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From Guidelines to Guarantees: A Graph-Based Evaluation Harness for Domain-Specific Evaluation of LLMs

- Rigorous evaluation of domain-specific language models requires benchmarks that are comprehensive, contamination-resistant, and maintainable. Static, manually curated datasets do not satisfy these...

Constrained Co-evolutionary Metamorphic Differential Testing for Autonomous Systems with an Interpretability Approach

- Autonomous systems, such as autonomous driving systems, evolve rapidly through frequent updates, risking unintended behavioral degradations. Effective system-level testing is challenging due to the...

CIS-BWE: Chaos-Informed Speech Bandwidth Extension

- Recovering high-frequency components lost to bandwidth constraints is crucial for applications ranging from telecommunications to high-fidelity audio on limited resources. We introduce NDSI-BWE, a...

Sampling-Based Global Optimal Control and Estimation via Semidefinite Programming

- Global optimization has gained attraction over the past decades, thanks to the development of both theoretical foundations and efficient numerical routines. Among recent advances, Kernel Sum of...

LMDeploy Accelerates Mixed-Precision LLM Inference with TurboMind

- Mixed-precision inference techniques reduce the memory and computational demands of Large Language Models (LLMs) by applying hybrid precision formats to model weights, activations, and KV caches....

Generalized Policy Gradient with History-Aware Decision Transformer for Reliable Routing over Graph Signals

- Reliable path planning in stochastic transportation networks requires decisions that account for uncertain and correlated travel times on irregular road graphs, rather than only minimizing expected...

DiscussLLM: Teaching Large Language Models When to Speak

- Large Language Models (LLMs) have demonstrated remarkable capabilities in understanding and generating human-like text, yet they largely operate as reactive agents, responding only when directly...

Navigating the Challenges of AI-Generated Image Detection in the Wild: What Truly Matters?

- As generative Artificial Intelligence (AI) advances, the realism of AI generated imagery has reached a threshold capable of deceiving even vigilant human observers. Yet, while current AI-generated...

Stabilizing Knowledge, Promoting Reasoning: Dual-Token Constraints for RLVR

- Reinforcement Learning with Verifiable Rewards (RLVR) has become an effective post-training method for improving the reasoning abilities of Large Language Models (LLMs). However, existing methods...

FAR: Function-preserving Attention Replacement for IMC-friendly Inference

- While transformers dominate modern vision and language models, their attention mechanism remains poorly suited for in-memory computing (IMC) devices due to intensive activation-to-activation...

Adapting Foundation Vision-Language Models to Medical Diagnosis via Query-Driven Expert Bridging

- Vision-language foundation models achieve promising performance in natural image classification, yet their direct application to medical imaging is limited by severe domain shifts, resolution...

Grounded Reinforcement Learning for Visual Reasoning

- While reinforcement learning (RL) over chains of thought has significantly advanced language models in tasks such as mathematics and coding, visual reasoning introduces added complexity by requiring...

A first view on the density of 5-planar graphs

- A key concept for many graph layout algorithms is planarity, a graph property that allows to draw vertices and edges crossing-free in the plane. Important is the generalization to $k$-planar graphs,...

Common Corpus: The Largest Collection of Ethical Data for LLM Pre-Training

- Large Language Models (LLMs) are pre-trained on large amounts of data from different sources and domains. Such datasets often contain trillions of tokens, including large portions of copyrighted or...

A projection-based framework for gradient-free and parallel learning

- We present a feasibility-seeking approach to neural network training. This mathematical optimization framework is distinct from conventional gradient-based loss minimization and uses projection...

Honey, I shrunk the hypothesis space (through logical preprocessing)

- Inductive logic programming (ILP) is a form of logical machine learning. The goal is to search a hypothesis space for a hypothesis that generalises training examples and background knowledge. We...

Leveraging Local and Global Knowledge Integration with Time-Frequency Calibrated Distillation for Speech Enhancement

- In this paper, we propose an intra-set and inter-set recursive fusion framework with time-frequency calibrated knowledge distillation (I$^2$SRF-TFCKD) for SE. Different from previous distillation...

The Hardness of Achieving Impact in AI for Social Impact Research: A Ground-Level View of Challenges & Opportunities

- AI for Social Impact (AI4SI) is an emergent field harnessing interdisciplinarities between the fields of artificial intelligence (AI), machine learning (ML), and the social sciences to address...

From Model Design to Organizational Design: Complexity Redistribution and Trade-Offs in Generative AI

- This paper introduces the Generality-Accuracy-Simplicity (GAS) framework to analyze how large language models (LLMs) are reshaping organizations and competitive strategy. We argue that viewing AI as...

JAM-Flow: Joint Audio-Motion Synthesis with Flow Matching

- The intrinsic link between facial motion and speech is often overlooked in generative modeling, where talking head synthesis and text-to-speech (TTS) are typically addressed as separate tasks. This...

Deep Double Q-learning

- Double Q-learning is a classical control algorithm that mitigates the maximization bias of Q-learning. To do so, it explicitly trains two independent action-value functions and uses them to decouple...