Generating Diverse TSP Tours via a Combination of Graph Pointer Network and Dispersioncs.AI updates on arXiv.org arXiv:2601.01132v2 Announce Type: replace-cross
Abstract: We address the Diverse Traveling Salesman Problem (D-TSP), a bi-criteria optimization challenge that seeks a set of $k$ distinct TSP tours. The objective requires every selected tour to have a length at most $c|T^*|$ (where $|T^*|$ is the optimal tour length) while minimizing the average Jaccard similarity across all tour pairs. This formulation is crucial for applications requiring both high solution quality and fault tolerance, such as logistics planning, robotics pathfinding or strategic patrolling. Current methods are limited: traditional heuristics, such as the Niching Memetic Algorithm (NMA) or bi-criteria optimization, incur high computational complexity $O(n^3)$, while modern neural approaches (e.g., RF-MA3S) achieve limited diversity quality and rely on complex, external mechanisms.
To overcome these limitations, we propose a novel hybrid framework that decomposes D-TSP into two efficient steps. First, we utilize a simple Graph Pointer Network (GPN), augmented with an approximated sequence entropy loss, to efficiently sample a large, diverse pool of high-quality tours. This simple modification effectively controls the quality-diversity trade-off without complex external mechanisms. Second, we apply a greedy algorithm that yields a 2-approximation for the dispersion problem to select the final $k$ maximally diverse tours from the generated pool. Our results demonstrate state-of-the-art performance. On the Berlin instance, our model achieves an average Jaccard index of $0.015$, significantly outperforming NMA ($0.081$) and RF-MA3S. By leveraging GPU acceleration, our GPN structure achieves a near-linear empirical runtime growth of $O(n)$. While maintaining solution diversity comparable to complex bi-criteria algorithms, our approach is over 360 times faster on large-scale instances (783 cities), delivering high-quality TSP solutions with unprecedented efficiency and simplicity.
arXiv:2601.01132v2 Announce Type: replace-cross
Abstract: We address the Diverse Traveling Salesman Problem (D-TSP), a bi-criteria optimization challenge that seeks a set of $k$ distinct TSP tours. The objective requires every selected tour to have a length at most $c|T^*|$ (where $|T^*|$ is the optimal tour length) while minimizing the average Jaccard similarity across all tour pairs. This formulation is crucial for applications requiring both high solution quality and fault tolerance, such as logistics planning, robotics pathfinding or strategic patrolling. Current methods are limited: traditional heuristics, such as the Niching Memetic Algorithm (NMA) or bi-criteria optimization, incur high computational complexity $O(n^3)$, while modern neural approaches (e.g., RF-MA3S) achieve limited diversity quality and rely on complex, external mechanisms.
To overcome these limitations, we propose a novel hybrid framework that decomposes D-TSP into two efficient steps. First, we utilize a simple Graph Pointer Network (GPN), augmented with an approximated sequence entropy loss, to efficiently sample a large, diverse pool of high-quality tours. This simple modification effectively controls the quality-diversity trade-off without complex external mechanisms. Second, we apply a greedy algorithm that yields a 2-approximation for the dispersion problem to select the final $k$ maximally diverse tours from the generated pool. Our results demonstrate state-of-the-art performance. On the Berlin instance, our model achieves an average Jaccard index of $0.015$, significantly outperforming NMA ($0.081$) and RF-MA3S. By leveraging GPU acceleration, our GPN structure achieves a near-linear empirical runtime growth of $O(n)$. While maintaining solution diversity comparable to complex bi-criteria algorithms, our approach is over 360 times faster on large-scale instances (783 cities), delivering high-quality TSP solutions with unprecedented efficiency and simplicity. Read More
Topological Signatures of ReLU Neural Network Activation Patternscs.AI updates on arXiv.org arXiv:2510.12700v2 Announce Type: replace-cross
Abstract: This paper explores the topological signatures of ReLU neural network activation patterns. We consider feedforward neural networks with ReLU activation functions and analyze the polytope decomposition of the feature space induced by the network. Mainly, we investigate how the Fiedler partition of the dual graph and show that it appears to correlate with the decision boundary — in the case of binary classification. Additionally, we compute the homology of the cellular decomposition — in a regression task — to draw similar patterns in behavior between the training loss and polyhedral cell-count, as the model is trained.
arXiv:2510.12700v2 Announce Type: replace-cross
Abstract: This paper explores the topological signatures of ReLU neural network activation patterns. We consider feedforward neural networks with ReLU activation functions and analyze the polytope decomposition of the feature space induced by the network. Mainly, we investigate how the Fiedler partition of the dual graph and show that it appears to correlate with the decision boundary — in the case of binary classification. Additionally, we compute the homology of the cellular decomposition — in a regression task — to draw similar patterns in behavior between the training loss and polyhedral cell-count, as the model is trained. Read More
CliCARE: Grounding Large Language Models in Clinical Guidelines for Decision Support over Longitudinal Cancer Electronic Health Recordscs.AI updates on arXiv.org arXiv:2507.22533v2 Announce Type: replace-cross
Abstract: Large Language Models (LLMs) hold significant promise for improving clinical decision support and reducing physician burnout by synthesizing complex, longitudinal cancer Electronic Health Records (EHRs). However, their implementation in this critical field faces three primary challenges: the inability to effectively process the extensive length and fragmented nature of patient records for accurate temporal analysis; a heightened risk of clinical hallucination, as conventional grounding techniques such as Retrieval-Augmented Generation (RAG) do not adequately incorporate process-oriented clinical guidelines; and unreliable evaluation metrics that hinder the validation of AI systems in oncology. To address these issues, we propose CliCARE, a framework for Grounding Large Language Models in Clinical Guidelines for Decision Support over Longitudinal Cancer Electronic Health Records. The framework operates by transforming unstructured, longitudinal EHRs into patient-specific Temporal Knowledge Graphs (TKGs) to capture long-range dependencies, and then grounding the decision support process by aligning these real-world patient trajectories with a normative guideline knowledge graph. This approach provides oncologists with evidence-grounded decision support by generating a high-fidelity clinical summary and an actionable recommendation. We validated our framework using large-scale, longitudinal data from a private Chinese cancer dataset and the public English MIMIC-IV dataset. In these settings, CliCARE significantly outperforms baselines, including leading long-context LLMs and Knowledge Graph-enhanced RAG methods. The clinical validity of our results is supported by a robust evaluation protocol, which demonstrates a high correlation with assessments made by oncologists.
arXiv:2507.22533v2 Announce Type: replace-cross
Abstract: Large Language Models (LLMs) hold significant promise for improving clinical decision support and reducing physician burnout by synthesizing complex, longitudinal cancer Electronic Health Records (EHRs). However, their implementation in this critical field faces three primary challenges: the inability to effectively process the extensive length and fragmented nature of patient records for accurate temporal analysis; a heightened risk of clinical hallucination, as conventional grounding techniques such as Retrieval-Augmented Generation (RAG) do not adequately incorporate process-oriented clinical guidelines; and unreliable evaluation metrics that hinder the validation of AI systems in oncology. To address these issues, we propose CliCARE, a framework for Grounding Large Language Models in Clinical Guidelines for Decision Support over Longitudinal Cancer Electronic Health Records. The framework operates by transforming unstructured, longitudinal EHRs into patient-specific Temporal Knowledge Graphs (TKGs) to capture long-range dependencies, and then grounding the decision support process by aligning these real-world patient trajectories with a normative guideline knowledge graph. This approach provides oncologists with evidence-grounded decision support by generating a high-fidelity clinical summary and an actionable recommendation. We validated our framework using large-scale, longitudinal data from a private Chinese cancer dataset and the public English MIMIC-IV dataset. In these settings, CliCARE significantly outperforms baselines, including leading long-context LLMs and Knowledge Graph-enhanced RAG methods. The clinical validity of our results is supported by a robust evaluation protocol, which demonstrates a high correlation with assessments made by oncologists. Read More
There are no Champions in Supervised Long-Term Time Series Forecastingcs.AI updates on arXiv.org arXiv:2502.14045v2 Announce Type: replace-cross
Abstract: Recent advances in long-term time series forecasting have introduced numerous complex supervised prediction models that consistently outperform previously published architectures. However, this rapid progression raises concerns regarding inconsistent benchmarking and reporting practices, which may undermine the reliability of these comparisons. In this study, we first perform a broad, thorough, and reproducible evaluation of the top-performing supervised models on the most popular benchmark and additional baselines representing the most active architecture families. This extensive evaluation assesses eight models on 14 datasets, encompassing $sim$5,000 trained networks for the hyperparameter (HP) searches. Then, through a comprehensive analysis, we find that slight changes to experimental setups or current evaluation metrics drastically shift the common belief that newly published results are advancing the state of the art. Our findings emphasize the need to shift focus away from pursuing ever-more complex models, towards enhancing benchmarking practices through rigorous and standardized evaluations that enable more substantiated claims, including reproducible HP setups and statistical testing. We offer recommendations for future research.
arXiv:2502.14045v2 Announce Type: replace-cross
Abstract: Recent advances in long-term time series forecasting have introduced numerous complex supervised prediction models that consistently outperform previously published architectures. However, this rapid progression raises concerns regarding inconsistent benchmarking and reporting practices, which may undermine the reliability of these comparisons. In this study, we first perform a broad, thorough, and reproducible evaluation of the top-performing supervised models on the most popular benchmark and additional baselines representing the most active architecture families. This extensive evaluation assesses eight models on 14 datasets, encompassing $sim$5,000 trained networks for the hyperparameter (HP) searches. Then, through a comprehensive analysis, we find that slight changes to experimental setups or current evaluation metrics drastically shift the common belief that newly published results are advancing the state of the art. Our findings emphasize the need to shift focus away from pursuing ever-more complex models, towards enhancing benchmarking practices through rigorous and standardized evaluations that enable more substantiated claims, including reproducible HP setups and statistical testing. We offer recommendations for future research. Read More
Conformity and Social Impact on AI Agentscs.AI updates on arXiv.org arXiv:2601.05384v1 Announce Type: new
Abstract: As AI agents increasingly operate in multi-agent environments, understanding their collective behavior becomes critical for predicting the dynamics of artificial societies. This study examines conformity, the tendency to align with group opinions under social pressure, in large multimodal language models functioning as AI agents. By adapting classic visual experiments from social psychology, we investigate how AI agents respond to group influence as social actors. Our experiments reveal that AI agents exhibit a systematic conformity bias, aligned with Social Impact Theory, showing sensitivity to group size, unanimity, task difficulty, and source characteristics. Critically, AI agents achieving near-perfect performance in isolation become highly susceptible to manipulation through social influence. This vulnerability persists across model scales: while larger models show reduced conformity on simple tasks due to improved capabilities, they remain vulnerable when operating at their competence boundary. These findings reveal fundamental security vulnerabilities in AI agent decision-making that could enable malicious manipulation, misinformation campaigns, and bias propagation in multi-agent systems, highlighting the urgent need for safeguards in collective AI deployments.
arXiv:2601.05384v1 Announce Type: new
Abstract: As AI agents increasingly operate in multi-agent environments, understanding their collective behavior becomes critical for predicting the dynamics of artificial societies. This study examines conformity, the tendency to align with group opinions under social pressure, in large multimodal language models functioning as AI agents. By adapting classic visual experiments from social psychology, we investigate how AI agents respond to group influence as social actors. Our experiments reveal that AI agents exhibit a systematic conformity bias, aligned with Social Impact Theory, showing sensitivity to group size, unanimity, task difficulty, and source characteristics. Critically, AI agents achieving near-perfect performance in isolation become highly susceptible to manipulation through social influence. This vulnerability persists across model scales: while larger models show reduced conformity on simple tasks due to improved capabilities, they remain vulnerable when operating at their competence boundary. These findings reveal fundamental security vulnerabilities in AI agent decision-making that could enable malicious manipulation, misinformation campaigns, and bias propagation in multi-agent systems, highlighting the urgent need for safeguards in collective AI deployments. Read More
How to Maximize Claude Code EffectivenessTowards Data Science Learn how to get the most out of agentic coding
The post How to Maximize Claude Code Effectiveness appeared first on Towards Data Science.
Learn how to get the most out of agentic coding
The post How to Maximize Claude Code Effectiveness appeared first on Towards Data Science. Read More
The latency trap: Smart warehouses abandon cloud for edgeAI News While the enterprise world rushes to migrate everything to the cloud, the warehouse floor is moving in the opposite direction. This article explores why the future of automation relies on edge AI to solve the fatal “latency gap” in modern logistics. In the sterilised promotional videos for smart warehouses, autonomous mobile robots (AMRs) glide in
The post The latency trap: Smart warehouses abandon cloud for edge appeared first on AI News.
While the enterprise world rushes to migrate everything to the cloud, the warehouse floor is moving in the opposite direction. This article explores why the future of automation relies on edge AI to solve the fatal “latency gap” in modern logistics. In the sterilised promotional videos for smart warehouses, autonomous mobile robots (AMRs) glide in
The post The latency trap: Smart warehouses abandon cloud for edge appeared first on AI News. Read More
Why Apple chose Google over OpenAI: What enterprise AI buyers can learn from the Gemini deal AI News
Why Apple chose Google over OpenAI: What enterprise AI buyers can learn from the Gemini dealAI News Apple’s multi-year agreement to integrate Google’s Gemini models into its revamped Siri marks more than just another Big Tech partnership. The deal, announced Monday, offers a rare window into how one of the world’s most selective technology companies evaluates foundation models—and the criteria should matter to any enterprise weighing similar decisions. The stakes were considerable. Apple had
The post Why Apple chose Google over OpenAI: What enterprise AI buyers can learn from the Gemini deal appeared first on AI News.
Apple’s multi-year agreement to integrate Google’s Gemini models into its revamped Siri marks more than just another Big Tech partnership. The deal, announced Monday, offers a rare window into how one of the world’s most selective technology companies evaluates foundation models—and the criteria should matter to any enterprise weighing similar decisions. The stakes were considerable. Apple had
The post Why Apple chose Google over OpenAI: What enterprise AI buyers can learn from the Gemini deal appeared first on AI News. Read More
ContractEval: A Benchmark for Evaluating Contract-Satisfying Assertions in Code Generationcs.AI updates on arXiv.org arXiv:2510.12047v3 Announce Type: replace
Abstract: Current code generation benchmarks measure functional correctness on well-formed inputs, as test cases are curated to satisfy input preconditions. This leaves a gap: generated programs may appear correct but fail to satisfy contracts — assertion-level validity constraints for rejecting ill-formed inputs. We introduce ContractEval, a benchmark for evaluating contract-satisfying assertions in code generation, i.e., whether code rejects contract-violating inputs by triggering intended assertions. Built on HumanEval+ and MBPP+, ContractEval augments each task with contract-violation tests derived from reference assertions. We synthesize these via a neuro-symbolic pipeline: an LLM converts assertion clauses into constraints, and an SMT solver enumerates satisfiable violation combinations to generate inputs that violate selected clauses while satisfying the rest. Across five code LLMs, standard prompting yields 0% contract satisfaction, while adding a few contract-violation examples boosts contract satisfaction to 49–53% while maintaining pass@1 by 92% of the original. Our code is available at https://github.com/suhanmen/ContractEval.
arXiv:2510.12047v3 Announce Type: replace
Abstract: Current code generation benchmarks measure functional correctness on well-formed inputs, as test cases are curated to satisfy input preconditions. This leaves a gap: generated programs may appear correct but fail to satisfy contracts — assertion-level validity constraints for rejecting ill-formed inputs. We introduce ContractEval, a benchmark for evaluating contract-satisfying assertions in code generation, i.e., whether code rejects contract-violating inputs by triggering intended assertions. Built on HumanEval+ and MBPP+, ContractEval augments each task with contract-violation tests derived from reference assertions. We synthesize these via a neuro-symbolic pipeline: an LLM converts assertion clauses into constraints, and an SMT solver enumerates satisfiable violation combinations to generate inputs that violate selected clauses while satisfying the rest. Across five code LLMs, standard prompting yields 0% contract satisfaction, while adding a few contract-violation examples boosts contract satisfaction to 49–53% while maintaining pass@1 by 92% of the original. Our code is available at https://github.com/suhanmen/ContractEval. Read More
KALE-LM-Chem: Vision and Practice Toward an AI Brain for Chemistrycs.AI updates on arXiv.org arXiv:2409.18695v3 Announce Type: replace
Abstract: Recent advancements in large language models (LLMs) have demonstrated strong potential for enabling domain-specific intelligence. In this work, we present our vision for building an AI-powered chemical brain, which frames chemical intelligence around four core capabilities: information extraction, semantic parsing, knowledge-based QA, and reasoning & planning. We argue that domain knowledge and logic are essential pillars for enabling such a system to assist and accelerate scientific discovery. To initiate this effort, we introduce our first generation of large language models for chemistry: KALE-LM-Chem and KALE-LM-Chem-1.5, which have achieved outstanding performance in tasks related to the field of chemistry. We hope that our work serves as a strong starting point, helping to realize more intelligent AI and promoting the advancement of human science and technology, as well as societal development.
arXiv:2409.18695v3 Announce Type: replace
Abstract: Recent advancements in large language models (LLMs) have demonstrated strong potential for enabling domain-specific intelligence. In this work, we present our vision for building an AI-powered chemical brain, which frames chemical intelligence around four core capabilities: information extraction, semantic parsing, knowledge-based QA, and reasoning & planning. We argue that domain knowledge and logic are essential pillars for enabling such a system to assist and accelerate scientific discovery. To initiate this effort, we introduce our first generation of large language models for chemistry: KALE-LM-Chem and KALE-LM-Chem-1.5, which have achieved outstanding performance in tasks related to the field of chemistry. We hope that our work serves as a strong starting point, helping to realize more intelligent AI and promoting the advancement of human science and technology, as well as societal development. Read More
