Collecting Real-Time Data with APIs: A Hands-On Guide Using PythonKDnuggets In this article, we’ll break down the essentials of using APIs for data collection — why they matter, how they work, and how to get started with them in Python.
In this article, we’ll break down the essentials of using APIs for data collection — why they matter, how they work, and how to get started with them in Python. Read More
Accelerating discovery with the AI for Math InitiativeGoogle DeepMind Blog The initiative brings together some of the world’s most prestigious research institutions to pioneer the use of AI in mathematical research.
The initiative brings together some of the world’s most prestigious research institutions to pioneer the use of AI in mathematical research. Read More
The Cost of Robustness: Tighter Bounds on Parameter Complexity for Robust Memorization in ReLU Netscs.AI updates on arXiv.org arXiv:2510.24643v1 Announce Type: cross
Abstract: We study the parameter complexity of robust memorization for $mathrm{ReLU}$ networks: the number of parameters required to interpolate any given dataset with $epsilon$-separation between differently labeled points, while ensuring predictions remain consistent within a $mu$-ball around each training sample. We establish upper and lower bounds on the parameter count as a function of the robustness ratio $rho = mu / epsilon$. Unlike prior work, we provide a fine-grained analysis across the entire range $rho in (0,1)$ and obtain tighter upper and lower bounds that improve upon existing results. Our findings reveal that the parameter complexity of robust memorization matches that of non-robust memorization when $rho$ is small, but grows with increasing $rho$.
arXiv:2510.24643v1 Announce Type: cross
Abstract: We study the parameter complexity of robust memorization for $mathrm{ReLU}$ networks: the number of parameters required to interpolate any given dataset with $epsilon$-separation between differently labeled points, while ensuring predictions remain consistent within a $mu$-ball around each training sample. We establish upper and lower bounds on the parameter count as a function of the robustness ratio $rho = mu / epsilon$. Unlike prior work, we provide a fine-grained analysis across the entire range $rho in (0,1)$ and obtain tighter upper and lower bounds that improve upon existing results. Our findings reveal that the parameter complexity of robust memorization matches that of non-robust memorization when $rho$ is small, but grows with increasing $rho$. Read More
Charting the European LLM Benchmarking Landscape: A New Taxonomy and a Set of Best Practicescs.AI updates on arXiv.org arXiv:2510.24450v1 Announce Type: cross
Abstract: While new benchmarks for large language models (LLMs) are being developed continuously to catch up with the growing capabilities of new models and AI in general, using and evaluating LLMs in non-English languages remains a little-charted landscape. We give a concise overview of recent developments in LLM benchmarking, and then propose a new taxonomy for the categorization of benchmarks that is tailored to multilingual or non-English use scenarios. We further propose a set of best practices and quality standards that could lead to a more coordinated development of benchmarks for European languages. Among other recommendations, we advocate for a higher language and culture sensitivity of evaluation methods.
arXiv:2510.24450v1 Announce Type: cross
Abstract: While new benchmarks for large language models (LLMs) are being developed continuously to catch up with the growing capabilities of new models and AI in general, using and evaluating LLMs in non-English languages remains a little-charted landscape. We give a concise overview of recent developments in LLM benchmarking, and then propose a new taxonomy for the categorization of benchmarks that is tailored to multilingual or non-English use scenarios. We further propose a set of best practices and quality standards that could lead to a more coordinated development of benchmarks for European languages. Among other recommendations, we advocate for a higher language and culture sensitivity of evaluation methods. Read More
Top 5 Text-to-Speech Open Source ModelsKDnuggets Discover the leading open-source text-to-speech models that rival premium tools in realism, emotion, and performance so that you can turn ideas into lifelike voices and power the next wave of creator audio.
Discover the leading open-source text-to-speech models that rival premium tools in realism, emotion, and performance so that you can turn ideas into lifelike voices and power the next wave of creator audio. Read More
Counterintuitive’s new chip aims escape the AI ‘twin trap’AI News AI startup company, Counterintuitive, has set out to build “reasoning-native computing,” enabling machines to understand rather than simply mimic. Such a breakthrough has the potential to shift AI from pattern recognition to genuine comprehension, paving the way for systems that can think and make decisions – in other words, to be more “human-like.” Counterintuitive Chairman,
The post Counterintuitive’s new chip aims escape the AI ‘twin trap’ appeared first on AI News.
AI startup company, Counterintuitive, has set out to build “reasoning-native computing,” enabling machines to understand rather than simply mimic. Such a breakthrough has the potential to shift AI from pattern recognition to genuine comprehension, paving the way for systems that can think and make decisions – in other words, to be more “human-like.” Counterintuitive Chairman,
The post Counterintuitive’s new chip aims escape the AI ‘twin trap’ appeared first on AI News. Read More
3D-Prover: Diversity Driven Theorem Proving With Determinantal Point Processescs.AI updates on arXiv.org arXiv:2410.11133v2 Announce Type: replace
Abstract: A key challenge in automated formal reasoning is the intractable search space, which grows exponentially with the depth of the proof. This branching is caused by the large number of candidate proof tactics which can be applied to a given goal. Nonetheless, many of these tactics are semantically similar or lead to an execution error, wasting valuable resources in both cases. We address the problem of effectively pruning this search, using only synthetic data generated from previous proof attempts. We first demonstrate that it is possible to generate semantically aware tactic representations which capture the effect on the proving environment, likelihood of success, and execution time. We then propose a novel filtering mechanism which leverages these representations to select semantically diverse and high quality tactics, using Determinantal Point Processes. Our approach, 3D- Prover, is designed to be general, and to augment any underlying tactic generator. We demonstrate the effectiveness of 3D-Prover on the miniF2F and LeanDojo benchmarks by augmenting popular open source proving LLMs. We show that our approach leads to an increase in the overall proof rate, as well as a significant improvement in the tactic success rate, execution time and diversity. We make our code available at https://github.com/sean-lamont/3D-Prover.
arXiv:2410.11133v2 Announce Type: replace
Abstract: A key challenge in automated formal reasoning is the intractable search space, which grows exponentially with the depth of the proof. This branching is caused by the large number of candidate proof tactics which can be applied to a given goal. Nonetheless, many of these tactics are semantically similar or lead to an execution error, wasting valuable resources in both cases. We address the problem of effectively pruning this search, using only synthetic data generated from previous proof attempts. We first demonstrate that it is possible to generate semantically aware tactic representations which capture the effect on the proving environment, likelihood of success, and execution time. We then propose a novel filtering mechanism which leverages these representations to select semantically diverse and high quality tactics, using Determinantal Point Processes. Our approach, 3D- Prover, is designed to be general, and to augment any underlying tactic generator. We demonstrate the effectiveness of 3D-Prover on the miniF2F and LeanDojo benchmarks by augmenting popular open source proving LLMs. We show that our approach leads to an increase in the overall proof rate, as well as a significant improvement in the tactic success rate, execution time and diversity. We make our code available at https://github.com/sean-lamont/3D-Prover. Read More
Migrating AI from Nvidia to Huawei: Opportunities and trade-offsAI News For many years, Nvidia has been the de facto leader in AI model training and inference infrastructure, thanks to its mature GPU range, the CUDA software stack, and a huge developer community. Moving away from that base is therefore a strategic and tactical consideration. Huawei AI represents an alternative to Nvidia, with the Chinese company
The post Migrating AI from Nvidia to Huawei: Opportunities and trade-offs appeared first on AI News.
For many years, Nvidia has been the de facto leader in AI model training and inference infrastructure, thanks to its mature GPU range, the CUDA software stack, and a huge developer community. Moving away from that base is therefore a strategic and tactical consideration. Huawei AI represents an alternative to Nvidia, with the Chinese company
The post Migrating AI from Nvidia to Huawei: Opportunities and trade-offs appeared first on AI News. Read More
DistDF: Time-Series Forecasting Needs Joint-Distribution Wasserstein Alignmentcs.AI updates on arXiv.org arXiv:2510.24574v1 Announce Type: cross
Abstract: Training time-series forecast models requires aligning the conditional distribution of model forecasts with that of the label sequence. The standard direct forecast (DF) approach resorts to minimize the conditional negative log-likelihood of the label sequence, typically estimated using the mean squared error. However, this estimation proves to be biased in the presence of label autocorrelation. In this paper, we propose DistDF, which achieves alignment by alternatively minimizing a discrepancy between the conditional forecast and label distributions. Because conditional discrepancies are difficult to estimate from finite time-series observations, we introduce a newly proposed joint-distribution Wasserstein discrepancy for time-series forecasting, which provably upper bounds the conditional discrepancy of interest. This discrepancy admits tractable, differentiable estimation from empirical samples and integrates seamlessly with gradient-based training. Extensive experiments show that DistDF improves the performance diverse forecast models and achieves the state-of-the-art forecasting performance. Code is available at https://anonymous.4open.science/r/DistDF-F66B.
arXiv:2510.24574v1 Announce Type: cross
Abstract: Training time-series forecast models requires aligning the conditional distribution of model forecasts with that of the label sequence. The standard direct forecast (DF) approach resorts to minimize the conditional negative log-likelihood of the label sequence, typically estimated using the mean squared error. However, this estimation proves to be biased in the presence of label autocorrelation. In this paper, we propose DistDF, which achieves alignment by alternatively minimizing a discrepancy between the conditional forecast and label distributions. Because conditional discrepancies are difficult to estimate from finite time-series observations, we introduce a newly proposed joint-distribution Wasserstein discrepancy for time-series forecasting, which provably upper bounds the conditional discrepancy of interest. This discrepancy admits tractable, differentiable estimation from empirical samples and integrates seamlessly with gradient-based training. Extensive experiments show that DistDF improves the performance diverse forecast models and achieves the state-of-the-art forecasting performance. Code is available at https://anonymous.4open.science/r/DistDF-F66B. Read More
ReCAP: Recursive Context-Aware Reasoning and Planning for Large Language Model Agentscs.AI updates on arXiv.org arXiv:2510.23822v1 Announce Type: new
Abstract: Long-horizon tasks requiring multi-step reasoning and dynamic re-planning remain challenging for large language models (LLMs). Sequential prompting methods are prone to context drift, loss of goal information, and recurrent failure cycles, while hierarchical prompting methods often weaken cross-level continuity or incur substantial runtime overhead. We introduce ReCAP (Recursive Context-Aware Reasoning and Planning), a hierarchical framework with shared context for reasoning and planning in LLMs. ReCAP combines three key mechanisms: (i) plan-ahead decomposition, in which the model generates a full subtask list, executes the first item, and refines the remainder; (ii) structured re-injection of parent plans, maintaining consistent multi-level context during recursive return; and (iii) memory-efficient execution, bounding the active prompt so costs scale linearly with task depth. Together these mechanisms align high-level goals with low-level actions, reduce redundant prompting, and preserve coherent context updates across recursion. Experiments demonstrate that ReCAP substantially improves subgoal alignment and success rates on various long-horizon reasoning benchmarks, achieving a 32% gain on synchronous Robotouille and a 29% improvement on asynchronous Robotouille under the strict pass@1 protocol.
arXiv:2510.23822v1 Announce Type: new
Abstract: Long-horizon tasks requiring multi-step reasoning and dynamic re-planning remain challenging for large language models (LLMs). Sequential prompting methods are prone to context drift, loss of goal information, and recurrent failure cycles, while hierarchical prompting methods often weaken cross-level continuity or incur substantial runtime overhead. We introduce ReCAP (Recursive Context-Aware Reasoning and Planning), a hierarchical framework with shared context for reasoning and planning in LLMs. ReCAP combines three key mechanisms: (i) plan-ahead decomposition, in which the model generates a full subtask list, executes the first item, and refines the remainder; (ii) structured re-injection of parent plans, maintaining consistent multi-level context during recursive return; and (iii) memory-efficient execution, bounding the active prompt so costs scale linearly with task depth. Together these mechanisms align high-level goals with low-level actions, reduce redundant prompting, and preserve coherent context updates across recursion. Experiments demonstrate that ReCAP substantially improves subgoal alignment and success rates on various long-horizon reasoning benchmarks, achieving a 32% gain on synchronous Robotouille and a 29% improvement on asynchronous Robotouille under the strict pass@1 protocol. Read More