arXiv Machine Learning
Jun 9
Learning from flowsheets: A generative transformer model for autocompletion of flowsheets
arXiv:2208. 00859v2 Announce Type: replace Abstract: We propose a novel method enabling autocompletion of chemical flowsheets.
Fine-tuning and adaptation
LoRA, PEFT, instruction tuning and domain adaptation — adapting a pretrained model without paying to train one.
arXiv Machine Learning
Jun 9
Now You (Still) See Me: Detecting Evasive Steganographic Payloads in LLMs
arXiv:2606. 09411v1 Announce Type: cross Abstract: Large language models can be fine-tuned to encode prompt-borne secrets into fluent, seemingly benign outputs.
arXiv Machine Learning
Jun 9
Community-Specific Slang and Entity Detection via Semantic Shift in Fine-Tuned Language Models
arXiv:2606. 07522v1 Announce Type: cross Abstract: We propose an unsupervised method of resolving slang, unique entities, and folklore from online communities by isolating words in the lexicon that have the highest magnitude of semantic shift.
arXiv AI
Jun 9
Decision-Aware Memory Cards: Counterfactual-Inspired Context Selection and Compression for Tool-Using LLM Agents
arXiv:2606. 08151v1 Announce Type: new Abstract: Tool-using LLM agents often fail not because relevant text is absent, but because decisive evidence is not selected, compressed, or surfaced at action time.
arXiv Machine Learning
Jun 9
Layer-wise Derivative Controlled Networks Achieve Competitive Accuracy and Gradient Stability Across Data Regimes
arXiv:2606. 07908v1 Announce Type: new Abstract: Derivative-controlled networks based on ChainzRule (CR) combine cubic polynomial layers with a lightweight forward-mode per-layer Jacobian penalty (DREG).
arXiv Machine Learning
Jun 9
Discovering Functionally Selective Brain Regions with a Deep Topographic Multimodal Model
arXiv:2606. 09770v1 Announce Type: cross Abstract: Nearby neurons in cortex share similar response profiles, producing systematic spatial organization across sensory and cognitive systems.
arXiv AI
Jun 9
Audio-FLAN: An Instruction-Following Dataset for Unified Audio Understanding and Generation of Speech, Music, and Sound
arXiv:2502. 16584v2 Announce Type: replace-cross Abstract: Recent advancements in audio tokenization have significantly enhanced the integration of audio capabilities into large language models (LLMs).
arXiv Machine Learning
Jun 9
Benchmarking Empirical Privacy Protection for Adaptations of Large Language Models
arXiv:2606. 09401v1 Announce Type: new Abstract: Recent work has applied differential privacy (DP) to adapt large language models (LLMs) for sensitive applications, offering theoretical guarantees.
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 Machine Learning
Jun 9
PriFT: Prior-Support Guided Supervised Fine-Tuning
arXiv:2606. 09396v1 Announce Type: cross Abstract: Supervised fine-tuning (SFT) is an efficient approach for downstream task adaptation and often serves as the initialization stage for reinforcement learning (RL), but it can show weaker generalization than RL.
arXiv AI
Jun 9
SurfDesign: Effective Protein Design on Molecular Surfaces
arXiv:2606. 07567v1 Announce Type: cross Abstract: Protein function is largely determined by molecular surface geometry and physicochemical complementarity, yet most protein design methods condition only on backbone structure.
arXiv AI
Jun 9
NutriMLLM: Multimodal Large Language Models for Dietary Micronutrient Analysis
arXiv:2606. 08948v1 Announce Type: cross Abstract: Comprehensive estimation of dietary micronutrients from food images could improve clinical nutrition care, but training such models requires large multimodal datasets linking diverse foods to complete nutrient profiles.
arXiv AI
Jun 9
Trait-space Monitoring for Emergent Misalignment During Supervised Finetuning
arXiv:2606. 07631v1 Announce Type: cross Abstract: Emergent misalignment (EM) occurs when narrow finetuning causes a model to behave dangerously outside the finetuning task.
arXiv AI
Jun 9
Phantom transitions in language model fine-tuning
arXiv:2606. 07559v1 Announce Type: cross Abstract: Fine-tuning a language model on contexts whose correct completion has a near-synonym competitor often fails silently.
arXiv AI
Jun 9
FineGen: A VLM-based Multi-Agent Framework for Fine-Grained Image-Text Dataset Construction
arXiv:2606. 07645v1 Announce Type: cross Abstract: The scarcity of hard negative samples in current vision-language datasets significantly hinders fine-grained perception.
arXiv Machine Learning
Jun 9
DICE: Entropy-Regularized Equilibrium Selection for Stable Multi-Agent LLM Coordination
arXiv:2606. 08068v1 Announce Type: new Abstract: Multi-agent large language model (LLM) systems often fail to reliably outperform a single strong model equipped with best-of-N sampling.
arXiv AI
Jun 9
NoRD: A Data-Efficient Vision-Language-Action Model that Drives without Reasoning
arXiv:2602. 21172v3 Announce Type: replace Abstract: Vision-Language-Action (VLA) models are advancing autonomous driving by replacing modular pipelines with unified end-to-end architectures.
arXiv Machine Learning
Jun 9
The Sample Complexity of Parameter-Free Stochastic Convex Optimization
arXiv:2506. 11336v2 Announce Type: replace Abstract: We study the sample complexity of stochastic convex optimization when problem parameters such as the distance to optimality and the Lipschitz constant are unknown.
arXiv Machine Learning
Jun 9
Zero and Few Shot Load Forecasting with Large Language Models
arXiv:2411. 11350v2 Announce Type: replace Abstract: Deep learning models have shown strong performance in load forecasting, but they generally require large amounts of data for model training before being applied to new scenarios, which limits their effectiveness in data-scarce scenarios.
arXiv Machine Learning
Jun 9
GNSS-FM: A Self-Supervised Foundation Model for Daily GNSS Displacement Time Series
arXiv:2606. 07725v1 Announce Type: cross Abstract: Displacement time series from Global Navigation Satellite Systems (GNSS) are essential for a wide range of applications, including monitoring tectonic crustal deformations and investigating the different stages of the earthquake cycle.