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...
Digital Metabolism: Decoupling Logic from Facts via Regenerative Unlearning -- Towards a Pure Neural Logic Core
arXiv:2601.10810v1 Announce Type: new
Abstract: Large language models (LLMs) currently suffer from parameter entanglement, where general reasoning capabilities (logic) and specific factual knowledge (facts) exist in a superposition state within shared weights. This coupling leads to the "memory wal...
Towards Reliable ML Feature Engineering via Planning in Constrained-Topology of LLM Agents
arXiv:2601.10820v1 Announce Type: new
Abstract: Recent advances in code generation models have unlocked unprecedented opportunities for automating feature engineering, yet their adoption in real-world ML teams remains constrained by critical challenges: (i) the scarcity of datasets capturing the it...
Japanese AI Agent System on Human Papillomavirus Vaccination: System Design
arXiv:2601.10718v1 Announce Type: new
Abstract: Human papillomavirus (HPV) vaccine hesitancy poses significant public health challenges, particularly in Japan where proactive vaccination recommendations were suspended from 2013 to 2021. The resulting information gap is exacerbated by misinformation...
Do You Trust Me? Cognitive-Affective Signatures of Trustworthiness in Large Language Models
arXiv:2601.10719v1 Announce Type: new
Abstract: Perceived trustworthiness underpins how users navigate online information, yet it remains unclear whether large language models (LLMs),increasingly embedded in search, recommendation, and conversational systems, represent this construct in psychologic...
Building AI Agents to Improve Job Referral Requests to Strangers
arXiv:2601.10726v1 Announce Type: new
Abstract: This paper develops AI agents that help job seekers write effective requests for job referrals in a professional online community. The basic workflow consists of an improver agent that rewrites the referral request and an evaluator agent that measures...
ORBITFLOW: SLO-Aware Long-Context LLM Serving with Fine-Grained KV Cache Reconfiguration
arXiv:2601.10729v1 Announce Type: new
Abstract: Serving long-context LLMs is challenging because request lengths and batch composition vary during token generation, causing the memory footprint to fluctuate significantly at runtime. Offloading KV caches to host memory limits effective memory usage,...
CTHA: Constrained Temporal Hierarchical Architecture for Stable Multi-Agent LLM Systems
arXiv:2601.10738v1 Announce Type: new
Abstract: Recently, multi-time-scale agent architectures have extended the ubiquitous single-loop paradigm by introducing temporal hierarchies with distinct cognitive layers. While yielding substantial performance gains, this diversification fundamentally compr...
The breakthrough that makes robot faces feel less creepy
Humans pay enormous attention to lips during conversation, and robots have struggled badly to keep up. A new robot developed at Columbia Engineering learned realistic lip movements by watching its own reflection and studying human videos online. This allowed it to speak and sing with synchronized fa...
Social Determinants of Health Prediction for ICD-9 Code with Reasoning Models
arXiv:2601.09709v1 Announce Type: new
Abstract: Social Determinants of Health correlate with patient outcomes but are rarely captured in structured data. Recent attention has been given to automatically extracting these markers from clinical text to supplement diagnostic systems with knowledge of p...
The Geometry of Thought: Disclosing the Transformer as a Tropical Polynomial Circuit
arXiv:2601.09775v1 Announce Type: new
Abstract: We prove that the Transformer self-attention mechanism in the high-confidence regime ($\beta \to \infty$, where $\beta$ is an inverse temperature) operates in the tropical semiring (max-plus algebra). In particular, we show that taking the tropical li...
TimeSAE: Sparse Decoding for Faithful Explanations of Black-Box Time Series Models
arXiv:2601.09776v1 Announce Type: new
Abstract: As black box models and pretrained models gain traction in time series applications, understanding and explaining their predictions becomes increasingly vital, especially in high-stakes domains where interpretability and trust are essential. However, ...