AINeutralarXiv – CS AI · Jun 26/10
🧠SIRIUS-SQL introduces a multi-candidate approach to Text-to-SQL generation that addresses redundancy, execution error classification, and selector limitations through difficulty-smoothing reinforcement learning, targeted repair mechanisms, and hybrid confidence-gated selection. The system achieves 75.88% accuracy on BIRD dev and 91.20% on SPIDER test, surpassing previous state-of-the-art multi-candidate systems.
AINeutralarXiv – CS AI · Jun 26/10
🧠Researchers have introduced DraDDP, the first publicly available English multimodal dataset for multi-party dialogue discourse parsing, containing 495 dialogue segments from American TV dramas with 6,374 utterances and 9.1 hours of video content. The dataset advances natural language understanding by enabling AI models to identify dependency structures and relation types in conversations across multiple speakers and modalities, with benchmarks demonstrating the value of combining visual and textual information.
AINeutralarXiv – CS AI · Jun 26/10
🧠MyoSem is a new framework that aligns electromyography (EMG) signals with natural language descriptions to enable semantic understanding of hand actions. Rather than classifying gestures into fixed categories, the system allows bidirectional retrieval between EMG signals and text queries, demonstrating strong generalization across users and action types.
AINeutralarXiv – CS AI · Jun 26/10
🧠Agentic-J is a containerized AI assistant system designed for ImageJ/Fiji that enables biologists to perform complex microscopy image analysis tasks using natural language commands. The system generates executable, documented scripts with specialized sub-agents handling plugin management, code generation, debugging, and statistical reporting, making advanced image analysis more accessible to researchers without extensive programming expertise.
AIBullisharXiv – CS AI · May 296/10
🧠Researchers have released mcp-proto-okn, a Python-based server that enables AI assistants to query and integrate scientific knowledge graphs through natural language via the Model Context Protocol. The tool democratizes access to complex biomedical and scientific data by removing technical barriers to cross-domain knowledge graph analysis.
AINeutralarXiv – CS AI · May 296/10
🧠Researchers introduced NRLB, a multi-agent AI framework designed to create plain language summaries accessible to diverse reader groups including elementary students, non-native speakers, and those with attention deficits. The system combines template-based planning with iterative refinement to improve readability while maintaining factual accuracy, achieving human preference rates of 55-76% in evaluations.
AINeutralarXiv – CS AI · May 296/10
🧠Researchers introduce SCOPE, a lightweight LLM framework designed to monitor pilot readbacks of Air Traffic Control instructions, addressing a critical aviation safety gap where readback anomalies contribute to approximately 80% of aviation incidents. The system achieves 91% accuracy in detecting anomalies and 96.63% correction rates while requiring minimal computational overhead, offering a practical deployment pathway for automated safety monitoring in high-stakes operational environments.
AINeutralarXiv – CS AI · May 296/10
🧠CORE-T introduces a training-free framework for improving table retrieval in text-to-SQL systems by combining dense retrieval with LLM-generated metadata and compatibility caching. The approach achieves significant performance gains—up to 22.7 points in table-selection F1 and 24.4 points in multi-table execution accuracy—while reducing inference tokens by 64-76% compared to LLM-intensive alternatives.
AINeutralarXiv – CS AI · May 286/10
🧠ESC-Skills introduces a novel framework for emotional support conversation systems that moves beyond end-to-end generation to create interpretable, executable skills. The system discovers support interventions from successful and failed dialogues, organizes them into a skills bank with applicability conditions and risk assessments, then self-improves through multi-profile simulations and systematic failure analysis.
AINeutralarXiv – CS AI · May 286/10
🧠MetaboT is an open-source LLM-based framework that translates natural-language questions into SPARQL queries for metabolomics knowledge graphs, significantly lowering technical barriers for researchers without programming expertise. The multi-agent architecture addresses hallucination and schema-compliance issues through specialized agents for validation, entity resolution, and query refinement, validated on the Experimental Natural Products Knowledge Graph.
AINeutralarXiv – CS AI · May 276/10
🧠Researchers introduce N2I-RAG, an AI framework that automates computation of legal indicators from normative texts using retrieval-augmented generation with built-in validation mechanisms. The system addresses hallucination risks in traditional language models by emphasizing traceability and evidence grounding, demonstrating strong performance on French marine environmental law.
AINeutralarXiv – CS AI · May 276/10
🧠SEAL introduces a two-stage semantic parsing framework that combines large language models with agentic learning to improve conversational question answering over knowledge graphs. The system self-evolves through dialog history and execution feedback without retraining, achieving state-of-the-art results on complex multi-hop reasoning and aggregation tasks while reducing computational costs.
AINeutralarXiv – CS AI · May 126/10
🧠Researchers introduced TrajPrism, a comprehensive benchmark dataset combining 300K real urban trajectories with natural language annotations across three cities, enabling AI models to understand the alignment between physical travel paths and human descriptions of movement intent, constraints, and preferences.
AINeutralarXiv – CS AI · May 126/10
🧠Researchers evaluated multiple code retrieval strategies using LLM-based rewriting, finding that full natural language transcription with query-corpus augmentation achieves the largest gains but corpus-only approaches often degrade performance. They introduced Delta H (token entropy) as a cheap, rewriter-agnostic metric to predict when LLM rewriting justifies its computational cost.
AINeutralarXiv – CS AI · May 126/10
🧠Researchers introduced Magis-Bench, a new benchmark for evaluating large language models on magistrate-level judicial tasks based on Brazilian competitive exams. Testing 23 state-of-the-art LLMs revealed that even top performers like Google's Gemini-3-Pro-Preview score below 70% on complex legal reasoning and judicial writing tasks, indicating significant gaps in AI legal capabilities.
🧠 Claude🧠 Gemini
AINeutralarXiv – CS AI · May 126/10
🧠Researchers introduce Lexical Acoustic Coding (LAC), a framework enabling LLM agents to transmit audio through natural language by converting sound into interpretable acoustic descriptors and verbalizing them as English text. The approach frames audio transmission as a quantization problem, balancing vocabulary size, transmission rate, and fidelity while keeping the transmitted text editable and human-readable.
AINeutralarXiv – CS AI · May 126/10
🧠Researchers conducted a systematic evaluation of large language models for part-of-speech tagging in Medieval Romance languages, comparing them against traditional taggers. The study demonstrates that LLM-based approaches with fine-tuning and cross-lingual transfer learning significantly outperform conventional methods, offering practical applications for digital humanities research on historical texts.
AINeutralarXiv – CS AI · May 125/10
🧠Researchers evaluate semantic search as a tool for analyzing 18th-century intellectual history, specifically tracking how John Locke's ideas circulated through paraphrases and implicit references. While semantic search substantially outperforms traditional lexical methods at capturing meaning-level correspondences, linguistic analysis reveals that retrieval remains constrained by surface-level vocabulary overlap, suggesting both promise and limitations for historical corpus analysis.
AIBullisharXiv – CS AI · May 126/10
🧠Researchers propose Semantic Softmax, a novel inference-time method that improves zero-shot LLM classification by recovering probability mass lost during constrained decoding. The approach aggregates scores from semantic synonyms, reducing calibration errors and boosting accuracy on emotion and toxicity detection tasks.
AIBullisharXiv – CS AI · May 116/10
🧠Researchers propose RRCM, a novel framework that enhances Large Language Model-based recommendation systems by dynamically retrieving relevant collaborative and metadata information. The system learns optimal context construction through ranking-driven optimization, addressing key challenges in balancing context quality with efficiency limitations.
AINeutralarXiv – CS AI · May 116/10
🧠Researchers developed a toxicity detection system for gaming chat using fine-tuned Llama 3.1 with synthetic data augmentation, achieving 4th place in the EEUCA 2026 shared task. The system classifies messages into six toxicity categories and reveals a critical "validation trap" phenomenon where high validation performance doesn't correlate with strong test set generalization.
🧠 Llama
AIBullisharXiv – CS AI · May 116/10
🧠Researchers present an end-to-end framework that uses Large Language Models to convert natural language specifications into PDDL planning models, with iterative refinement through hardcoded and dynamic agents, then generates executable plans. The system demonstrates strong performance across multiple domains including classic planning problems where LLMs typically struggle, and integrates with established planning engines.
🧠 Gemini
AINeutralarXiv – CS AI · May 76/10
🧠Researchers introduce StoryRMB, the first benchmark for evaluating reward models on story generation preferences, and develop StoryReward, a specialized reward model achieving 66.3% accuracy where existing models struggle. The work addresses the challenge of modeling subjective human preferences in narrative generation, enabling better alignment between LLM-generated stories and human expectations.
AINeutralarXiv – CS AI · May 16/10
🧠Researchers have developed an agentic framework that uses knowledge graphs to help large language models understand and reason about AI policy documents. The system was tested on multiple AI safety regulations, demonstrating that knowledge graph augmentation improves LLM performance across various reasoning tasks from simple entity lookup to complex cross-policy inference.
AINeutralarXiv – CS AI · May 16/10
🧠A comprehensive survey examines how large language models can assist or automate peer review processes across academia, synthesizing techniques for review generation, post-review tasks, and evaluation methods. The research catalogs datasets and modeling approaches while addressing ethical concerns and practical implementation challenges for integrating AI into scholarly publishing workflows.
