Online Algorithms with Unreliable Guidance
arXiv:2602.20706v1 Announce Type: new Abstract: This paper introduces a new model for ML-augmented online decision making, called online algorithms with unreliable guidance (OAG). This model completely separates between the predictive and algorithmic components, thus offering a single well-defined analysis framework...
Buffer Matters: Unleashing the Power of Off-Policy Reinforcement Learning in Large Language Model Reasoning
arXiv:2602.20722v1 Announce Type: new Abstract: Traditional on-policy Reinforcement Learning with Verifiable Rewards (RLVR) frameworks suffer from experience waste and reward homogeneity, which directly hinders learning efficiency on difficult samples during large language models post-training. In this paper, we introduce Batch...
Modality-Guided Mixture of Graph Experts with Entropy-Triggered Routing for Multimodal Recommendation
arXiv:2602.20723v1 Announce Type: new Abstract: Multimodal recommendation enhances ranking by integrating user-item interactions with item content, which is particularly effective under sparse feedback and long-tail distributions. However, multimodal signals are inherently heterogeneous and can conflict in specific contexts, making effective...
Balancing Multiple Objectives in Urban Traffic Control with Reinforcement Learning from AI Feedback
arXiv:2602.20728v1 Announce Type: new Abstract: Reward design has been one of the central challenges for real world reinforcement learning (RL) deployment, especially in settings with multiple objectives. Preference-based RL offers an appealing alternative by learning from human preferences over pairs...
PyVision-RL: Forging Open Agentic Vision Models via RL
arXiv:2602.20739v1 Announce Type: new Abstract: Reinforcement learning for agentic multimodal models often suffers from interaction collapse, where models learn to reduce tool usage and multi-turn reasoning, limiting the benefits of agentic behavior. We introduce PyVision-RL, a reinforcement learning framework for...
POMDPPlanners: Open-Source Package for POMDP Planning
arXiv:2602.20810v1 Announce Type: new Abstract: We present POMDPPlanners, an open-source Python package for empirical evaluation of Partially Observable Markov Decision Process (POMDP) planning algorithms. The package integrates state-of-the-art planning algorithms, a suite of benchmark environments with safety-critical variants, automated hyperparameter...
Pressure Reveals Character: Behavioural Alignment Evaluation at Depth
arXiv:2602.20813v1 Announce Type: new Abstract: Evaluating alignment in language models requires testing how they behave under realistic pressure, not just what they claim they would do. While alignment failures increasingly cause real-world harm, comprehensive evaluation frameworks with realistic multi-turn scenarios...
Diagnosing Causal Reasoning in Vision-Language Models via Structured Relevance Graphs
arXiv:2602.20878v1 Announce Type: new Abstract: Large Vision-Language Models (LVLMs) achieve strong performance on visual question answering benchmarks, yet often rely on spurious correlations rather than genuine causal reasoning. Existing evaluations primarily assess the correctness of the answers, making it unclear...
Predicting Sentence Acceptability Judgments in Multimodal Contexts
arXiv:2602.20918v1 Announce Type: new Abstract: Previous work has examined the capacity of deep neural networks (DNNs), particularly transformers, to predict human sentence acceptability judgments, both independently of context, and in document contexts. We consider the effect of prior exposure to...
Architecting AgentOS: From Token-Level Context to Emergent System-Level Intelligence
arXiv:2602.20934v1 Announce Type: new Abstract: The paradigm of Large Language Models is undergoing a fundamental transition from static inference engines to dynamic autonomous cognitive systems.While current research primarily focuses on scaling context windows or optimizing prompt engineering the theoretical bridge...
LogicGraph : Benchmarking Multi-Path Logical Reasoning via Neuro-Symbolic Generation and Verification
arXiv:2602.21044v1 Announce Type: new Abstract: Evaluations of large language models (LLMs) primarily emphasize convergent logical reasoning, where success is defined by producing a single correct proof. However, many real-world reasoning problems admit multiple valid derivations, requiring models to explore diverse...
Tool Building as a Path to "Superintelligence"
arXiv:2602.21061v1 Announce Type: new Abstract: The Diligent Learner framework suggests LLMs can achieve superintelligence via test-time search, provided a sufficient step-success probability $\gamma$. In this work, we design a benchmark to measure $\gamma$ on logical out-of-distribution inference. We construct a...
The Initial Exploration Problem in Knowledge Graph Exploration
arXiv:2602.21066v1 Announce Type: new Abstract: Knowledge Graphs (KGs) enable the integration and representation of complex information across domains, but their semantic richness and structural complexity create substantial barriers for lay users without expertise in semantic web technologies. When encountering an...
CG-DMER: Hybrid Contrastive-Generative Framework for Disentangled Multimodal ECG Representation Learning
arXiv:2602.21154v1 Announce Type: new Abstract: Accurate interpretation of electrocardiogram (ECG) signals is crucial for diagnosing cardiovascular diseases. Recent multimodal approaches that integrate ECGs with accompanying clinical reports show strong potential, but they still face two main concerns from a modality...
Interpretable Medical Image Classification using Prototype Learning and Privileged Information
arXiv:2310.15741v1 Announce Type: cross Abstract: Interpretability is often an essential requirement in medical imaging. Advanced deep learning methods are required to address this need for explainability and high performance. In this work, we investigate whether additional information available during the...
ConceptRM: The Quest to Mitigate Alert Fatigue through Consensus-Based Purity-Driven Data Cleaning for Reflection Modelling
arXiv:2602.20166v1 Announce Type: cross Abstract: In many applications involving intelligent agents, the overwhelming volume of alerts (mostly false) generated by the agents may desensitize users and cause them to overlook critical issues, leading to the so-called ''alert fatigue''. A common...
Benchmarking Early Deterioration Prediction Across Hospital-Rich and MCI-Like Emergency Triage Under Constrained Sensing
arXiv:2602.20168v1 Announce Type: cross Abstract: Emergency triage decisions are made under severe information constraints, yet most data-driven deterioration models are evaluated using signals unavailable during initial assessment. We present a leakage-aware benchmarking framework for early deterioration prediction that evaluates model...
InterviewSim: A Scalable Framework for Interview-Grounded Personality Simulation
arXiv:2602.20294v1 Announce Type: new Abstract: Simulating real personalities with large language models requires grounding generation in authentic personal data. Existing evaluation approaches rely on demographic surveys, personality questionnaires, or short AI-led interviews as proxies, but lack direct assessment against what...
What Makes a Good Query? Measuring the Impact of Human-Confusing Linguistic Features on LLM Performance
arXiv:2602.20300v1 Announce Type: new Abstract: Large Language Model (LLM) hallucinations are usually treated as defects of the model or its decoding strategy. Drawing on classical linguistics, we argue that a query's form can also shape a listener's (and model's) response....
No One Size Fits All: QueryBandits for Hallucination Mitigation
arXiv:2602.20332v1 Announce Type: new Abstract: Advanced reasoning capabilities in Large Language Models (LLMs) have led to more frequent hallucinations; yet most mitigation work focuses on open-source models for post-hoc detection and parameter editing. The dearth of studies focusing on hallucinations...
Natural Language Processing Models for Robust Document Categorization
arXiv:2602.20336v1 Announce Type: new Abstract: This article presents an evaluation of several machine learning methods applied to automated text classification, alongside the design of a demonstrative system for unbalanced document categorization and distribution. The study focuses on balancing classification accuracy...
How communicatively optimal are exact numeral systems? Once more on lexicon size and morphosyntactic complexity
arXiv:2602.20372v1 Announce Type: new Abstract: Recent research argues that exact recursive numeral systems optimize communicative efficiency by balancing a tradeoff between the size of the numeral lexicon and the average morphosyntactic complexity (roughly length in morphemes) of numeral terms. We...
Disentangling Geometry, Performance, and Training in Language Models
arXiv:2602.20433v1 Announce Type: new Abstract: Geometric properties of Transformer weights, particularly the unembedding matrix, have been widely useful in language model interpretability research. Yet, their utility for estimating downstream performance remains unclear. In this work, we systematically investigate the relationship...
A Hierarchical Multi-Agent System for Autonomous Discovery in Geoscientific Data Archives
arXiv:2602.21351v1 Announce Type: new Abstract: The rapid accumulation of Earth science data has created a significant scalability challenge; while repositories like PANGAEA host vast collections of datasets, citation metrics indicate that a substantial portion remains underutilized, limiting data reusability. Here...
ARLArena: A Unified Framework for Stable Agentic Reinforcement Learning
arXiv:2602.21534v1 Announce Type: new Abstract: Agentic reinforcement learning (ARL) has rapidly gained attention as a promising paradigm for training agents to solve complex, multi-step interactive tasks. Despite encouraging early results, ARL remains highly unstable, often leading to training collapse. This...
Distill and Align Decomposition for Enhanced Claim Verification
arXiv:2602.21857v1 Announce Type: new Abstract: Complex claim verification requires decomposing sentences into verifiable subclaims, yet existing methods struggle to align decomposition quality with verification performance. We propose a reinforcement learning (RL) approach that jointly optimizes decomposition quality and verifier alignment...
ProactiveMobile: A Comprehensive Benchmark for Boosting Proactive Intelligence on Mobile Devices
arXiv:2602.21858v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) have made significant progress in mobile agent development, yet their capabilities are predominantly confined to a reactive paradigm, where they merely execute explicit user commands. The emerging paradigm of proactive...
Semantic Partial Grounding via LLMs
arXiv:2602.22067v1 Announce Type: new Abstract: Grounding is a critical step in classical planning, yet it often becomes a computational bottleneck due to the exponential growth in grounded actions and atoms as task size increases. Recent advances in partial grounding have...
Inference-time Alignment via Sparse Junction Steering
arXiv:2602.21215v1 Announce Type: cross Abstract: Token-level steering has emerged as a pivotal approach for inference-time alignment, enabling fine grained control over large language models by modulating their output distributions without parameter updates. While effective, existing methods rely on dense intervention...
EQ-5D Classification Using Biomedical Entity-Enriched Pre-trained Language Models and Multiple Instance Learning
arXiv:2602.21216v1 Announce Type: cross Abstract: The EQ-5D (EuroQol 5-Dimensions) is a standardized instrument for the evaluation of health-related quality of life. In health economics, systematic literature reviews (SLRs) depend on the correct identification of publications that use the EQ-5D, but...