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Compositional Objectives: Learning Structure in Structure

arXiv:2609.32566v1 Announce Type: new Abstract: Intelligence is defined in many ways. One of these definitions defines intelligence as the pursuit of learnable novelty. However, learnable novelty can

Compressing Value Predictions for Learning-Augmented Metrical Task Systems

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DGX agent

arXiv:2609.33580v1 Announce Type: new Abstract: Learning-augmented algorithms for metrical task systems (MTS) can exploit predictions of canonical dual values, but existing formulations typically requ

researcharxiv-cs-lg

29 Sept 2026

Compute Time Scaling with Recursive Models for Combinatorial Optimization

arXiv:2609.34585v1 Announce Type: new Abstract: We propose Tiny Recursive Models for Combinatorial Optimization (ours{}), a general neural method for combinatorial optimization that scales both depth

ConCAD: Constraint-Aware Image-to-CAD Generation with Dual-Granularity Rewards

arXiv:2609.34494v1 Announce Type: new Abstract: Image-to-CAD generation seeks executable parametric programs that recover both the geometry and design intent of a reference object. Existing systems ar

safetyarxiv-cs-cv

Concept-Guided Fine-Tuning: Steering ViTs away from Spurious Correlations to Improve Robustness

arXiv:2603.08309v3 Announce Type: replace Abstract: Vision Transformers (ViTs) often degrade under distribution shifts because they rely on spurious correlations, such as background cues, rather than

safetyarxiv-cs-cv

Concept Score Relearning: A Unified Cross-Architecture Attack on Concept Erasure

arXiv:2609.33445v1 Announce Type: new Abstract: Concept erasure aims to suppress undesirable knowledge in text-to-image generative models. However, existing robustness evaluations typically rely on re

Concepts Complement Dense Semantics: Learning Compact Sparse Spaces for Text-Image Retrieval

arXiv:2609.32671v1 Announce Type: cross Abstract: Cross-modal retrieval has been advanced by vision-language pre-trained models that encode images and texts into a shared dense embedding space. While

researcharxiv-cs-cv

Conditioned Direct Feedback Alignment via Activity and Error Geometry

arXiv:2607.18574v2 Announce Type: replace Abstract: Direct feedback alignment (DFA) trains hidden layers with fixed random projections of the output error. Even when this feedback provides useful cred

ConflictVLA-Bench: Benchmarking Behavioral Responses of Vision-Language-Action Models to Premise Conflicts

model-releasesarxiv-cs-ro

arXiv:2609.31792v1 Announce Type: cross Abstract: While Vision-Language-Action (VLA) models perform strongly on manipulation tasks, their responses to invalid task premises remain underexplored. Exist

Conformal Prediction and Conditional Coverage for Tabular Foundation Models

arXiv:2609.34887v1 Announce Type: cross Abstract: Tabular foundation models (TFMs) provide predictive distributions for regression, but their prediction regions can exhibit undercoverage or overcovera

Conformal Robust Set Estimation

arXiv:2604.18441v2 Announce Type: replace-cross Abstract: We introduce a robust conformal prediction method based on the half-mass radius P(x):=inf{r>0:P(B(x,r))>1/2}. Its empirical version is exactly

Congrats to @hardmaru @risi1979 @yujin_tang and Risto! Neuroevolution is where I got started in AI, and neuroevolution is still a playground…

Congrats to @hardmaru @risi1979 @yujin_tang and Risto! Neuroevolution is where I got started in AI, and neuroevolution is still a playground for original ideas with neural networks. This book is worth

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Agents

🚨🔥 Congress (@RepJoshG @ValerieFoushee @tedlieu) to OpenAI, Anthropic, Google, Meta, SpaceX on their inadequate reporting of their inadequ…

🚨🔥 Congress (@RepJoshG @ValerieFoushee @tedlieu) to OpenAI, Anthropic, Google, Meta, SpaceX on their inadequate reporting of their inadequately-controlled agents: “Your companies have since notified t

agentsgary-marcus--x

ConRAG: Lightweight inference of multi-hop relations

arXiv:2609.35193v1 Announce Type: new Abstract: Understanding how two entities are connected often requires tracing multi-hop relations across documents to identify intermediate entities and supportin

Constella Preview: Swap Query Models Without Re-Embedding

As query traffic grows, so does the compute bill for embedding it. On a low-power device, a large model may not fit in memory. A smaller query model could reduce that cost, but switching usually means

model-releasesqdrant

Constrained Edit Fields for Training-Free Flow Editing

arXiv:2609.33735v1 Announce Type: new Abstract: Text-guided image editing aims to perform a desired edit while preserving source content unrelated to it. Pretrained rectified-flow models enable traini

safetyarxiv-cs-cv

Constrained Flow Policy Updates: A Generalized Schrodinger Bridge View

arXiv:2609.32952v1 Announce Type: new Abstract: Online safe reinforcement learning (RL) seeks policies that maximize reward while satisfying safety constraints. Reward and safety can induce multimodal

Contact-Aware Impedance Controller for Robot-Assisted Ultrasound Imaging

arXiv:2609.34400v1 Announce Type: new Abstract: Safe robot-assisted ultrasound imaging requires a reliable controller able to detect and localize probe--tissue interaction. In this paper, we present a

safetyarxiv-cs-ro

Contamination, Prior, or Evidence? Decomposing and Training Evidence Use in Whole-Slide Vision-Language Models

arXiv:2609.32185v1 Announce Type: new Abstract: Pathology vision-language models (VLMs) are conventionally evaluated by accuracy, but accuracy alone does not measure evidence use: it may conflate data

Context-dependent time-series prediction via HyperReservoirs

arXiv:2609.34847v1 Announce Type: new Abstract: Time series prediction is a common application of reservoir computing. When the training and testing time series data contains multiple dynamical regime

Contextual Distributionally Robust Optimization with Causal and Continuous Structure

arXiv:2601.11016v3 Announce Type: replace-cross Abstract: We propose a framework for contextual distributionally robust optimization (DRO) that considers the causal and continuous structure of the und

Continual Data Unlearning in Diffusion Models via Transition-based Regularization

arXiv:2609.32328v1 Announce Type: new Abstract: Data unlearning in diffusion models aims to remove the influence of specific training examples without suppressing the broader concepts they represent.

Continual Learning via Self-Probe Gradients

arXiv:2609.32771v1 Announce Type: new Abstract: Adapting pretrained models to new data can cause catastrophic forgetting of previously learned behavior. When only a few past samples remain, they give

Applications

Continuous-Time Trajectory Generation from Discrete Observations with Stochasticity

arXiv:2609.32026v1 Announce Type: cross Abstract: Physical systems evolve continuously in time, yet their states are typically observed only at discrete times. Generating trajectories consistent with

applicationsarxiv-cs-lg

Continuous Variational Synthesis

arXiv:2609.35083v1 Announce Type: cross Abstract: Biological machine learning was long bottlenecked by the ability to synthesize designed DNA. Variational synthesis models control chemical reactions t

Contrast Matters: Understanding Robustness of In-Context Fine-Tuning to Target-Context Relatedness

arXiv:2604.01601v2 Announce Type: replace Abstract: In-context fine-tuning (IC-Train), training an LLM with labeled examples in-context, is increasingly used in place of standard fine-tuning for domai

Local Ai

Control-Geometry Straightening for Sampling-Based Latent Planning

arXiv:2609.35603v1 Announce Type: new Abstract: Joint-embedding predictive architectures enable planning with latent world models, but accurate transition prediction alone does not ensure that the pla

local-aiarxiv-cs-lg

ControlGS: Conditioning Neural Gaussians for Downstream-Processing-Aware XR Rendering

arXiv:2609.32038v1 Announce Type: new Abstract: Extended Reality (XR) users do not directly perceive the output of a rendering engine. Instead, rendered images pass through a post-processing pipeline

researcharxiv-cs-cv

Applications

Controllable GNN Explanations via Multi-Metric Preference Selection

arXiv:2609.32436v1 Announce Type: cross Abstract: Mechanisms for generating GNN explanations are crucial for building trust and mitigating biases in Graph Neural Networks (GNNs), especially in high-st

applicationsarxiv-cs-lg

Controlling Speaking Rate in Autoregressive TTS via Activation Steering

arXiv:2609.33810v1 Announce Type: cross Abstract: Autoregressive text-to-speech (TTS) systems synthesize natural speech but, once trained, offer little control over speaking rate. We show that speakin

ControlTrace: Recovering Control Fields for Hidden-Content Recognition

arXiv:2609.34807v1 Announce Type: new Abstract: Spatially conditioned diffusion models can embed words and contours in natural-looking images, but vision-language models (VLMs) may fail to recognize t

ConvCue: Complementary Visual Inductive Biases for Vision-Language Models

arXiv:2609.34196v1 Announce Type: new Abstract: Modern vision-language models (VLMs) achieve strong performance across a broad range of multimodal tasks, yet still struggle with visual questions that

Applications

Convergence-Aware Pareto Selection of Covariate Scaling Transformations for Markov Deterioration Hazard Models: Evidence from Bridge Inspection Data

arXiv:2609.31786v1 Announce Type: cross Abstract: Maximum likelihood estimation of Markov deterioration hazard models for infrastructure asset management is sensitive to the numerical conditioning of

applicationsarxiv-cs-lg

Convergence of Practical Muon

arXiv:2609.33152v1 Announce Type: new Abstract: Muon is emerging as a promising alternative to AdamW for large-scale neural network training, yet theoretical understanding of its practical implementat

Convergent Plug-and-Play Image Restoration with Annealed Noise Levels

arXiv:2609.32393v1 Announce Type: cross Abstract: Plug-and-Play (PnP) methods solve imaging inverse problems by incorporating deep denoisers into iterative optimization algorithms. Although practical

Convex Optimization Is Free When Accuracy Is Expensive

arXiv:2609.35418v1 Announce Type: cross Abstract: This paper studies convex optimization when the gradient cannot be evaluated exactly, but only approximated by a hierarchy of algorithms whose compute

Agents

Coordinated Lane-Level Variable Speed Limits and Ramp Metering for Successive Weaving Segments Considering Merging/Diverging Risks: A Hybrid Model Predictive Control and Multi-Agent Reinforcement Learning Approach

arXiv:2609.35152v1 Announce Type: cross Abstract: Successive weaving segments (SWSs) on urban expressways are bottlenecks prone to recurrent congestion and collisions, requiring fine-grained active tr

agentsarxiv-cs-lg

Copper-Policy: Focus on the Representation for Robust Robot Manipulation

arXiv:2609.32779v1 Announce Type: cross Abstract: World Action Models (WAMs) acquire behavioral priors by modeling future scene evolution, but predicting detailed futures in pixel or latent space incu

Tutorials

Copy the Same, Distill the Difference: Initializing Linear Vision Transformers

arXiv:2609.35745v1 Announce Type: new Abstract: Linear Vision Transformers (ViTs) are designed to replace the attention in Softmax ViTs with the linear-complexity attention operator for more efficient

tutorialsarxiv-cs-cv

Coreset-Based Task Selection for Sample-Efficient Meta-Reinforcement Learning

arXiv:2502.02332v3 Announce Type: replace-cross Abstract: We study task selection to enhance sample efficiency in model-agnostic meta-reinforcement learning (MAML-RL). Traditional meta-RL typically as

Correct then Forecast: Observer State-Space Models for Time Series Forecasting

arXiv:2609.33566v1 Announce Type: new Abstract: Time series forecasting requires extrapolating the dynamics of an observed process beyond the last available measurement. Yet recurrent forecasting mode

Correcting Spectra Outside the Backbone: A Model-Agnostic Rectifier for Hyperspectral Image Super-Resolution

arXiv:2601.21338v2 Announce Type: replace Abstract: Hyperspectral image super-resolution (HSI-SR) aims to recover spatial detail while preserving the spectral shape on which quantitative analysis reli

researcharxiv-cs-cv

Correcting the Dropout-LayerNorm Expectation Gap Improves Protein Structure Models

arXiv:2609.32062v1 Announce Type: cross Abstract: Although E[dropout(x)] = x, here we show that E[LayerNorm(dropout(x))] is not equal to LayerNorm(x). Accordingly, the pattern of a Dropout layer follo

Correction-space Cross-variate Interaction for Test-time Adaptation in Time Series Forecasting

arXiv:2609.34638v1 Announce Type: new Abstract: Test-time adaptation (TTA) is a promising paradigm for handling distribution shift in time-series forecasting (TSF), where models adapt at inference tim

CORTEX: A Verified Experience Layer for Generalist Agents

arXiv:2609.33260v1 Announce Type: cross Abstract: An agent can solve a task today and face the same task under new facts, tools, or governing knowledge tomorrow. Most agent systems can retrieve releva

CoSE-E: A Benchmark for Code-switched Speech Evaluation in Enterprise Settings

arXiv:2609.35645v1 Announce Type: cross Abstract: Code-switching (CS), a seamless alternation between languages within a single utterance, remains a critical challenge in automatic speech recognition

Cost-free Spectral Estimation for Adaptive Newton--Schulz in Matrix Optimizers

arXiv:2609.33047v1 Announce Type: new Abstract: Matrix optimizers such as Muon transform each momentum matrix through an approximate orthogonalization, typically implemented by a small number of Newto

could anything prove more decisively that Jensen Huang is a poor judge of character? his trust in OpenAI is entirely misplaced.

could anything prove more decisively that Jensen Huang is a poor judge of character? his trust in OpenAI is entirely misplaced. 🚨BREAKING: “Months before OpenAI's artificial intelligence went rogue, t

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