arXiv:2601.14266v1 Announce Type: new
Abstract: While most LLMs are autoregressive, diffusion-based LLMs have recently emerged as an alternative method for generation. Greedy Coordinate Gradient (GCG) attacks have proven effective against autoregressive models, but their applicability to diffusion ...
Divide and Refine: Enhancing Multimodal Representation and Explainability for Emotion Recognition in Conversation
arXiv:2601.14274v1 Announce Type: new
Abstract: Multimodal emotion recognition in conversation (MERC) requires representations that effectively integrate signals from multiple modalities. These signals include modality-specific cues, information shared across modalities, and interactions that emerg...
Quality or Quantity? Error-Informed Selective Online Learning with Gaussian Processes in Multi-Agent Systems: Extended Version
arXiv:2601.14275v1 Announce Type: new
Abstract: Effective cooperation is pivotal in distributed learning for multi-agent systems, where the interplay between the quantity and quality of the machine learning models is crucial. This paper reveals the irrationality of indiscriminate inclusion of all m...
Which Quantization Should I Use? A Unified Evaluation of llama.cpp Quantization on Llama-3.1-8B-Instruct
arXiv:2601.14277v1 Announce Type: new
Abstract: Quantization is a practical technique for making large language models easier to deploy by reducing the precision used to store and operate on model weights. This can lower memory use and improve runtime feasibility on constrained hardware, which is e...
VisTIRA: Closing the Image-Text Modality Gap in Visual Math Reasoning via Structured Tool Integration
arXiv:2601.14440v1 Announce Type: new
Abstract: Vision-language models (VLMs) lag behind text-only language models on mathematical reasoning when the same problems are presented as images rather than text. We empirically characterize this as a modality gap: the same question in text form yields mar...
On the Generalization Gap in LLM Planning: Tests and Verifier-Reward RL
arXiv:2601.14456v1 Announce Type: new
Abstract: Recent work shows that fine-tuned Large Language Models (LLMs) can achieve high valid plan rates on PDDL planning tasks. However, it remains unclear whether this reflects transferable planning competence or domain-specific memorization. In this work, ...
Stanford HAI and Swiss National AI Institute Form Alliance to Advance Open, Human-Centered AI
Stanford, ETH Zurich, and EPFL will develop open-source foundation models that prioritize societal values over commercial interests, strengthening academia's role in shaping AI's future.
The UK can unite its advanced AI expertise with its strict regulatory framework and its high-quality product development with the rapid expansion of Saudi retail operations.
The human brain may work more like AI than anyone expected
Scientists have discovered that the human brain understands spoken language in a way that closely resembles how advanced AI language models work. By tracking brain activity as people listened to a long podcast, researchers found that meaning unfolds step by step—much like the layered processing insi...
CSyMR: Benchmarking Compositional Symbolic Muisc Reasoning With MIR Tool Integration
arXiv:2601.11556v1 Announce Type: new
Abstract: Large Language Models (LLMs) are leveraged in symbolic music reasoning, yet existing benchmarks emphasize isolated knowledge or atomic analyses rather than the integrative compositional reasoning needed to connect musical structures. To address this, ...
AdaFRUGAL: Adaptive Memory-Efficient Training with Dynamic Control
arXiv:2601.11568v1 Announce Type: new
Abstract: Training Large Language Models (LLMs) is highly memory-intensive due to optimizer state overhead. The FRUGAL framework mitigates this with gradient splitting, but its static hyperparameters -- the subspace ratio ($\rho$) and update frequency ($T$) -- ...
Discrete Semantic States and Hamiltonian Dynamics in LLM Embedding Spaces
arXiv:2601.11572v1 Announce Type: new
Abstract: We investigate the structure of Large Language Model (LLM) embedding spaces using mathematical concepts, particularly linear algebra and the Hamiltonian formalism, drawing inspiration from analogies with quantum mechanical systems. Motivated by the ob...
GRADE: Replacing Policy Gradients with Backpropagation for LLM Alignment
arXiv:2601.11574v1 Announce Type: new
Abstract: Reinforcement learning from human feedback (RLHF) has become the dominant paradigm for aligning large language models with human preferences. However, policy gradient methods such as PPO suffer from high variance gradient estimates, requiring careful ...
arXiv:2601.11604v1 Announce Type: new
Abstract: Multi-objective reinforcement learning (MORL) enables agents to optimize vector-valued rewards while respecting user preferences. CAPQL, a preference-conditioned actor-critic method, achieves this by conditioning on weight vectors w and restricts data...
MIMIC-RD: Can LLMs differentially diagnose rare diseases in real-world clinical settings?
arXiv:2601.11559v1 Announce Type: new
Abstract: Despite rare diseases affecting 1 in 10 Americans, their differential diagnosis remains challenging. Due to their impressive recall abilities, large language models (LLMs) have been recently explored for differential diagnosis. Existing approaches to ...
Dynamical Systems Analysis Reveals Functional Regimes in Large Language Models
arXiv:2601.11622v1 Announce Type: new
Abstract: Large language models perform text generation through high-dimensional internal dynamics, yet the temporal organisation of these dynamics remains poorly understood. Most interpretability approaches emphasise static representations or causal interventi...
Reasoning Stabilization Point: A Training-Time Signal for Stable Evidence and Shortcut Reliance
arXiv:2601.11625v1 Announce Type: new
Abstract: Fine-tuning pretrained language models can improve task performance while subtly altering the evidence a model relies on. We propose a training-time interpretability view that tracks token-level attributions across finetuning epochs. We define explana...
Stanford researchers have developed a deep learning model that transforms overwhelming brain data into clear trajectories, opening new possibilities for understanding thought, emotion, and neurological disease.
DiffuCoder: Understanding and Improving Masked Diffusion Models for Code Generation
Diffusion large language models (dLLMs) are compelling alternatives to autoregressive (AR) models because their denoising models operate over the entire sequence. The global planning and iterative refinement features of dLLMs are particularly useful for code generation. However, current training and...
Multimodal reinforcement learning with agentic verifier for AI agents
Argos improves multimodal RL by evaluating whether an agent’s reasoning aligns with what it observes over time. The approach reduces visual hallucinations and produces more reliable, data-efficient agents for real-world applications.
The post Multimodal reinforcement learning with agentic verifier f...
Unbreakable? Researchers warn quantum computers have serious security flaws
Quantum computers could revolutionize everything from drug discovery to business analytics—but their incredible power also makes them surprisingly vulnerable. New research from Penn State warns that today’s quantum machines are not just futuristic tools, but potential gold mines for hackers. The stu...
Unified Optimization of Source Weights and Transfer Quantities in Multi-Source Transfer Learning: An Asymptotic Framework
arXiv:2601.10779v1 Announce Type: new
Abstract: Transfer learning plays a vital role in improving model performance in data-scarce scenarios. However, naive uniform transfer from multiple source tasks may result in negative transfer, highlighting the need to properly balance the contributions of he...
Towards Tensor Network Models for Low-Latency Jet Tagging on FPGAs
arXiv:2601.10801v1 Announce Type: new
Abstract: We present a systematic study of Tensor Network (TN) models $\unicode{x2013}$ Matrix Product States (MPS) and Tree Tensor Networks (TTN) $\unicode{x2013}$ for real-time jet tagging in high-energy physics, with a focus on low-latency deployment on Fiel...