announce Type: new Abstract: Artificial Intelligence (AI) applications, such as Large Language Models, are primarily driven and executed by Graphics Processing Units (GPUs). These GPU programs (kernels) consume substantial amounts of energy, yet software developers often lack the hardware expertise and ad hoc knowledge required to optimize for power efficiency. We propose FlipFlop, a framework using static code analysis to predict energy consumption and recommend Pareto-optimal thread block configurations considering both power consumption and execution time. Our framework r


CodeContests-O: Powering LLMs via Feedback-Driven Iterative Test Case Generation

Summary: arXiv:2601.13682v1 Announce Type: new Abstract: The rise of reasoning models necessitates large-scale verifiable data, for which programming tasks serve as an ideal source. However, while competitive programming platforms provide abundant problems and solutions, high-quality test cases for verification remain scarce. Existing approaches attempt to synthesize test cases using Large Language Models (LLMs), but rely solely on the model's intrinsic generation capabilities without external feedback, frequently resulting in insufficiently diverse cases. To address this limitation, we propose a $\tex


Summary: arXiv:2601.12349v1 Announce Type: cross Abstract: Large multimodal model powered GUI agents are emerging as high-privilege operators on mobile platforms, entrusted with perceiving screen content and injecting inputs. However, their design operates under the implicit assumption of Visual Atomicity: that the UI state remains invariant between observation and action. We demonstrate that this assumption is fundamentally invalid in Android, creating a critical attack surface. We present Action Rebinding, a novel attack that allows a seemingly-benign app with zero dangerous permissions to rebind an


Vision Language Models for Optimization-Driven Intent Processing in Autonomous Networks

Summary: arXiv:2601.12744v1 Announce Type: cross Abstract: Intent-Based Networking (IBN) allows operators to specify high-level network goals rather than low-level configurations. While recent work demonstrates that large language models can automate configuration tasks, a distinct class of intents requires generating optimization code to compute provably optimal solutions for traffic engineering, routing, and resource allocation. Current systems assume text-based intent expression, requiring operators to enumerate topologies and parameters in prose. Network practitioners naturally reason about structu


Google appeals landmark antitrust verdict over search monopoly

Summary: The tech giant is seeking a pause on court-ordered fixes aimed at limiting its monopoly power.


Apple turns to Google to power AI upgrade for Siri

Summary: Analysts say the deal is likely to be welcomed by consumers - but reflects Apple's failure to develop its own AI tools.


Inside the sub-zero lair of the world's most powerful computer

Summary: Faisal Islam gets rare access to Willow - Google's quantum computer.


Nvidia unveils self-driving car tech as it seeks to power more products with AI

Summary: The chip giant is looking for ways to move the AI revolution beyond software and into physical products.


'It's amazing' – the wonder material very few can make

Summary: Just a handful of companies can make cadmium zinc telluride, a material with powerful properties.


Show HN: ChartGPU – WebGPU-powered charting library (1M points at 60fps)

Summary:
Article URL: https://github.com/ChartGPU/ChartGPU

Comments URL: https://news.ycombinator.com/item?id=46706528

Points: 7

# Comments: 2


Summary:
Article URL: https://github.com/ChartGPU/ChartGPU

Comments URL: https://news.ycombinator.com/item?id=46706528

Points: 34

# Comments: 5


Summary:
Article URL: https://github.com/ChartGPU/ChartGPU

Comments URL: https://news.ycombinator.com/item?id=46706528

Points: 66

# Comments: 17


Summary:
Creator here. I built ChartGPU because I kept hitting the same wall: charting libraries that claim to be "fast" but choke past 100K data points.
The core insight: Canvas2D is fundamentally CPU-bound. Even WebGL chart libraries still do most computation on the CPU. So I moved everything to the GPU via WebGPU:
- LTTB downsampling runs as a compute shader - Hit-testing for tooltips/hover is GPU-accelerated - Rendering uses instanced draws (one draw call per series)
The result: 1M points at 60fps with smooth zoom/pan.
Live demo: <a href="https://chartgpu.github.io/ChartGPU/examples/mill


Large Language Model-Powered Evolutionary Code Optimization on a Phylogenetic Tree

Summary: arXiv:2601.14523v1 Announce Type: new Abstract: Optimizing scientific computing algorithms for modern GPUs is a labor-intensive and iterative process involving repeated code modification, benchmarking, and tuning across complex hardware and software stacks. Recent work has explored large language model (LLM)-assisted evolutionary methods for automated code optimization, but these approaches primarily rely on outcome-based selection and random mutation, underutilizing the rich trajectory information generated during iterative optimization. We propose PhyloEvolve, an LLM-agent system that refram


CI4A: Semantic Component Interfaces for Agents Empowering Web Automation

Summary: arXiv:2601.14790v1 Announce Type: new Abstract: While Large Language Models demonstrate remarkable proficiency in high-level semantic planning, they remain limited in handling fine-grained, low-level web component manipulations. To address this limitation, extensive research has focused on enhancing model grounding capabilities through techniques such as Reinforcement Learning. However, rather than compelling agents to adapt to human-centric interfaces, we propose constructing interaction interfaces specifically optimized for agents. This paper introduces Component Interface for Agent (CI4A),


Guardrails for trust, safety, and ethical development and deployment of Large Language Models (LLM)

Summary: arXiv:2601.14298v1 Announce Type: cross Abstract: The AI era has ushered in Large Language Models (LLM) to the technological forefront, which has been much of the talk in 2023, and is likely to remain as such for many years to come. LLMs are the AI models that are the power house behind generative AI applications such as ChatGPT. These AI models, fueled by vast amounts of data and computational prowess, have unlocked remarkable capabilities, from human-like text generation to assisting with natural language understanding (NLU) tasks. They have quickly become the foundation upon which countless


Agentic AI Meets Edge Computing in Autonomous UAV Swarms

Summary: arXiv:2601.14437v1 Announce Type: cross Abstract: The integration of agentic AI, powered by large language models (LLMs) with autonomous reasoning, planning, and execution, into unmanned aerial vehicle (UAV) swarms opens new operational possibilities and brings the vision of the Internet of Drones closer to reality. However, infrastructure constraints, dynamic environments, and the computational demands of multi-agent coordination limit real-world deployment in high-risk scenarios such as wildfires and disaster response. This paper investigates the integration of LLM-based agentic AI and edge


Tokenomics: Quantifying Where Tokens Are Used in Agentic Software Engineering

Summary: arXiv:2601.14470v1 Announce Type: cross Abstract: LLM-based Multi-Agent (LLM-MA) systems are increasingly applied to automate complex software engineering tasks such as requirements engineering, code generation, and testing. However, their operational efficiency and resource consumption remain poorly understood, hindering practical adoption due to unpredictable costs and environmental impact. To address this, we conduct an analysis of token consumption patterns in an LLM-MA system within the Software Development Life Cycle (SDLC), aiming to understand where tokens are consumed across distinct


Summary: arXiv:2601.14298v1 Announce Type: new Abstract: The AI era has ushered in Large Language Models (LLM) to the technological forefront, which has been much of the talk in 2023, and is likely to remain as such for many years to come. LLMs are the AI models that are the power house behind generative AI applications such as ChatGPT. These AI models, fueled by vast amounts of data and computational prowess, have unlocked remarkable capabilities, from human-like text generation to assisting with natural language understanding (NLU) tasks. They have quickly become the foundation upon which countless a


Towards Transparent Malware Detection With Granular Explainability: Backtracking Meta-Coarsened Explanations Onto Assembly Flow Graphs With Graph Neural Networks

Summary: arXiv:2601.14511v1 Announce Type: new Abstract: As malware continues to become increasingly sophisticated, threatening, and evasive, malware detection systems must keep pace and become equally intelligent, powerful, and transparent. In this paper, we propose Assembly Flow Graph (AFG) to comprehensively represent the assembly flow of a binary executable as graph data. Importantly, AFG can be used to extract granular explanations needed to increase transparency for malware detection using Graph Neural Networks (GNNs). However, since AFGs may be large in practice, we also propose a Meta-Coarsenin


SpooFL: Spoofing Federated Learning

Summary: arXiv:2601.15055v1 Announce Type: new Abstract: Traditional defenses against Deep Leakage (DL) attacks in Federated Learning (FL) primarily focus on obfuscation, introducing noise, transformations or encryption to degrade an attacker's ability to reconstruct private data. While effective to some extent, these methods often still leak high-level information such as class distributions or feature representations, and are frequently broken by increasingly powerful denoising attacks. We propose a fundamentally different perspective on FL defense: framing it as a spoofing problem.We introduce SpooF


Formal Power Series on Algebraic Cryptanalysis

Summary: arXiv:2007.14729v4 Announce Type: replace-cross Abstract: In the complexity estimation for an attack that reduces a cryptosystem to solving a system of polynomial equations, the degree of regularity and an upper bound of the first fall degree are often used in cryptanalysis. While the degree of regularity can be easily computed using a univariate formal power series under the semi-regularity assumption, determining an upper bound of the first fall degree requires investigating the concrete syzygies of an input system. In this paper, we investigate an upper bound of the first fall degree for a


Summary: arXiv:2601.14470v1 Announce Type: new Abstract: LLM-based Multi-Agent (LLM-MA) systems are increasingly applied to automate complex software engineering tasks such as requirements engineering, code generation, and testing. However, their operational efficiency and resource consumption remain poorly understood, hindering practical adoption due to unpredictable costs and environmental impact. To address this, we conduct an analysis of token consumption patterns in an LLM-MA system within the Software Development Life Cycle (SDLC), aiming to understand where tokens are consumed across distinct so


Reclaiming Software Engineering as the Enabling Technology for the Digital Age

Summary: arXiv:2601.14861v1 Announce Type: new Abstract: Software engineering is the invisible infrastructure of the digital age. Every breakthrough in artificial intelligence, quantum computing, photonics, and cybersecurity relies on advances in software engineering, yet the field is too often treated as a supportive digital component rather than as a strategic, enabling discipline. In policy frameworks, including major European programmes, software appears primarily as a building block within other technologies, while the scientific discipline of software engineering remains largely absent. This posi


RovoDev Code Reviewer: A Large-Scale Online Evaluation of LLM-based Code Review Automation at Atlassian

Summary: arXiv:2601.01129v2 Announce Type: replace Abstract: Large Language Models (LLMs)-powered code review automation has the potential to transform code review workflows. Despite the advances of LLM-powered code review comment generation approaches, several practical challenges remain for designing enterprise-grade code review automation tools. In particular, this paper aims at answering the practical question: how can we design a review-guided, context-aware, quality-checked code review comment generation without fine-tuning? In this paper, we present RovoDev Code Reviewer, an enterprise-grade LLM


Summary: The tech giant is seeking a pause on court-ordered fixes aimed at limiting its monopoly power.


Summary: Analysts say the deal is likely to be welcomed by consumers - but reflects Apple's failure to develop its own AI tools.


Summary: Faisal Islam gets rare access to Willow - Google's quantum computer.


Summary: The chip giant is looking for ways to move the AI revolution beyond software and into physical products.


Summary: Just a handful of companies can make cadmium zinc telluride, a material with powerful properties.


Summary:
Creator here. I built ChartGPU because I kept hitting the same wall: charting libraries that claim to be "fast" but choke past 100K data points.
The core insight: Canvas2D is fundamentally CPU-bound. Even WebGL chart libraries still do most computation on the CPU. So I moved everything to the GPU via WebGPU:
- LTTB downsampling runs as a compute shader - Hit-testing for tooltips/hover is GPU-accelerated - Rendering uses instanced draws (one draw call per series)
The result: 1M points at 60fps with smooth zoom/pan.
Live demo: <a href="https://chartgpu.github.io/ChartGPU/examples/mill