arXiv AI
Jun 2
AlphaToken: Decoupling Adaptation and Stability for Path-Aware Response Token Valuation in LLM Post-Training
arXiv:2606. 01635v1 Announce Type: cross Abstract: Token selection is pivotal for effective LLM post-training.
Fine-tuning and adaptation
LoRA, PEFT, instruction tuning and domain adaptation — adapting a pretrained model without paying to train one.
arXiv Machine Learning
Jun 2
Representation Signatures and Risk-Feedback Alignment in LLM Trading Agents
arXiv:2605. 28850v2 Announce Type: replace Abstract: We study behavioral alignment and representation dynamics of large language model (LLM) agents in financial decision environments.
arXiv Machine Learning
Jun 2
Scalable Inference-Time Annealing with Surrogate Likelihood Estimators
arXiv:2605. 31498v2 Announce Type: replace Abstract: A long standing challenge in computational chemistry and biophysics is efficiently sampling the Boltzmann distribution of molecules.
arXiv Machine Learning
Jun 2
Causal Evaluation of Membership Inference Attacks
arXiv:2602. 02819v4 Announce Type: replace Abstract: Membership Inference Attacks (MIAs) aim to distinguish training points (members) from unseen data (non-members), and are widely used to quantify memorization and assess privacy risks.
arXiv AI
Jun 2
MOSS-Audio Technical Report
arXiv:2606. 01802v1 Announce Type: cross Abstract: MOSS-Audio is a unified audio-language model for speech, environmental sound, and music understanding, supporting audio captioning, time-aware question answering, timestamped transcription, and audio-grounded reasoning.
arXiv Machine Learning
Jun 2
CAREF: Calibration-Aware Regularization for Explanation Faithfulness Without Rationale Supervision
arXiv:2605. 27835v2 Announce Type: replace Abstract: We introduce CAREF, a parameter-efficient fine-tuning framework that jointly optimizes predictive accuracy and explanation faithfulness via calibration-aware regularization.
arXiv AI
Jun 2
From "Weak" Signals to Strong Models: Preference Delta Aggregation with LoRA Merging
arXiv:2606. 00357v1 Announce Type: new Abstract: Training strong large language models (LLMs) requires high-quality supervision, which is often scarce.
arXiv AI
Jun 2
Distilling Neuro-Symbolic Programs into 3D Multi-modal LLMs
arXiv:2606. 01215v1 Announce Type: cross Abstract: Current 3D spatial reasoning methods face a fundamental trade-off: neuro-symbolic 3D (NS3D) concept learners achieve interpretable reasoning through compositional programs but are constrained to closed-set concept vocabularies and simple programs; end-to-end 3D multi-modal LLMs (3D MLLMs) could handle complex natural language and open-vocabulary concepts but suffer from black-box reasoning without explicit spatial verification.
arXiv AI
Jun 2
Attention mechanisms and transfer learning for robust peach leaf damage classification under domain shift
arXiv:2606. 02045v1 Announce Type: cross Abstract: Artificial intelligence provides a practical framework for crop damage assessment from imagery data, supporting early decision-making in agricultural management.
arXiv Machine Learning
Jun 2
Learning Fine-grained Parameter Sharing via Sparse Tensor Decomposition
arXiv:2411. 09816v5 Announce Type: replace Abstract: Large neural networks achieve state-of-the-art performance on many tasks, yet their sheer size hinders deployment on resource-constrained devices.
arXiv Machine Learning
Jun 2
Task Structure Reverses Layerwise State Encoding in Sequence Models
arXiv:2606. 00926v1 Announce Type: new Abstract: Mechanistic studies of sequence models often treat layerwise state encodings as architectural traits: recurrent models concentrate readable state, attention-based models distribute it.
arXiv AI
Jun 2
Train, Test, Re-evaluate: Schedule-Sensitive Evaluation of Generative Data for Hand Detection
arXiv:2606. 01896v1 Announce Type: cross Abstract: Generated (or synthetic) image data is increasingly used to augment or replace real training datasets when target imagery is scarce, expensive, or biased.
arXiv AI
Jun 2
LinguIUTics at PsyDefDetect: Iterative Imbalance-Aware Fine-tuning of Qwen3-8B for Psychological Defense Mechanism Classification
arXiv:2606. 00647v1 Announce Type: cross Abstract: Detecting psychological defense mechanisms in conversational text remains a challenging clinical NLP problem.
arXiv AI
Jun 2
An Open-Source Benchmark and Baseline for Multi-temporal Referring Segmentation
arXiv:2606. 00987v1 Announce Type: cross Abstract: Large Vision-Language Models (LVLMs) have shown strong visual understanding and language-guided grounding abilities, yet their capacity for multi-temporal visual reasoning remains underexplored.
arXiv AI
Jun 2
U-Cast: A Surprisingly Simple and Efficient Frontier Probabilistic AI Weather Forecaster
arXiv:2604. 09041v2 Announce Type: replace-cross Abstract: AI-based weather forecasting now rivals traditional physics-based ensembles, but state-of-the-art (SOTA) models rely on specialized architectures and massive computational budgets, creating a high barrier to entry.
arXiv Machine Learning
Jun 2
From Reward-Free Representations to Preferences: Rethinking Offline Preference-Based Reinforcement Learning
arXiv:2606. 01123v1 Announce Type: new Abstract: Preference-based reinforcement learning (PbRL) avoids explicit reward engineering by learning from pairwise human preference feedback.
arXiv AI
Jun 2
TInR: Exploring Tool-Internalized Reasoning in Large Language Models
arXiv:2604. 10788v2 Announce Type: replace-cross Abstract: Tool-Integrated Reasoning (TIR) has emerged as a promising direction by extending Large Language Models' (LLMs) capabilities with external tools during reasoning.
arXiv Machine Learning
Jun 2
Task-Induced Representational Invariances Depend on Learning Objective in Deep RL
arXiv:2606. 01868v1 Announce Type: new Abstract: Reinforcement Learning (RL) has long served as a model for goal-directed animal behavior in neuroscience.
arXiv Machine Learning
Jun 2
BERT4beam: Large AI Model Enabled Generalized Beamforming Optimization
arXiv:2509. 11056v2 Announce Type: replace-cross Abstract: Artificial intelligence (AI) is anticipated to emerge as a pivotal enabler for the forthcoming sixth-generation (6G) wireless communication systems.
arXiv AI
Jun 2
Ryze: Evidence-Enriched Data Synthesis from Biomedical Papers
arXiv:2606. 00902v1 Announce Type: new Abstract: General-purpose VLMs remain unreliable for biomedical research because valid answers in scientific papers depend on evidence split across figures, tables, charts, captions, and referring text.