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AINeutralarXiv – CS AI · May 126/10
🧠Researchers identify capability erosion in self-evolving LLM agents, where systems adapting to new tasks progressively lose previously learned abilities across workflow, skill, model, and memory dimensions. The study proposes Capability-Preserving Evolution (CPE), a stabilization framework that maintains performance on existing tasks while enabling new adaptations, demonstrating improvements in retained capability stability across all evolution channels.
🧠 GPT-5
AINeutralarXiv – CS AI · May 126/10
🧠Researchers present SKG-VLA, an AI system that uses Scene Knowledge Graphs to improve decision-making in large-scale complaint handling by integrating multimodal evidence (text, images, metadata) with structured reasoning about entities, policies, and temporal events. The approach demonstrates improved accuracy and robustness across policy-grounded reasoning and long-tail scenarios.
AINeutralarXiv – CS AI · May 126/10
🧠Researchers introduce DSAT, a native SAT solver designed to work directly with discrete variables rather than converting them to binary Boolean variables. The solver applies traditional SAT techniques like unit resolution and clause learning to discrete logic, offering potential computational and semantic advantages over existing binarization approaches for applications in probabilistic reasoning, planning, and explainable AI.
AINeutralarXiv – CS AI · May 126/10
🧠Researchers discover that neural networks across different modalities (vision, point clouds, language) converge toward shared representations, with non-language modalities systematically moving toward language's neighborhood structure rather than vice versa. Using directional analysis, they attribute this asymmetry to language representations occupying more compact feature space, proposing that language serves as the asymptotic attractor in multimodal representation learning.
AINeutralarXiv – CS AI · May 126/10
🧠Researchers introduce Probabilistic Logical Knowledge Tracing (PLKT), an interpretable AI framework that uses Beta-distributed probabilistic embeddings to model student knowledge states and predict learning performance. Unlike conventional deep learning approaches that rely on opaque deterministic embeddings, PLKT constructs transparent reasoning paths showing how past interactions influence predictions while maintaining superior accuracy compared to existing methods.
AINeutralarXiv – CS AI · May 126/10
🧠Researchers introduce MarsTSC, a novel framework combining Vision Language Models with agentic reasoning for few-shot multimodal time series classification. The system uses collaborative AI roles—Generator, Reflector, and Modifier—to iteratively refine knowledge and improve classification accuracy across 12 benchmarks while providing interpretable explanations.
AINeutralarXiv – CS AI · May 126/10
🧠Researchers present a game-theoretic model showing that unequal access to AI-powered cybersecurity tools creates persistent vulnerabilities, with weak defenders unable to afford strong protection. They propose that targeted subsidies for committed defenders adopting advanced AI defenses significantly improve overall system resilience and suppress attacks more effectively than commitment alone.
AINeutralarXiv – CS AI · May 126/10
🧠Researchers introduce Neuro-Symbolic Experience Replay (NSER), a framework that enhances reinforcement learning by combining Large Language Models with symbolic logic to transform passive memory buffers into active knowledge construction systems. The approach grounds LLM-generated behavioral rules into differentiable logic representations, enabling more efficient policy optimization across multiple benchmark environments.
AIBullisharXiv – CS AI · May 126/10
🧠Researchers introduce VulTriage, an LLM-based framework that enhances vulnerability detection in source code through triple-path context augmentation combining control flow analysis, vulnerability knowledge retrieval, and semantic summarization. The approach achieves state-of-the-art results on benchmark datasets and demonstrates strong generalization to low-resource scenarios.
AINeutralarXiv – CS AI · May 126/10
🧠Researchers introduce DUDE, a framework that teaches AI web agents to resist deceptive interface elements through hybrid-reward learning and experience summarization. The accompanying RUC benchmark demonstrates the framework reduces susceptibility to deception by 53.8% while preserving task performance, addressing a critical vulnerability in autonomous GUI interaction systems.
AINeutralarXiv – CS AI · May 126/10
🧠WindINR is a machine learning framework that enables fast, localized wind forecasting in complex terrain by using implicit neural representations to query wind conditions at specific user-defined locations rather than generating dense grid-based forecasts. The system achieves 2.6x speedup in corrections by updating only a compact latent state instead of retraining full networks, making it practical for real-time wind estimation applications.
AINeutralarXiv – CS AI · May 125/10
🧠Researchers introduce weighted rules under stable model semantics, combining logic programming with probabilistic methods similar to Markov Logic Networks. This advancement enables answer set programs to handle inconsistencies, rank solutions, assign probabilities, and perform statistical inference—moving beyond the deterministic limitations of traditional logic-based systems.
AINeutralarXiv – CS AI · May 125/10
🧠Researchers demonstrate how functional stable model semantics enhances Answer Set Programming Modulo Theories (ASPMT), enabling integration of intensional functions that derive values from other predicates rather than pre-defined sources. The framework allows tight ASPMT programs to translate into SMT instances, extending the theoretical foundations of logic programming.
AINeutralarXiv – CS AI · May 125/10
🧠Cplus2ASP Version 2 is a new system that translates action language C+ into answer set programming, offering significant performance improvements over the Causal Calculator through modern ASP solving techniques. The tool supports incremental execution, external atoms via Lua integration, and extensible translations for other action languages, making it relevant for automated reasoning and planning applications.
AINeutralarXiv – CS AI · May 126/10
🧠Researchers introduce TESSERA, a neuro-symbolic framework that combines Large Language Models with Monte Carlo Tree Search to extract multi-step explanations from knowledge graphs, specifically for drug-disease mechanism discovery. The system uses LLMs for local judgments rather than autonomous generation, enforcing structural constraints through knowledge graphs while employing MCTS for principled credit assignment across extended reasoning chains.
AINeutralarXiv – CS AI · May 126/10
🧠Researchers introduce TIDE-Bench, a comprehensive evaluation benchmark for tool-integrated reasoning (TIR) systems that assess how well large language models leverage external tools. The benchmark addresses critical gaps in existing evaluations by combining traditional tasks with novel experimental design and interactive scenarios, measuring not just accuracy but tool efficiency and inference costs.
AINeutralarXiv – CS AI · May 126/10
🧠Researchers introduced PDEAgent-Bench, the first comprehensive benchmark for evaluating AI systems that generate numerical solvers from partial differential equations (PDEs). The benchmark contains 645 test cases across multiple PDE families and finite-element libraries, revealing that while current LLMs can produce runnable code, they substantially fail when accuracy and efficiency requirements are enforced.
AINeutralarXiv – CS AI · May 126/10
🧠CodeClinic introduces a benchmark for evaluating whether large language model agents can autonomously generate clinical skills rather than relying on pre-built tool libraries. The research demonstrates that an offline autoformalization pipeline converting clinical guidelines into Python libraries improves consistency and reduces token usage by 40% compared to zero-shot code generation.
AINeutralarXiv – CS AI · May 126/10
🧠Researchers introduce Absurd World, a benchmarking framework that tests large language models' logical reasoning by creating logically coherent but unrealistic scenarios derived from real-world problems. The framework reveals whether LLMs can reason independently of learned patterns by breaking down real-world models into symbols, actions, sequences, and events, then systematically altering them while preserving underlying logic.
AINeutralarXiv – CS AI · May 126/10
🧠Researchers demonstrate that unpredictability in language agents does not equate to effective control, finding that structured decision-making mechanisms significantly outperform stochastic sampling across 74,352 test cases. The study challenges assumptions about randomness and control in AI systems, with implications for agent reliability and interpretability.
AINeutralarXiv – CS AI · May 126/10
🧠Researchers propose MedMSA, a framework combining language models with formal probabilistic models to enable AI systems to make transparent, calibrated clinical predictions under uncertainty. The approach addresses critical limitations in current medical AI by producing verifiable differential diagnoses that explain patient symptoms with uncertainty weighting, marking progress toward safer clinical decision support.
AINeutralarXiv – CS AI · May 126/10
🧠Researchers introduce primal-dual guided decoding, an inference-time method for discrete diffusion models that enforces global constraints during token generation through adaptive Lagrangian multipliers and KL-regularized optimization. The approach requires no model retraining, supports multiple simultaneous constraints, and demonstrates effectiveness across text generation, molecular design, and music applications.
AINeutralarXiv – CS AI · May 126/10
🧠Researchers from UTS achieved second place in a psychological defense mechanism classification competition using a multi-agent AI system that identifies defense patterns through absence-based reasoning rather than presence detection. The system combines Gemini 2.5 agents with fine-tuned Qwen models to achieve an F1 score of 0.406, addressing critical biases in minority class prediction through structured ensemble methods.
🧠 Gemini
AINeutralarXiv – CS AI · May 126/10
🧠Researchers develop a generative AI model that integrates social determinants of health (SDoH) with multi-organ sensor data and medical events to improve disease prediction and personalized clinical decision support. Tested on UK Biobank data spanning nearly 500,000 medical histories, the model outperforms existing autoregressive disease prediction systems by explicitly modeling socioeconomic factors alongside imaging and biomarker data.
AINeutralarXiv – CS AI · May 126/10
🧠Researchers have developed attribution techniques that explain decision-making in Markov Decision Processes (MDPs), extending explainability methods beyond static inputs to sequential decision-making systems. The approach assigns importance scores to states and execution paths, enabling more interpretable AI agents in dynamic environments.
