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Do Temporal Link Predictors Need Learned Memory? A Smoothed-Count Baseline with a Handful of Parameters

arXiv:2609.35364v1 Announce Type: new Abstract: Many temporal link predictors summarize past interactions through learned node representations. We examine whether simple counts of recurring interactio

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Do We Really Need KL Divergence for On-Policy Distillation of Large Language Models?

DGX agent

arXiv:2609.33791v1 Announce Type: new Abstract: Since the advent of knowledge distillation, KL divergence has been the standard loss in distillation. Recently, on-policy distillation (OPD) has emerged

safetyarxiv-cs-lg

29 Sept 2026

Do World Models Learn Global Understanding?

arXiv:2609.34058v1 Announce Type: new Abstract: AI systems often feel brittle and fragmented. A large language model (LLM) may correctly explain a concept but fail to apply it, or follow safety instru

do you guys actually tell ChatGPT stuff you wouldn't tell anyone else?

kinda realized i type things into AI chats that i'd never say to a friend. money problems, work stuff, health worries, random late-night thoughts lol then i found out all of it is stored on their serv

model-releasesr-chatgpt

Does Adversarial Training Improve Generalization in Multi-View VLAs? Revealing and Mitigating View Collapse

arXiv:2609.33707v1 Announce Type: cross Abstract: Vision-language-action (VLA) models adapt pretrained vision-language models (VLMs) for closed-loop robot control, transferring their perceptual and se

safetyarxiv-cs-ro

Hardware

Does Execution Require Target KV Fidelity? A Mixed-Fidelity KV Runtime for LLM Serving

arXiv:2609.33536v1 Announce Type: new Abstract: Large language model (LLM) serving is increasingly constrained by the GPU memory consumed by key-value (KV) caches. Existing compression, eviction, and

hardwarearxiv-cs-lg

Does Joint-Embedding Predictive Architecture Pretraining Help Time Series Forecasting?

arXiv:2609.31680v1 Announce Type: new Abstract: Joint-embedding predictive architectures (JEPA) have emerged as a promising self-supervised pretraining paradigm for time series, learning representatio

Does Native 3D Texture Generation Necessarily Require 3D Assets for Training?

arXiv:2609.34621v1 Announce Type: new Abstract: Native 3D texture generation synthesizes colors directly in 3D space for a given geometry, conditioned on multi-view reference images. It is generally b

Does post training make LLMs funnier?

model-releasesr-localllama

We did a study: does post training actually make LLMs funnier? We used open models that publish every stage of post training, so we could compare a base model with the future models it became: Tulu 3

Does Transolver really need a Transformer?

arXiv:2609.32525v1 Announce Type: new Abstract: The widely used Transolver family of neural operators is based on physics-attention, which softly assigns the points of an unstructured mesh to a small

Does Vision-Language Pretraining Granularity Matter? A Controlled Evaluation of Vision-Language Objectives Across Chest X-Ray Interpretation Tasks

arXiv:2609.31985v1 Announce Type: new Abstract: Vision-language pretraining objectives differ in the spatial granularity of their supervision, yet the implications of this distribution for chest X-ray

DOHF: Online Diffusion Fine-tuning with Doob's h-transform Guidance

arXiv:2609.31882v1 Announce Type: new Abstract: Reward-based diffusion fine-tuning faces practical challenges when desirable outcomes are rare or conditioning corrections are costly to estimate. In th

Domain-Adapted Diffusion Models for Conditional Independence Testing

arXiv:2609.33490v1 Announce Type: cross Abstract: Conditional independence (CI) is a fundamental concept in statistics and machine learning. Recent advances in conditional generative modeling provide

safetyarxiv-cs-lg

Domain-adaptive Zero-Shot Image Enhancement via Locality-Constrained Diffusion Guidance

arXiv:2609.35289v1 Announce Type: new Abstract: Denoising Diffusion Probabilistic Models have shown remarkable performance in unconditional image generation. In order to generate images with desired s

local-aiarxiv-cs-cv

Domain Generalization under Sampling Pattern Shifts in Irregular Time Series

arXiv:2609.33279v1 Announce Type: new Abstract: Irregularly sampled multivariate time series (ISMTS) are prevalent in real-world applications, where both observation times and available measurements c

Don't Forget! Decomposing the Training Dynamics of Memorization in Language Models

arXiv:2609.34933v1 Announce Type: new Abstract: Memorization has been proposed as a mechanism to explain how language models fit the tail of their training distributions, but its training dynamics are

safetyarxiv-cs-lg

Don't stop me now: How Validation Criteria Affect Checkpoint Selection and Early Stopping

arXiv:2602.22107v2 Announce Type: replace Abstract: Checkpoint selection is a standard component of neural network training, yet the validation criterion used to select a checkpoint is often chosen he

Don't Throw Away the Tail: Action Upcycling for Policy Acceleration

arXiv:2609.34911v1 Announce Type: cross Abstract: Modern robot policies predict a chunk of future actions from a single observation, execute only a prefix, and discard the rest before replanning. Choo

safetyarxiv-cs-cv

dOPT: Differentiating Conic Optimization via Geometric Reduction

arXiv:2609.33828v1 Announce Type: new Abstract: Optimization layers enable the incorporation of structured constraints and decision problems into learning systems. Training such systems requires diffe

DORA: Dynamic Online Reinforcement Agent for Token Pruning in Vision Transformers

arXiv:2609.34325v1 Announce Type: new Abstract: Vision Transformers (ViTs) incur quadratic self-attention cost in the number of tokens. Most token-reduction methods adapt token identities within a pre

safetyarxiv-cs-cv

Industry

Dots are here! A new way to use AI that works 24/7 for you; get more of your time and attention back to work at a higher level. https://open…

OpenAI introduced “Dots,” an AI system built to run continuously and free users’ time so they can focus on higher‑level tasks. The announcement was posted on September 29 2026 by CEO Sam Altman via Tw

industrysam-altman--x

Applications

Dots will be available in ChatGPT on web, mobile, and desktop across Pro, Business Premium, and Enterprise users in eligible markets. To get…

Dots will be available in ChatGPT on web, mobile, and desktop across Pro, Business Premium, and Enterprise users in eligible markets. To get started, create your first dot in the ChatGPT desktop app o

applicationsopenai--x

Double-Edged Sword of Mediated Visibility: How Visual Framing Undermines Congresswomen's Perceived Competence

arXiv:2609.31970v1 Announce Type: new Abstract: Women's presence in Congress has stalled below 30%. That institutional underrepresentation is mirrored by their limited visibility on televised news, wh

Tutorials

Downstream-Aware Context Selection for Online In-Context Reinforcement Learning

arXiv:2609.33166v1 Announce Type: new Abstract: In-context reinforcement learning (ICRL) enables large language model agents to adapt to new environments using their interaction history without updati

tutorialsarxiv-cs-lg

DP-Rec: Towards Dynamic Patching for Efficient Long-Sequence Recommendation

arXiv:2609.32215v1 Announce Type: new Abstract: Transformers have redefined sequential recommendation by effectively modeling dynamic user behaviors and long-range dependencies. However, they remain i

DPAMixerSR: An Efficient Degradation-Pattern-Aware Model for Image Super-Resolution

arXiv:2609.32705v1 Announce Type: new Abstract: While content-adaptive schemes have delivered notable advances in image super-resolution (SR), existing approaches typically focus on texture complexity

Agents

Dr. MAS: Stable Reinforcement Learning for Multi-Agent LLM Systems

arXiv:2602.08847v2 Announce Type: replace Abstract: Multi-agent LLM systems enable advanced reasoning and tool use via role specialization, yet reliable reinforcement learning (RL) post-training for s

agentsarxiv-cs-lg

DR-net-Mamba: Selective State-Space Modeling for Long-Range ECG Time-Series Denoising

arXiv:2609.35634v1 Announce Type: new Abstract: Electrocardiogram (ECG) recordings are corrupted by non-stationary noise sources that degrade diagnostic reliability, particularly in ambulatory and lon

DraftAttention2: Fast Video Diffusion with Low-Resolution-Guided Mixed-Precision Attention

arXiv:2609.32628v1 Announce Type: new Abstract: Video generation has broad applications in content creation and entertainment. Diffusion transformers have advanced the quality of generated videos, but

DRAM: Delta-rule Recurrent Associative Memory for Robot Manipulation Policies

arXiv:2609.32453v1 Announce Type: new Abstract: Robotic manipulation is inherently history-dependent, yet most pretrained robotic policies condition on only the current observation or a short temporal

safetyarxiv-cs-ro

DrawingsDreamer: A Unified Multi-View Engineering Drawings Generation Model

arXiv:2609.35242v1 Announce Type: new Abstract: Scalable Vector Graphics (SVG) are essential for modern industrial Computer-Aided Design (CAD). However, existing autoregressive SVG generation models a

local-aiarxiv-cs-cv

Hardware

dream job token maxxing till open source wins

dream job token maxxing till open source wins getting acquired by @nvidia = hugging face can now hire people we couldn't as a small startup and give them a decade to make open-source AI win! if you're

hardwareclem-delangue--x

DreamingGoose: Staged Distillation from Autoregressive Transformers to Bidirectional Recurrent Diffusion Language Models

arXiv:2609.34253v1 Announce Type: new Abstract: Pretrained autoregressive Transformers represent a large sunk investment in compute. Existing conversion methods reuse that investment by changing eithe

DRIFT: Data Selection for LLM Instruction Tuning via On-Policy Attribution

arXiv:2606.18307v2 Announce Type: replace Abstract: Data selection for instruction tuning is usually studied for efficiency, reaching strong performance with a fraction of the training data. We ask wh

safetyarxiv-cs-lg

DRIFT: Disentangled Responsive-Invariant Flow Transport for Single-Cell Perturbation Prediction

arXiv:2609.35106v1 Announce Type: new Abstract: Predicting cellular responses to perturbations is a central problem in cellular biology, with broad applications in systems biology and drug discovery.

DroneWAM: Efficient World Action Model for Drone Visual Navigation

model-releasesarxiv-cs-cv

arXiv:2609.33148v1 Announce Type: new Abstract: World-action models give visual navigation agents a way to anticipate how candidate actions will change future observations and to act from the predicte

Drug-Target Interaction Prediction via Hierarchical Sequential Cross-Attention over Chemical and Protein Language Models

arXiv:2609.34921v1 Announce Type: new Abstract: Predicting Drug-Target Interactions~(DTIs) is a central task in computational drug discovery, with direct applications in virtual screening, drug repurp

DS-VLA: A Dendritic-inspired Vision-Language-Action Model for Robust Action Control

arXiv:2609.32253v1 Announce Type: new Abstract: Vision-language-action (VLA) models have achieved strong performance in language-conditioned manipulation, yet success under nominal evaluation does not

researcharxiv-cs-ro

DuoOPD: Learning from Joint Teacher-Student Outcomes for Multi-Task On-Policy Distillation

arXiv:2609.33711v1 Announce Type: new Abstract: On-policy distillation (OPD) trains a student on its own responses with token-level feedback from a stronger teacher, yet the teacher can fail on questi

Dynamic Buffers: Cost-Efficient Planning for Tabletop Rearrangement with Stacking

arXiv:2509.22828v2 Announce Type: replace Abstract: Rearranging objects in cluttered tabletop environments remains a long-standing challenge in robotics. Classical planners often generate inefficient,

researcharxiv-cs-ro

Dynamic Image Prompt Adapter for Scalable Zero-shot Personalized Text-to-Image Generation

arXiv:2512.09814v2 Announce Type: replace Abstract: Personalized Text-to-Image (PT2I) generation aims to produce customized images based on reference images. A prominent interest pertains to the integ

Dynamic Kuramoto-Hodge Operators for PDEs on Complex Geometries and Topologies

arXiv:2609.33693v1 Announce Type: new Abstract: Learning PDE operators on complex domains requires capturing interactions among fields on vertices, edges, and faces, alongside global responses shaped

Dynamic Manipulation with World-Action Models via Counterfactual Planning

arXiv:2609.33172v1 Announce Type: cross Abstract: World-Action models (WAMs) trained on static demonstrations often fail to manipulate moving targets even when they possess the required manipulation s

safetyarxiv-cs-cv

Dynamic Model Identification and Gravity Compensation for the dVRK-Si Patient Side Manipulator

arXiv:2603.12099v2 Announce Type: replace Abstract: The da Vinci Research Kit (dVRK) has been widely used for research in robot-assisted surgery over the past decade, and its latest generation, the dV

researcharxiv-cs-ro

DynamicDx: Evaluating Evidence Acquisition in Video-Based Diagnosis

arXiv:2609.32957v1 Announce Type: new Abstract: Diagnosing a patient from video requires more than recognizing the sign: a vision-language model must turn what it sees into hypotheses, questions and t

DynaTokens: Teaching Dynamics to Camera-Controlled Video Models at Test Time

arXiv:2609.35704v1 Announce Type: new Abstract: Video generation must account for two sources of motion, one induced by the observer's camera path and the other caused by scene dynamics. An ideal came

local-aiarxiv-cs-cv

DynGraphAgentBench: A Benchmark for Agentic Lifecycle Control in Dynamic Graph Anomaly Detection

arXiv:2609.33980v1 Announce Type: new Abstract: Dynamic graph anomaly detection requires repeated decisions as graph structure and class prevalence drift, yet detector benchmarks usually score a fixed

Applications

E-CONAN (Entailment, CONtradition And Neutral) Diagnostics Dataset Investigating Linguistic Phenomena in Arabic Natural Language Understanding

arXiv:2609.33530v1 Announce Type: cross Abstract: Natural Language Understanding (NLU) plays a crucial role in various applications, yet its performance suffers from weaknesses in handling the complex

applicationsarxiv-cs-lg