arXiv Machine Learning
Jun 2
An Algebraic View of the Expressivity of Recurrent Language Models
arXiv:2606. 01765v1 Announce Type: cross Abstract: What formal languages can a recurrent neural language model recognize?
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 2
Optimizing Diversity and Quality through Base-Aligned Model Collaboration
arXiv:2511. 05650v2 Announce Type: replace-cross Abstract: Alignment has greatly improved large language models (LLMs)' output quality at the cost of diversity, yielding highly similar outputs across generations, especially in open-ended generation tasks.
arXiv AI
Jun 2
SortingHat: Redefining Operating Systems Education with a Tailored Digital Teaching Assistant
arXiv:2606. 00015v1 Announce Type: cross Abstract: Operating Systems (OS) courses are among the most challenging in computer science education due to the complexity of internal structures and the diversity of running environments.
arXiv AI
Jun 2
CSRP: Chain-of-Thought Reasoning for Chinese Text Correction via Reinforcement Learning with Efficiency-Aware Rewards
arXiv:2606. 00020v1 Announce Type: cross Abstract: Large Language Model (LLM) based Chinese Grammatical Error Correction (CGEC) systems face two critical challenges: general-purpose models lack specialized linguistic priors for subtle grammatical distinctions, and Supervised Fine-Tuning (SFT) with Maximum Likelihood Estimation fails to optimize for precision-focused metrics, leading to systematic over-correction.
arXiv AI
Jun 2
RadioMaster: Multi-Agent System for Autonomous Radio Signal Generation
arXiv:2606. 01862v1 Announce Type: cross Abstract: Translating user intents into physical radio signals represents the critical yet notoriously tedious final step in wireless prototyping, as it requires intricate knowledge of physical layer details and presents immense implementation challenges.
arXiv AI
Jun 2
Multimodal Function Vectors for Visual Relations
arXiv:2510. 02528v2 Announce Type: replace Abstract: Large Multimodal Models (LMMs) demonstrate impressive in-context learning abilities from few multimodal demonstrations, yet the internal mechanisms supporting such task learning remain opaque.
arXiv AI
Jun 2
Bayesian Spectral Emotion Transition Discovery from Multi-Annotator Disagreement
arXiv:2606. 01906v1 Announce Type: new Abstract: Emotions evolve through the dynamics of conversation, and understanding their transition structure is foundational to applications ranging from mental-health screening to dialogue systems.
arXiv AI
Jun 2
VLM4VLA: Revisiting Vision-Language-Models in Vision-Language-Action Models
arXiv:2601. 03309v2 Announce Type: replace-cross Abstract: Vision-Language-Action (VLA) models, which integrate pretrained large Vision-Language Models (VLM) into their policy backbone, are gaining significant attention for their promising generalization capabilities.
arXiv AI
Jun 2
DyLLM: Efficient Diffusion LLM Inference via Saliency-based Token Selection and Partial Attention
arXiv:2603. 08026v2 Announce Type: replace-cross Abstract: Masked diffusion language models enable parallel token decoding, providing a promising alternative to the sequential nature of autoregressive generation.
arXiv AI
Jun 2
AgentProcessBench: Diagnosing Step-Level Process Quality in Tool-Using Agents
arXiv:2603. 14465v2 Announce Type: replace Abstract: While Large Language Models (LLMs) have evolved into tool-using agents, they remain brittle in long-horizon interactions.
arXiv Machine Learning
Jun 2
Perception First: A Frontier Native-Video Model with Self-Consistency for Implicit Video Question Answering
arXiv:2606. 01485v1 Announce Type: cross Abstract: We describe our submission to the VRR Challenge @ CVPR 2026, built on the \emph{ImplicitQA} / \emph{VRR-QA} benchmark~\cite{implicitqa}: multiple-choice video question answering in which answers are deliberately \emph{not} observable in any single frame and must be inferred from spatial layout, motion, depth, viewpoint, causality, and social context across discontinuous frames of creative video.
arXiv AI
Jun 2
DAStatFormer: A Hybrid Multibranch Transformer with Statistical Feature Integration for DAS-Based Pattern Recognitions
arXiv:2606. 00081v1 Announce Type: cross Abstract: Distributed Acoustic Sensing (DAS) enables large-scale monitoring through optical fibers, but its high dimensionality and complex spatio-temporal patterns make event classification demanding.
arXiv AI
Jun 2
From Layers to Submodules: Rethinking Granularity in Replacement-Based LLM Compression
arXiv:2606. 02559v1 Announce Type: cross Abstract: Post-training compression of Large Language Models (LLMs) removes entire architectural components, either deleting them or replacing them with fitted modules.
arXiv AI
Jun 2
Vision Language Models Cannot Reason About Physical Transformation
arXiv:2603. 07109v2 Announce Type: replace Abstract: Understanding physical transformations is fundamental for reasoning in dynamic environments.
arXiv AI
Jun 2
Test-Time Deep Thinking to Explore Implicit Rules
arXiv:2605. 24828v2 Announce Type: replace Abstract: With the continuous advancement of Large Language Models (LLMs), intelligent agents are becoming increasingly vital.
arXiv AI
Jun 2
LayerRoute: Input-Conditioned Adaptive Layer Skipping via LoRA Fine-Tuning for Agentic Language Models
arXiv:2606. 01838v1 Announce Type: cross Abstract: Agentic language model systems alternate between two structurally distinct step types: structured tool calls (short, deterministic, low perplexity) and open-ended planning/reasoning steps (long, complex, high perplexity).
arXiv AI
Jun 2
Adaptive Auto-Harness: Sustained Self-Improvement for Agentic System Deployment on Open-Ended Task Streams
arXiv:2606. 01770v1 Announce Type: cross Abstract: Auto-harness systems such as A-Evolve, GEPA, and Meta-Harness improve LLM agents by optimizing prompts, skills, tools, memories, and supporting infrastructure from execution feedback, but they are typically evaluated on fixed offline benchmarks.
arXiv AI
Jun 2
Large Language Models in Transportation Systems Management and Operations: From Text Reasoning to Multi-modal Decision Support
arXiv:2606. 00991v1 Announce Type: new Abstract: Transportation systems management and operations (TSMO) increasingly depends on timely interpretation of heterogeneous data, from various sensor streams, incident reports, traveler feedback, and visual observations.
arXiv Machine Learning
Jun 2
Observation, Not Prediction: Conversation-Level Disaggregated Scheduling for Agentic Serving
arXiv:2606. 01839v1 Announce Type: cross Abstract: LLM-based agents resolve a user task through many turns of dependent inference and tool calls, producing a workload whose total cost is unknown when the task arrives.
arXiv AI
Jun 2
SPADE-Bench: Evaluating Spontaneous Strategic Deception in Agents via Plan-Action Divergence
arXiv:2606. 02380v1 Announce Type: cross Abstract: As LLM-based agents expand their operational scope, reliability becomes a prerequisite for real-world deployment.