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


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


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


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


Earth from Space: The Fate of a Giant

Summary:
Article URL: https://www.esa.int/ESA_Multimedia/Images/2026/01/Earth_from_Space_The_fate_of_a_giant

Comments URL: https://news.ycombinator.com/item?id=46649813

Points: 3

# Comments: 0


Earth from Space: The Fate of a Giant


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: 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


Earth from Space: The Fate of a Giant

Summary:
Article URL: https://www.esa.int/ESA_Multimedia/Images/2026/01/Earth_from_Space_The_fate_of_a_giant

Comments URL: https://news.ycombinator.com/item?id=46649813

Points: 6

# Comments: 1


Earth from Space: The Fate of a Giant


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.


Earth from Space: The Fate of a Giant

Summary:
Article URL: https://www.esa.int/ESA_Multimedia/Images/2026/01/Earth_from_Space_The_fate_of_a_giant

Comments URL: https://news.ycombinator.com/item?id=46649813

Points: 7

# Comments: 2


Earth from Space: The Fate of a Giant


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.


Earth from Space: The Fate of a Giant

Summary:
Article URL: https://www.esa.int/ESA_Multimedia/Images/2026/01/Earth_from_Space_The_fate_of_a_giant

Comments URL: https://news.ycombinator.com/item?id=46649813

Points: 14

# Comments: 2


Earth from Space: The Fate of a Giant


Earth from Space: The Fate of a Giant

Summary:
Article URL: https://www.esa.int/ESA_Multimedia/Images/2026/01/Earth_from_Space_The_fate_of_a_giant

Comments URL: https://news.ycombinator.com/item?id=46649813

Points: 15

# Comments: 2


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.


Earth from Space: The Fate of a Giant

Summary:
Article URL: https://www.esa.int/ESA_Multimedia/Images/2026/01/Earth_from_Space_The_fate_of_a_giant

Comments URL: https://news.ycombinator.com/item?id=46649813

Points: 17

# Comments: 2


Earth from Space: The Fate of a Giant


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.


Earth from Space: The Fate of a Giant

Summary:
Article URL: https://www.esa.int/ESA_Multimedia/Images/2026/01/Earth_from_Space_The_fate_of_a_giant

Comments URL: https://news.ycombinator.com/item?id=46649813

Points: 21

# Comments: 2


Earth from Space: The Fate of a Giant


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.


Earth from Space: The Fate of a Giant

Summary:
Article URL: https://www.esa.int/ESA_Multimedia/Images/2026/01/Earth_from_Space_The_fate_of_a_giant

Comments URL: https://news.ycombinator.com/item?id=46649813

Points: 21

# Comments: 2


Earth from Space: The Fate of a Giant


Ask HN: Claude Opus performance affected by time of day?

Summary:
I am a big fan of Claude Opus as it has been very good at understanding feature requests and generally staying consistent with my codebase (completely written from scratch using Opus).
I've noticed recently that when I am using Opus at night (Eastern US), I am seeing it go down extreme rabbit holes on the same types of requests I am putting through on a regular basis. It is more likely to undertake refactors that break the code and then iterates on those errors in a sort of spiral. A request that would normally take 3-4 minutes will turn into a 10 minute adventure before I revert the chan


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.


Ask HN: Is it still worth pursuing a software startup?

Summary:
Considering there is very little moat left in software and big companies can copy your product in no time?

Comments URL: https://news.ycombinator.com/item?id=46654726

Points: 4

# Comments: 1


Summary:
Considering there is very little moat left in software and big companies can copy your product in no time?

Comments URL: https://news.ycombinator.com/item?id=46654726

Points: 23

# Comments: 13


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:
Considering there is very little moat left in software and big companies can copy your product in no time?

Comments URL: https://news.ycombinator.com/item?id=46654726

Points: 44

# Comments: 31


Summary:
Considering there is very little moat left in software and big companies can copy your product in no time?

Comments URL: https://news.ycombinator.com/item?id=46654726

Points: 74

# Comments: 58


Summary:
Considering there is very little moat left in software and big companies can copy your product in no time?

Comments URL: https://news.ycombinator.com/item?id=46654726

Points: 81

# Comments: 62


Summary:
Considering there is very little moat left in software and big companies can copy your product in no time?

Comments URL: https://news.ycombinator.com/item?id=46654726

Points: 91

# Comments: 76


Summary:
Considering there is very little moat left in software and big companies can copy your product in no time?

Comments URL: https://news.ycombinator.com/item?id=46654726

Points: 94

# Comments: 82


Summary:
Considering there is very little moat left in software and big companies can copy your product in no time?

Comments URL: https://news.ycombinator.com/item?id=46654726

Points: 101

# Comments: 97