· ai
As we continue to push the boundaries of AI advancements, I
As we continue to push the boundaries of AI advancements, I pose a question that challenges us to think beyond the conventional realm of bias: Can we create an...
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- · ai
[Paper] QFed: Parameter-Compact Quantum-Classical Federated Learning
Organizations and enterprises across domains such as healthcare, finance, and scientific research are increasingly required to extract collective intelligence f...
- · ai
AI models are starting to crack high-level math problems
Since the release of GPT 5.2, AI tools have become inescapable in high-level mathematics....
- · ai
[Paper] Fast-ThinkAct: Efficient Vision-Language-Action Reasoning via Verbalizable Latent Planning
Vision-Language-Action (VLA) tasks require reasoning over complex visual scenes and executing adaptive actions in dynamic environments. While recent studies on ...
- · ai
[Paper] Value-Aware Numerical Representations for Transformer Language Models
Transformer-based language models often achieve strong results on mathematical reasoning benchmarks while remaining fragile on basic numerical understanding and...
- · ai
[Paper] ShortCoder: Knowledge-Augmented Syntax Optimization for Token-Efficient Code Generation
Code generation tasks aim to automate the conversion of user requirements into executable code, significantly reducing manual development efforts and enhancing ...
- · ai
[Paper] LLMs can Compress LLMs: Adaptive Pruning by Agents
As Large Language Models (LLMs) continue to scale, post-training pruning has emerged as a promising approach to reduce computational costs while preserving perf...
- · ai
[Paper] Contrastive Geometric Learning Unlocks Unified Structure- and Ligand-Based Drug Design
Structure-based and ligand-based computational drug design have traditionally relied on disjoint data sources and modeling assumptions, limiting their joint use...
- · ai
[Paper] Routing with Generated Data: Annotation-Free LLM Skill Estimation and Expert Selection
Large Language Model (LLM) routers dynamically select optimal models for given inputs. Existing approaches typically assume access to ground-truth labeled data,...
- · ai
[Paper] Disentangling Task Conflicts in Multi-Task LoRA via Orthogonal Gradient Projection
Multi-Task Learning (MTL) combined with Low-Rank Adaptation (LoRA) has emerged as a promising direction for parameter-efficient deployment of Large Language Mod...
- · ai
[Paper] Automating Supply Chain Disruption Monitoring via an Agentic AI Approach
Modern supply chains are increasingly exposed to disruptions from geopolitical events, demand shocks, trade restrictions, to natural disasters. While many of th...
- · ai
[Paper] Collaborative Multi-Agent Test-Time Reinforcement Learning for Reasoning
Multi-agent systems have evolved into practical LLM-driven collaborators for many applications, gaining robustness from diversity and cross-checking. However, m...