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Newest papers, page 51
Implicit Beliefs as Scalable Process Rewards for Strategic Persuasion
Training large language models (LLMs) for multi-turn strategic persuasion faces two coupled obstacles: (i) realistic simulation of psychologically grounded persuadees, and (ii) dense, faithful…SkillShift-Bench: Benchmarking Skill Learning under Environment Evolution for Web Agents
Skill learning enables web agents to reuse prior experience for long-horizon interactive tasks.SEDATE: Do Large Language Models Share an Aggression Circuit Across Languages?
As the applications of AI continue to expand across various multimodal and multilingual domains, ensuring safe and responsible deployment of these systems has become an important concern.Trajectory2Gym: Decision-Grounded Compositional Interaction Synthesis for Tool-Use Agents
Successful tool-use trajectories are not necessarily useful supervision.When the Weakest Language Keeps Moving: ARMOR-DP for Multilingual BabyLM
Multilingual BabyLM systems usually fix language sampling proportions even though the weakest language can change during training.Words Are the Voice of the Mind: Depression Severity Estimation from Social Traces via Mental-Guided Evidence Reasoning
Depression severity estimation is crucial for mental health assessment and intervention, yet traditional approaches depend on clinical interviews or self-report questionnaires that require users'…Mycelium: A Meta-Study of Hallucination Detection Across Provenance, Typology, and Granularities
Hallucination detection is increasingly important for evaluating and deploying large language models.RSI Arena: Towards Recursive Self-Improvement with Human Feedback
Can AI agents autonomously train useful language models and improve them using feedback from users?A Model-Theoretic Formalization of AlKhwaremis ilm alfaraid
This paper presents a model theoretic and computational formalization of AlKhwarizmis treatment of inheritance within the framework of Computational Fiqh.PreView: Enhancing GUI Grounding with Preprocessed Views and Cues
GUI grounding requires locating small controls in cluttered screenshots and identifying which control a natural-language instruction refers to.Jointly Modeling Preferences and Imperfect Decision-Making in Large Language Models
Do preferences Large Language Models (LLMs) express in standard elicitation tasks predict their strategic behaviour?Meta-CLIP: Metadata-Conditioned Contrastive Learning for Zero-Shot Disease Inference
Medical vision--language pretraining learns transferable representations from paired images and radiology reports, yet differences in scanners and acquisition protocols can impair transfer.PathKGen: Pathology Knowledge Assisted MLLM for Explainable Histopathology
Whole Slide Image (WSI) analysis serves as the fundamental method for the clinical diagnosis of cancer in digital histopathology.Embedding-Based Educational Taxonomies
Educational taxonomies organize educational content, such as exercise problems, into concepts, which are also called skills or knowledge components.Gender Associations in LLM-Mediated ADHD Self-Diagnosis
Large Language Models (LLMs) are increasingly used for medical self-diagnosis.Beyond Prompts: A Systematic Study of LLM-Based Machine Translation Evaluators
LLM-based evaluators have rapidly become a standard tool for machine translation evaluation, yet the design choices that shape their behavior remain poorly understood.Refined Assessment for Translation Evaluation: Rethinking Machine Translation Evaluation in the Era of Human-Level Systems
As machine translation systems approach human-level quality, traditional evaluation methodologies struggle to detect subtle translation errors.RoleHear: Pseudo-Labeling for Semi-Supervised Role-Attributed Streaming Speech Recognition
Streaming automatic speech recognition transcribes at low latency but produces an unattributed word stream, while real-time applications such as customer-service analytics need each word tied to a…StateWise: Teaching Language Models to Track and Obey Evolving Constraints in Multi-Turn Dialogue
Users of conversational assistants rarely state all requirements up front: they add constraints, amend them, withdraw them, digress, and return.The Hitchhiker's Guide to System 1 Models: Fast Decision-Making Beyond LLMS
Large Language Models excel at reasoning and generation but their autoregressive, token-by-token inference makes them unnecessarily slow, expensive, and poorly calibrated for the many routine…