arXiv Machine Learning
Jun 10
CodeAlchemy: Synthetic Code Rewriting at Scale
arXiv:2606. 10087v1 Announce Type: cross Abstract: Pre-training on raw code teaches syntax but provides sparse signal for diverse real-world task formats.
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 10
Modeling Complex Behaviors: Multi-Personality Composition and Dynamic Switching in Vision-Language Models
arXiv:2606. 11074v1 Announce Type: cross Abstract: With the widespread deployment of Multimodal Large Language Models (MLLMs) in social interaction, understanding and controlling their behavior under complex personality conditions is essential.
arXiv AI
Jun 10
Towards Robust Arabic Speech Emotion Recognition with Deep Learning
arXiv:2606. 10278v1 Announce Type: cross Abstract: Speech Emotion Recognition (SER) aims to identify a speaker's emotional state from audio signals.
arXiv AI
Jun 10
Unsupervised Style Representation Learning for AI-Text Detection via Paraphrase Inversion
arXiv:2606. 10099v1 Announce Type: cross Abstract: The rapid development of large language models (LLMs) has raised concerns about misuse such as plagiarism, misinformation, and automated influence operations, motivating the need for robust detectors.
arXiv AI
Jun 10
Frontier Coding Agents Use Metaprogramming to Adapt to Unfamiliar Programming Languages
arXiv:2606. 10933v1 Announce Type: new Abstract: LLM-based coding agents are usually evaluated in familiar software settings: mainstream languages, common libraries, and public repositories.
arXiv AI
Jun 10
ComBench: A Benchmark for Rigorous Proof Reasoning and Constructive Realization in Olympiad-Level Combinatorics
arXiv:2606. 10479v1 Announce Type: new Abstract: Combinatorics is central to Olympiad-level mathematical problem solving, requiring deep discrete reasoning, creative constructions, and rigorous structural insight.
arXiv AI
Jun 10
MMClima: A Framework for Multimodal Climate Science Data and Evaluation
arXiv:2606. 10194v1 Announce Type: cross Abstract: Climate change research increasingly requires AI systems that reason across text, dynamic visual content, and scientific figures, yet existing climate QA benchmarks are small, mostly textual, and cover a narrow range of models.
arXiv AI
Jun 10
ReflectiChain: Epistemic Grounding in LLM-Driven World Models for Supply Chain Resilience
arXiv:2606. 10359v1 Announce Type: new Abstract: AI agents in supply chains face a fundamental epistemic gap: large language models (LLMs) interpret policies but lack physical grounding, while reinforcement learning (RL) optimizes flows but is semantically blind to unstructured constraints.
arXiv AI
Jun 10
Null-Space Constrained Low-Rank Adaptation for Response-Specified Large Language Model Unlearning
arXiv:2606. 10989v1 Announce Type: new Abstract: Large language model unlearning aims to suppress designated undesirable knowledge while preserving benign capabilities.
arXiv AI
Jun 10
VFUSE: Virulent Feature Understanding with Sparse autoEncoders
arXiv:2606. 10080v1 Announce Type: cross Abstract: Generative models have shown remarkable progress in a variety of domains such as protein design, but such power enables the opaque generation of hazardous proteins.
arXiv AI
Jun 10
AuRA: Internalizing Audio Understanding into LLMs as LoRA
arXiv:2606. 11033v1 Announce Type: cross Abstract: Recent efforts to extend large language models (LLMs) to speech inputs typically rely on cascaded ASR-LLM pipelines, end-to-end speech-language models, or bridge/distillation-based adaptation.
arXiv AI
Jun 10
Instruction Finetuning DeepSeek-R1-8B Model Using LoRA and NEFTune
arXiv:2606. 10392v1 Announce Type: new Abstract: Financial named-entity recognition (NER) is essential for translating unstructured financial reports and news into structured knowledge graphs.
arXiv AI
Jun 10
Does Normalization Choice Matter for Causal Large Time-Series Models?
arXiv:2606. 09954v1 Announce Type: cross Abstract: Large models for time-series forecasting have been emerged as a promising paradigm for training models on heterogeneous collections of signals.
arXiv AI
Jun 10
PatchSTG: Scalable Spatiotemporal Graph Transformers for Traffic Forecasting on Irregular Sensor Networks
arXiv:2606. 09872v1 Announce Type: cross Abstract: Traffic forecasting is a fundamental component of intelligent transportation systems, yet remains challenging in real-world settings due to irregular sensor distributions and the high computational cost of modeling large-scale spatiotemporal dependencies.
arXiv AI
Jun 10
Inside the Latent Flow: Causal Deciphering of Attention Dynamics in Audio Separation Foundation Models
arXiv:2606. 10046v1 Announce Type: cross Abstract: Flow-matching transformers achieve strong audio separation, yet their attention dynamics are opaque.
arXiv AI
Jun 10
NuWa: Deriving Lightweight Class-Specific Vision Transformers for Edge Devices
arXiv:2504. 03118v2 Announce Type: replace-cross Abstract: Vision Transformers (ViTs) often need to be compressed for deployment on resource-constrained edge devices like drones and smart vehicles.
arXiv AI
Jun 10
From Context-Aware to Conflict-Aware: Generalizing Contrastive Decoding for Knowledge Conflict in LLMs
arXiv:2606. 10298v1 Announce Type: new Abstract: When large language models generate from retrieved or augmented contexts, conflicts between external context and parametric priors remain a central reliability bottleneck.
arXiv AI
Jun 10
From Senses to Decisions: The Information Flow of Auditory and Visual Perception in Multimodal LLMs
arXiv:2606. 10147v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) can listen and see, but how do audio and visual signals actually travel through the network to shape an answer?
arXiv Machine Learning
Jun 10
Do LLMsMakeNeural Distinguishers Wise?
arXiv:2606. 10692v1 Announce Type: cross Abstract: Neural distinguishers are a cryptanalysis method for symmetric-key cryptography that trains machine learning models on pairs of plaintexts and ciphertexts with specific differences in order to recover a secret key.
arXiv AI
Jun 10
Optimality of FSQ Tokens for Continuous Diffusion for Categorical Data with Application to Text-to-Speech
arXiv:2606. 09962v1 Announce Type: cross Abstract: Continuous diffusion for categorical data is a framework belonging to the diffusion family and aiming at generating discrete data.