StethoLM: Audio Language Model for Cardiopulmonary Analysis Across Clinical Tasks
arXiv:2603.00355v1 Announce Type: new Abstract: Listening to heart and lung sounds - auscultation - is one of the first and most fundamental steps in a clinical examination. Despite being fast and non-invasive, it demands years of experience to interpret subtle...
Improving Full Waveform Inversion in Large Model Era
arXiv:2603.00377v1 Announce Type: new Abstract: Full Waveform Inversion (FWI) is a highly nonlinear and ill-posed problem that aims to recover subsurface velocity maps from surface-recorded seismic waveforms data. Existing data-driven FWI typically uses small models, as available datasets have limited...
Physics-Aware Learnability: From Set-Theoretic Independence to Operational Constraints
arXiv:2603.00417v1 Announce Type: new Abstract: Beyond binary classification, learnability can become a logically fragile notion: in EMX, even the class of all finite subsets of $[0,1]$ is learnable in some models of ZFC and not in others. We argue the...
Benchmarking Few-shot Transferability of Pre-trained Models with Improved Evaluation Protocols
arXiv:2603.00478v1 Announce Type: new Abstract: Few-shot transfer has been revolutionized by stronger pre-trained models and improved adaptation algorithms.However, there lacks a unified, rigorous evaluation protocol that is both challenging and realistic for real-world usage. In this work, we establish FEWTRANS,...
Alibaba’s Qwen tech lead steps down after major AI push
Reactions rippled through Alibaba's Qwen team after tech lead Junyang Lin stepped down following a major model launch.
Humans and LLMs Diverge on Probabilistic Inferences
arXiv:2602.23546v1 Announce Type: new Abstract: Human reasoning often involves working over limited information to arrive at probabilistic conclusions. In its simplest form, this involves making an inference that is not strictly entailed by a premise, but rather only likely given...
From Static Benchmarks to Dynamic Protocol: Agent-Centric Text Anomaly Detection for Evaluating LLM Reasoning
arXiv:2602.23729v1 Announce Type: new Abstract: The evaluation of large language models (LLMs) has predominantly relied on static datasets, which offer limited scalability and fail to capture the evolving reasoning capabilities of recent models. To overcome these limitations, we propose an...
Structured Prompt Optimization for Few-Shot Text Classification via Semantic Alignment in Latent Space
arXiv:2602.23753v1 Announce Type: new Abstract: This study addresses the issues of semantic entanglement, unclear label structure, and insufficient feature representation in few-shot text classification, and proposes an optimization framework based on structured prompts to enhance semantic understanding and task adaptation...
ARGUS: Seeing the Influence of Narrative Features on Persuasion in Argumentative Texts
arXiv:2602.24109v1 Announce Type: new Abstract: Can narratives make arguments more persuasive? And to this end, which narrative features matter most? Although stories are often seen as powerful tools for persuasion, their specific role in online, unstructured argumentation remains underexplored. To...
CoME: Empowering Channel-of-Mobile-Experts with Informative Hybrid-Capabilities Reasoning
arXiv:2602.24142v1 Announce Type: new Abstract: Mobile Agents can autonomously execute user instructions, which requires hybrid-capabilities reasoning, including screen summary, subtask planning, action decision and action function. However, existing agents struggle to achieve both decoupled enhancement and balanced integration of these...
Task-Centric Acceleration of Small-Language Models
arXiv:2602.24174v1 Announce Type: new Abstract: Small language models (SLMs) have emerged as efficient alternatives to large language models for task-specific applications. However, they are often employed in high-volume, low-latency settings, where efficiency is crucial. We propose TASC, Task-Adaptive Sequence Compression,...
Do LLMs Benefit From Their Own Words?
arXiv:2602.24287v1 Announce Type: new Abstract: Multi-turn interactions with large language models typically retain the assistant's own past responses in the conversation history. In this work, we revisit this design choice by asking whether large language models benefit from conditioning on...
UTPTrack: Towards Simple and Unified Token Pruning for Visual Tracking
arXiv:2602.23734v1 Announce Type: cross Abstract: One-stream Transformer-based trackers achieve advanced performance in visual object tracking but suffer from significant computational overhead that hinders real-time deployment. While token pruning offers a path to efficiency, existing methods are fragmented. They typically prune...
SWE-rebench V2: Language-Agnostic SWE Task Collection at Scale
arXiv:2602.23866v1 Announce Type: cross Abstract: Software engineering agents (SWE) are improving rapidly, with recent gains largely driven by reinforcement learning (RL). However, RL training is constrained by the scarcity of large-scale task collections with reproducible execution environments and reliable test...
LK Losses: Direct Acceptance Rate Optimization for Speculative Decoding
arXiv:2602.23881v1 Announce Type: cross Abstract: Speculative decoding accelerates autoregressive large language model (LLM) inference by using a lightweight draft model to propose candidate tokens that are then verified in parallel by the target model. The speedup is significantly determined by...
RewardUQ: A Unified Framework for Uncertainty-Aware Reward Models
arXiv:2602.24040v1 Announce Type: cross Abstract: Reward models are central to aligning large language models (LLMs) with human preferences. Yet most approaches rely on pointwise reward estimates that overlook the epistemic uncertainty in reward models arising from limited human feedback. Recent...
Uncertainty-aware Language Guidance for Concept Bottleneck Models
arXiv:2602.23495v1 Announce Type: new Abstract: Concept Bottleneck Models (CBMs) provide inherent interpretability by first mapping input samples to high-level semantic concepts, followed by a combination of these concepts for the final classification. However, the annotation of human-understandable concepts requires extensive...
Active Value Querying to Minimize Additive Error in Subadditive Set Function Learning
arXiv:2602.23529v1 Announce Type: new Abstract: Subadditive set functions play a pivotal role in computational economics (especially in combinatorial auctions), combinatorial optimization or artificial intelligence applications such as interpretable machine learning. However, specifying a set function requires assigning values to an...
Dynamics of Learning under User Choice: Overspecialization and Peer-Model Probing
arXiv:2602.23565v1 Announce Type: new Abstract: In many economically relevant contexts where machine learning is deployed, multiple platforms obtain data from the same pool of users, each of whom selects the platform that best serves them. Prior work in this setting...
SDMixer: Sparse Dual-Mixer for Time Series Forecasting
arXiv:2602.23581v1 Announce Type: new Abstract: Multivariate time series forecasting is widely applied in fields such as transportation, energy, and finance. However, the data commonly suffers from issues of multi-scale characteristics, weak correlations, and noise interference, which limit the predictive performance...
When Does Multimodal Learning Help in Healthcare? A Benchmark on EHR and Chest X-Ray Fusion
arXiv:2602.23614v1 Announce Type: new Abstract: Machine learning holds promise for advancing clinical decision support, yet it remains unclear when multimodal learning truly helps in practice, particularly under modality missingness and fairness constraints. In this work, we conduct a systematic benchmark...
FlexGuard: Continuous Risk Scoring for Strictness-Adaptive LLM Content Moderation
arXiv:2602.23636v1 Announce Type: new Abstract: Ensuring the safety of LLM-generated content is essential for real-world deployment. Most existing guardrail models formulate moderation as a fixed binary classification task, implicitly assuming a fixed definition of harmfulness. In practice, enforcement strictness -...
Disentangled Mode-Specific Representations for Tensor Time Series via Contrastive Learning
arXiv:2602.23663v1 Announce Type: new Abstract: Multi-mode tensor time series (TTS) can be found in many domains, such as search engines and environmental monitoring systems. Learning representations of a TTS benefits various applications, but it is also challenging since the complexities...
MAGE: Multi-scale Autoregressive Generation for Offline Reinforcement Learning
arXiv:2602.23770v1 Announce Type: new Abstract: Generative models have gained significant traction in offline reinforcement learning (RL) due to their ability to model complex trajectory distributions. However, existing generation-based approaches still struggle with long-horizon tasks characterized by sparse rewards. Some hierarchical...
TradeFM: A Generative Foundation Model for Trade-flow and Market Microstructure
arXiv:2602.23784v1 Announce Type: new Abstract: Foundation models have transformed domains from language to genomics by learning general-purpose representations from large-scale, heterogeneous data. We introduce TradeFM, a 524M-parameter generative Transformer that brings this paradigm to market microstructure, learning directly from billions...
GRAIL: Post-hoc Compensation by Linear Reconstruction for Compressed Networks
arXiv:2602.23795v1 Announce Type: new Abstract: Structured deep model compression methods are hardware-friendly and substantially reduce memory and inference costs. However, under aggressive compression, the resulting accuracy degradation often necessitates post-compression finetuning, which can be impractical due to missing labeled data...
Actor-Critic Pretraining for Proximal Policy Optimization
arXiv:2602.23804v1 Announce Type: new Abstract: Reinforcement learning (RL) actor-critic algorithms enable autonomous learning but often require a large number of environment interactions, which limits their applicability in robotics. Leveraging expert data can reduce the number of required environment interactions. A...
Beyond State-Wise Mirror Descent: Offline Policy Optimization with Parameteric Policies
arXiv:2602.23811v1 Announce Type: new Abstract: We investigate the theoretical aspects of offline reinforcement learning (RL) under general function approximation. While prior works (e.g., Xie et al., 2021) have established the theoretical foundations of learning a good policy from offline data...
Inferring Chronic Treatment Onset from ePrescription Data: A Renewal Process Approach
arXiv:2602.23824v1 Announce Type: new Abstract: Longitudinal electronic health record (EHR) data are often left-censored, making diagnosis records incomplete and unreliable for determining disease onset. In contrast, outpatient prescriptions form renewal-based trajectories that provide a continuous signal of disease management. We...