HiVAE: Hierarchical Latent Variables for Scalable Theory of Mind
arXiv:2602.16826v1 Announce Type: new Abstract: Theory of mind (ToM) enables AI systems to infer agents' hidden goals and mental states, but existing approaches focus mainly on small human understandable gridworld spaces. We introduce HiVAE, a hierarchical variational architecture that scales...
VAM: Verbalized Action Masking for Controllable Exploration in RL Post-Training -- A Chess Case Study
arXiv:2602.16833v1 Announce Type: new Abstract: Exploration remains a key bottleneck for reinforcement learning (RL) post-training of large language models (LLMs), where sparse feedback and large action spaces can lead to premature collapse into repetitive behaviors. We propose Verbalized Action Masking...
Training Large Reasoning Models Efficiently via Progressive Thought Encoding
arXiv:2602.16839v1 Announce Type: new Abstract: Large reasoning models (LRMs) excel on complex problems but face a critical barrier to efficiency: reinforcement learning (RL) training requires long rollouts for outcome-based rewards, where autoregressive decoding dominates time and memory usage. While sliding-window...
Position: Why a Dynamical Systems Perspective is Needed to Advance Time Series Modeling
arXiv:2602.16864v1 Announce Type: new Abstract: Time series (TS) modeling has come a long way from early statistical, mainly linear, approaches to the current trend in TS foundation models. With a lot of hype and industrial demand in this field, it...
ML-driven detection and reduction of ballast information in multi-modal datasets
arXiv:2602.16876v1 Announce Type: new Abstract: Modern datasets often contain ballast as redundant or low-utility information that increases dimensionality, storage requirements, and computational cost without contributing meaningful analytical value. This study introduces a generalized, multimodal framework for ballast detection and reduction...
Multi-Agent Lipschitz Bandits
arXiv:2602.16965v1 Announce Type: new Abstract: We study the decentralized multi-player stochastic bandit problem over a continuous, Lipschitz-structured action space where hard collisions yield zero reward. Our objective is to design a communication-free policy that maximizes collective reward, with coordination costs...
A Unified Framework for Locality in Scalable MARL
arXiv:2602.16966v1 Announce Type: new Abstract: Scalable Multi-Agent Reinforcement Learning (MARL) is fundamentally challenged by the curse of dimensionality. A common solution is to exploit locality, which hinges on an Exponential Decay Property (EDP) of the value function. However, existing conditions...
Early-Warning Signals of Grokking via Loss-Landscape Geometry
arXiv:2602.16967v1 Announce Type: new Abstract: Grokking -- the abrupt transition from memorization to generalization after prolonged training -- has been linked to confinement on low-dimensional execution manifolds in modular arithmetic. Whether this mechanism extends beyond arithmetic remains open. We study...
Fail-Closed Alignment for Large Language Models
arXiv:2602.16977v1 Announce Type: new Abstract: We identify a structural weakness in current large language model (LLM) alignment: modern refusal mechanisms are fail-open. While existing approaches encode refusal behaviors across multiple latent features, suppressing a single dominant feature$-$via prompt-based jailbreaks$-$can cause...
Discovering Universal Activation Directions for PII Leakage in Language Models
arXiv:2602.16980v1 Announce Type: new Abstract: Modern language models exhibit rich internal structure, yet little is known about how privacy-sensitive behaviors, such as personally identifiable information (PII) leakage, are represented and modulated within their hidden states. We present UniLeak, a mechanistic-interpretability...
Action-Graph Policies: Learning Action Co-dependencies in Multi-Agent Reinforcement Learning
arXiv:2602.17009v1 Announce Type: new Abstract: Coordinating actions is the most fundamental form of cooperation in multi-agent reinforcement learning (MARL). Successful decentralized decision-making often depends not only on good individual actions, but on selecting compatible actions across agents to synchronize behavior,...
Sign Lock-In: Randomly Initialized Weight Signs Persist and Bottleneck Sub-Bit Model Compression
arXiv:2602.17063v1 Announce Type: new Abstract: Sub-bit model compression seeks storage below one bit per weight; as magnitudes are aggressively compressed, the sign bit becomes a fixed-cost bottleneck. Across Transformers, CNNs, and MLPs, learned sign matrices resist low-rank approximation and are...
Spatio-temporal dual-stage hypergraph MARL for human-centric multimodal corridor traffic signal control
arXiv:2602.17068v1 Announce Type: new Abstract: Human-centric traffic signal control in corridor networks must increasingly account for multimodal travelers, particularly high-occupancy public transportation, rather than focusing solely on vehicle-centric performance. This paper proposes STDSH-MARL (Spatio-Temporal Dual-Stage Hypergraph based Multi-Agent Reinforcement Learning),...
MeGU: Machine-Guided Unlearning with Target Feature Disentanglement
arXiv:2602.17088v1 Announce Type: new Abstract: The growing concern over training data privacy has elevated the "Right to be Forgotten" into a critical requirement, thereby raising the demand for effective Machine Unlearning. However, existing unlearning approaches commonly suffer from a fundamental...
Synergizing Transport-Based Generative Models and Latent Geometry for Stochastic Closure Modeling
arXiv:2602.17089v1 Announce Type: new Abstract: Diffusion models recently developed for generative AI tasks can produce high-quality samples while still maintaining diversity among samples to promote mode coverage, providing a promising path for learning stochastic closure models. Compared to other types...
Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy
Why creators are ditching ad revenue for chocolate bars and fintech acquisitions
The creator economy is evolving fast, and ad revenue alone isn’t cutting it anymore. YouTubers are launching product lines, acquiring startups, and building actual business empires. In fact, MrBeast’s company bought fintech startup Step, and his chocolate business is out-earning...
TechCrunch Disrupt 2026 Super Early Bird rates end in 1 week
The lowest ticket rates of the year for TechCrunch Disrupt 2026 end next Friday, February 27. Save up to $680 on your pass. Register now before prices increase.
OpenAI says 18- to 24-year-olds account for nearly 50% of ChatGPT usage in India
The company said on Friday that users between 18 and 24 years of age account for nearly 50% of all messages sent by Indians to ChatGPT, and users under 30 account for 80% of usage in the country.
General Catalyst commits $5B to India over five years
The pledge marks a sharp jump from General Catalyst's earlier $500 million–$1 billion India earmark.
KD4MT: A Survey of Knowledge Distillation for Machine Translation
arXiv:2602.15845v1 Announce Type: new Abstract: Knowledge Distillation (KD) as a research area has gained a lot of traction in recent years as a compression tool to address challenges related to ever-larger models in NLP. Remarkably, Machine Translation (MT) offers a...
Multi-source Heterogeneous Public Opinion Analysis via Collaborative Reasoning and Adaptive Fusion: A Systematically Integrated Approach
arXiv:2602.15857v1 Announce Type: new Abstract: The analysis of public opinion from multiple heterogeneous sources presents significant challenges due to structural differences, semantic variations, and platform-specific biases. This paper introduces a novel Collaborative Reasoning and Adaptive Fusion (CRAF) framework that systematically...
P-RAG: Prompt-Enhanced Parametric RAG with LoRA and Selective CoT for Biomedical and Multi-Hop QA
arXiv:2602.15874v1 Announce Type: new Abstract: Large Language Models (LLMs) demonstrate remarkable capabilities but remain limited by their reliance on static training data. Retrieval-Augmented Generation (RAG) addresses this constraint by retrieving external knowledge during inference, though it still depends heavily on...
Quality-constrained Entropy Maximization Policy Optimization for LLM Diversity
arXiv:2602.15894v1 Announce Type: new Abstract: Recent research indicates that while alignment methods significantly improve the quality of large language model(LLM) outputs, they simultaneously reduce the diversity of the models' output. Although some methods have been proposed to enhance LLM output...
Every Little Helps: Building Knowledge Graph Foundation Model with Fine-grained Transferable Multi-modal Tokens
arXiv:2602.15896v1 Announce Type: new Abstract: Multi-modal knowledge graph reasoning (MMKGR) aims to predict the missing links by exploiting both graph structure information and multi-modal entity contents. Most existing works are designed for a transductive setting, which learns dataset-specific embeddings and...
Language Statistics and False Belief Reasoning: Evidence from 41 Open-Weight LMs
arXiv:2602.16085v1 Announce Type: new Abstract: Research on mental state reasoning in language models (LMs) has the potential to inform theories of human social cognition--such as the theory that mental state reasoning emerges in part from language exposure--and our understanding of...
Updating Parametric Knowledge with Context Distillation Retains Post-Training Capabilities
arXiv:2602.16093v1 Announce Type: new Abstract: Post-training endows pretrained LLMs with a variety of desirable skills, including instruction-following, reasoning, and others. However, these post-trained LLMs only encode knowledge up to a cut-off date, necessitating continual adaptation. Unfortunately, existing solutions cannot simultaneously...
Missing-by-Design: Certifiable Modality Deletion for Revocable Multimodal Sentiment Analysis
arXiv:2602.16144v1 Announce Type: new Abstract: As multimodal systems increasingly process sensitive personal data, the ability to selectively revoke specific data modalities has become a critical requirement for privacy compliance and user autonomy. We present Missing-by-Design (MBD), a unified framework for...
Balancing Faithfulness and Performance in Reasoning via Multi-Listener Soft Execution
arXiv:2602.16154v1 Announce Type: new Abstract: Chain-of-thought (CoT) reasoning sometimes fails to faithfully reflect the true computation of a large language model (LLM), hampering its utility in explaining how LLMs arrive at their answers. Moreover, optimizing for faithfulness and interpretability in...
Beyond Learning: A Training-Free Alternative to Model Adaptation
arXiv:2602.16189v1 Announce Type: new Abstract: Despite the continuous research and evolution of language models, they sometimes underperform previous versions. Existing approaches to overcome these challenges are resource-intensive, highlighting the need for alternatives that enable immediate action. We assume that each...