arXiv AI
Jun 9
FieldWorkArena: Agentic AI Benchmark for Real Field Work Tasks
arXiv:2505. 19662v4 Announce Type: replace Abstract: This paper introduces FieldWorkArena, a benchmark for agentic AI targeting real-world field work.
Large language models
Model releases, architecture work and prompting research on large language models — from frontier-lab announcements to the arXiv papers behind them.
arXiv AI
Jun 9
FlashCP: Load-Balanced Communication-Efficient Context Parallelism for LLM Training
arXiv:2606. 08476v1 Announce Type: cross Abstract: Context parallelism (CP) is essential for training large-scale, long-context language models, as it partitions sequences to reduce memory overhead.
arXiv AI
Jun 9
DynamicPO: Dynamic Preference Optimization for Recommendation
arXiv:2605. 00327v2 Announce Type: replace-cross Abstract: In large language model (LLM)-based recommendation systems, direct preference optimization (DPO) effectively aligns recommendations with user preferences, requiring multi-negative objective functions to leverage abundant implicit-feedback negatives and sharpen preference boundaries.
arXiv AI
Jun 9
Towards Long-Horizon Vessel Trajectory and Destination Forecasting with Reasoning Large Language Models
arXiv:2606. 08633v1 Announce Type: new Abstract: Long-horizon maritime trajectory prediction is important for shipping management, logistics planning, and maritime risk analysis, yet month-level forecasting remains insufficiently studied.
arXiv AI
Jun 9
A Universal Dense Football Event Representation Based on TabTransformer
arXiv:2606. 09327v1 Announce Type: cross Abstract: Football event data constitute a rich spatiotemporal source for quantitative analysis of player actions in team sports.
arXiv Machine Learning
Jun 9
Diffuse AI Control on Fuzzy Tasks
arXiv:2606. 08892v1 Announce Type: new Abstract: AI models deployed in critical domains, such as AI safety research, may subtly sabotage our efforts due to misalignment.
arXiv AI
Jun 9
INFUSER: Influence-Guided Self-Evolution Improves Reasoning
arXiv:2606. 09052v1 Announce Type: cross Abstract: Self-evolution offers a scalable path to stronger reasoning: a pretrained language model improves itself with only minimal external supervision.
arXiv AI
Jun 9
Hacking Generative Perplexity: Why Unconditional Text Evaluation Needs Distributional Metrics
arXiv:2606. 08417v1 Announce Type: cross Abstract: Diffusion and continuous flow-based language models have emerged as the leading non-autoregressive alternatives to language modeling.
arXiv AI
Jun 9
Hiding in Plain Floats: Steganographic Carriers for Indirect Prompt and Content Injection
arXiv:2606. 08403v1 Announce Type: cross Abstract: Text-centered prompt-injection defenses assume that the malicious signal is visible in one of the inspected text views.
arXiv AI
Jun 9
Minibatch Selection via Partition Matroid Constrained Gradient Matching
arXiv:2606. 07954v1 Announce Type: cross Abstract: Training large language models (LLMs) on heterogeneous data requires selecting minibatches that balance convergence speed with coverage across domains.
arXiv AI
Jun 9
SecureClaw: Clawing Back Control of LLM Agents
arXiv:2606. 09549v1 Announce Type: cross Abstract: Tool-using large language model (LLM) agents face two distinct security failures: unauthorized external actions and exposure of sensitive plaintext inside the runtime before any final output check can intervene.
arXiv AI
Jun 9
SEF-CLGC at SemEval-2026 Task 11: Logical Notation Impact on Language Model Performance
arXiv:2606. 09157v1 Announce Type: cross Abstract: This paper revisits our pipeline called Syllogistic Evaluation Framework-Common Logic Grammar Construction (SEF-CLGC).
arXiv AI
Jun 9
More Yap Less Meaning: Uncovering Self-Improvement Behavior in SLMs
arXiv:2606. 08471v1 Announce Type: cross Abstract: Recently, language models have made rapid progress across various domains and applications.
arXiv AI
Jun 9
"I understand your perspective": LLM Persuasion and Sycophancy through the Lens of Communicative Action Theory
arXiv:2606. 08076v1 Announce Type: cross Abstract: Large Language Models (LLMs) can generate high-quality arguments, yet their ability to engage in nuanced and persuasive communicative actions remains largely unexplored.
arXiv AI
Jun 9
GIScholarBench: Benchmarking LLM Overconfidence in GIS Research
arXiv:2606. 08036v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used in academic research workflows, but scholarly tasks require high factual precision and therefore expose a key weakness: overconfidence.
arXiv AI
Jun 9
Why Limit the Residual Stream to Layers and Not Tokens? Persistent Memory for Continuous Latent Reasoning
arXiv:2606. 07720v1 Announce Type: new Abstract: Large language models (LLMs) have demonstrated remarkable reasoning abilities on mathematical and multi-hop planning tasks.
arXiv Machine Learning
Jun 9
Sequential statistical inference for Large Language Models: Representation, validity, and monitoring
arXiv:2606. 07624v1 Announce Type: new Abstract: This discussion argues that sequential statistical inference can naturally contribute to LLM trustworthiness.
arXiv AI
Jun 9
Beyond Pass Rate: A Multilingual, Execution-Grounded Evaluation of Open Code LLMs
arXiv:2606. 08840v1 Announce Type: new Abstract: Code generation models are typically compared using compact execution benchmarks and aggregate pass rates, but such summaries obscure how performance varies across programming languages, problem families, and failure modes.
arXiv AI
Jun 9
ConflictRAG: Detecting and Resolving Knowledge Conflicts in Retrieval Augmented Generation
arXiv:2605. 17301v2 Announce Type: replace-cross Abstract: Retrieval-Augmented Generation (RAG) systems implicitly assume mutual consistency among retrieved documents -- an assumption that frequently fails in practice.
arXiv AI
Jun 9
InA-Probe: Instruction-Aware Active Probing for Time Series Forecasting with LLMs
arXiv:2606. 08601v1 Announce Type: new Abstract: Large Language Models (LLMs) have recently demonstrated impressive potential for time series forecasting.