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Bringing Value Models Back: Generative Critics for Value Modeling in LLM Reinforcement Learning9h◆Subagents vs Agent Skills: Executing Reusable Knowledge for Long-Horizon Agentic Tasks9h◆Distribution-Consistent Inference for Dynamic Sparse Mixture-of-Experts9h◆In RAG We Trust? Measuring Robustness of Retrieval-Augmented Generation Under Document Poisoning9h◆FrontierChallenge: Evaluating Scientific Workflow Completion9h◆IBIB: A Protocol for Measuring Enterprise AI Systems by Serving Route, Not Model Identifier9h◆AgenticGen: Reward-Guided Agentic Video Generation for Advertising9h◆Strangers to Themselves: What Language Models Say About Themselves Is Generic9h◆Omni Interaction Agent Technical Report9h◆CoGReV: A Confidence-Gated Post-Hoc Non-Monotonic Belief Revision Framework for Phishing Website Classification9h◆Bit-Flip Attacks on Vision-Language-Action Models: Action-Decoding Architecture Shapes the Vulnerability9h◆Can Foundation Models Moderate Online Content? Evaluating Instruction- vs. Example-Driven Policy Operationalization9h◆RiLM: Parameter-Efficient Language Modeling via Geodesic Decoding9h◆Tracing Computation Density in LLMs9h◆Cultural Binding Heads in Language Models9h◆Direct Diversity Optimization for Diverse Successful Trajectories in Preference Post-Training9h◆Left-Branching Transformers Excel at Right-Branching Languages: Data Shapes Word Order Preferences in Language Models9h◆Palmyra x6 Technical Report: An Agentic, Tool-Use Model Post-Trained via Anchored Supervised Fine-Tuning9h◆'Ghaib in Translation' aka Unseen Harm: Measuring Cross-Script Safety Inconsistency with 'Missed-in-Urdu' Scores in LLM Hate Speech Detection9h◆DexterSQL: Deep Schema Exploration and Rule-based Correction for Text-to-SQL Generation9h◆Bringing Value Models Back: Generative Critics for Value Modeling in LLM Reinforcement Learning9h◆Subagents vs Agent Skills: Executing Reusable Knowledge for Long-Horizon Agentic Tasks9h◆Distribution-Consistent Inference for Dynamic Sparse Mixture-of-Experts9h◆In RAG We Trust? Measuring Robustness of Retrieval-Augmented Generation Under Document Poisoning9h◆FrontierChallenge: Evaluating Scientific Workflow Completion9h◆IBIB: A Protocol for Measuring Enterprise AI Systems by Serving Route, Not Model Identifier9h◆AgenticGen: Reward-Guided Agentic Video Generation for Advertising9h◆Strangers to Themselves: What Language Models Say About Themselves Is Generic9h◆Omni Interaction Agent Technical Report9h◆CoGReV: A Confidence-Gated Post-Hoc Non-Monotonic Belief Revision Framework for Phishing Website Classification9h◆Bit-Flip Attacks on Vision-Language-Action Models: Action-Decoding Architecture Shapes the Vulnerability9h◆Can Foundation Models Moderate Online Content? Evaluating Instruction- vs. Example-Driven Policy Operationalization9h◆RiLM: Parameter-Efficient Language Modeling via Geodesic Decoding9h◆Tracing Computation Density in LLMs9h◆Cultural Binding Heads in Language Models9h◆Direct Diversity Optimization for Diverse Successful Trajectories in Preference Post-Training9h◆Left-Branching Transformers Excel at Right-Branching Languages: Data Shapes Word Order Preferences in Language Models9h◆Palmyra x6 Technical Report: An Agentic, Tool-Use Model Post-Trained via Anchored Supervised Fine-Tuning9h◆'Ghaib in Translation' aka Unseen Harm: Measuring Cross-Script Safety Inconsistency with 'Missed-in-Urdu' Scores in LLM Hate Speech Detection9h◆DexterSQL: Deep Schema Exploration and Rule-based Correction for Text-to-SQL Generation9h◆
Tag

#transfer-learning

8 articles tagged #transfer-learning

arxivJul 13

Pitfalls and Remedies for Multi-Task Bayesian Optimization

arXiv:2607.09073v1 Announce Type: new Abstract: Bayesian optimization routinely warm-starts a target experiment with data from related source tasks, and the multi-task Gaussian process is the textbook surrogate for the job. We revisit this default in a controlled setting and find that it misestimate

GA1 model#machine-learning#optimization#transfer-learningRead on arxiv →
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arxivJul 2bullish

Efficient Multilingual Reasoning Transfer via Progressive Code-Switching

arXiv:2607.00485v1 Announce Type: new Abstract: Large reasoning models (LRMs) have achieved strong reasoning capabilities in English, yet their performance degrades significantly when required to reason in other languages. A natural solution is to transfer the model's English reasoning ability to ta

#language-models#transfer-learning#reasoningRead on arxiv →
arxivJun 25bullish

NeuroShield: A Device-Agnostic Foundation Model for EEG Authentication

arXiv:2606.20673v2 Announce Type: replace Abstract: A central challenge in EEG authentication is that models are typically tied to the acquisition settings in which they are trained. In particular, variations in headset hardware, channel layout, and signal duration create heterogeneous recordings th

NE1 model#eeg#authentication#transfer-learningRead on arxiv →
arxivJun 11bullish

UniReason-Med: A Shared Grounded Reasoning Interface for 2D-to-3D Transfer in Medical VQA

arXiv:2606.11740v1 Announce Type: cross Abstract: We study whether grounded reasoning supervision from abundant 2D medical images can improve 3D medical VQA when both input types are aligned through a common reasoning interface. We introduce UniReason-Med, a single-checkpoint framework that processe

UN1 model#medical-imaging#vqa#grounded-reasoningRead on arxiv →
arxivMay 29

Uncertainty-Aware Transfer Learning for Cross-Building Energy Forecasting: Toward Robust and Scalable District-Level Energy Management

arXiv:2605.29733v1 Announce Type: new Abstract: Scaling data-driven energy forecasting to district level requires models that can be re-used across buildings with minimal target-domain data and honest uncertainty estimates. We present an uncertainty-aware transfer learning (TL) framework for cross-b

TE1 model#energy-forecasting#transfer-learning#uncertainty-estimationRead on arxiv →
arxivMay 26

Universal Activation Verbalizer: A Unified Framework for Cross-Model Activation Explanation

arXiv:2605.25903v1 Announce Type: new Abstract: Activation verbalization explains hidden representations in natural language, but existing methods are mostly limited to self-explanation, where each model explains only its own activations. We introduce Universal Activation Verbalizer (UAV), a framewo

UN1 model#explanation#transfer-learning#natural-languageRead on arxiv →
arxivMay 11bullish

Don't Retrain, Align: Adapting Autoregressive LMs to Diffusion LMs via Representation Alignment

arXiv:2605.06885v1 Announce Type: cross Abstract: Diffusion language models (DLMs) have recently demonstrated capabilities that complement standard autoregressive (AR) models, particularly in non-sequential generation and bidirectional editing. Although recent work has shown that pretrained autoregr

DIAU2 models#diffusion#language-models#representation-learningRead on arxiv →
arxivApr 16

Guided Transfer Learning for Discrete Diffusion Models

arXiv:2512.10877v4 Announce Type: replace Abstract: Discrete diffusion models (DMs) have achieved strong performance in language and other discrete domains, offering a compelling alternative to autoregressive modeling. Yet this performance typically depends on large training datasets, challenging th

#diffusion-models#transfer-learning#language-modelingRead on arxiv →