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Better Supervision Is Nearby: Neighborhood On-Policy Self-Distillation

arXiv:2609.39687v1 Announce Type: new Abstract: On-policy self-distillation (OPSD) trains mathematical reasoning models using a privileged teacher that sees a reference solution and supervises student

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Model Releases

Beyond Layers: Position-Resolved Gradient Conflict and Position-Aware Modulation for Unified Multimodal Models

DGX agent

arXiv:2609.38485v1 Announce Type: new Abstract: Unified multimodal models (UMMs) train image understanding and autoregressive image generation on shared parameters, and the two objectives are known to

model-releasesarxiv-cs-cv

1 Oct 2026

Beyond Model Ranking: Regime Diagnosis for Distributional-Statistical Misspecification in Industrial Time-Series Forecasting

arXiv:2609.40117v1 Announce Type: new Abstract: Time-series forecasting models achieve strong benchmark performance but exhibit severe systematic bias in industrial deployments. This train--deploy gap

Beyond Oracle Communication: Benchmarking Interactive Intent Alignment Under Miscommunication and Evolving User Intent

arXiv:2609.38604v1 Announce Type: cross Abstract: Modern LLM agents increasingly tackle complex tasks through interactive, long-horizon exchanges with users, while existing benchmarks generally assume

Beyond Policy Alignment: Closing the Planning-Learning Loop for Robot Control with Learned World Models

model-releasesarxiv-cs-ro

arXiv:2609.39751v1 Announce Type: new Abstract: Planning with learned world models combines online trajectory optimization with learned value and policy functions for high-dimensional control. Because

Beyond Text: LLM-Based Dimensional Emotion Evaluation in Multimodal Dialogue

arXiv:2609.39072v1 Announce Type: cross Abstract: Emotion recognition in conversation has been widely studied, but applying Large Language Models (LLMs) to continuous dimensional emotion evaluation in

Beyond the Remembered World: Predictive 4D Belief for Persistent Navigation in Evolving Worlds

arXiv:2609.39166v1 Announce Type: new Abstract: Persistent spatial memory enables embodied agents to navigate familiar environments across repeated visits. However, targets may move while unobserved,

Beyond Uniform Compression: Budgeted Transmission Allocation for Extreme Federated Learning

arXiv:2609.39646v1 Announce Type: new Abstract: Federated learning faces severe communication bottlenecks when clients upload high-dimensional model updates. Existing methods often compress these upda

Breaking Babel: A Self-Evolving Multi-Agent System for Long-Form Subtitle Translation

arXiv:2609.38660v1 Announce Type: cross Abstract: Long-form subtitle translation requires reasoning over discourse and cultural context spanning episodes or entire series, while maintaining consistent

Can an AI Agent Rediscover a Blaschke-Curve Invariant?

arXiv:2609.38369v1 Announce Type: new Abstract: We study generalized Blaschke curves as a controlled environment for AI-assisted mathematical rediscovery. For one fixed degree-four Blaschke product, a

Can Computation from Earlier Problems Help LLMs Solve New Ones?

arXiv:2609.39394v1 Announce Type: new Abstract: Large language models often solve independent problems in the same conversation. Can computation from earlier problems help them solve new ones? To answ

Caption-Mediated Perceived-Safety Estimation for Pedestrian Routing

arXiv:2609.38479v1 Announce Type: cross Abstract: This paper presents an explainable approach to pedestrian routing, in which perceived safety is estimated from street-level imagery through an explici

CARAT: Do Materials LLMs Reason or Recite?

arXiv:2609.38340v1 Announce Type: new Abstract: When a materials LLM answers a question about crystal structure, does it reason from the structure or copy an answer already printed in its input? Accur

Cascadia: A Control-Plane-Free Alternative to Hyperconverged AI Infrastructure

arXiv:2609.38697v1 Announce Type: cross Abstract: We present Cascadia, a system for serving large language models on fleets of commodity Intel AIPCs using their CPU, integrated-GPU, and NPU resources.

CAST: Causal Advantage-Structured Training with Spatially Grounded Compositional Rewards for Diffusion Models

arXiv:2609.39441v1 Announce Type: cross Abstract: Online reinforcement learning has been extended to flow matching for diffusion model (DM) image generation. However, this paradigm faces three limitat

CATCH: A Controllable Analysis Testbed for Reward Hacking in Coding RL

arXiv:2609.39533v1 Announce Type: new Abstract: During reinforcement learning with verifiable rewards (RLVR), large language models (LLMs) can exploit loopholes in their environments to obtain high re

Certification-Based Differentially Private Learning

arXiv:2609.39629v1 Announce Type: new Abstract: Differential privacy (DP) in machine learning is typically achieved by adding noise to model parameters (private learning) or to model outputs (private

Certified Approximation for Interpretable Representer Landmarks

arXiv:2609.38901v1 Announce Type: new Abstract: Representer explanations rank the training landmarks that most influence a self-supervised representation. At scale, this ranking rests on up to four st

ChartDensity-Bench: Benchmarking MLLMs for Numerical Data Reconstruction under Visual Density

arXiv:2609.38781v1 Announce Type: cross Abstract: Multimodal large language models (MLLMs) offer a promising approach for recovering numerical data from scientific charts, but their ability to reconst

Cheap to Draw, Expensive to Trust: Certifying Test-Time Scaling Curves

arXiv:2609.40190v1 Announce Type: cross Abstract: Sampling several answers and keeping the one a verifier scores highest is one of the simplest ways to buy accuracy at test time. Its effect is reporte

Closing the Approximation Gap in Simulation-free Latent SDEs

arXiv:2606.16138v2 Announce Type: replace-cross Abstract: Recovering dynamical systems from noisy observations is a recurring challenge across scientific domains, including neuroscience and physics. L

Cluster Attention Neural Operators for Solving Parametric Partial Differential Equations

arXiv:2609.39914v1 Announce Type: cross Abstract: Traditional simulations of parametric partial differential equations (PDEs) rely on repetitive computations for each parameter, which makes high-fidel

Cogentic: Multi-Agent Orchestration for Automated Proof Discovery

arXiv:2609.40324v1 Announce Type: new Abstract: We present Cogentic, a multi-agent harness for automated proof discovery on open research problems. While frontier language models can generate strong m

CombEval: A Framework for Evaluating Combinatorial Counting in Large Language Models

arXiv:2606.19788v2 Announce Type: replace Abstract: We present CombEval, a dynamic benchmark for evaluating combinatorial counting in large language models. CombEval represents each problem as a typed

Community-Driven API and AI Writer Design for Openly Scaling Community Notes

arXiv:2609.40067v1 Announce Type: cross Abstract: Community Notes is a crowd-sourced approach for adding context to posts on X. Contributors propose and rate notes, forming the inputs to an open-sourc

Comparison of techniques for fine-tuning open-weight models for entity extraction from radiology reports

arXiv:2609.40236v1 Announce Type: new Abstract: Converting free-text radiology reports into structured labels supports cohort building, quality assurance, and monitoring of clinical imaging models, bu

CompoWorld: Compositional Environment Scaling for General Agents

arXiv:2609.33665v2 Announce Type: replace Abstract: Automatically generated environments provide a scalable source of interaction data for training general agents. However, existing approaches mainly

Conformal Factuality Control for Multi-Hop Retrieval-Augmented Generation

arXiv:2609.38222v1 Announce Type: new Abstract: Retrieval-augmented generation (RAG) can ground large language models in external evidence, but retrieved context does not guarantee that generated clai

Consistent Plan-Act for Long-Horizon Agentic Tasks

arXiv:2609.38891v1 Announce Type: new Abstract: Long-horizon agentic tasks demand strong reasoning and efficient execution across successive interactions with dynamic environments. A common approach d

Continual Learning of Dynamical Systems in Recurrent Neural Networks through Recyclable Unit Gating

arXiv:2609.38356v1 Announce Type: new Abstract: Dynamical Systems Reconstruction (DSR) aims to infer models from observed time series that reproduce a system's qualitative long-term behavior. Continua

Convergence of Practical Muon with Finite Newton-Schulz Iterations and Nesterov Momentum

arXiv:2609.39595v1 Announce Type: new Abstract: Practical Muon maintains momentum and performs a small, fixed number of Newton--Schulz iterations separately for each parameter matrix, often with a Nes

Covert Assistance: Helpful LLM Agents Evade Oversight in Multi-Agent Systems

arXiv:2609.39050v1 Announce Type: cross Abstract: As multi-agent systems enter high-stakes domains, the possibility that agents may circumvent safety boundaries is a growing concern. Prior work has ex

CraftSPH: A high-accuracy and composable differentiable SPH solver implemented in PyTorch

arXiv:2609.38208v1 Announce Type: cross Abstract: Smoothed Particle Hydrodynamics (SPH) is well suited to a range of problems, particularly those involving large deformations in fluid dynamics. In rec

Cybersecurity in Edge Computing: A Trust-Aware Federated Hybrid Intrusion Detection Framework

arXiv:2609.39584v1 Announce Type: cross Abstract: Edge computing has emerged as a critical computing paradigm in modern distributed systems by migrating data processing closer to end users and Interne

D-Scope: Decomposing and Steering Diffusion Transformers with Sparse Autoencoders

arXiv:2609.39625v1 Announce Type: cross Abstract: Sparse autoencoders (SAEs) reveal visual structure in diffusion transformers (DiTs), but interpreting a feature does not establish whether it can be u

DAGent: Evaluate-then-Grow Planning for Deep Research Agents

arXiv:2609.39154v1 Announce Type: cross Abstract: Deep research tasks require agents to navigate large knowledge spaces, synthesize evidence across many sources, and adapt their plans as findings emer

DataFlex: A Unified Framework for Data-Centric Dynamic Training of Large Language Models

arXiv:2603.26164v2 Announce Type: replace-cross Abstract: Data-centric training has emerged as a promising direction for improving large language models (LLMs) by optimizing not only model parameters

dattri-LLM: A Unified and Efficient Library for Training Data Attribution at LLM Scale

arXiv:2609.38767v1 Announce Type: cross Abstract: Training data attribution (TDA) estimates the contribution of individual training examples to model outputs. Most scalable TDA methods rely on per-exa

DC-SAE: Deep Compression Semantic Autoencoder for Faster Diffusion Convergence

arXiv:2609.39222v1 Announce Type: new Abstract: High-compression tokenizers are essential for scaling latent image generative models. However, aggressive compression creates a fundamental tradeoff bet

Decision-Oriented Recommendation Reranking: An Empirical Study of Jev

arXiv:2609.40241v1 Announce Type: cross Abstract: Large language models (LLMs) have shown promise for recommendation reranking, but their use introduces an important tradeoff between recommendation qu

DeCoPrune: Efficient KV-Cache Pruning for Autoregressive Video Diffusion via Denoising Consistency

arXiv:2609.39096v1 Announce Type: new Abstract: Autoregressive video diffusion supports streaming generation and interactive control, but its KV cache grows with the generated history. Existing compre

Decoupled and Distilled: Task-Adaptive LoRA-Teachers with Ensemble Knowledge Transfer for Few-Shot Class-Incremental Learning

arXiv:2609.39390v1 Announce Type: new Abstract: Few-Shot Class-Incremental Learning (FSCIL) addresses the challenge of learning new classes from very limited samples while retaining knowledge of previ

DiDA: Video Object Segmentation with Distillation Learning of Deformable Attention

arXiv:2401.13937v3 Announce Type: replace Abstract: Video object segmentation is a fundamental research problem in computer vision. Recent techniques have often applied attention mechanism to object r

DiFF: Doppler-informed Flow Matching for Human Motion Flow

arXiv:2609.39098v1 Announce Type: cross Abstract: Perceiving human motion via privacy-preserving 4D millimeter-wave (mmWave) radar is critical for next-generation human-robot interaction (HRI), where

Diffusion-2BC: Hybrid Diffusion and Regression Training for Offline Behavior Cloning in Autonomous Driving

arXiv:2609.38472v1 Announce Type: cross Abstract: Behavior cloning provides an offline route to autonomous-driving policy learning, but mean-squared-error regression is poorly matched to demonstration

DiffWAM: A Fast and Efficient Navigation World Action Model

arXiv:2609.39763v1 Announce Type: cross Abstract: Pretrained video foundation models encode rich semantic and spatiotemporal priors for embodied navigation, yet converting these priors into UAV motion

Discrete Forcing: Infusing Discrete Guidance into Continuous Denoising for Few-Step Action Experts

model-releasesarxiv-cs-ro

arXiv:2609.39526v1 Announce Type: new Abstract: Efficient action generation in vision-language-action (VLA) models requires capturing both coarse action structure and fine-grained details. Discrete ac

Discrete Score Matching Enables Causal Discovery from Count Data

arXiv:2609.39326v1 Announce Type: cross Abstract: Count data pose a challenge for score-matching-based causal discovery: derivatives are unavailable, and simply replacing them with finite differences