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Language Models as Semantic Teachers: Post-Training Alignment for Medical Audio Understanding

arXiv:2512.04847v2 Announce Type: replace-cross Abstract: Pre-trained audio models excel at detecting acoustic patterns in auscultation sounds but often fail to grasp their clinical significance, limi

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Language, Place, and Social Media: Geographic Dialect Alignment in New Zealand

DGX agent

arXiv:2604.15744v1 Announce Type: new Abstract: This thesis investigates geographic dialect alignment in place-informed social media communities, focussing on New Zealand-related Reddit communities. B

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20 Apr 2026

Large Language Models for Market Research: A Data-augmentation Approach

arXiv:2412.19363v3 Announce Type: replace Abstract: Large Language Models (LLMs) have transformed artificial intelligence by excelling in complex natural language processing tasks. Their ability to ge

Learning to Look before Learning to Like: Incorporating Human Visual Cognition into Aesthetic Quality Assessment

arXiv:2604.15853v1 Announce Type: new Abstract: Automated Aesthetic Quality Assessment (AQA) treats images primarily as static pixel vectors, aligning predictions with human-rating scores largely thro

literally my basic model since 1998. crazy that some people still haven’t figured this out.

literally my basic model since 1998. crazy that some people still haven’t figured this out. My basic model of capabilities: LLMs are good at problems similar to those that appear in their training dat

LLMs are “ good at math style problems, where you tell them A, B and C are true, and then ask them to figure out D [but] extremely bad at an…

LLMs are “ good at math style problems, where you tell them A, B and C are true, and then ask them to figure out D [but] extremely bad at anything involving what I would call mature scholarship .. [so

Long-Term Memory for VLA-based Agents in Open-World Task Execution

arXiv:2604.15671v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models have demonstrated significant potential for embodied decision-making; however, their application in complex chemical

M3R: Localized Rainfall Nowcasting with Meteorology-Informed MultiModal Attention

arXiv:2604.15377v1 Announce Type: cross Abstract: Accurate and timely rainfall nowcasting is crucial for disaster mitigation and water resource management. Despite recent advances in deep learning, pr

MFC-RFNet: A Multi-scale Guided Rectified Flow Network for Radar Sequence Prediction

arXiv:2601.03633v2 Announce Type: replace-cross Abstract: Accurate and high-resolution precipitation nowcasting from radar echo sequences is crucial for disaster mitigation and economic planning, yet

Multi-objective Reinforcement Learning With Augmented States Requires Rewards After Deployment

arXiv:2604.15757v1 Announce Type: new Abstract: This research note identifies a previously overlooked distinction between multi-objective reinforcement learning (MORL), and more conventional single-ob

On the Rejection Criterion for Proxy-based Test-time Alignment

arXiv:2604.16146v1 Announce Type: new Abstract: Recent works proposed test-time alignment methods that rely on a small aligned model as a proxy that guides the generation of a larger base (unaligned)

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On theCUBE Pod: IBM’s AI strategy, infrastructure bottlenecks and ecosystem partnerships reshape markets

Artificial intelligence infrastructure is now the deciding force behind enterprise competitiveness. What was once a backend concern has moved directly into the center of business strategy, shaping how

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One-Shot Cross-Geometry Skill Transfer through Part Decomposition

arXiv:2604.15455v1 Announce Type: new Abstract: Given a demonstration, a robot should be able to generalize a skill to any object it encounters-but existing approaches to skill transfer often fail to

OpenAI’s market share is slipping.

OpenAI’s market share is slipping. ChatGPT is dying. OpenAI lost the crown as their market share fell below 40 percent for the first time ever. It stands at just 38.7 percent now after four straight m

Order: India's CCI sets May hearing on penalties after Apple failed to submit info on alleged app market abuse; Apple said it fears it could be fined up to $38B (Aditya Kalra/Reuters)

Aditya Kalra / Reuters: Order: India's CCI sets May hearing on penalties after Apple failed to submit info on alleged app market abuse; Apple said it fears it could be fined up to $38B — Apple has not

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PAWN: Piece Value Analysis with Neural Networks

arXiv:2604.15585v1 Announce Type: cross Abstract: Predicting the relative value of any given chess piece in a position remains an open challenge, as a piece's contribution depends on its spatial relat

Persona-Assigned Large Language Models Exhibit Human-Like Motivated Reasoning

arXiv:2506.20020v2 Announce Type: replace Abstract: Reasoning in humans is prone to biases due to underlying motivations like identity protection, that undermine rational decision-making and judgment.

PLAF: Pixel-wise Language-Aligned Feature Extraction for Efficient 3D Scene Understanding

arXiv:2604.15770v1 Announce Type: new Abstract: Accurate open-vocabulary 3D scene understanding requires semantic representations that are both language-aligned and spatially precise at the pixel leve

Preregistered Belief Revision Contracts

arXiv:2604.15558v1 Announce Type: new Abstract: Deliberative multi-agent systems allow agents to exchange messages and revise beliefs over time. While this interaction is meant to improve performance,

Prototype-Grounded Concept Models for Verifiable Concept Alignment

arXiv:2604.16076v1 Announce Type: cross Abstract: Concept Bottleneck Models (CBMs) aim to improve interpretability in Deep Learning by structuring predictions through human-understandable concepts, bu

Puppets or partners? Governing cyborg propaganda in the digital public square

arXiv:2602.13088v2 Announce Type: replace-cross Abstract: The distinction between genuine grassroots activism and automated influence operations is collapsing. While contemporary policy debates priori

Reckoning with the Political Economy of AI: Avoiding Decoys in Pursuit of Accountability

arXiv:2604.16106v1 Announce Type: cross Abstract: The Project of AI is a world-building endeavor, wherein those who fund and develop AI systems both operate through and seek to sustain networks of pow

Revisiting Entropy Regularization: Adaptive Coefficient Unlocks Its Potential for LLM Reinforcement Learning

arXiv:2510.10959v3 Announce Type: replace-cross Abstract: Reasoning ability has become a defining capability of Large Language Models (LLMs), with Reinforcement Learning with Verifiable Rewards (RLVR)

Reward Weighted Classifier-Free Guidance as Policy Improvement in Autoregressive Models

arXiv:2604.15577v1 Announce Type: cross Abstract: Consider an auto-regressive model that produces outputs x (e.g., answers to questions, molecules) each of which can be summarized by an attribute vect

Robust Multispectral Semantic Segmentation under Missing or Full Modalities via Structured Latent Projection

arXiv:2604.15856v1 Announce Type: cross Abstract: Multimodal remote sensing data provide complementary information for semantic segmentation, but in real-world deployments, some modalities may be unav

Robust Synchronisation for Federated Learning in The Face of Correlated Device Failure

arXiv:2604.16090v1 Announce Type: cross Abstract: Probabilistic Synchronous Parallel (PSP) is a technique in distributed learning systems to reduce synchronization bottlenecks by sampling a subset of

Safe and Energy-Aware Multi-Robot Density Control via PDE-Constrained Optimization for Long-Duration Autonomy

arXiv:2604.15524v1 Announce Type: cross Abstract: This paper presents a novel density control framework for multi-robot systems with spatial safety and energy sustainability guarantees. Stochastic rob

Safe Deep Reinforcement Learning for Building Heating Control and Demand-side Flexibility

arXiv:2604.16033v1 Announce Type: cross Abstract: Buildings account for approximately 40% of global energy consumption, and with the growing share of intermittent renewable energy sources, enabling de

Sample Complexity Bounds for Stochastic Shortest Path with a Generative Model

arXiv:2604.16111v1 Announce Type: new Abstract: We study the sample complexity of learning an epsilon-optimal policy in the Stochastic Shortest Path (SSP) problem. We first derive sample complexity bo

Scalable Multi-Task Learning through Spiking Neural Networks with Adaptive Task-Switching Policy for Intelligent Autonomous Agents

arXiv:2504.13541v5 Announce Type: replace-cross Abstract: Training resource-constrained autonomous agents on multiple tasks simultaneously is crucial for adapting to diverse real-world environments. R

Scalable Unseen Objects 6-DoF Absolute Pose Estimation with Robotic Integration

arXiv:2503.05578v4 Announce Type: replace Abstract: Pose estimation-guided unseen object 6-DoF robotic manipulation is a key task in robotics. However, the scalability of current pose estimation metho

Self-Distillation as a Performance Recovery Mechanism for LLMs: Counteracting Compression and Catastrophic Forgetting

arXiv:2604.15794v1 Announce Type: cross Abstract: Large Language Models (LLMs) have achieved remarkable success, underpinning diverse AI applications. However, they often suffer from performance degra

Seriously. If you don’t understand the below, you really shouldn’t speculate about AI. It’s absolutely fundamental.

Seriously. If you don’t understand the below, you really shouldn’t speculate about AI. It’s absolutely fundamental. literally my basic model since 1998. crazy that some people still haven’t figured th

SIMMER: Cross-Modal Food Image--Recipe Retrieval via MLLM-Based Embedding

arXiv:2604.15628v1 Announce Type: cross Abstract: Cross-modal retrieval between food images and recipe texts is an important task with applications in nutritional management, dietary logging, and cook

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Skill-RAG: Failure-State-Aware Retrieval Augmentation via Hidden-State Probing and Skill Routing

arXiv:2604.15771v1 Announce Type: new Abstract: Retrieval-Augmented Generation (RAG) has emerged as a foundational paradigm for grounding large language models in external knowledge. While adaptive re

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Subliminal Transfer of Unsafe Behaviors in AI Agent Distillation

arXiv:2604.15559v1 Announce Type: new Abstract: Recent work on subliminal learning demonstrates that language models can transmit semantic traits through data that is semantically unrelated to those t

Symbolic Guardrails for Domain-Specific Agents: Stronger Safety and Security Guarantees Without Sacrificing Utility

arXiv:2604.15579v1 Announce Type: cross Abstract: AI agents that interact with their environments through tools enable powerful applications, but in high-stakes business settings, unintended actions c

'Taking Stock at FAccT': Using Participatory Design to Co-Create a Vision for the Fairness, Accountability and Transparency Community

arXiv:2604.16224v1 Announce Type: cross Abstract: As a relatively new forum, ACM FAccT has become a key space for activists and scholars to critically examine emerging AI and ML technologies. It bring

Targeted Exploration via Unified Entropy Control for Reinforcement Learning

arXiv:2604.14646v2 Announce Type: replace Abstract: Recent advances in reinforcement learning (RL) have improved the reasoning capabilities of large language models (LLMs) and vision-language models (

Temporal Contrastive Decoding: A Training-Free Method for Large Audio-Language Models

arXiv:2604.15383v1 Announce Type: cross Abstract: Large audio-language models (LALMs) generalize across speech, sound, and music, but unified decoders can exhibit a temporal smoothing bias: transient

The AI industry insists they can manage the risks of superintelligence, but there are in fact zero widely agreed on or accepted solutions to…

The AI industry insists they can manage the risks of superintelligence, but there are in fact zero widely agreed on or accepted solutions to the problem of how one could even control something vastly

safetyconnor-leahy--x

The Harder Path: Last Iterate Convergence for Uncoupled Learning in Zero-Sum Games with Bandit Feedback

arXiv:2604.16087v1 Announce Type: new Abstract: We study the problem of learning in zero-sum matrix games with repeated play and bandit feedback. Specifically, we focus on developing uncoupled algorit

The Informational Cost of Agency: A Bounded Measure of Interaction Efficiency for Deployed Reinforcement Learning

arXiv:2603.01283v2 Announce Type: replace Abstract: Deployed RL agents operate in closed-loop systems where reliable performance depends on maintaining coherent coupling between observations, actions,

The Price of Paranoia: Robust Risk-Sensitive Cooperation in Non-Stationary Multi-Agent Reinforcement Learning

arXiv:2604.15695v1 Announce Type: cross Abstract: Cooperative equilibria are fragile. When agents learn alongside each other rather than in a fixed environment, the process of learning destabilizes th

Towards Intrinsic Interpretability of Large Language Models:A Survey of Design Principles and Architectures

arXiv:2604.16042v1 Announce Type: cross Abstract: While Large Language Models (LLMs) have achieved strong performance across many NLP tasks, their opaque internal mechanisms hinder trustworthiness and

Towards Robust Endogenous Reasoning: Unifying Drift Adaptation in Non-Stationary Tuning

arXiv:2604.15705v1 Announce Type: new Abstract: Reinforcement Fine-Tuning (RFT) has established itself as a critical paradigm for the alignment of Multi-modal Large Language Models (MLLMs) with comple

Trajectory Planning for Safe Dual Control with Active Exploration

arXiv:2604.15507v1 Announce Type: new Abstract: Planning safe trajectories under model uncertainty is a fundamental challenge. Robust planning ensures safety by considering worst-case realizations, ye

Truncated Kernel Stochastic Gradient Descent with General Losses and Spherical Radial Basis Functions

arXiv:2510.04237v5 Announce Type: replace Abstract: In this paper, we propose a novel kernel stochastic gradient descent (SGD) algorithm for large-scale supervised learning with general losses. Compar