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...
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...
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 -...
Selective Denoising Diffusion Model for Time Series Anomaly Detection
arXiv:2602.23662v1 Announce Type: new Abstract: Time series anomaly detection (TSAD) has been an important area of research for decades, with reconstruction-based methods, mostly based on generative models, gaining popularity and demonstrating success. Diffusion models have recently attracted attention due to...
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...
FedNSAM:Consistency of Local and Global Flatness for Federated Learning
arXiv:2602.23827v1 Announce Type: new Abstract: In federated learning (FL), multi-step local updates and data heterogeneity usually lead to sharper global minima, which degrades the performance of the global model. Popular FL algorithms integrate sharpness-aware minimization (SAM) into local training to...
From Logs to Language: Learning Optimal Verbalization for LLM-Based Recommendation in Production
arXiv:2602.20558v1 Announce Type: new Abstract: Large language models (LLMs) are promising backbones for generative recommender systems, yet a key challenge remains underexplored: verbalization, i.e., converting structured user interaction logs into effective natural language inputs. Existing methods rely on rigid templates...
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...
Qwen-BIM: developing large language model for BIM-based design with domain-specific benchmark and dataset
arXiv:2602.20812v1 Announce Type: new Abstract: As the construction industry advances toward digital transformation, BIM (Building Information Modeling)-based design has become a key driver supporting intelligent construction. Despite Large Language Models (LLMs) have shown potential in promoting BIM-based design, the lack...
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...
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...
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...
NoRD: A Data-Efficient Vision-Language-Action Model that Drives without Reasoning
arXiv:2602.21172v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models are advancing autonomous driving by replacing modular pipelines with unified end-to-end architectures. However, current VLAs face two expensive requirements: (1) massive dataset collection, and (2) dense reasoning annotations. In this work, we...
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...
A Dynamic Survey of Soft Set Theory and Its Extensions
arXiv:2602.21268v1 Announce Type: new Abstract: Soft set theory provides a direct framework for parameterized decision modeling by assigning to each attribute (parameter) a subset of a given universe, thereby representing uncertainty in a structured way [1, 2]. Over the past...
Latent Context Compilation: Distilling Long Context into Compact Portable Memory
arXiv:2602.21221v1 Announce Type: cross Abstract: Efficient long-context LLM deployment is stalled by a dichotomy between amortized compression, which struggles with out-of-distribution generalization, and Test-Time Training, which incurs prohibitive synthetic data costs and requires modifying model weights, creating stateful parameters that...
Equitable Evaluation via Elicitation
arXiv:2602.21327v1 Announce Type: cross Abstract: Individuals with similar qualifications and skills may vary in their demeanor, or outward manner: some tend toward self-promotion while others are modest to the point of omitting crucial information. Comparing the self-descriptions of equally qualified...
Alignment-Weighted DPO: A principled reasoning approach to improve safety alignment
arXiv:2602.21346v1 Announce Type: cross Abstract: Recent advances in alignment techniques such as Supervised Fine-Tuning (SFT), Reinforcement Learning from Human Feedback (RLHF), and Direct Preference Optimization (DPO) have improved the safety of large language models (LLMs). However, these LLMs remain vulnerable...
Towards single-shot coherent imaging via overlap-free ptychography
arXiv:2602.21361v1 Announce Type: cross Abstract: Ptychographic imaging at synchrotron and XFEL sources requires dense overlapping scans, limiting throughput and increasing dose. Extending coherent diffractive imaging to overlap-free operation on extended samples remains an open problem. Here, we extend PtychoPINN (O....
FedVG: Gradient-Guided Aggregation for Enhanced Federated Learning
arXiv:2602.21399v1 Announce Type: cross Abstract: Federated Learning (FL) enables collaborative model training across multiple clients without sharing their private data. However, data heterogeneity across clients leads to client drift, which degrades the overall generalization performance of the model. This effect...
Graph Your Way to Inspiration: Integrating Co-Author Graphs with Retrieval-Augmented Generation for Large Language Model Based Scientific Idea Generation
arXiv:2602.22215v1 Announce Type: new Abstract: Large Language Models (LLMs) demonstrate potential in the field of scientific idea generation. However, the generated results often lack controllable academic context and traceable inspiration pathways. To bridge this gap, this paper proposes a scientific...
FIRE: A Comprehensive Benchmark for Financial Intelligence and Reasoning Evaluation
arXiv:2602.22273v1 Announce Type: new Abstract: We introduce FIRE, a comprehensive benchmark designed to evaluate both the theoretical financial knowledge of LLMs and their ability to handle practical business scenarios. For theoretical assessment, we curate a diverse set of examination questions...
ConstraintBench: Benchmarking LLM Constraint Reasoning on Direct Optimization
arXiv:2602.22465v1 Announce Type: new Abstract: Large language models are increasingly applied to operational decision-making where the underlying structure is constrained optimization. Existing benchmarks evaluate whether LLMs can formulate optimization problems as solver code, but leave open a complementary question. Can...
VeRO: An Evaluation Harness for Agents to Optimize Agents
arXiv:2602.22480v1 Announce Type: new Abstract: An important emerging application of coding agents is agent optimization: the iterative improvement of a target agent through edit-execute-evaluate cycles. Despite its relevance, the community lacks a systematic understanding of coding agent performance on this...
A Mathematical Theory of Agency and Intelligence
arXiv:2602.22519v1 Announce Type: new Abstract: To operate reliably under changing conditions, complex systems require feedback on how effectively they use resources, not just whether objectives are met. Current AI systems process vast information to produce sophisticated predictions, yet predictions can...
Scaling In, Not Up? Testing Thick Citation Context Analysis with GPT-5 and Fragile Prompts
arXiv:2602.22359v1 Announce Type: new Abstract: This paper tests whether large language models (LLMs) can support interpretative citation context analysis (CCA) by scaling in thick, text-grounded readings of a single hard case rather than scaling up typological labels. It foregrounds prompt-sensitivity...
Causality $\neq$ Invariance: Function and Concept Vectors in LLMs
arXiv:2602.22424v1 Announce Type: new Abstract: Do large language models (LLMs) represent concepts abstractly, i.e., independent of input format? We revisit Function Vectors (FVs), compact representations of in-context learning (ICL) tasks that causally drive task performance. Across multiple LLMs, we show...
dLLM: Simple Diffusion Language Modeling
arXiv:2602.22661v1 Announce Type: new Abstract: Although diffusion language models (DLMs) are evolving quickly, many recent models converge on a set of shared components. These components, however, are distributed across ad-hoc research codebases or lack transparent implementations, making them difficult to...
The Poly Problem in Zoning: Redefining “Family” for a Changing Society lawreview - Minnesota Law Review
By ARIC SHORT & TANYA PIERCE. Full Text. Single-family zoning has long dictated not only where people may live but also with whom. Although extensively critiqued for perpetuating racial and economic exclusion, these laws also privilege relationships defined by blood,...