The Promptware Kill Chain: How Prompt Injections Gradually Evolved Into a Multi-Step Malware

Summary: arXiv:2601.09625v1 Announce Type: new Abstract: The rapid adoption of large language model (LLM)-based systems -- from chatbots to autonomous agents capable of executing code and financial transactions -- has created a new attack surface that existing security frameworks inadequately address. The dominant framing of these threats as "prompt injection" -- a catch-all phrase for security failures in LLM-based systems -- obscures a more complex reality: Attacks on LLM-based systems increasingly involve multi-step sequences that mirror traditional malware campaigns. In this paper, we propose that


SAGA: Synthetic Audit Log Generation for APT Campaigns

Summary: arXiv:2411.13138v2 Announce Type: replace Abstract: With the increasing sophistication of Advanced Persistent Threats (APTs), the demand for effective detection and mitigation strategies and methods has escalated. Program execution leaves traces in the system audit log, which can be analyzed to detect malicious activities. However, collecting and analyzing large volumes of audit logs over extended periods is challenging, further compounded by insufficient labeling that hinders their usability. Addressing these challenges, this paper introduces SAGA (Synthetic Audit log Generation for APT campa


A New Strategy for Verifying Reach-Avoid Specifications in Neural Feedback Systems

Summary: arXiv:2601.08065v1 Announce Type: new Abstract: Forward reachability analysis is the predominant approach for verifying reach-avoid properties in neural feedback systems (dynamical systems controlled by neural networks). This dominance stems from the limited scalability of existing backward reachability methods. In this work, we introduce new algorithms that compute both over- and under-approximations of backward reachable sets for such systems. We further integrate these backward algorithms with established forward analysis techniques to yield a unified verification framework for neural feedb


Resisting Manipulative Bots in Memecoin Copy Trading: A Multi-Agent Approach with Chain-of-Thought Reasoning

Summary: arXiv:2601.08641v1 Announce Type: new Abstract: The launch of \$Trump coin ignited a wave in meme coin investment. Copy trading, as a strategy-agnostic approach that eliminates the need for deep trading knowledge, quickly gains widespread popularity in the meme coin market. However, copy trading is not a guarantee of profitability due to the prevalence of manipulative bots, the uncertainty of the followed wallets' future performance, and the lag in trade execution. Recently, large language models (LLMs) have shown promise in financial applications by effectively understanding multi-modal data


Hierarchical Sparse Plus Low Rank Compression of LLM

Summary: arXiv:2601.07839v1 Announce Type: cross Abstract: Modern large language models (LLMs) place extraordinary pressure on memory and compute budgets, making principled compression indispensable for both deployment and continued training. We present Hierarchical Sparse Plus Low-Rank (HSS) compression, a two-stage scheme that (i) removes the largest-magnitude weights into a sparse matrix S and (ii) applies a recursive Hierarchically Sparse Separable (HSS) low-rank factorisation to the dense residual matrix. A recursive rank-reducing strategy and a reverse Cuthill-Mckee (RCM) permutation are introduc


Continuum Memory Architectures for Long-Horizon LLM Agents

Summary: arXiv:2601.09913v1 Announce Type: new Abstract: Retrieval-augmented generation (RAG) has become the default strategy for providing large language model (LLM) agents with contextual knowledge. Yet RAG treats memory as a stateless lookup table: information persists indefinitely, retrieval is read-only, and temporal continuity is absent. We define the \textit{Continuum Memory Architecture} (CMA), a class of systems that maintain and update internal state across interactions through persistent storage, selective retention, associative routing, temporal chaining, and consolidation into higher-order


Show HN: I built a text-based business simulator to replace video courses

Summary:
I am a solo developer, and I built Core MBA because I was frustrated with the "video course" default in business education.
I wanted to build a "compiler for business logic"—a tool where I could read a concept in 5 minutes and immediately test it in a hostile environment to see if my strategy actually compiles or throws a runtime error.
The project is a business simulator built on React 19 and TypeScript.
The core technical innovation isn't just using AI; it's the architecture of a closed loop between a deterministic economic engine and a generative AI validation layer.
The biggest


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.


Sketch-Based Facade Renovation With Generative AI: A Streamlined Framework for Bypassing As-Built Modelling in Industrial Adaptive Reuse

Summary: arXiv:2601.08531v1 Announce Type: new Abstract: Facade renovation offers a more sustainable alternative to full demolition, yet producing design proposals that preserve existing structures while expressing new intent remains challenging. Current workflows typically require detailed as-built modelling before design, which is time-consuming, labour-intensive, and often involves repeated revisions. To solve this issue, we propose a three-stage framework combining generative artificial intelligence (AI) and vision-language models (VLM) that directly processes rough structural sketch and textual de


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


Lego unveils tech-filled Smart Bricks - to play experts' unease

Summary: Lego says its new tech-enabled products are its "most revolutionary innovation" since it launched its mini-figurines in 1978.


Project Synapse: A Hierarchical Multi-Agent Framework with Hybrid Memory for Autonomous Resolution of Last-Mile Delivery Disruptions

Summary: arXiv:2601.08156v1 Announce Type: new Abstract: This paper introduces Project Synapse, a novel agentic framework designed for the autonomous resolution of last-mile delivery disruptions. Synapse employs a hierarchical multi-agent architecture in which a central Resolution Supervisor agent performs strategic task decomposition and delegates subtasks to specialized worker agents responsible for tactical execution. The system is orchestrated using LangGraph to manage complex and cyclical workflows. To validate the framework, a benchmark dataset of 30 complex disruption scenarios was curated from


Prism: Towards Lowering User Cognitive Load in LLMs via Complex Intent Understanding

Summary: arXiv:2601.08653v1 Announce Type: new Abstract: Large Language Models are rapidly emerging as web-native interfaces to social platforms. On the social web, users frequently have ambiguous and dynamic goals, making complex intent understanding-rather than single-turn execution-the cornerstone of effective human-LLM collaboration. Existing approaches attempt to clarify user intents through sequential or parallel questioning, yet they fall short of addressing the core challenge: modeling the logical dependencies among clarification questions. Inspired by the Cognitive Load Theory, we propose Pris


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


NOVAK: Unified adaptive optimizer for deep neural networks

Summary: arXiv:2601.07876v1 Announce Type: cross Abstract: This work introduces NOVAK, a modular gradient-based optimization algorithm that integrates adaptive moment estimation, rectified learning-rate scheduling, decoupled weight regularization, multiple variants of Nesterov momentum, and lookahead synchronization into a unified, performance-oriented framework. NOVAK adopts a dual-mode architecture consisting of a streamlined fast path designed for production. The optimizer employs custom CUDA kernels that deliver substantial speedups (3-5 for critical operations) while preserving numerical stability


The Real Menace of Cloning Attacks on SGX Applications

Summary: arXiv:2601.09273v1 Announce Type: new Abstract: Trusted Execution Environments (TEEs) are gaining popularity as an effective means to provide confidentiality in the cloud. TEEs, such as Intel SGX, suffer from so-called rollback and cloning attacks (often referred to as forking attacks). Rollback attacks are enabled by the lack of freshness guarantees for sealed data; cloning attacks stem from the inability to determine if other instances of an enclave are running on the same platform. While rollback attacks have been extensively studied by the community, cloning attacks have been, unfortunatel


StateFi: Effectively Identifying Wi-Fi Devices through State Transitions

Summary: arXiv:2507.02478v2 Announce Type: replace Abstract: Randomized MAC addresses aim to prevent passive device tracking, yet Wi-Fi management frames still leak structured behavioral patterns. Prior work has relied primarily on syntactic probe-request features such as Information Elements (IEs), sequence numbers (SEQ), or RSSI correlations, which degrade in dense environments and fail under aggressive randomization. We introduce StateFi, a fingerprinting framework that models device behavior as finite-state machines (FSMs), capturing both structural transition patterns and temporal execution logic.


VIGIL: Defending LLM Agents Against Tool Stream Injection via Verify-Before-Commit

Summary: arXiv:2601.05755v2 Announce Type: replace Abstract: LLM agents operating in open environments face escalating risks from indirect prompt injection, particularly within the tool stream where manipulated metadata and runtime feedback hijack execution flow. Existing defenses encounter a critical dilemma as advanced models prioritize injected rules due to strict alignment while static protection mechanisms sever the feedback loop required for adaptive reasoning. To reconcile this conflict, we propose \textbf{VIGIL}, a framework that shifts the paradigm from restrictive isolation to a verify-before


Forward Symbolic Execution for Trustworthy Automation of Binary Code Verification

Summary: arXiv:2304.08848v2 Announce Type: replace-cross Abstract: Control flow in unstructured programs can be complex and dynamic, which makes static analysis difficult. Yet, automated reasoning about unstructured control flow is important when certifying properties of binary (machine) code in trustworthy systems, e.g., cryptographic routines. We present a theory of forward symbolic execution for unstructured programs suitable for use in theorem provers that enables automated verification of both functional and non-functional program properties. The theory's foundation is a set of inference rules whe


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


Path-optimal symbolic execution of heap-manipulating programs

Summary: arXiv:2407.16827v2 Announce Type: replace Abstract: Symbolic execution is at the core of many techniques for program analysis and test generation. Traditional symbolic execution of programs with numeric inputs enjoys the property of forking as many analysis traces as the number of analyzed program paths, a property that in this paper we refer to as path optimality. On the contrary, current approaches for symbolic execution of heap-manipulating programs fail to satisfy this property, thereby incurring crucial path explosion effects. This paper introduces POSE, path-optimal symbolic execution, a


Toward Ultra-Long-Horizon Agentic Science: Cognitive Accumulation for Machine Learning Engineering

Summary: arXiv:2601.10402v1 Announce Type: new Abstract: The advancement of artificial intelligence toward agentic science is currently bottlenecked by the challenge of ultra-long-horizon autonomy, the ability to sustain strategic coherence and iterative correction over experimental cycles spanning days or weeks. While Large Language Models (LLMs) have demonstrated prowess in short-horizon reasoning, they are easily overwhelmed by execution details in the high-dimensional, delayed-feedback environments of real-world research, failing to consolidate sparse feedback into coherent long-term guidance. Here


R-LAM: Reproducibility-Constrained Large Action Models for Scientific Workflow Automation

Summary: arXiv:2601.09749v1 Announce Type: cross Abstract: Large Action Models (LAMs) extend large language models by enabling autonomous decision-making and tool execution, making them promising for automating scientific workflows. However, scientific workflows impose strict requirements on reproducibility, auditability, and deterministic execution, which are not satisfied by generic LLM-based agents. Unconstrained action generation can lead to silent state changes, non-deterministic executions, and irreproducible experimental results, limiting the applicability of LAMs in scientific settings. In this


Summary: arXiv:2601.09749v1 Announce Type: new Abstract: Large Action Models (LAMs) extend large language models by enabling autonomous decision-making and tool execution, making them promising for automating scientific workflows. However, scientific workflows impose strict requirements on reproducibility, auditability, and deterministic execution, which are not satisfied by generic LLM-based agents. Unconstrained action generation can lead to silent state changes, non-deterministic executions, and irreproducible experimental results, limiting the applicability of LAMs in scientific settings. In this p


S$^2$F: Principled Hybrid Testing With Fuzzing, Symbolic Execution, and Sampling

Summary: arXiv:2601.10068v1 Announce Type: new Abstract: Hybrid testing that integrates fuzzing, symbolic execution, and sampling has demonstrated superior testing efficiency compared to individual techniques. However, the state-of-the-art (SOTA) hybrid testing tools do not fully exploit the capabilities of symbolic execution and sampling in two key aspects. First, the SOTA hybrid testing tools employ tailored symbolic execution engines that tend to over-prune branches, leading to considerable time wasted waiting for seeds from the fuzzer and missing opportunities to discover crashes. Second, existing


STELP: Secure Transpilation and Execution of LLM-Generated Programs

Summary: arXiv:2601.05467v3 Announce Type: replace Abstract: Rapid evolution of Large Language Models (LLMs) has achieved major advances in reasoning, planning, and function-calling capabilities. Multi-agentic collaborative frameworks using such LLMs place them at the center of solving software development-related tasks such as code generation. However, direct use of LLM generated code in production software development systems is problematic. The code could be unstable or erroneous and contain vulnerabilities such as data poisoning, malicious attacks, and hallucinations that could lead to widespread s


The spectrum of isolation: From bare metal to WebAssembly

Summary:
Article URL: https://buildsoftwaresystems.com/post/guide-to-execution-environments/

Comments URL: https://news.ycombinator.com/item?id=46644661

Points: 3

# Comments: 1


Summary: Lego says its new tech-enabled products are its "most revolutionary innovation" since it launched its mini-figurines in 1978.


Summary:
Article URL: https://buildsoftwaresystems.com/post/guide-to-execution-environments/

Comments URL: https://news.ycombinator.com/item?id=46644661

Points: 12

# Comments: 6


Summary:
Article URL: https://buildsoftwaresystems.com/post/guide-to-execution-environments/

Comments URL: https://news.ycombinator.com/item?id=46644661

Points: 34

# Comments: 15


Summary:
Article URL: https://buildsoftwaresystems.com/post/guide-to-execution-environments/

Comments URL: https://news.ycombinator.com/item?id=46644661

Points: 39

# Comments: 15


Summary:
Article URL: https://buildsoftwaresystems.com/post/guide-to-execution-environments/

Comments URL: https://news.ycombinator.com/item?id=46644661

Points: 59

# Comments: 19


Show HN: I built a text-based business simulator to replace video courses

Summary:
I am a solo developer, and I built Core MBA because I was frustrated with the "video course" default in business education.
I wanted to build a "compiler for business logic"—a tool where I could read a concept in 5 minutes and immediately test it in a hostile environment to see if my strategy actually compiles or throws a runtime error.
The project is a business simulator built on React 19 and TypeScript.
The core technical innovation isn't just using AI; it's the architecture of a closed loop between a deterministic economic engine and a generative AI validation layer.
The biggest


Lego unveils tech-filled Smart Bricks - to play experts' unease


Summary:
Article URL: https://buildsoftwaresystems.com/post/guide-to-execution-environments/

Comments URL: https://news.ycombinator.com/item?id=46644661

Points: 62

# Comments: 19


Summary:
Article URL: https://buildsoftwaresystems.com/post/guide-to-execution-environments/

Comments URL: https://news.ycombinator.com/item?id=46644661

Points: 66

# Comments: 20


Summary:
Article URL: https://buildsoftwaresystems.com/post/guide-to-execution-environments/

Comments URL: https://news.ycombinator.com/item?id=46644661

Points: 68

# Comments: 20


Summary:
Article URL: https://buildsoftwaresystems.com/post/guide-to-execution-environments/

Comments URL: https://news.ycombinator.com/item?id=46644661

Points: 68

# Comments: 23


Summary:
Article URL: https://buildsoftwaresystems.com/post/guide-to-execution-environments/

Comments URL: https://news.ycombinator.com/item?id=46644661

Points: 70

# Comments: 23


Summary:
Article URL: https://buildsoftwaresystems.com/post/guide-to-execution-environments/

Comments URL: https://news.ycombinator.com/item?id=46644661

Points: 70

# Comments: 24


Summary:
Article URL: https://buildsoftwaresystems.com/post/guide-to-execution-environments/

Comments URL: https://news.ycombinator.com/item?id=46644661

Points: 71

# Comments: 24


Summary: arXiv:2601.10402v1 Announce Type: new Abstract: The advancement of artificial intelligence toward agentic science is currently bottlenecked by the challenge of ultra-long-horizon autonomy, the ability to sustain strategic coherence and iterative correction over experimental cycles spanning days or weeks. While Large Language Models (LLMs) have demonstrated prowess in short-horizon reasoning, they are easily overwhelmed by execution details in the high-dimensional, delayed-feedback environments of real-world research, failing to consolidate sparse feedback into coherent long-term guidance. Here


Summary: arXiv:2601.09749v1 Announce Type: cross Abstract: Large Action Models (LAMs) extend large language models by enabling autonomous decision-making and tool execution, making them promising for automating scientific workflows. However, scientific workflows impose strict requirements on reproducibility, auditability, and deterministic execution, which are not satisfied by generic LLM-based agents. Unconstrained action generation can lead to silent state changes, non-deterministic executions, and irreproducible experimental results, limiting the applicability of LAMs in scientific settings. In this


Summary:
Article URL: https://buildsoftwaresystems.com/post/guide-to-execution-environments/

Comments URL: https://news.ycombinator.com/item?id=46644661

Points: 72

# Comments: 24


Summary:
Article URL: https://buildsoftwaresystems.com/post/guide-to-execution-environments/

Comments URL: https://news.ycombinator.com/item?id=46644661

Points: 74

# Comments: 24


Summary: Lego says its new tech-enabled products are its "most revolutionary innovation" since it launched its mini-figurines in 1978.


Summary:
Article URL: https://buildsoftwaresystems.com/post/guide-to-execution-environments/

Comments URL: https://news.ycombinator.com/item?id=46644661

Points: 76

# Comments: 24


Summary:
Article URL: https://buildsoftwaresystems.com/post/guide-to-execution-environments/

Comments URL: https://news.ycombinator.com/item?id=46644661

Points: 85

# Comments: 30


Summary:
Article URL: https://buildsoftwaresystems.com/post/guide-to-execution-environments/

Comments URL: https://news.ycombinator.com/item?id=46644661

Points: 86

# Comments: 31


Summary:
Article URL: https://buildsoftwaresystems.com/post/guide-to-execution-environments/

Comments URL: https://news.ycombinator.com/item?id=46644661

Points: 87

# Comments: 31


Why are more bosses sharing the top job?

Summary: More bosses are sharing the top job giving them more time for family and breaks.


Integrating Attendance Tracking and Emotion Detection for Enhanced Student Engagement in Smart Classrooms

Summary: arXiv:2601.08049v1 Announce Type: new Abstract: The increasing adoption of smart classroom technologies in higher education has mainly focused on automating attendance, with limited attention given to students' emotional and cognitive engagement during lectures. This limits instructors' ability to identify disengagement and adapt teaching strategies in real time. This paper presents SCASED (Smart Classroom Attendance System with Emotion Detection), an IoT-based system that integrates automated attendance tracking with facial emotion recognition to support classroom engagement monitoring. The s


Forecast Aware Deep Reinforcement Learning for Efficient Electricity Load Scheduling in Dairy Farms

Summary: arXiv:2601.08052v1 Announce Type: new Abstract: Dairy farming is an energy intensive sector that relies heavily on grid electricity. With increasing renewable energy integration, sustainable energy management has become essential for reducing grid dependence and supporting the United Nations Sustainable Development Goal 7 on affordable and clean energy. However, the intermittent nature of renewables poses challenges in balancing supply and demand in real time. Intelligent load scheduling is therefore crucial to minimize operational costs while maintaining reliability. Reinforcement Learning ha


Embedded AI Companion System on Edge Devices

Summary: arXiv:2601.08128v1 Announce Type: new Abstract: Computational resource constraints on edge devices make it difficult to develop a fully embedded AI companion system with a satisfactory user experience. AI companion and memory systems detailed in existing literature cannot be directly used in such an environment due to lack of compute resources and latency concerns. In this paper, we propose a memory paradigm that alternates between active and inactive phases: during phases of user activity, the system performs low-latency, real-time dialog using lightweight retrieval over existing memories and


ZeroDVFS: Zero-Shot LLM-Guided Core and Frequency Allocation for Embedded Platforms

Summary: arXiv:2601.08166v1 Announce Type: new Abstract: Dynamic voltage and frequency scaling (DVFS) and task-to-core allocation are critical for thermal management and balancing energy and performance in embedded systems. Existing approaches either rely on utilization-based heuristics that overlook stall times, or require extensive offline profiling for table generation, preventing runtime adaptation. We propose a model-based hierarchical multi-agent reinforcement learning (MARL) framework for thermal- and energy-aware scheduling on multi-core platforms. Two collaborative agents decompose the exponen


Adapting Rules of Official International Mahjong for Online Players

Summary: arXiv:2601.08211v1 Announce Type: new Abstract: As one of the worldwide spread traditional game, Official International Mahjong can be played and promoted online through remote devices instead of requiring face-to-face interaction. However, online players have fragmented playtime and unfixed combination of opponents in contrary to offline players who have fixed opponents for multiple rounds of play. Therefore, the rules designed for offline players need to be modified to ensure the fairness of online single-round play. Specifically, We employ a world champion AI to engage in self-play competit


Sparsity Is Necessary: Polynomial-Time Stability for Agentic LLMs in Large Action Spaces

Summary: arXiv:2601.08271v1 Announce Type: new Abstract: Tool-augmented LLM systems expose a control regime that learning theory has largely ignored: sequential decision-making with a massive discrete action universe (tools, APIs, documents) in which only a small, unknown subset is relevant for any fixed task distribution. We formalize this setting as Sparse Agentic Control (SAC), where policies admit block-sparse representations over M >> 1 actions and rewards depend on sparse main effects and (optionally) sparse synergies. We study ell_{1,2}-regularized policy learning through a convex surrogate and


Deconstructing Pre-training: Knowledge Attribution Analysis in MoE and Dense Models

Summary: arXiv:2601.08383v1 Announce Type: new Abstract: Mixture-of-Experts (MoE) architectures decouple model capacity from per-token computation, enabling scaling beyond the computational limits imposed by dense scaling laws. Yet how MoE architectures shape knowledge acquisition during pre-training, and how this process differs from dense architectures, remains unknown. To address this issue, we introduce Gated-LPI (Log-Probability Increase), a neuron-level attribution metric that decomposes log-probability increase across neurons. We present a time-resolved comparison of knowledge acquisition dynami


Hybrid Distillation with CoT Guidance for Edge-Drone Control Code Generation

Summary: arXiv:2601.08412v1 Announce Type: new Abstract: With large language models demonstrating significant potential in code generation tasks, their application to onboard control of resource-constrained Unmanned Aerial Vehicles has emerged as an important research direction. However, a notable contradiction exists between the high resource consumption of large models and the real-time, lightweight requirements of UAV platforms. This paper proposes an integrated approach that combines knowledge distillation, chain-of-thought guidance, and supervised fine-tuning for UAV multi-SDK control tasks, aimin


What If TSF: A Benchmark for Reframing Forecasting as Scenario-Guided Multimodal Forecasting

Summary: arXiv:2601.08509v1 Announce Type: new Abstract: Time series forecasting is critical to real-world decision making, yet most existing approaches remain unimodal and rely on extrapolating historical patterns. While recent progress in large language models (LLMs) highlights the potential for multimodal forecasting, existing benchmarks largely provide retrospective or misaligned raw context, making it unclear whether such models meaningfully leverage textual inputs. In practice, human experts incorporate what-if scenarios with historical evidence, often producing distinct forecasts from the same o


Sketch-Based Facade Renovation With Generative AI: A Streamlined Framework for Bypassing As-Built Modelling in Industrial Adaptive Reuse

Summary: arXiv:2601.08531v1 Announce Type: new Abstract: Facade renovation offers a more sustainable alternative to full demolition, yet producing design proposals that preserve existing structures while expressing new intent remains challenging. Current workflows typically require detailed as-built modelling before design, which is time-consuming, labour-intensive, and often involves repeated revisions. To solve this issue, we propose a three-stage framework combining generative artificial intelligence (AI) and vision-language models (VLM) that directly processes rough structural sketch and textual de


WaterCopilot: An AI-Driven Virtual Assistant for Water Management

Summary: arXiv:2601.08559v1 Announce Type: new Abstract: Sustainable water resource management in transboundary river basins is challenged by fragmented data, limited real-time access, and the complexity of integrating diverse information sources. This paper presents WaterCopilot-an AI-driven virtual assistant developed through collaboration between the International Water Management Institute (IWMI) and Microsoft Research for the Limpopo River Basin (LRB) to bridge these gaps through a unified, interactive platform. Built on Retrieval-Augmented Generation (RAG) and tool-calling architectures, WaterCop


Photometric Redshift Estimation Using Scaled Ensemble Learning

Summary: arXiv:2601.07292v1 Announce Type: cross Abstract: The development of the state-of-the-art telescopic systems capable of performing expansive sky surveys such as the Sloan Digital Sky Survey, Euclid, and the Rubin Observatory's Legacy Survey of Space and Time (LSST) has significantly advanced efforts to refine cosmological models. These advances offer deeper insight into persistent challenges in astrophysics and our understanding of the Universe's evolution. A critical component of this progress is the reliable estimation of photometric redshifts (Pz). To improve the precision and efficiency of


A survey: Information search time optimization based on RAG (Retrieval Augmentation Generation) chatbot

Summary: arXiv:2601.07838v1 Announce Type: cross Abstract: Retrieval-Augmented Generation (RAG) based chatbots are not only useful for information retrieval through questionanswering but also for making complex decisions based on injected private data.we present a survey on how much search time can be saved when retrieving complex information within an organization called "X Systems"(a stealth mode company) by using a RAG-based chatbot compared to traditional search methods. We compare the information retrieval time using standard search techniques versus the RAG-based chatbot for the same queries. Our


Rigorous and Generalized Proof of Security of Bitcoin Protocol with Bounded Network Delay

Summary: arXiv:2601.09082v1 Announce Type: new Abstract: A proof of the security of the Bitcoin protocol is made rigorous, and simplified in certain parts. A computational model in which an adversary can delay transmission of blocks by time $\Delta$ is considered. The protocol is generalized to allow blocks of different scores and a proof within this more general model is presented. An approach used in a previous paper that used random walk theory is shown through a counterexample to be incorrect; an approach involving a punctured block arrival process is shown to remedy this error. Thus, it is proven


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


CallShield: Secure Caller Authentication over Real-Time Audio Channels

Summary: arXiv:2601.09327v1 Announce Type: new Abstract: We present CallShield, the first caller identity authentication system that operates entirely at the audio layer, without relying on speech transcription, internet connectivity, or trusted infrastructure. CallShield introduces a real-time neural watermarking technique that enables per-bit embedding and recovery within 40-millisecond frames of live 8 kHz speech. This capability allows CallShield to transform the real-time audio channel into a noisy serial communication medium. To ensure reliable data transmission, CallShield implements a low-bitra


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


VIGIL: Defending LLM Agents Against Tool Stream Injection via Verify-Before-Commit

Summary: arXiv:2601.05755v2 Announce Type: replace Abstract: LLM agents operating in open environments face escalating risks from indirect prompt injection, particularly within the tool stream where manipulated metadata and runtime feedback hijack execution flow. Existing defenses encounter a critical dilemma as advanced models prioritize injected rules due to strict alignment while static protection mechanisms sever the feedback loop required for adaptive reasoning. To reconcile this conflict, we propose \textbf{VIGIL}, a framework that shifts the paradigm from restrictive isolation to a verify-before


EZInput: A Cross-Environment Python Library for Easy UI Generation in Scientific Computing

Summary: arXiv:2601.08859v1 Announce Type: new Abstract: Researchers face a persistent barrier when applying computational algorithms with parameter configuration typically demanding programming skills, interfaces differing across environments, and settings rarely persisting between sessions. This fragmentation forces repetitive input, slows iterative exploration, and undermines reproducibility because parameter choices are difficult to record, share, and reuse. We present EZInput, a cross-runtime environment Python library enabling algorithm developers to automatically generate graphical user interfac


On the Flakiness of LLM-Generated Tests for Industrial and Open-Source Database Management Systems

Summary: arXiv:2601.08998v1 Announce Type: new Abstract: Flaky tests are a common problem in software testing. They produce inconsistent results when executed multiple times on the same code, invalidating the assumption that a test failure indicates a software defect. Recent work on LLM-based test generation has identified flakiness as a potential problem with generated tests. However, its prevalence and underlying causes are unclear. We examined the flakiness of LLM-generated tests in the context of four relational database management systems: SAP HANA, DuckDB, MySQL, and SQLite. We amplified test sui


DepRadar: Agentic Coordination for Context Aware Defect Impact Analysis in Deep Learning Libraries

Summary: arXiv:2601.09440v1 Announce Type: new Abstract: Deep learning libraries like Transformers and Megatron are now widely adopted in modern AI programs. However, when these libraries introduce defects, ranging from silent computation errors to subtle performance regressions, it is often challenging for downstream users to assess whether their own programs are affected. Such impact analysis requires not only understanding the defect semantics but also checking whether the client code satisfies complex triggering conditions involving configuration flags, runtime environments, and indirect API usage.


release-1.27.4

Summary: [nginx-1.27.4](https://nginx.org/en/download.html) mainline version has been released, featuring [optimized resource usage for complex SSL configurations](https://blog.nginx.org/blog/optimizing-resource-usage-for-complex-ssl-configurations), and with a fix for the [SSL session reuse](https://nginx.org/en/security_advisories.html) vulnerability (CVE-2025-23419). See official [CHANGES](https://nginx.org/en/CHANGES) on nginx.org. Below is a release summary generated by GitHub. ## What's Changed * Version bump. by @arut in https://github.com/nginx/nginx/pull/361 * QUIC: fixed client request timeout in 0-RTT scenarios. by @nandsky in https://github.com/nginx/nginx/pull/353 * Updated security policy to clarify experimental features by @jzebor-at-f5 in https://github.com/nginx/nginx/pull/404 * QU


release-1.27.4


3.9.0 / 2026-01-06

Summary: ## Note for users of Native Histograms In version 3.9, Native Histograms is no longer experimental, and the feature flag `native-histogram` has no effect. You must now turn on the config setting `scrape_native_histograms` to collect Native Histogram samples from exporters. ## Changelog - [CHANGE] Native Histograms are no longer experimental! Make the `native-histogram` feature flag a no-op. Use `scrape_native_histograms` config option instead. #17528 - [CHANGE] API: Add maximum limit of 10,000 sets of statistics to TSDB status endpoint. #17647 - [FEATURE] API: Add /api/v1/features for clients to understand which features are supported. #17427 - [FEATURE] Promtool: Add `start_timestamp` field for unit tests. #17636 - [FEATURE] Promtool: Add `--format seriesjson` option to `tsdb dump` to out


3.9.0 / 2026-01-06


3.9.0-rc.0 / 2025-12-18

Summary: **Note:** In version 3.9, Native Histograms is no longer experimental, and the feature flag `native-histogram` has no effect. You must now turn on the config setting `scrape_native_histogram` to collect Native Histogram samples from exporters. - [CHANGE] Native Histograms are no longer experimental! Make the `native-histogram` feature flag a no-op. Use `scrape_native_histograms` config option instead. #17528 - [CHANGE] API: Add maximum limit of 10,000 sets of statistics to TSDB status endpoint. #17647 - [FEATURE] API: Add /api/v1/features for clients to understand which features are supported. #17427 - [FEATURE] Promtool: Add `start_timestamp` field for unit tests. #17636 - [FEATURE] Promtool: Add `--format seriesjson` option to `tsdb dump` to output just series labels in JSON format. #134


3.9.0-rc.0 / 2025-12-18


3.8.0-rc.1 / 2025-11-21

Summary: * [CHANGE] Remote-write 2 (receiving): Update to [2.0-rc.4 spec](https://github.com/prometheus/docs/blob/60c24e450010df38cfcb4f65df874f6f9b26dbcb/docs/specs/prw/remote_write_spec_2_0.md). "created timestamp" (CT) is now called "start timestamp" (ST). #17411 * [FEATURE] OAuth2: support jwt-bearer grant-type (RFC7523 3.1). #17592


3.8.0-rc.1 / 2025-11-21


12.1.4

Summary: [Download page](https://grafana.com/grafana/download/12.1.4) [What's new highlights](https://grafana.com/docs/grafana/latest/whatsnew/) ### Bug fixes - **Alerting:** Fix unmarshalling of GettableStatus to include time intervals [#112733](https://github.com/grafana/grafana/pull/112733), [@yuri-tceretian](https://github.com/yuri-tceretian) - **AnalyticsSummaries:** Fix dashboard rollup not resetting "last X days" metrics to zero (Enterprise) - **AnalyticsSummaries:** Fix dashboard rollup totals resetting incorrectly (Enterprise) - **Security:** fix for CVE-2025-41115 in SCIM (System for Cross-domain Identity Management) (Enterprise)


12.1.4


Why are more bosses sharing the top job?

Summary: More bosses are sharing the top job giving them more time for family and breaks.


Thinking Long, but Short: Stable Sequential Test-Time Scaling for Large Reasoning Models

Summary: arXiv:2601.09855v1 Announce Type: new Abstract: Sequential test-time scaling is a promising training-free method to improve large reasoning model accuracy, but as currently implemented, significant limitations have been observed. Inducing models to think for longer can increase their accuracy, but as the length of reasoning is further extended, it has also been shown to result in accuracy degradation and model instability. This work presents a novel sequential test-time scaling method, Min-Seek, which improves model accuracy significantly over a wide range of induced thoughts, stabilizing the


SPRInG: Continual LLM Personalization via Selective Parametric Adaptation and Retrieval-Interpolated Generation

Summary: arXiv:2601.09974v1 Announce Type: new Abstract: Personalizing Large Language Models typically relies on static retrieval or one-time adaptation, assuming user preferences remain invariant over time. However, real-world interactions are dynamic, where user interests continuously evolve, posing a challenge for models to adapt to preference drift without catastrophic forgetting. Standard continual learning approaches often struggle in this context, as they indiscriminately update on noisy interaction streams, failing to distinguish genuine preference shifts from transient contexts. To address thi


Memo-SQL: Structured Decomposition and Experience-Driven Self-Correction for Training-Free NL2SQL

Summary: arXiv:2601.10011v1 Announce Type: new Abstract: Existing NL2SQL systems face two critical limitations: (1) they rely on in-context learning with only correct examples, overlooking the rich signal in historical error-fix pairs that could guide more robust self-correction; and (2) test-time scaling approaches often decompose questions arbitrarily, producing near-identical SQL candidates across runs and diminishing ensemble gains. Moreover, these methods suffer from a stark accuracy-efficiency trade-off: high performance demands excessive computation, while fast variants compromise quality. We pr


Is More Context Always Better? Examining LLM Reasoning Capability for Time Interval Prediction

Summary: arXiv:2601.10132v1 Announce Type: new Abstract: Large Language Models (LLMs) have demonstrated impressive capabilities in reasoning and prediction across different domains. Yet, their ability to infer temporal regularities from structured behavioral data remains underexplored. This paper presents a systematic study investigating whether LLMs can predict time intervals between recurring user actions, such as repeated purchases, and how different levels of contextual information shape their predictive behavior. Using a simple but representative repurchase scenario, we benchmark state-of-the-art


History Is Not Enough: An Adaptive Dataflow System for Financial Time-Series Synthesis

Summary: arXiv:2601.10143v1 Announce Type: new Abstract: In quantitative finance, the gap between training and real-world performance-driven by concept drift and distributional non-stationarity-remains a critical obstacle for building reliable data-driven systems. Models trained on static historical data often overfit, resulting in poor generalization in dynamic markets. The mantra "History Is Not Enough" underscores the need for adaptive data generation that learns to evolve with the market rather than relying solely on past observations. We present a drift-aware dataflow system that integrates machin


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


How does downsampling affect needle electromyography signals? A generalisable workflow for understanding downsampling effects on high-frequency time series

Summary: arXiv:2601.10191v1 Announce Type: new Abstract: Automated analysis of needle electromyography (nEMG) signals is emerging as a tool to support the detection of neuromuscular diseases (NMDs), yet the signals' high and heterogeneous sampling rates pose substantial computational challenges for feature-based machine-learning models, particularly for near real-time analysis. Downsampling offers a potential solution, but its impact on diagnostic signal content and classification performance remains insufficiently understood. This study presents a workflow for systematically evaluating information los


LLMdoctor: Token-Level Flow-Guided Preference Optimization for Efficient Test-Time Alignment of Large Language Models

Summary: arXiv:2601.10416v1 Announce Type: new Abstract: Aligning Large Language Models (LLMs) with human preferences is critical, yet traditional fine-tuning methods are computationally expensive and inflexible. While test-time alignment offers a promising alternative, existing approaches often rely on distorted trajectory-level signals or inefficient sampling, fundamentally capping performance and failing to preserve the generative diversity of the base model. This paper introduces LLMdoctor, a novel framework for efficient test-time alignment that operates via a patient-doctor paradigm. It integrate


Clinical Document Metadata Extraction: A Scoping Review

Summary: arXiv:2601.09730v1 Announce Type: cross Abstract: Clinical document metadata, such as document type, structure, author role, medical specialty, and encounter setting, is essential for accurate interpretation of information captured in clinical documents. However, vast documentation heterogeneity and drift over time challenge harmonization of document metadata. Automated extraction methods have emerged to coalesce metadata from disparate practices into target schema. This scoping review aims to catalog research on clinical document metadata extraction, identify methodological trends and applica


Formal Safety Guarantees for Autonomous Vehicles using Barrier Certificates

Summary: arXiv:2601.09740v1 Announce Type: cross Abstract: Modern AI technologies enable autonomous vehicles to perceive complex scenes, predict human behavior, and make real-time driving decisions. However, these data-driven components often operate as black boxes, lacking interpretability and rigorous safety guarantees. Autonomous vehicles operate in dynamic, mixed-traffic environments where interactions with human-driven vehicles introduce uncertainty and safety challenges. This work develops a formally verified safety framework for Connected and Autonomous Vehicles (CAVs) that integrates Barrier Ce


Heterogeneous computing platform for real-time robotics

Summary: arXiv:2601.09755v1 Announce Type: cross Abstract: After Industry 4.0 has embraced tight integration between machinery (OT), software (IT), and the Internet, creating a web of sensors, data, and algorithms in service of efficient and reliable production, a new concept of Society 5.0 is emerging, in which infrastructure of a city will be instrumented to increase reliability, efficiency, and safety. Robotics will play a pivotal role in enabling this vision that is pioneered by the NEOM initiative - a smart city, co-inhabited by humans and robots. In this paper we explore the computing platform th


A Risk-Stratified Benchmark Dataset for Bad Randomness (SWC-120) Vulnerabilities in Ethereum Smart Contracts

Summary: arXiv:2601.09836v1 Announce Type: new Abstract: Many Ethereum smart contracts rely on block attributes such as block.timestamp or blockhash to generate random numbers for applications like lotteries and games. However, these values are predictable and miner-manipulable, creating the Bad Randomness vulnerability (SWC-120) that has led to real-world exploits. Current detection tools identify only simple patterns and fail to verify whether protective modifiers actually guard vulnerable code. A major obstacle to improving these tools is the lack of large, accurately labeled datasets. This paper pr


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


Malware Classification using Diluted Convolutional Neural Network with Fast Gradient Sign Method

Summary: arXiv:2601.09933v1 Announce Type: new Abstract: Android malware has become an increasingly critical threat to organizations, society and individuals, posing significant risks to privacy, data security and infrastructure. As malware continues to evolve in terms of complexity and sophistication, the mitigation and detection of these malicious software instances have become more time consuming and challenging particularly due to the requirement of large number of features to identify potential malware. To address these challenges, this research proposes Fast Gradient Sign Method with Diluted Conv


Fuzzychain-edge: A novel Fuzzy logic-based adaptive Access control model for Blockchain in Edge Computing

Summary: arXiv:2601.10105v1 Announce Type: new Abstract: The rapid integration of IoT with edge computing has revolutionized various domains, particularly healthcare, by enabling real-time data sharing, remote monitoring, and decision-making. However, it introduces critical challenges, including data privacy breaches, security vulnerabilities, especially in environments dealing with sensitive information. Traditional access control mechanisms and centralized security systems do not address these issues, leaving IoT environments exposed to unauthorized access and data misuse. This research proposes Fuzz


Advanced Encryption Technique for Multimedia Data Using Sudoku-Based Algorithms for Enhanced Security

Summary: arXiv:2601.10119v1 Announce Type: new Abstract: Encryption and Decryption is the process of sending a message in a ciphered way that appears meaningless and could be deciphered using a key for security purposes to avoid data breaches. This paper expands on the previous work on Sudoku-based encryption methods, applying it to other forms of media including images, audio and video. It also enhances the security of key generation and usage by making it dependent on the timestamp of when the message was transmitted. It is a versatile system that works on multimodal data and functions as a block-bas


XuanJia: A Comprehensive Virtualization-Based Code Obfuscator for Binary Protection

Summary: arXiv:2601.10261v1 Announce Type: new Abstract: Virtualization-based binary obfuscation is widely adopted to protect software intellectual property, yet existing approaches leave exception-handling (EH) metadata unprotected to preserve ABI compatibility. This exposed metadata leaks rich structural information, such as stack layouts, control-flow boundaries, and object lifetimes, which can be exploited to facilitate reverse engineering. In this paper, we present XuanJia, a comprehensive VM-based binary obfuscation framework that provides end-to-end protection for both executable code and except


Hybrid Encryption with Certified Deletion in Preprocessing Model

Summary: arXiv:2601.10542v1 Announce Type: new Abstract: Certified deletion allows Alice to outsource data to Bob and, at a later time, obtain a verifiable guarantee that the file has been irreversibly deleted at her request. The functionality, while impossible using classical information alone, can be achieved using quantum information. Existing approaches, rely on one-time pad (OTP) encryption, or use computational hardness assumptions that may be vulnerable to future advances in classical or quantum computing. In this work, we introduce and formalize hybrid encryption with certified deletion in the


Distortion maps for elliptic curves over finite fields

Summary: arXiv:2601.09904v1 Announce Type: cross Abstract: The Weil pairing on elliptic curves has deep links with discrete logarithm problems. In practice, to better suit the functionalities of cryptosystems, one often needs to modify the original Weil pairing via what is called a distortion map. We propose a study on the question of the existence of distortion maps for elliptic curves over finite fields. We revisit results from the literature and provide detailed proofs. We also propose new perspectives at times.


Adoption and Evolution of Code Style and Best Programming Practices in Open-Source Projects

Summary: arXiv:2601.09832v1 Announce Type: new Abstract: Following code style conventions in software projects is essential for maintaining overall code quality. Adhering to these conventions improves maintainability, understandability, and extensibility. Additionally, following best practices during software development enhances performance and reduces the likelihood of errors. This paper analyzes 1,036 popular open-source JAVA projects on GITHUB to study how code style and programming practices are adopted and evolve over time, examining their prevalence and the most common violations. Additionally,


S$^2$F: Principled Hybrid Testing With Fuzzing, Symbolic Execution, and Sampling

Summary: arXiv:2601.10068v1 Announce Type: new Abstract: Hybrid testing that integrates fuzzing, symbolic execution, and sampling has demonstrated superior testing efficiency compared to individual techniques. However, the state-of-the-art (SOTA) hybrid testing tools do not fully exploit the capabilities of symbolic execution and sampling in two key aspects. First, the SOTA hybrid testing tools employ tailored symbolic execution engines that tend to over-prune branches, leading to considerable time wasted waiting for seeds from the fuzzer and missing opportunities to discover crashes. Second, existing


Mark My Works Autograder for Programming Courses

Summary: arXiv:2601.10093v1 Announce Type: new Abstract: Large programming courses struggle to provide timely, detailed feedback on student code. We developed Mark My Works, a local autograding system that combines traditional unit testing with LLM-generated explanations. The system uses role-based prompts to analyze submissions, critique code quality, and generate pedagogical feedback while maintaining transparency in its reasoning process. We piloted the system in a 191-student engineering course, comparing AI-generated assessments with human grading on 79 submissions. While AI scores showed no linea


Towards Online Malware Detection using Process Resource Utilization Metrics

Summary: arXiv:2601.10164v1 Announce Type: new Abstract: The rapid growth of Cloud Computing and Internet of Things (IoT) has significantly increased the interconnection of computational resources, creating an environment where malicious software (malware) can spread rapidly. To address this challenge, researchers are increasingly utilizing Machine Learning approaches to identify malware through behavioral (i.e. dynamic) cues. However, current approaches are limited by their reliance on large labeled datasets, fixed model training, and the assumption that a trained model remains effective over time-dis


Developer Interaction Patterns with Proactive AI: A Five-Day Field Study

Summary: arXiv:2601.10253v1 Announce Type: cross Abstract: Current in-IDE AI coding tools typically rely on time-consuming manual prompting and context management, whereas proactive alternatives that anticipate developer needs without explicit invocation remain underexplored. Understanding when humans are receptive to such proactive AI assistance during their daily work remains an open question in human-AI interaction research. We address this gap through a field study of proactive AI assistance in professional developer workflows. We present a five-day in-the-wild study with 15 developers who interact


Multi-Threaded Software Model Checking via Parallel Trace Abstraction Refinement

Summary: arXiv:2509.13699v2 Announce Type: replace-cross Abstract: Automatic software verification is a valuable means for software quality assurance. However, automatic verification and in particular software model checking can be time-consuming, which hinders their practical applicability e.g., the use in continuous integration. One solution to address the issue is to reduce the response time of the verification procedure by leveraging today's multi-core CPUs. In this paper, we propose a solution to parallelize trace abstraction, an abstraction-based approach to software model checking. The underlyin


release-1.27.4

Summary: [nginx-1.27.4](https://nginx.org/en/download.html) mainline version has been released, featuring [optimized resource usage for complex SSL configurations](https://blog.nginx.org/blog/optimizing-resource-usage-for-complex-ssl-configurations), and with a fix for the [SSL session reuse](https://nginx.org/en/security_advisories.html) vulnerability (CVE-2025-23419). See official [CHANGES](https://nginx.org/en/CHANGES) on nginx.org. Below is a release summary generated by GitHub. ## What's Changed * Version bump. by @arut in https://github.com/nginx/nginx/pull/361 * QUIC: fixed client request timeout in 0-RTT scenarios. by @nandsky in https://github.com/nginx/nginx/pull/353 * Updated security policy to clarify experimental features by @jzebor-at-f5 in https://github.com/nginx/nginx/pull/404 * QU


release-1.27.4


3.9.0 / 2026-01-06

Summary: ## Note for users of Native Histograms In version 3.9, Native Histograms is no longer experimental, and the feature flag `native-histogram` has no effect. You must now turn on the config setting `scrape_native_histograms` to collect Native Histogram samples from exporters. ## Changelog - [CHANGE] Native Histograms are no longer experimental! Make the `native-histogram` feature flag a no-op. Use `scrape_native_histograms` config option instead. #17528 - [CHANGE] API: Add maximum limit of 10,000 sets of statistics to TSDB status endpoint. #17647 - [FEATURE] API: Add /api/v1/features for clients to understand which features are supported. #17427 - [FEATURE] Promtool: Add `start_timestamp` field for unit tests. #17636 - [FEATURE] Promtool: Add `--format seriesjson` option to `tsdb dump` to out


3.9.0 / 2026-01-06


3.9.0-rc.0 / 2025-12-18

Summary: **Note:** In version 3.9, Native Histograms is no longer experimental, and the feature flag `native-histogram` has no effect. You must now turn on the config setting `scrape_native_histogram` to collect Native Histogram samples from exporters. - [CHANGE] Native Histograms are no longer experimental! Make the `native-histogram` feature flag a no-op. Use `scrape_native_histograms` config option instead. #17528 - [CHANGE] API: Add maximum limit of 10,000 sets of statistics to TSDB status endpoint. #17647 - [FEATURE] API: Add /api/v1/features for clients to understand which features are supported. #17427 - [FEATURE] Promtool: Add `start_timestamp` field for unit tests. #17636 - [FEATURE] Promtool: Add `--format seriesjson` option to `tsdb dump` to output just series labels in JSON format. #134


3.9.0-rc.0 / 2025-12-18


3.8.0-rc.1 / 2025-11-21

Summary: * [CHANGE] Remote-write 2 (receiving): Update to [2.0-rc.4 spec](https://github.com/prometheus/docs/blob/60c24e450010df38cfcb4f65df874f6f9b26dbcb/docs/specs/prw/remote_write_spec_2_0.md). "created timestamp" (CT) is now called "start timestamp" (ST). #17411 * [FEATURE] OAuth2: support jwt-bearer grant-type (RFC7523 3.1). #17592


3.8.0-rc.1 / 2025-11-21


Why are more bosses sharing the top job?

Summary: More bosses are sharing the top job giving them more time for family and breaks.


Ask HN: How to make my website exist for 100 years?

Summary:
I have personal website and a lot of writings that I want to keep, also for my children one day will read those. How do I make my domain + content exist for a really long time? Domain + Server must be paid of annually, do I need to switch to other way of hosting?

Comments URL: https://news.ycombinator.com/item?id=46642372

Points: 10

# Comments: 12


Show HN: BGP Scout – BGP Network Browser

Summary:
Hi HN,
When working with BGP data, I kept running into the same friction: it’s easy to get raw data, but surprisingly hard to browse networks over time — especially by when they appeared, where they operate, and what they actually look like at a glance.
I built a small tool, bgpscout.io, to scratch that itch.
It lets you:
Browse ASNs by registration date and geography
See where a given network appears to have presence
View commonly scattered public data about an ASN in one place
Save searches to track when new networks matching certain criteria appear
All of this data is pub


Thinking Long, but Short: Stable Sequential Test-Time Scaling for Large Reasoning Models

Summary: arXiv:2601.09855v1 Announce Type: new Abstract: Sequential test-time scaling is a promising training-free method to improve large reasoning model accuracy, but as currently implemented, significant limitations have been observed. Inducing models to think for longer can increase their accuracy, but as the length of reasoning is further extended, it has also been shown to result in accuracy degradation and model instability. This work presents a novel sequential test-time scaling method, Min-Seek, which improves model accuracy significantly over a wide range of induced thoughts, stabilizing the


SPRInG: Continual LLM Personalization via Selective Parametric Adaptation and Retrieval-Interpolated Generation

Summary: arXiv:2601.09974v1 Announce Type: new Abstract: Personalizing Large Language Models typically relies on static retrieval or one-time adaptation, assuming user preferences remain invariant over time. However, real-world interactions are dynamic, where user interests continuously evolve, posing a challenge for models to adapt to preference drift without catastrophic forgetting. Standard continual learning approaches often struggle in this context, as they indiscriminately update on noisy interaction streams, failing to distinguish genuine preference shifts from transient contexts. To address thi


Memo-SQL: Structured Decomposition and Experience-Driven Self-Correction for Training-Free NL2SQL

Summary: arXiv:2601.10011v1 Announce Type: new Abstract: Existing NL2SQL systems face two critical limitations: (1) they rely on in-context learning with only correct examples, overlooking the rich signal in historical error-fix pairs that could guide more robust self-correction; and (2) test-time scaling approaches often decompose questions arbitrarily, producing near-identical SQL candidates across runs and diminishing ensemble gains. Moreover, these methods suffer from a stark accuracy-efficiency trade-off: high performance demands excessive computation, while fast variants compromise quality. We pr


Is More Context Always Better? Examining LLM Reasoning Capability for Time Interval Prediction

Summary: arXiv:2601.10132v1 Announce Type: new Abstract: Large Language Models (LLMs) have demonstrated impressive capabilities in reasoning and prediction across different domains. Yet, their ability to infer temporal regularities from structured behavioral data remains underexplored. This paper presents a systematic study investigating whether LLMs can predict time intervals between recurring user actions, such as repeated purchases, and how different levels of contextual information shape their predictive behavior. Using a simple but representative repurchase scenario, we benchmark state-of-the-art


History Is Not Enough: An Adaptive Dataflow System for Financial Time-Series Synthesis

Summary: arXiv:2601.10143v1 Announce Type: new Abstract: In quantitative finance, the gap between training and real-world performance-driven by concept drift and distributional non-stationarity-remains a critical obstacle for building reliable data-driven systems. Models trained on static historical data often overfit, resulting in poor generalization in dynamic markets. The mantra "History Is Not Enough" underscores the need for adaptive data generation that learns to evolve with the market rather than relying solely on past observations. We present a drift-aware dataflow system that integrates machin


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


How does downsampling affect needle electromyography signals? A generalisable workflow for understanding downsampling effects on high-frequency time series

Summary: arXiv:2601.10191v1 Announce Type: new Abstract: Automated analysis of needle electromyography (nEMG) signals is emerging as a tool to support the detection of neuromuscular diseases (NMDs), yet the signals' high and heterogeneous sampling rates pose substantial computational challenges for feature-based machine-learning models, particularly for near real-time analysis. Downsampling offers a potential solution, but its impact on diagnostic signal content and classification performance remains insufficiently understood. This study presents a workflow for systematically evaluating information los


LLMdoctor: Token-Level Flow-Guided Preference Optimization for Efficient Test-Time Alignment of Large Language Models

Summary: arXiv:2601.10416v1 Announce Type: new Abstract: Aligning Large Language Models (LLMs) with human preferences is critical, yet traditional fine-tuning methods are computationally expensive and inflexible. While test-time alignment offers a promising alternative, existing approaches often rely on distorted trajectory-level signals or inefficient sampling, fundamentally capping performance and failing to preserve the generative diversity of the base model. This paper introduces LLMdoctor, a novel framework for efficient test-time alignment that operates via a patient-doctor paradigm. It integrate


Clinical Document Metadata Extraction: A Scoping Review

Summary: arXiv:2601.09730v1 Announce Type: cross Abstract: Clinical document metadata, such as document type, structure, author role, medical specialty, and encounter setting, is essential for accurate interpretation of information captured in clinical documents. However, vast documentation heterogeneity and drift over time challenge harmonization of document metadata. Automated extraction methods have emerged to coalesce metadata from disparate practices into target schema. This scoping review aims to catalog research on clinical document metadata extraction, identify methodological trends and applica


Formal Safety Guarantees for Autonomous Vehicles using Barrier Certificates

Summary: arXiv:2601.09740v1 Announce Type: cross Abstract: Modern AI technologies enable autonomous vehicles to perceive complex scenes, predict human behavior, and make real-time driving decisions. However, these data-driven components often operate as black boxes, lacking interpretability and rigorous safety guarantees. Autonomous vehicles operate in dynamic, mixed-traffic environments where interactions with human-driven vehicles introduce uncertainty and safety challenges. This work develops a formally verified safety framework for Connected and Autonomous Vehicles (CAVs) that integrates Barrier Ce


Heterogeneous computing platform for real-time robotics

Summary: arXiv:2601.09755v1 Announce Type: cross Abstract: After Industry 4.0 has embraced tight integration between machinery (OT), software (IT), and the Internet, creating a web of sensors, data, and algorithms in service of efficient and reliable production, a new concept of Society 5.0 is emerging, in which infrastructure of a city will be instrumented to increase reliability, efficiency, and safety. Robotics will play a pivotal role in enabling this vision that is pioneered by the NEOM initiative - a smart city, co-inhabited by humans and robots. In this paper we explore the computing platform th


release-1.27.4

Summary: [nginx-1.27.4](https://nginx.org/en/download.html) mainline version has been released, featuring [optimized resource usage for complex SSL configurations](https://blog.nginx.org/blog/optimizing-resource-usage-for-complex-ssl-configurations), and with a fix for the [SSL session reuse](https://nginx.org/en/security_advisories.html) vulnerability (CVE-2025-23419). See official [CHANGES](https://nginx.org/en/CHANGES) on nginx.org. Below is a release summary generated by GitHub. ## What's Changed * Version bump. by @arut in https://github.com/nginx/nginx/pull/361 * QUIC: fixed client request timeout in 0-RTT scenarios. by @nandsky in https://github.com/nginx/nginx/pull/353 * Updated security policy to clarify experimental features by @jzebor-at-f5 in https://github.com/nginx/nginx/pull/404 * QU


release-1.27.4


3.9.0 / 2026-01-06

Summary: ## Note for users of Native Histograms In version 3.9, Native Histograms is no longer experimental, and the feature flag `native-histogram` has no effect. You must now turn on the config setting `scrape_native_histograms` to collect Native Histogram samples from exporters. ## Changelog - [CHANGE] Native Histograms are no longer experimental! Make the `native-histogram` feature flag a no-op. Use `scrape_native_histograms` config option instead. #17528 - [CHANGE] API: Add maximum limit of 10,000 sets of statistics to TSDB status endpoint. #17647 - [FEATURE] API: Add /api/v1/features for clients to understand which features are supported. #17427 - [FEATURE] Promtool: Add `start_timestamp` field for unit tests. #17636 - [FEATURE] Promtool: Add `--format seriesjson` option to `tsdb dump` to out


3.9.0 / 2026-01-06


3.9.0-rc.0 / 2025-12-18

Summary: **Note:** In version 3.9, Native Histograms is no longer experimental, and the feature flag `native-histogram` has no effect. You must now turn on the config setting `scrape_native_histogram` to collect Native Histogram samples from exporters. - [CHANGE] Native Histograms are no longer experimental! Make the `native-histogram` feature flag a no-op. Use `scrape_native_histograms` config option instead. #17528 - [CHANGE] API: Add maximum limit of 10,000 sets of statistics to TSDB status endpoint. #17647 - [FEATURE] API: Add /api/v1/features for clients to understand which features are supported. #17427 - [FEATURE] Promtool: Add `start_timestamp` field for unit tests. #17636 - [FEATURE] Promtool: Add `--format seriesjson` option to `tsdb dump` to output just series labels in JSON format. #134


3.9.0-rc.0 / 2025-12-18


3.8.0-rc.1 / 2025-11-21

Summary: * [CHANGE] Remote-write 2 (receiving): Update to [2.0-rc.4 spec](https://github.com/prometheus/docs/blob/60c24e450010df38cfcb4f65df874f6f9b26dbcb/docs/specs/prw/remote_write_spec_2_0.md). "created timestamp" (CT) is now called "start timestamp" (ST). #17411 * [FEATURE] OAuth2: support jwt-bearer grant-type (RFC7523 3.1). #17592


3.8.0-rc.1 / 2025-11-21


Summary: More bosses are sharing the top job giving them more time for family and breaks.


On Being a Human Being in the Time of Collapse (2022) [pdf]

Summary:
Article URL: https://web.cs.ucdavis.edu/~rogaway/papers/crisis/crisis.pdf

Comments URL: https://news.ycombinator.com/item?id=46644962

Points: 8

# Comments: 0


Summary:
Hi HN,
When working with BGP data, I kept running into the same friction: it’s easy to get raw data, but surprisingly hard to browse networks over time — especially by when they appeared, where they operate, and what they actually look like at a glance.
I built a small tool, bgpscout.io, to scratch that itch.
It lets you:
Browse ASNs by registration date and geography
See where a given network appears to have presence
View commonly scattered public data about an ASN in one place
Save searches to track when new networks matching certain criteria appear
All of this data is pub


Summary:
Article URL: https://web.cs.ucdavis.edu/~rogaway/papers/crisis/crisis.pdf

Comments URL: https://news.ycombinator.com/item?id=46644962

Points: 110

# Comments: 86


Summary:
Article URL: https://web.cs.ucdavis.edu/~rogaway/papers/crisis/crisis.pdf

Comments URL: https://news.ycombinator.com/item?id=46644962

Points: 115

# Comments: 92


Show HN: I built a text-based business simulator to replace video courses

Summary:
I am a solo developer, and I built Core MBA because I was frustrated with the "video course" default in business education.
I wanted to build a "compiler for business logic"—a tool where I could read a concept in 5 minutes and immediately test it in a hostile environment to see if my strategy actually compiles or throws a runtime error.
The project is a business simulator built on React 19 and TypeScript.
The core technical innovation isn't just using AI; it's the architecture of a closed loop between a deterministic economic engine and a generative AI validation layer.
The biggest


Cyberattack in Venezuela Demonstrated Precision of U.S. Capabilities

Summary:
Article URL: https://www.nytimes.com/2026/01/15/us/politics/cyberattack-venezuela-military.html

Comments URL: https://news.ycombinator.com/item?id=46647038

Points: 7

# Comments: 1


Summary: arXiv:2601.09855v1 Announce Type: new Abstract: Sequential test-time scaling is a promising training-free method to improve large reasoning model accuracy, but as currently implemented, significant limitations have been observed. Inducing models to think for longer can increase their accuracy, but as the length of reasoning is further extended, it has also been shown to result in accuracy degradation and model instability. This work presents a novel sequential test-time scaling method, Min-Seek, which improves model accuracy significantly over a wide range of induced thoughts, stabilizing the


Summary: arXiv:2601.09974v1 Announce Type: new Abstract: Personalizing Large Language Models typically relies on static retrieval or one-time adaptation, assuming user preferences remain invariant over time. However, real-world interactions are dynamic, where user interests continuously evolve, posing a challenge for models to adapt to preference drift without catastrophic forgetting. Standard continual learning approaches often struggle in this context, as they indiscriminately update on noisy interaction streams, failing to distinguish genuine preference shifts from transient contexts. To address thi


Summary: arXiv:2601.10011v1 Announce Type: new Abstract: Existing NL2SQL systems face two critical limitations: (1) they rely on in-context learning with only correct examples, overlooking the rich signal in historical error-fix pairs that could guide more robust self-correction; and (2) test-time scaling approaches often decompose questions arbitrarily, producing near-identical SQL candidates across runs and diminishing ensemble gains. Moreover, these methods suffer from a stark accuracy-efficiency trade-off: high performance demands excessive computation, while fast variants compromise quality. We pr


Summary: arXiv:2601.10132v1 Announce Type: new Abstract: Large Language Models (LLMs) have demonstrated impressive capabilities in reasoning and prediction across different domains. Yet, their ability to infer temporal regularities from structured behavioral data remains underexplored. This paper presents a systematic study investigating whether LLMs can predict time intervals between recurring user actions, such as repeated purchases, and how different levels of contextual information shape their predictive behavior. Using a simple but representative repurchase scenario, we benchmark state-of-the-art


Summary: arXiv:2601.10143v1 Announce Type: new Abstract: In quantitative finance, the gap between training and real-world performance-driven by concept drift and distributional non-stationarity-remains a critical obstacle for building reliable data-driven systems. Models trained on static historical data often overfit, resulting in poor generalization in dynamic markets. The mantra "History Is Not Enough" underscores the need for adaptive data generation that learns to evolve with the market rather than relying solely on past observations. We present a drift-aware dataflow system that integrates machin


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


Summary: arXiv:2601.10191v1 Announce Type: new Abstract: Automated analysis of needle electromyography (nEMG) signals is emerging as a tool to support the detection of neuromuscular diseases (NMDs), yet the signals' high and heterogeneous sampling rates pose substantial computational challenges for feature-based machine-learning models, particularly for near real-time analysis. Downsampling offers a potential solution, but its impact on diagnostic signal content and classification performance remains insufficiently understood. This study presents a workflow for systematically evaluating information los


Summary: arXiv:2601.10416v1 Announce Type: new Abstract: Aligning Large Language Models (LLMs) with human preferences is critical, yet traditional fine-tuning methods are computationally expensive and inflexible. While test-time alignment offers a promising alternative, existing approaches often rely on distorted trajectory-level signals or inefficient sampling, fundamentally capping performance and failing to preserve the generative diversity of the base model. This paper introduces LLMdoctor, a novel framework for efficient test-time alignment that operates via a patient-doctor paradigm. It integrate


Summary: arXiv:2601.09730v1 Announce Type: cross Abstract: Clinical document metadata, such as document type, structure, author role, medical specialty, and encounter setting, is essential for accurate interpretation of information captured in clinical documents. However, vast documentation heterogeneity and drift over time challenge harmonization of document metadata. Automated extraction methods have emerged to coalesce metadata from disparate practices into target schema. This scoping review aims to catalog research on clinical document metadata extraction, identify methodological trends and applica


Summary: arXiv:2601.09740v1 Announce Type: cross Abstract: Modern AI technologies enable autonomous vehicles to perceive complex scenes, predict human behavior, and make real-time driving decisions. However, these data-driven components often operate as black boxes, lacking interpretability and rigorous safety guarantees. Autonomous vehicles operate in dynamic, mixed-traffic environments where interactions with human-driven vehicles introduce uncertainty and safety challenges. This work develops a formally verified safety framework for Connected and Autonomous Vehicles (CAVs) that integrates Barrier Ce


Summary: arXiv:2601.09755v1 Announce Type: cross Abstract: After Industry 4.0 has embraced tight integration between machinery (OT), software (IT), and the Internet, creating a web of sensors, data, and algorithms in service of efficient and reliable production, a new concept of Society 5.0 is emerging, in which infrastructure of a city will be instrumented to increase reliability, efficiency, and safety. Robotics will play a pivotal role in enabling this vision that is pioneered by the NEOM initiative - a smart city, co-inhabited by humans and robots. In this paper we explore the computing platform th


release-1.27.4

Summary: [nginx-1.27.4](https://nginx.org/en/download.html) mainline version has been released, featuring [optimized resource usage for complex SSL configurations](https://blog.nginx.org/blog/optimizing-resource-usage-for-complex-ssl-configurations), and with a fix for the [SSL session reuse](https://nginx.org/en/security_advisories.html) vulnerability (CVE-2025-23419). See official [CHANGES](https://nginx.org/en/CHANGES) on nginx.org. Below is a release summary generated by GitHub. ## What's Changed * Version bump. by @arut in https://github.com/nginx/nginx/pull/361 * QUIC: fixed client request timeout in 0-RTT scenarios. by @nandsky in https://github.com/nginx/nginx/pull/353 * Updated security policy to clarify experimental features by @jzebor-at-f5 in https://github.com/nginx/nginx/pull/404 * QU


release-1.27.4


3.9.0 / 2026-01-06

Summary: ## Note for users of Native Histograms In version 3.9, Native Histograms is no longer experimental, and the feature flag `native-histogram` has no effect. You must now turn on the config setting `scrape_native_histograms` to collect Native Histogram samples from exporters. ## Changelog - [CHANGE] Native Histograms are no longer experimental! Make the `native-histogram` feature flag a no-op. Use `scrape_native_histograms` config option instead. #17528 - [CHANGE] API: Add maximum limit of 10,000 sets of statistics to TSDB status endpoint. #17647 - [FEATURE] API: Add /api/v1/features for clients to understand which features are supported. #17427 - [FEATURE] Promtool: Add `start_timestamp` field for unit tests. #17636 - [FEATURE] Promtool: Add `--format seriesjson` option to `tsdb dump` to out


3.9.0 / 2026-01-06


3.9.0-rc.0 / 2025-12-18

Summary: **Note:** In version 3.9, Native Histograms is no longer experimental, and the feature flag `native-histogram` has no effect. You must now turn on the config setting `scrape_native_histogram` to collect Native Histogram samples from exporters. - [CHANGE] Native Histograms are no longer experimental! Make the `native-histogram` feature flag a no-op. Use `scrape_native_histograms` config option instead. #17528 - [CHANGE] API: Add maximum limit of 10,000 sets of statistics to TSDB status endpoint. #17647 - [FEATURE] API: Add /api/v1/features for clients to understand which features are supported. #17427 - [FEATURE] Promtool: Add `start_timestamp` field for unit tests. #17636 - [FEATURE] Promtool: Add `--format seriesjson` option to `tsdb dump` to output just series labels in JSON format. #134


3.9.0-rc.0 / 2025-12-18


3.8.0-rc.1 / 2025-11-21

Summary: * [CHANGE] Remote-write 2 (receiving): Update to [2.0-rc.4 spec](https://github.com/prometheus/docs/blob/60c24e450010df38cfcb4f65df874f6f9b26dbcb/docs/specs/prw/remote_write_spec_2_0.md). "created timestamp" (CT) is now called "start timestamp" (ST). #17411 * [FEATURE] OAuth2: support jwt-bearer grant-type (RFC7523 3.1). #17592


3.8.0-rc.1 / 2025-11-21


Why are more bosses sharing the top job?

Summary: More bosses are sharing the top job giving them more time for family and breaks.


Summary:
Article URL: https://www.nytimes.com/2026/01/15/us/politics/cyberattack-venezuela-military.html

Comments URL: https://news.ycombinator.com/item?id=46647038

Points: 13

# Comments: 6


Summary:
I am a solo developer, and I built Core MBA because I was frustrated with the "video course" default in business education.
I wanted to build a "compiler for business logic"—a tool where I could read a concept in 5 minutes and immediately test it in a hostile environment to see if my strategy actually compiles or throws a runtime error.
The project is a business simulator built on React 19 and TypeScript.
The core technical innovation isn't just using AI; it's the architecture of a closed loop between a deterministic economic engine and a generative AI validation layer.
The biggest


Summary: arXiv:2601.09855v1 Announce Type: new Abstract: Sequential test-time scaling is a promising training-free method to improve large reasoning model accuracy, but as currently implemented, significant limitations have been observed. Inducing models to think for longer can increase their accuracy, but as the length of reasoning is further extended, it has also been shown to result in accuracy degradation and model instability. This work presents a novel sequential test-time scaling method, Min-Seek, which improves model accuracy significantly over a wide range of induced thoughts, stabilizing the


Summary: arXiv:2601.09974v1 Announce Type: new Abstract: Personalizing Large Language Models typically relies on static retrieval or one-time adaptation, assuming user preferences remain invariant over time. However, real-world interactions are dynamic, where user interests continuously evolve, posing a challenge for models to adapt to preference drift without catastrophic forgetting. Standard continual learning approaches often struggle in this context, as they indiscriminately update on noisy interaction streams, failing to distinguish genuine preference shifts from transient contexts. To address thi


Summary: arXiv:2601.10011v1 Announce Type: new Abstract: Existing NL2SQL systems face two critical limitations: (1) they rely on in-context learning with only correct examples, overlooking the rich signal in historical error-fix pairs that could guide more robust self-correction; and (2) test-time scaling approaches often decompose questions arbitrarily, producing near-identical SQL candidates across runs and diminishing ensemble gains. Moreover, these methods suffer from a stark accuracy-efficiency trade-off: high performance demands excessive computation, while fast variants compromise quality. We pr


Summary: arXiv:2601.10132v1 Announce Type: new Abstract: Large Language Models (LLMs) have demonstrated impressive capabilities in reasoning and prediction across different domains. Yet, their ability to infer temporal regularities from structured behavioral data remains underexplored. This paper presents a systematic study investigating whether LLMs can predict time intervals between recurring user actions, such as repeated purchases, and how different levels of contextual information shape their predictive behavior. Using a simple but representative repurchase scenario, we benchmark state-of-the-art


Summary: arXiv:2601.10143v1 Announce Type: new Abstract: In quantitative finance, the gap between training and real-world performance-driven by concept drift and distributional non-stationarity-remains a critical obstacle for building reliable data-driven systems. Models trained on static historical data often overfit, resulting in poor generalization in dynamic markets. The mantra "History Is Not Enough" underscores the need for adaptive data generation that learns to evolve with the market rather than relying solely on past observations. We present a drift-aware dataflow system that integrates machin


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


Summary: arXiv:2601.10191v1 Announce Type: new Abstract: Automated analysis of needle electromyography (nEMG) signals is emerging as a tool to support the detection of neuromuscular diseases (NMDs), yet the signals' high and heterogeneous sampling rates pose substantial computational challenges for feature-based machine-learning models, particularly for near real-time analysis. Downsampling offers a potential solution, but its impact on diagnostic signal content and classification performance remains insufficiently understood. This study presents a workflow for systematically evaluating information los


Summary: arXiv:2601.10416v1 Announce Type: new Abstract: Aligning Large Language Models (LLMs) with human preferences is critical, yet traditional fine-tuning methods are computationally expensive and inflexible. While test-time alignment offers a promising alternative, existing approaches often rely on distorted trajectory-level signals or inefficient sampling, fundamentally capping performance and failing to preserve the generative diversity of the base model. This paper introduces LLMdoctor, a novel framework for efficient test-time alignment that operates via a patient-doctor paradigm. It integrate


Summary: arXiv:2601.09730v1 Announce Type: cross Abstract: Clinical document metadata, such as document type, structure, author role, medical specialty, and encounter setting, is essential for accurate interpretation of information captured in clinical documents. However, vast documentation heterogeneity and drift over time challenge harmonization of document metadata. Automated extraction methods have emerged to coalesce metadata from disparate practices into target schema. This scoping review aims to catalog research on clinical document metadata extraction, identify methodological trends and applica


Summary: arXiv:2601.09740v1 Announce Type: cross Abstract: Modern AI technologies enable autonomous vehicles to perceive complex scenes, predict human behavior, and make real-time driving decisions. However, these data-driven components often operate as black boxes, lacking interpretability and rigorous safety guarantees. Autonomous vehicles operate in dynamic, mixed-traffic environments where interactions with human-driven vehicles introduce uncertainty and safety challenges. This work develops a formally verified safety framework for Connected and Autonomous Vehicles (CAVs) that integrates Barrier Ce


Summary: arXiv:2601.09755v1 Announce Type: cross Abstract: After Industry 4.0 has embraced tight integration between machinery (OT), software (IT), and the Internet, creating a web of sensors, data, and algorithms in service of efficient and reliable production, a new concept of Society 5.0 is emerging, in which infrastructure of a city will be instrumented to increase reliability, efficiency, and safety. Robotics will play a pivotal role in enabling this vision that is pioneered by the NEOM initiative - a smart city, co-inhabited by humans and robots. In this paper we explore the computing platform th


release-1.27.4

Summary: [nginx-1.27.4](https://nginx.org/en/download.html) mainline version has been released, featuring [optimized resource usage for complex SSL configurations](https://blog.nginx.org/blog/optimizing-resource-usage-for-complex-ssl-configurations), and with a fix for the [SSL session reuse](https://nginx.org/en/security_advisories.html) vulnerability (CVE-2025-23419). See official [CHANGES](https://nginx.org/en/CHANGES) on nginx.org. Below is a release summary generated by GitHub. ## What's Changed * Version bump. by @arut in https://github.com/nginx/nginx/pull/361 * QUIC: fixed client request timeout in 0-RTT scenarios. by @nandsky in https://github.com/nginx/nginx/pull/353 * Updated security policy to clarify experimental features by @jzebor-at-f5 in https://github.com/nginx/nginx/pull/404 * QU


release-1.27.4


3.9.0 / 2026-01-06

Summary: ## Note for users of Native Histograms In version 3.9, Native Histograms is no longer experimental, and the feature flag `native-histogram` has no effect. You must now turn on the config setting `scrape_native_histograms` to collect Native Histogram samples from exporters. ## Changelog - [CHANGE] Native Histograms are no longer experimental! Make the `native-histogram` feature flag a no-op. Use `scrape_native_histograms` config option instead. #17528 - [CHANGE] API: Add maximum limit of 10,000 sets of statistics to TSDB status endpoint. #17647 - [FEATURE] API: Add /api/v1/features for clients to understand which features are supported. #17427 - [FEATURE] Promtool: Add `start_timestamp` field for unit tests. #17636 - [FEATURE] Promtool: Add `--format seriesjson` option to `tsdb dump` to out


3.9.0 / 2026-01-06


3.9.0-rc.0 / 2025-12-18

Summary: **Note:** In version 3.9, Native Histograms is no longer experimental, and the feature flag `native-histogram` has no effect. You must now turn on the config setting `scrape_native_histogram` to collect Native Histogram samples from exporters. - [CHANGE] Native Histograms are no longer experimental! Make the `native-histogram` feature flag a no-op. Use `scrape_native_histograms` config option instead. #17528 - [CHANGE] API: Add maximum limit of 10,000 sets of statistics to TSDB status endpoint. #17647 - [FEATURE] API: Add /api/v1/features for clients to understand which features are supported. #17427 - [FEATURE] Promtool: Add `start_timestamp` field for unit tests. #17636 - [FEATURE] Promtool: Add `--format seriesjson` option to `tsdb dump` to output just series labels in JSON format. #134


3.9.0-rc.0 / 2025-12-18


3.8.0-rc.1 / 2025-11-21

Summary: * [CHANGE] Remote-write 2 (receiving): Update to [2.0-rc.4 spec](https://github.com/prometheus/docs/blob/60c24e450010df38cfcb4f65df874f6f9b26dbcb/docs/specs/prw/remote_write_spec_2_0.md). "created timestamp" (CT) is now called "start timestamp" (ST). #17411 * [FEATURE] OAuth2: support jwt-bearer grant-type (RFC7523 3.1). #17592


3.8.0-rc.1 / 2025-11-21


Summary: More bosses are sharing the top job giving them more time for family and breaks.


Summary:
Article URL: https://www.nytimes.com/2026/01/15/us/politics/cyberattack-venezuela-military.html

Comments URL: https://news.ycombinator.com/item?id=46647038

Points: 19

# Comments: 11


Summary:
I am a solo developer, and I built Core MBA because I was frustrated with the "video course" default in business education.
I wanted to build a "compiler for business logic"—a tool where I could read a concept in 5 minutes and immediately test it in a hostile environment to see if my strategy actually compiles or throws a runtime error.
The project is a business simulator built on React 19 and TypeScript.
The core technical innovation isn't just using AI; it's the architecture of a closed loop between a deterministic economic engine and a generative AI validation layer.
The biggest


Summary:
Article URL: https://www.nytimes.com/2026/01/15/us/politics/cyberattack-venezuela-military.html

Comments URL: https://news.ycombinator.com/item?id=46647038

Points: 36

# Comments: 34


Summary: arXiv:2601.09855v1 Announce Type: new Abstract: Sequential test-time scaling is a promising training-free method to improve large reasoning model accuracy, but as currently implemented, significant limitations have been observed. Inducing models to think for longer can increase their accuracy, but as the length of reasoning is further extended, it has also been shown to result in accuracy degradation and model instability. This work presents a novel sequential test-time scaling method, Min-Seek, which improves model accuracy significantly over a wide range of induced thoughts, stabilizing the


Summary: arXiv:2601.09974v1 Announce Type: new Abstract: Personalizing Large Language Models typically relies on static retrieval or one-time adaptation, assuming user preferences remain invariant over time. However, real-world interactions are dynamic, where user interests continuously evolve, posing a challenge for models to adapt to preference drift without catastrophic forgetting. Standard continual learning approaches often struggle in this context, as they indiscriminately update on noisy interaction streams, failing to distinguish genuine preference shifts from transient contexts. To address thi