Multi-Agent Debate for Belief Optimization in Defeasible Domains
An arXiv paper finds multi-agent debate causes confidence escalation and martingale belief trajectories rather than better reasoning.
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An arXiv paper finds multi-agent debate causes confidence escalation and martingale belief trajectories rather than better reasoning.
Researchers introduced a benchmark exposing limitations in Vision-Language Model explainability caused by text biases and redundancy.
Research demonstrates network traffic side-channel attacks on cloud LLMs, inferring models and tasks from packet patterns despite encryption.
Researchers propose ReCoPII, a relation-grounded framework for generating contextual PII masking datasets using synthetic augmentation.
Researchers propose FinLLM-KG, combining knowledge graphs with LLMs for explainable financial sentiment analysis in algorithmic trading.
Researchers propose a low-rank multilinear decomposition method for detecting hallucinations by analyzing internal LLM hidden states.
Researchers introduce a framework to evaluate task handoffs and information preservation between collaborative AI agents.
Researchers propose a quantitative approach to measuring Asch conformity in frontier LLMs by repeatedly sampling a single question.
A preprint analysis examines whether token confidence scoring in reasoning language models merely reflects chain length.
Researchers propose a harness to reduce over-defensive writing in AI agents caused by optimizing against adversarial reviewer prompts.
Researchers introduced DocSkill, an agentic framework for multilingual multi-hop document question answering across complex layouts.
Researchers evaluate LLM ability to predict user non-compliance by comparing verbal promises against decision records in human-agent chats.
Researchers study decision models for identifying knowledge-conflict types in RAG retrieval sources to improve generator accuracy.
Researchers introduce TrawMem, an agent memory framework framing LLM memory as task-oriented role-adaptive workspaces rather than static retrieval.
Researchers introduced DocXEval, a benchmark framework for evaluating explainability in multimodal document understanding models.
An audit of 51 multilingual benchmarks reveals wide but thin coverage and methodological gaps across 219 languages and 242 datasets.
A research paper evaluates decision models as faster, lower-cost judges for hallucination detection across six benchmark datasets.
Researchers introduced UCVE, a graph-structured retrieval framework designed to improve automated coverage and compliance validation.
A new arXiv study audits pretraining disclosure across 41 models from 17 families using Foundation Model Transparency Index indicators.
Researchers propose a metacognitive controller for diffusion language models to dynamically adapt token generation and compute usage.
Researchers propose GEPA-Curator, a method to filter noisy learning signals for self-improving multi-turn LLM agent workflows.
Researchers propose SCOPE, a multi-agent system for profiling news media outlets regarding bias, factuality, and credibility.
An arXiv paper examines self-distillation methods for open-weight models doing financial quantitative reasoning under data residency.
A new research paper proposes a control map for evaluating test-time language model updates beyond simple update counts.
Researchers propose a decision-theoretic framework to resolve knowledge conflicts between retrieval context and parametric memory in language models.
Researchers introduced PRISM-BENCH, a benchmark evaluating multimodal model reasoning groundedness, accuracy, and inference costs.
Researchers introduce QESChunker, a question-evidence-supervised chunking method optimizing RAG preprocessing indices.
Researchers propose batching reflective text optimization loops to run parallel proposals, cutting wall-clock execution time.
Researchers propose GUI-JEPA, a decoder-free mobile GUI agent policy using Joint-Embedding Predictive Architectures to improve speed.
Researchers introduced RubricAtlas to extract type-level rubrics from expert reports for evaluating deep research agents.