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CLEAR: Revealing How Noise and Ambiguity Degrade Reliability in LLMs for Medicine

arXiv:2605.01011v1 Announce Type: new Abstract: Medical large language model (LLM) evaluations rely on simplified, exam-style benchmarks that rarely reflect the ambiguity of real-world medical inquiri

applicationsarxiv-cs-cl

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CleverCatch: A Knowledge-Guided Weak Supervision Model for Fraud Detection

DGX agent

arXiv:2510.13205v3 Announce Type: replace Abstract: Healthcare fraud detection remains a critical challenge due to limited availability of labeled data, constantly evolving fraud tactics, and the high

applicationsarxiv-cs-lg

5 May 2026

Closed-Loop CO2 Storage Control With History-Based Reinforcement Learning and Latent Model-Based Adaptation

arXiv:2605.02405v1 Announce Type: new Abstract: Closed-loop management of geological CO2 storage requires control policies that adapt to uncertain reservoir behavior while relying on observations that

applicationsarxiv-cs-lg

Tutorials

Closed-Loop LLM Discovery of Non-Standard Channel Priors in Vision Models

arXiv:2601.08517v2 Announce Type: replace Abstract: Channel-configuration search, the optimization of layer specifications such as channel widths in deep neural networks, presents a combinatorial chal

tutorialsarxiv-cs-cv

Cloud Engineer’s AI Toolkit: Sign up Now for a Developer Workshop Near You!

model-releasesgoogle-cloud-ai

The world of AI is rapidly shifting from experimental Large Language Models to an era of Agentic AI. In the agentic era, autonomous software agents act on behalf of employees and consumers—driving a f

CNN-based Multi-In-Multi-Out Model for Efficient Spatiotemporal Prediction

model-releasesarxiv-cs-cv

arXiv:2605.01277v1 Announce Type: new Abstract: Recently, Convolutional Neural Network (CNN) or Transformer architecture based models have been proposed to overcome the limitations of Recurrent Neural

CNNs for Vis-NIR Chemometrics: From Contradiction to Conditional Design

arXiv:2605.02636v1 Announce Type: new Abstract: Near-infrared (NIR; a.k.a. NIRS) deep-learning studies in chemometrics increasingly report mutually inconsistent conclusions regarding convolutional neu

researcharxiv-cs-lg

CoAction: Cross-task Correlation-aware Pareto Set Learning

arXiv:2605.01712v1 Announce Type: new Abstract: Pareto set learning (PSL) is an emerging paradigm in multi-objective optimization that trains neural networks to map preference vectors to Pareto optima

COCORELI: Enforcing Execution Preconditions for Reliable Collaborative Instruction Following

arXiv:2509.04470v2 Announce Type: replace Abstract: Autonomous agents executing human instructions must operate reliably even when instructions are incomplete. While recent approaches improve detectio

Code-switching in text and speech challenges information-theoretic speaker design

arXiv:2408.04596v2 Announce Type: replace Abstract: In this work, we use language modeling to investigate the factors that influence insertional code-switching. Code-switching occurs when a speaker al

researcharxiv-cs-cl

Codex is gaining steam

Codex, likely referring to OpenAI's code generation model, is experiencing increased adoption and usage. The article from Ben's Bites discusses the growing momentum and applications of this AI coding

industryben-s-bites

Tutorials

Coding agents are accelerating different types of software work to different degrees. When we architect teams, understanding these distincti…

Coding agents are accelerating different types of software work to different degrees. When we architect teams, understanding these distinctions helps us to have realistic expectations. Listing functio

tutorialsandrew-ng--x

CoFrGeNet: Continued Fraction Architectures for Language Generation

arXiv:2601.21766v3 Announce Type: replace Abstract: Transformers are arguably the preferred architecture for language generation. In this paper, inspired by continued fractions, we introduce a new fun

researcharxiv-cs-cl

Coinbase CEO Brian Armstrong announces the company is cutting ~700 jobs, or ~14% of its global workforce, to reduce costs, saying 'AI is changing how we work' (Reuters)

Reuters: Coinbase CEO Brian Armstrong announces the company is cutting ~700 jobs, or ~14% of its global workforce, to reduce costs, saying “AI is changing how we work” — Coinbase (COIN.O) said on Tues

industrytechmeme

Coinbase cuts staff by 14%, citing crypto market downturn and AI adoption

Coinbase Global Inc. Chief Executive Brian Armstrong announced today that the company intends to cut its headcount by 14%, or roughly 700 jobs, citing a combination of market forces and artificial int

industrysiliconangle

Colinearity Decay: Training Quantization-Friendly ViTs with Outlier Decay

arXiv:2605.01330v1 Announce Type: new Abstract: Low-bit quantization is a practical route for efficiently deploying vision Transformers, yet activation outliers complicate fully quantized deployment.

safetyarxiv-cs-cv

CombinationTS: A Modular Framework for Understanding Time-Series Forecasting Models

arXiv:2605.01231v1 Announce Type: new Abstract: Recent progress in time-series forecasting has led to rapidly increasing architectural complexity, yet many reported State-of-the-Art gains are statisti

Combining Facial Videos and Biosignals for Stress Estimation During Driving

arXiv:2601.04376v3 Announce Type: replace Abstract: Reliable stress recognition is critical in applications such as medical monitoring and safety-critical systems, including real-world driving. While

safetyarxiv-cs-cv

Combining Trained Models in Reinforcement Learning

arXiv:2605.02159v1 Announce Type: new Abstract: Deep reinforcement learning (DRL) has delivered strong results in domains such as Atari and Go, but it still suffers from high sample cost and weak tran

safetyarxiv-cs-lg

Agents

Come and join us, we're hiring across almost all roles 🚀https://www.langchain.com/careers

Come and join us, we're hiring across almost all roles 🚀https://www.langchain.com/careers Companies I'd consider going to if I ever had the urge to try something new. 1 Thinking Machines Lab 2 OpenAI

agentsharrison-chase--x

come for the rate limits, stay for the best model

Sam Altman suggests that OpenAI's competitive advantage extends beyond just offering competitive API rate limits; the superior quality of their models themselves is what keeps users engaged long-term.

industrysam-altman--x

Comparative Evaluation of Convolutional and Transformer-Based Detectors for Automated Weed Detection in Precision Agriculture

arXiv:2605.00908v1 Announce Type: new Abstract: This paper presents a comparative evaluation of convolutional and transformer-based object detection architectures for early weed detection in realistic

Compared to What? Baselines and Metrics for Counterfactual Prompting

arXiv:2605.01048v1 Announce Type: new Abstract: Counterfactual prompting (i.e., perturbing a single factor and measuring output change) is widely used to evaluate things like LLM bias and CoT faithful

safetyarxiv-cs-cl

Agents

Compiling Deterministic Structure into SLM Harnesses

arXiv:2604.17450v2 Announce Type: replace Abstract: Enterprise SLM deployment faces epistemic asymmetry: small models cannot self-correct reasoning errors, while frontier LLMs incur prohibitive costs

agentsarxiv-cs-ai

Complex Diffusion Maps with omega-Parameterized Kernels Revealing Inherent Harmonic Representations

arXiv:2605.01691v1 Announce Type: new Abstract: In this paper, we propose Complex Diffusion Maps (CDM), a novel diffusion mapping framework that aims to reveal the dominant complex harmonics of high-d

applicationsarxiv-cs-lg

Compliance-Aware Agentic Payments on Stablecoin Rails

arXiv:2605.00071v1 Announce Type: cross Abstract: Agentic payment systems extend delegated action to financial transfers, but scaling them on stablecoin rails in regulated settings requires safeguards

safetyarxiv-cs-ai

Component-Aware Self-Speculative Decoding in Hybrid Language Models

arXiv:2605.01106v1 Announce Type: new Abstract: Speculative decoding accelerates autoregressive inference by drafting candidate tokens with a fast model and verifying them in parallel with the target.

Compositional Multi-hop Factual Error Correction via Decomposition-and-Injection

arXiv:2605.02277v1 Announce Type: new Abstract: Factual Error Correction (FEC) aims to revise inaccurate text into statements that are factually consistent with external evidence. Although recent meth

applicationsarxiv-cs-cl

Compression as Adaptation: Implicit Visual Representation with Diffusion Foundation Models

arXiv:2603.07615v2 Announce Type: replace-cross Abstract: Modern visual generative models acquire rich visual knowledge through large-scale training, yet existing visual representations (such as pixel

Compute Optimal Tokenization

arXiv:2605.01188v1 Announce Type: new Abstract: Scaling laws enable the optimal selection of data amount and language model size, yet the impact of the data unit, the token, on this relationship remai

Concepts Whisper While Syntax Shouts: Spectral Anti-Concentration and the Dual Geometry of Transformer Representations

arXiv:2605.01609v1 Announce Type: new Abstract: We test whether the causal inner product of itet{park2024linear} -- defined by the unembedding covariance Sigma -- enables cross-lingual concept transpo

Confident, Calibrated, or Complicit: Safety Alignment and Ideological Bias in LLM Hate Speech Detection

arXiv:2509.00673v2 Announce Type: replace Abstract: We investigate the efficacy of Large Language Models (LLMs) in detecting implicit and explicit hate speech, examining how models with minimal safety

safetyarxiv-cs-cl

Tools

Confirmed that this is an experimental branch, not a decision to switch languages

Confirmed that this is an experimental branch, not a decision to switch languages @mariusdotdev @simonw I want to see what a viable side by side comparison looks like. Does it use less memory? How man

toolssimon-willison--x

Congestion-Aware Dynamic Axonal Delay for Spiking Neural Networks

arXiv:2605.01291v1 Announce Type: new Abstract: Spiking Neural Networks (SNNs) are widely regarded as an energy-efficient paradigm for modeling and processing temporal and event-driven information. In

Constructing Interpretable Features from Compositional Neuron Groups

arXiv:2506.10920v2 Announce Type: replace Abstract: A central goal for mechanistic interpretability has been to identify the right units of analysis in large language models (LLMs) that causally expla

ContextualJailbreak: Evolutionary Red-Teaming via Simulated Conversational Priming

arXiv:2605.02647v1 Announce Type: new Abstract: Large language models (LLMs) remain vulnerable to jailbreak attacks that bypass safety alignment and elicit harmful responses. A growing body of work sh

Continual Few-shot Adaptation for Synthetic Fingerprint Detection

arXiv:2603.14632v2 Announce Type: replace Abstract: The quality and realism of synthetically generated fingerprint images have increased significantly over the past decade fueled by advancements in ge

Tutorials

Continual Learning of Feedback-based Molecular Communication

arXiv:2605.01020v1 Announce Type: new Abstract: This paper proposes and evaluates a new performance estimation method that leverages continual learning (CL) algorithms to carry out sequential simulati

tutorialsarxiv-cs-lg

Continuous quantification of viral plaque dynamics using ultra-large-area label-free imaging enables rapid antiviral susceptibility testing

arXiv:2605.01738v1 Announce Type: cross Abstract: The plaque reduction assay (PRA) remains the gold standard for antiviral susceptibility testing, evaluating drug potency by measuring reductions in pl

Continuous Temporal Representations of Event-Based Signals via Interference-Based Wave Modeling

arXiv:2605.01270v1 Announce Type: new Abstract: Spatio-temporal signals arising from event-driven biological processes, such as surface electromyography (sEMG), exhibit asynchronous and highly structu

researcharxiv-cs-lg

Contrastive Residual Energy Test-time Adaptation

arXiv:2505.19607v2 Announce Type: replace Abstract: Test-time adaptation (TTA) enhances model robustness by enabling adaptation to target distributions that differ from training distributions, improvi

safetyarxiv-cs-lg

Control Reinforcement Learning: Interpretable Token-Level Steering of LLMs via Sparse Autoencoder Features

arXiv:2602.10437v3 Announce Type: replace-cross Abstract: Sparse autoencoders (SAEs) decompose language model activations into interpretable features, but existing methods reveal only which features a

Controllable Logical Hypothesis Generation for Abductive Reasoning in Knowledge Graphs

model-releasesarxiv-cs-ai

arXiv:2505.20948v3 Announce Type: replace Abstract: Abductive reasoning in knowledge graphs aims to generate plausible logical hypotheses from observed entities, with broad applications in areas such

Controlled Paraphrase Geometry in Sentence Embedding Space: Local Manifold Modeling and Latent Probing

arXiv:2605.01073v1 Announce Type: new Abstract: The paper studies the local geometry of embedding clouds induced by controlled local classes of semantically close sentences. The central question is ho

researcharxiv-cs-cl

Coopetition-Gym v1: A Formally Grounded Platform for Mixed-Motive Multi-Agent Reinforcement Learning under Strategic Coopetition

arXiv:2605.02063v1 Announce Type: cross Abstract: We present Coopetition-Gym v1, a benchmark platform for mixed-motive multi-agent reinforcement learning under strategic coopetition. The platform comp

CoRAL: Contact-Rich Adaptive LLM-based Control for Robotic Manipulation

arXiv:2605.02600v1 Announce Type: new Abstract: While Large Language Models (LLMs) and Vision-Language Models (VLMs) demonstrate remarkable capabilities in high-level reasoning and semantic understand

applicationsarxiv-cs-ro

Correlates of Image Memorability in Vision Encoders: Activations, Attention Entropy, Patch Uniformity and Autoencoder Losses

arXiv:2509.01453v2 Announce Type: replace Abstract: Images vary in how memorable they are to humans. Inspired by findings from cognitive science and computer vision, we explore correlates of image mem

CorrSteer: Generation-Time LLM Steering via Correlated Sparse Autoencoder Features

arXiv:2508.12535v3 Announce Type: replace Abstract: Sparse Autoencoders (SAEs) can extract interpretable features from large language models (LLMs) without supervision. However, their effectiveness in