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AINeutralarXiv – CS AI · Mar 47/104
🧠Researchers introduced NeuroCognition, a new benchmark for evaluating LLMs based on neuropsychological tests, revealing that while models show unified capability across tasks, they struggle with foundational cognitive abilities. The study found LLMs perform well on text but degrade with images and complexity, suggesting current models lack core adaptive cognition compared to human intelligence.
AINeutralarXiv – CS AI · Mar 46/102
🧠Researchers have released LiveAgentBench, a comprehensive benchmark featuring 104 real-world scenarios to evaluate AI agent performance across practical applications. The benchmark uses a novel Social Perception-Driven Data Generation method to ensure tasks reflect actual user requirements and includes 374 total tasks for testing various AI models and frameworks.
AIBullisharXiv – CS AI · Mar 47/102
🧠Researchers propose SUN (Shared Use of Next-token Prediction), a novel approach for multi-LLM serving that enables cross-model sharing of decode execution by decomposing transformers into separate prefill and decode modules. The system achieves up to 2.0x throughput improvement per GPU while maintaining accuracy comparable to full fine-tuning, with a quantized version (QSUN) providing additional 45% speedup.
AIBullisharXiv – CS AI · Mar 46/104
🧠Researchers introduce AgentAssay, the first framework for regression testing AI agent workflows, achieving 78-100% cost reduction while maintaining statistical guarantees. The system uses behavioral fingerprinting and stochastic testing methods to detect regressions in autonomous AI agents across multiple models including GPT-5.2, Claude Sonnet 4.6, and others.
AINeutralarXiv – CS AI · Mar 47/104
🧠Researchers have introduced SorryDB, a dynamic benchmark for evaluating AI systems' ability to prove mathematical theorems using the Lean proof assistant. The benchmark draws from 78 real-world formalization projects and addresses limitations of static benchmarks by providing continuously updated tasks that better reflect community needs.
AIBullisharXiv – CS AI · Mar 46/102
🧠Researchers propose NAR-CP, a new method to improve Large Language Models' performance in high-frequency decision-making tasks like UAV pursuit. The approach uses normalized action rewards and consistency policy optimization to address limitations in current LLM-based agents that struggle with rapid, precise numerical state updates.
AIBullisharXiv – CS AI · Mar 47/104
🧠Researchers introduce Retrieval-Augmented Robotics (RAR), a new paradigm enabling robots to actively retrieve and use external visual documentation to execute complex tasks. The system uses a Retrieve-Reason-Act loop where robots search unstructured visual manuals, align 2D diagrams with 3D objects, and synthesize executable plans for assembly tasks.
AIBullisharXiv – CS AI · Mar 46/104
🧠Researchers have developed EvoSkill, an automated framework that enables AI agents to discover and refine domain-specific skills through iterative failure analysis. The system demonstrated significant performance improvements on specialized tasks, with accuracy gains of 7.3% on financial data analysis and 12.1% on search-augmented QA, while showing transferable capabilities across different domains.
AIBullisharXiv – CS AI · Mar 46/102
🧠Researchers introduce BehaveSim, a new method to measure algorithmic similarity by analyzing problem-solving behavior rather than code syntax. The approach enhances AI-driven algorithm design frameworks and enables systematic analysis of AI-generated algorithms through behavioral clustering.
AIBullisharXiv – CS AI · Mar 46/104
🧠Researchers present a new framework for evaluating logical reasoning AI agents using an "assessor agent" that can issue tasks, enforce execution limits, and record structured failure types. Their auto-formalization agent achieved 86.70% accuracy on logical reasoning tasks, outperforming traditional chain-of-thought approaches by nearly 13 percentage points.
AIBullisharXiv – CS AI · Mar 47/103
🧠Researchers developed GLEAN, a new AI verification framework that improves reliability of LLM-powered agents in high-stakes decisions like clinical diagnosis. The system uses expert guidelines and Bayesian logistic regression to better verify AI agent decisions, showing 12% improvement in accuracy and 50% better calibration in medical diagnosis tests.
AINeutralarXiv – CS AI · Mar 47/103
🧠Research compares Transformers, State Space Models (SSMs), and hybrid architectures for in-context retrieval tasks, finding hybrid models excel at information-dense retrieval while Transformers remain superior for position-based tasks. SSM-based models develop unique locality-aware embeddings that create interpretable positional structures, explaining their specific strengths and limitations.
AIBullisharXiv – CS AI · Mar 46/102
🧠Researchers developed SAE-based Transferability Score (STS), a new metric using sparse autoencoders to predict how well fine-tuned large language models will perform across different domains without requiring actual training. The method achieves correlation coefficients above 0.7 with actual performance changes and provides interpretable insights into model adaptation.
AINeutralarXiv – CS AI · Mar 46/105
🧠Researchers propose a framework for developing trustworthy AI agents that function as epistemic entities, capable of pursuing knowledge goals and shaping information environments. The paper argues that as AI models increasingly replace traditional search methods and provide specialized advice, their calibration to human epistemic norms becomes critical to prevent cognitive deskilling and epistemic drift.
AIBearisharXiv – CS AI · Mar 46/103
🧠Researchers introduce SpatialText, a diagnostic framework to test whether large language models can truly reason about spatial relationships or merely rely on linguistic patterns. The study reveals that current AI models fail at egocentric perspective reasoning despite proficiency in basic spatial fact retrieval.
AIBullisharXiv – CS AI · Mar 46/102
🧠Researchers have developed OrchMAS, a new multi-agent AI framework that uses specialized expert agents and dynamic orchestration to improve reasoning in scientific domains. The system addresses limitations of existing multi-agent frameworks by enabling flexible role allocation, prompt refinement, and heterogeneous model integration for complex scientific tasks.
AIBullisharXiv – CS AI · Mar 46/104
🧠Researchers present REGAL, a registry-driven architecture that enables AI agents to work deterministically with enterprise telemetry data from systems like CI/CD pipelines and observability platforms. The system addresses key challenges of grounding Large Language Models on private enterprise data through structured data processing and version-controlled action spaces.
AIBullisharXiv – CS AI · Mar 46/103
🧠Researchers have developed TikZilla, a new AI model that generates high-quality scientific figures from text descriptions using TikZ code. The model uses a dataset four times larger than previous versions and combines supervised learning with reinforcement learning to achieve performance matching GPT-5 while using much smaller model sizes.
AIBullisharXiv – CS AI · Mar 46/103
🧠Researchers introduce RAPO (Retrieval-Augmented Policy Optimization), a new reinforcement learning framework that improves LLM agent training by incorporating retrieval mechanisms for broader exploration. The method achieves 5% performance gains across 14 datasets and 1.2x faster training efficiency by using hybrid-policy rollouts and retrieval-aware optimization.
AINeutralarXiv – CS AI · Mar 46/102
🧠Researchers propose PURE, a new framework for AI-powered recommendation systems that addresses preference-inconsistent explanations - where AI provides factually correct but unconvincing reasoning that conflicts with user preferences. The system uses a select-then-generate approach to improve both evidence selection and explanation generation, demonstrating reduced hallucinations while maintaining recommendation accuracy.
AIBullisharXiv – CS AI · Mar 47/103
🧠Researchers present Odin, the first production-deployed graph intelligence engine that autonomously discovers patterns in knowledge graphs without predefined queries. The system uses a novel COMPASS scoring metric combining structural, semantic, temporal, and community-aware signals, and has been successfully deployed in regulated healthcare and insurance environments.
AIBearisharXiv – CS AI · Mar 47/102
🧠Researchers introduce Procedure-Aware Evaluation (PAE) framework to assess how AI agents complete tasks, not just if they succeed. The study reveals that 27-78% of reported AI agent successes are actually "corrupt successes" that mask underlying procedural violations and reliability issues.
AIBullisharXiv – CS AI · Mar 46/103
🧠Researchers have developed an agentic AI-driven workflow using Large Language Models to automate coverage analysis for formal verification in integrated chip development. The approach systematically identifies coverage gaps and generates required formal properties, demonstrating measurable improvements in coverage metrics that correlate with design complexity.
AIBullisharXiv – CS AI · Mar 47/102
🧠Researchers have enhanced the Saarthi AI framework for formal verification, achieving 70% better accuracy in generating SystemVerilog assertions and 50% fewer iterations to reach coverage closure. The framework uses multi-agent collaboration and improved RAG techniques to move toward domain-specific AI intelligence for verification tasks.
AIBullisharXiv – CS AI · Mar 46/102
🧠Researchers developed a method to improve EEG-based music identification by using artificial neural networks that distinguish between acoustic and expectation-related brain representations. The approach combines both types of neural representations to achieve better performance than traditional methods, potentially advancing brain-computer interfaces and neural decoding applications.
