Found-RL: foundation model-enhanced reinforcement learning for autonomous driving
arXiv:2602.10458v1 Announce Type: new
Abstract: Reinforcement Learning (RL) has emerged as a dominant paradigm for end-to-end autonomous driving (AD). However, RL suffers from sample inefficiency and a lack of semantic interpretability in complex scenarios. Foundation Models, particularly Vision-La...
MERIT Feedback Elicits Better Bargaining in LLM Negotiators
arXiv:2602.10467v1 Announce Type: new
Abstract: Bargaining is often regarded as a logical arena rather than an art or a matter of intuition, yet Large Language Models (LLMs) still struggle to navigate it due to limited strategic depth and difficulty adapting to complex human factors. Current benchm...
Abstraction Generation for Generalized Planning with Pretrained Large Language Models
arXiv:2602.10485v1 Announce Type: new
Abstract: Qualitative Numerical Planning (QNP) serves as an important abstraction model for generalized planning (GP), which aims to compute general plans that solve multiple instances at once. Recent works show that large language models (LLMs) can function as...
Mapping the Design Space of User Experience for Computer Use Agents
Large language model (LLM)-based computer use agents execute user commands by interacting with available UI elements, but little is known about how users want to interact with these agents or what design factors matter for their user experience (UX). We conducted a two-phase study to map the UX desi...
Trace Length is a Simple Uncertainty Signal in Reasoning Models
Uncertainty quantification for LLMs is a key research direction towards addressing hallucination and other issues that limit their reliable deployment. In this work, we show that reasoning trace length is a simple and useful confidence estimator in large reasoning models. Through comprehensive exper...
Enhanced Graph Transformer with Serialized Graph Tokens
arXiv:2602.09065v1 Announce Type: new
Abstract: Transformers have demonstrated success in graph learning, particularly for node-level tasks. However, existing methods encounter an information bottleneck when generating graph-level representations. The prevalent single token paradigm fails to fully ...
Spectral Disentanglement and Enhancement: A Dual-domain Contrastive Framework for Representation Learning
arXiv:2602.09066v1 Announce Type: new
Abstract: Large-scale multimodal contrastive learning has recently achieved impressive success in learning rich and transferable representations, yet it remains fundamentally limited by the uniform treatment of feature dimensions and the neglect of the intrinsi...
Learning to Remember, Learn, and Forget in Attention-Based Models
arXiv:2602.09075v1 Announce Type: new
Abstract: In-Context Learning (ICL) in transformers acts as an online associative memory and is believed to underpin their high performance on complex sequence processing tasks. However, in gated linear attention models, this memory has a fixed capacity and is ...
Patient foundation model for risk stratification in low-risk overweight patients
arXiv:2602.09079v1 Announce Type: new
Abstract: Accurate risk stratification in patients with overweight or obesity is critical for guiding preventive care and allocating high-cost therapies such as GLP-1 receptor agonists. We present PatientTPP, a neural temporal point process (TPP) model trained ...
Looping Back to Move Forward: Recursive Transformers for Efficient and Flexible Large Multimodal Models
arXiv:2602.09080v1 Announce Type: new
Abstract: Large Multimodal Models (LMMs) have achieved remarkable success in vision-language tasks, yet their vast parameter counts are often underutilized during both training and inference. In this work, we embrace the idea of looping back to move forward: re...
A Small-Scale System for Autoregressive Program Synthesis Enabling Controlled Experimentation
arXiv:2602.09112v1 Announce Type: new
Abstract: What research can be pursued with small models trained to complete true programs? Typically, researchers study program synthesis via large language models (LLMs) which introduce issues such as knowing what is in or out of distribution, understanding f...
Uncertainty-Aware Multimodal Emotion Recognition through Dirichlet Parameterization
arXiv:2602.09121v1 Announce Type: new
Abstract: In this work, we present a lightweight and privacy-preserving Multimodal Emotion Recognition (MER) framework designed for deployment on edge devices. To demonstrate framework's versatility, our implementation uses three modalities - speech, text and f...
PABU: Progress-Aware Belief Update for Efficient LLM Agents
arXiv:2602.09138v1 Announce Type: new
Abstract: Large Language Model (LLM) agents commonly condition actions on full action-observation histories, which introduce task-irrelevant information that easily leads to redundant actions and higher inference cost. We propose Progress-Aware Belief Update (P...
CoMMa: Contribution-Aware Medical Multi-Agents From A Game-Theoretic Perspective
arXiv:2602.09159v1 Announce Type: new
Abstract: Recent multi-agent frameworks have broadened the ability to tackle oncology decision support tasks that require reasoning over dynamic, heterogeneous patient data. We propose Contribution-Aware Medical Multi-Agents (CoMMa), a decentralized LLM-agent f...
FlyAOC: Evaluating Agentic Ontology Curation of Drosophila Scientific Knowledge Bases
arXiv:2602.09163v1 Announce Type: new
Abstract: Scientific knowledge bases accelerate discovery by curating findings from primary literature into structured, queryable formats for both human researchers and emerging AI systems. Maintaining these resources requires expert curators to search relevant...
arXiv:2602.06993v1 Announce Type: new
Abstract: Transformers achieve strong language modeling accuracy, yet their position-wise feed-forward networks (FFNs) are dense, globally shared, and typically updated end to end. These properties create two practical tensions. First, dense FFNs spend the same...
Neural Sabermetrics with World Model: Play-by-play Predictive Modeling with Large Language Model
arXiv:2602.07030v1 Announce Type: new
Abstract: Classical sabermetrics has profoundly shaped baseball analytics by summarizing long histories of play into compact statistics. While these metrics are invaluable for valuation and retrospective analysis, they do not define a generative model of how ba...
TransConv-DDPM: Enhanced Diffusion Model for Generating Time-Series Data in Healthcare
arXiv:2602.07033v1 Announce Type: new
Abstract: The lack of real-world data in clinical fields poses a major obstacle in training effective AI models for diagnostic and preventive tools in medicine. Generative AI has shown promise in increasing data volume and enhancing model training, particularly...
AVERE: Improving Audiovisual Emotion Reasoning with Preference Optimization
arXiv:2602.07054v1 Announce Type: new
Abstract: Emotion understanding is essential for building socially intelligent agents. Although recent multimodal large language models have shown strong performance on this task, two key challenges remain - spurious associations between emotions and irrelevant...
LLM-FSM: Scaling Large Language Models for Finite-State Reasoning in RTL Code Generation
arXiv:2602.07032v1 Announce Type: new
Abstract: Finite-state reasoning, the ability to understand and implement state-dependent behavior, is central to hardware design. In this paper, we present LLM-FSM, a benchmark that evaluates how well large language models (LLMs) can recover finite-state machi...
ST-Raptor: An Agentic System for Semi-Structured Table QA
arXiv:2602.07034v1 Announce Type: new
Abstract: Semi-structured table question answering (QA) is a challenging task that requires (1) precise extraction of cell contents and positions and (2) accurate recovery of key implicit logical structures, hierarchical relationships, and semantic associations...
DLLM-Searcher: Adapting Diffusion Large Language Model for Search Agents
arXiv:2602.07035v1 Announce Type: new
Abstract: Recently, Diffusion Large Language Models (dLLMs) have demonstrated unique efficiency advantages, enabled by their inherently parallel decoding mechanism and flexible generation paradigm. Meanwhile, despite the rapid advancement of Search Agents, thei...