EDM-ARS: A Domain-Specific Multi-Agent System for Automated Educational Data Mining Research
arXiv:2603.18273v1 Announce Type: new Abstract: In this technical report, we present the Educational Data Mining Automated Research System (EDM-ARS), a domain-specific multi-agent pipeline that automates end-to-end educational data mining (EDM) research. We conceptualize EDM-ARS as a general framework for domain-aware...
Expert Personas Improve LLM Alignment but Damage Accuracy: Bootstrapping Intent-Based Persona Routing with PRISM
arXiv:2603.18507v1 Announce Type: new Abstract: Persona prompting can steer LLM generation towards a domain-specific tone and pattern. This behavior enables use cases in multi-agent systems where diverse interactions are crucial and human-centered tasks require high-level human alignment. Prior works provide...
Correlation-Weighted Multi-Reward Optimization for Compositional Generation
arXiv:2603.18528v1 Announce Type: new Abstract: Text-to-image models produce images that align well with natural language prompts, but compositional generation has long been a central challenge. Models often struggle to satisfy multiple concepts within a single prompt, frequently omitting some concepts...
DynaRAG: Bridging Static and Dynamic Knowledge in Retrieval-Augmented Generation
arXiv:2603.18012v1 Announce Type: new Abstract: We present DynaRAG, a retrieval-augmented generation (RAG) framework designed to handle both static and time-sensitive information needs through dynamic knowledge integration. Unlike traditional RAG pipelines that rely solely on static corpora, DynaRAG selectively invokes external...
LGESynthNet: Controlled Scar Synthesis for Improved Scar Segmentation in Cardiac LGE-MRI Imaging
arXiv:2603.18356v1 Announce Type: new Abstract: Segmentation of enhancement in LGE cardiac MRI is critical for diagnosing various ischemic and non-ischemic cardiomyopathies. However, creating pixel-level annotations for these images is challenging and labor-intensive, leading to limited availability of annotated data. Generative...
Agentic Flow Steering and Parallel Rollout Search for Spatially Grounded Text-to-Image Generation
arXiv:2603.18627v1 Announce Type: new Abstract: Precise Text-to-Image (T2I) generation has achieved great success but is hindered by the limited relational reasoning of static text encoders and the error accumulation in open-loop sampling. Without real-time feedback, initial semantic ambiguities during the...
Large-Scale Analysis of Political Propaganda on Moltbook
arXiv:2603.18349v1 Announce Type: new Abstract: We present an NLP-based study of political propaganda on Moltbook, a Reddit-style platform for AI agents. To enable large-scale analysis, we develop LLM-based classifiers to detect political propaganda, validated against expert annotation (Cohen's $\kappa$= 0.64-0.74)....
How Confident Is the First Token? An Uncertainty-Calibrated Prompt Optimization Framework for Large Language Model Classification and Understanding
arXiv:2603.18009v1 Announce Type: new Abstract: With the widespread adoption of large language models (LLMs) in natural language processing, prompt engineering and retrieval-augmented generation (RAG) have become mainstream to enhance LLMs' performance on complex tasks. However, LLMs generate outputs autoregressively, leading...
D-Mem: A Dual-Process Memory System for LLM Agents
arXiv:2603.18631v1 Announce Type: new Abstract: Driven by the development of persistent, self-adapting autonomous agents, equipping these systems with high-fidelity memory access for long-horizon reasoning has emerged as a critical requirement. However, prevalent retrieval-based memory frameworks often follow an incremental processing...
Prune-then-Quantize or Quantize-then-Prune? Understanding the Impact of Compression Order in Joint Model Compression
arXiv:2603.18426v1 Announce Type: new Abstract: What happens when multiple compression methods are combined-does the order in which they are applied matter? Joint model compression has emerged as a powerful strategy to achieve higher efficiency by combining multiple methods such as...
TherapyGym: Evaluating and Aligning Clinical Fidelity and Safety in Therapy Chatbots
arXiv:2603.18008v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used for mental-health support; yet prevailing evaluation methods--fluency metrics, preference tests, and generic dialogue benchmarks--fail to capture the clinically critical dimensions of psychotherapy. We introduce THERAPYGYM, a framework that...
Learned but Not Expressed: Capability-Expression Dissociation in Large Language Models
arXiv:2603.18013v1 Announce Type: new Abstract: Large language models (LLMs) demonstrate the capacity to reconstruct and trace learned content from their training data under specific elicitation conditions, yet this capability does not manifest in standard generation contexts. This empirical observational study...
Cognitive Mismatch in Multimodal Large Language Models for Discrete Symbol Understanding
arXiv:2603.18472v1 Announce Type: new Abstract: While Multimodal Large Language Models (MLLMs) have achieved remarkable success in interpreting natural scenes, their ability to process discrete symbols -- the fundamental building blocks of human cognition -- remains a critical open question. Unlike...
TeachingCoach: A Fine-Tuned Scaffolding Chatbot for Instructional Guidance to Instructors
arXiv:2603.18189v1 Announce Type: new Abstract: Higher education instructors often lack timely and pedagogically grounded support, as scalable instructional guidance remains limited and existing tools rely on generic chatbot advice or non-scalable teaching center human-human consultations. We present TeachingCoach, a pedagogically...
MemArchitect: A Policy Driven Memory Governance Layer
arXiv:2603.18330v1 Announce Type: new Abstract: Persistent Large Language Model (LLM) agents expose a critical governance gap in memory management. Standard Retrieval-Augmented Generation (RAG) frameworks treat memory as passive storage, lacking mechanisms to resolve contradictions, enforce privacy, or prevent outdated information...
A Concept is More Than a Word: Diversified Unlearning in Text-to-Image Diffusion Models
arXiv:2603.18767v1 Announce Type: new Abstract: Concept unlearning has emerged as a promising direction for reducing the risks of harmful content generation in text-to-image diffusion models by selectively erasing undesirable concepts from a model's parameters. Existing approaches typically rely on keywords...
Do Large Language Models Possess a Theory of Mind? A Comparative Evaluation Using the Strange Stories Paradigm
arXiv:2603.18007v1 Announce Type: new Abstract: The study explores whether current Large Language Models (LLMs) exhibit Theory of Mind (ToM) capabilities -- specifically, the ability to infer others' beliefs, intentions, and emotions from text. Given that LLMs are trained on language...
MineDraft: A Framework for Batch Parallel Speculative Decoding
arXiv:2603.18016v1 Announce Type: new Abstract: Speculative decoding (SD) accelerates large language model inference by using a smaller draft model to propose draft tokens that are subsequently verified by a larger target model. However, the performance of standard SD is often...
GRAFITE: Generative Regression Analysis Framework for Issue Tracking and Evaluation
arXiv:2603.18173v1 Announce Type: new Abstract: Large language models (LLMs) are largely motivated by their performance on popular topics and benchmarks at the time of their release. However, over time, contamination occurs due to significant exposure of benchmark data during training....
From Noise to Signal: When Outliers Seed New Topics
arXiv:2603.18358v1 Announce Type: new Abstract: Outliers in dynamic topic modeling are typically treated as noise, yet we show that some can serve as early signals of emerging topics. We introduce a temporal taxonomy of news-document trajectories that defines how documents...
Synthetic Data Generation for Training Diversified Commonsense Reasoning Models
arXiv:2603.18361v1 Announce Type: new Abstract: Conversational agents are required to respond to their users not only with high quality (i.e. commonsense bearing) responses, but also considering multiple plausible alternative scenarios, reflecting the diversity in their responses. Despite the growing need...
AutoScreen-FW: An LLM-based Framework for Resume Screening
arXiv:2603.18390v1 Announce Type: new Abstract: Corporate recruiters often need to screen many resumes within a limited time, which increases their burden and may cause suitable candidates to be overlooked. To address these challenges, prior work has explored LLM-based automated resume...
TopoChunker: Topology-Aware Agentic Document Chunking Framework
arXiv:2603.18409v1 Announce Type: new Abstract: Current document chunking methods for Retrieval-Augmented Generation (RAG) typically linearize text. This forced linearization strips away intrinsic topological hierarchies, creating ``semantic fragmentation'' that degrades downstream retrieval quality. In this paper, we propose TopoChunker, an agentic...
Multimodal Task Interference: A Benchmark and Analysis of History-Target Mismatch in Multimodal LLMs
arXiv:2603.18425v1 Announce Type: new Abstract: Task interference, the performance degradation caused by task switches within a single conversation, has been studied exclusively in text-only settings despite the growing prevalence of multimodal dialogue systems. We introduce a benchmark for evaluating this...
When Names Change Verdicts: Intervention Consistency Reveals Systematic Bias in LLM Decision-Making
arXiv:2603.18530v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used for high-stakes decisions, yet their susceptibility to spurious features remains poorly characterized. We introduce ICE-Guard, a framework applying intervention consistency testing to detect three types of spurious feature...
Cross-Lingual LLM-Judge Transfer via Evaluation Decomposition
arXiv:2603.18557v1 Announce Type: new Abstract: As large language models are increasingly deployed across diverse real-world applications, extending automated evaluation beyond English has become a critical challenge. Existing evaluation approaches are predominantly English-focused, and adapting them to other languages is hindered...
ICE: Intervention-Consistent Explanation Evaluation with Statistical Grounding for LLMs
arXiv:2603.18579v1 Announce Type: new Abstract: Evaluating whether explanations faithfully reflect a model's reasoning remains an open problem. Existing benchmarks use single interventions without statistical testing, making it impossible to distinguish genuine faithfulness from chance-level performance. We introduce ICE (Intervention-Consistent Explanation),...
Language Model Maps for Prompt-Response Distributions via Log-Likelihood Vectors
arXiv:2603.18593v1 Announce Type: new Abstract: We propose a method that represents language models by log-likelihood vectors over prompt-response pairs and constructs model maps for comparing their conditional distributions. In this space, distances between models approximate the KL divergence between the...
RADIUS: Ranking, Distribution, and Significance - A Comprehensive Alignment Suite for Survey Simulation
arXiv:2603.19002v1 Announce Type: new Abstract: Simulation of surveys using LLMs is emerging as a powerful application for generating human-like responses at scale. Prior work evaluates survey simulation using metrics borrowed from other domains, which are often ad hoc, fragmented, and...
InfoMamba: An Attention-Free Hybrid Mamba-Transformer Model
arXiv:2603.18031v1 Announce Type: new Abstract: Balancing fine-grained local modeling with long-range dependency capture under computational constraints remains a central challenge in sequence modeling. While Transformers provide strong token mixing, they suffer from quadratic complexity, whereas Mamba-style selective state-space models (SSMs)...