Search: “models”

Motion Concept Unlearning in Video Diffusion Models

arXiv:2609.36832v1 Announce Type: new Abstract: Text-to-video (T2V) diffusion models can generate realistic depictions of actions such as kicking, stabbing, and shooting, raising safety concerns that

One Geometry, Different Outcomes: Readout-Dependent Effects of the Modality Gap in Vision-Language Models

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DGX agent

arXiv:2609.36101v1 Announce Type: cross Abstract: Contrastive vision-language models learn shared embedding spaces by aligning matched image-text pairs, yet their representations remain separated by a

safetyarxiv-cs-ai

30 Sept 2026

Towards Mitigating Deceptive Safety Alignment in Large Reasoning Models

arXiv:2609.36254v1 Announce Type: new Abstract: Large Reasoning Models (LRMs) are commonly trained with reinforcement learning (RL) to improve their generation of chain-of-thought (CoT) reasoning befo

safetyarxiv-cs-ai

Tracing mechanisms of sycophantic agreement in language models

arXiv:2609.35822v1 Announce Type: new Abstract: Sycophantic agreement in language models refers to the tendency to overly affirm a user's stated beliefs or preferences, often at the expense of factual

safetyarxiv-cs-cl

Tools

We built a new way to train contextual embedding models, which encode each chunk of a document with the whole document in view. pplx-embed-v…

We built a new way to train contextual embedding models, which encode each chunk of a document with the whole document in view. pplx-embed-v2-context-9b-preview sets a new state of the art on ConTEB a

toolsperplexity--x

Tutorials

Why Cross-Skeleton Retargeting Is Non-Identifiable: Structural Limits of Generative Motion Models

arXiv:2609.37297v1 Announce Type: new Abstract: Cross-skeleton motion generation trains generative models to carry action structure and motion intention from one body to another. Yet a target motion t

tutorialsarxiv-cs-cv

A Continuous-Time Reinforcement Learning Framework for Fine-Tuning Discrete Diffusion Models

arXiv:2607.14522v2 Announce Type: replace Abstract: We formulate reinforcement learning (RL) in continuous time with discrete state spaces and possibly arbitrary action spaces via a stochastic control

safetyarxiv-cs-lg

Beyond Reconstruction Loss in Post-Training Quantization: Balanced Fitting for Large Vision-Language Models

arXiv:2609.34765v1 Announce Type: new Abstract: Post-training quantization (PTQ) enables efficient deployment of large vision-language models (LVLMs), but is typically calibrated on a small set while

Beyond Saying Less: Fine-Grained Alignment for Informative and Faithful Vision-Language Models

arXiv:2609.35294v1 Announce Type: new Abstract: Object hallucination remains a major challenge for large vision-language models. While off-policy preference optimization proves to be an effective solu

CapField-OPD: Learning Continuous Capability Fields via Joint-Anchored Multi-Teacher On-Policy Distillation for Flow Models

arXiv:2609.34658v1 Announce Type: new Abstract: Reward-specialized post-training produces strong experts for flow-based generative models, while multi-teacher on-policy distillation (OPD) consolidates

Certifying Interventional Agreement Among Observationally Equivalent Causal Models

arXiv:2609.32247v1 Announce Type: cross Abstract: Observationally equivalent causal models can still disagree about what happens under intervention, because interventions create inputs that never occu

Climbing the Hill: Prompt Injection Red-Teaming Against Frontier Models with Curriculum Reinforcement Learning

model-releasesarxiv-cs-lg

arXiv:2609.33628v1 Announce Type: new Abstract: Prompt injection is a leading security risk for LLMs and LLM-based applications such as agents. State-of-the-art red-teaming methods for prompt injectio

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

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

Local Ai

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

From Static to Dynamic: On-Policy Distillation from Image to Video Diffusion Models

arXiv:2609.34371v1 Announce Type: new Abstract: On-policy distillation (OPD) specializes pretrained video diffusion models through teacher supervision along the student's own generation trajectory. Al

Geometric Encoding for Spatial Reasoning in Vision-Language Models

arXiv:2609.34148v1 Announce Type: new Abstract: Vision-Language Models (VLMs) are far more reliable at recognizing what appears in a video than at reasoning about its spatial and temporal properties,

Grounding Vision-Language Models in Driving Semantics: A Multi-Dataset Predicate Framework for Explainable Reasoning

arXiv:2609.31636v1 Announce Type: cross Abstract: Vision-language models are increasingly used for driving-scene understanding, yet the semantic relations expressed in their outputs are often difficul

Hamiltonian JEPA: Action-Conditioned World Models with an Inherited Control State

model-releasesarxiv-cs-lg

arXiv:2609.33497v1 Announce Type: new Abstract: Planning from pixels needs more than a latent space that is stable and predictable. The state the planner scores must also be organized by how actions m

Tutorials

Hierarchical Response Preservation for Continual Adaptation of Zero-Shot Graph-Text Models

arXiv:2609.33607v1 Announce Type: new Abstract: Pretrained graph-text models align graph representations with textual semantics, enabling recognition of unseen classes and transfer across graph domain

tutorialsarxiv-cs-lg

How to Tame a Multi-Headed Hydra? Adaptive Multi-Category Safety Steering for Large Language Models

arXiv:2609.34514v1 Announce Type: cross Abstract: As large language models (LLMs) become increasingly widespread, preventing unsafe responses to harmful prompts is essential for their safe deployment.

safetyarxiv-cs-lg

Local Ai

Instance-Adaptive Prompts as Context for Time-Series Foundation Models

arXiv:2609.34786v1 Announce Type: new Abstract: Longer histories can improve time-series foundation models (TSFMs), but require substantially higher inference cost. We therefore ask whether contextual

local-aiarxiv-cs-lg

Agents

Kafila: Serving Large Language Models on a Trusted Set of Heterogeneous Commodity Machines

arXiv:2609.34045v1 Announce Type: cross Abstract: Between them, the members of a research group or a circle of friends own several consumer computers, none large enough to run a capable large language

agentsarxiv-cs-lg

Learning What to Recall: Adaptive Multi-Cue Episodic Memory for World Models

arXiv:2609.34677v1 Announce Type: cross Abstract: World models predict future observations from current experience and actions, yet prediction can depend on observations seen far in the past. Episodic

LFPO: Likelihood-Free Policy Optimization for Masked Diffusion Models

arXiv:2603.01563v2 Announce Type: replace Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) has achieved remarkable success in improving autoregressive models, especially in domains requ

safetyarxiv-cs-lg

M3-Score: Fidelity, Memorization and Coverage as Separate Axes for Evaluating Generative Radiology Image Models

arXiv:2609.33769v1 Announce Type: new Abstract: Quantitative evaluation of generative models for radiology remains challenging. Clinically relevant structures are often small and infrequent, feature s

On Temporal Binding in Large Audio Language Models

arXiv:2609.34806v1 Announce Type: cross Abstract: Reasoning about temporal structure of audio recordings requires Large Audio Language Models (LALMs) to associate sound events with their temporal posi

Industry

OpenAI apologizes for its AI models breaching Australian government websites, pledges cyber defense funding, and plans to form a task force as part of reforms (Bloomberg)

Bloomberg: OpenAI apologizes for its AI models breaching Australian government websites, pledges cyber defense funding, and plans to form a task force as part of reforms — OpenAI on Tuesday apologized

industrytechmeme

Readout is not Recovery: Dissociating Coordinate Emission from Visual-Corruption Repair in Vision-Language Models

model-releasesarxiv-cs-cv

arXiv:2609.32757v1 Announce Type: cross Abstract: VLM bounding-box localization is both language generation and spatial commitment. Parseable fields such as bbox_2d make localization easy to score, bu

Sam Altman says OpenAI won’t go public until its models are safe

For months, people have wondered when OpenAI will go public. CEO Sam Altman says it won't happen until the company can make better promises about model safety, with no firm timeline in sight. 'We inte

safetythe-verge-ai

SyncEdit: Rethinking Lip Synchronization as Editing with Audio-Driven Diffusion Models

arXiv:2603.09084v2 Announce Type: replace Abstract: Lip synchronization refers to the task of modifying facial lip movements such that they are temporally aligned with a given audio signal. It is a fu

The Temporal Tug-of-War: Visualizing and Detecting RAG Conflicts in Diffusion Models via Trajectory Variance

arXiv:2609.31684v1 Announce Type: cross Abstract: Retrieval-Augmented Generation (RAG) introduces a specific failure mode in discrete diffusion language models: when retrieved context contradicts para

Toward Comprehensive 3D Grounding: Orientation Grounding through Vision-Language Models

arXiv:2609.33109v1 Announce Type: new Abstract: Grounding is a core capability of spatial vision-language models, yet most existing work focuses only on where a referred object is. Many 3D tasks also

Agents

We let open models access the outside world during evals on tasks from public benchmarks. Here's what happened: Most went and found upstream…

We let open models access the outside world during evals on tasks from public benchmarks. Here's what happened: Most went and found upstream commits containing fixes for the tasks they were supposed t

agentsai21-labs--x

When Is an SAE Feature Interpretable? A Validation Ladder for EEG Foundation Models

arXiv:2609.34091v1 Announce Type: new Abstract: Sparse autoencoders (SAEs) decompose dense model activations into discrete latents, making individual features easy to interpret--and easy to misinterpr

Local Ai

DyMD: Preserving Interaction Dynamics through Distribution Matching Distillation in Few-Step Video World Models

arXiv:2609.31349v1 Announce Type: cross Abstract: Large video diffusion models offer expressive priors for embodied prediction and learning, yet their many-step sampling remains costly for interactive

local-aiarxiv-cs-ai

Symbiotic Architecture for Post-Hoc Audio Extension of Frozen Language Models

arXiv:2609.30784v1 Announce Type: cross Abstract: This paper proposes an architecture for equipping large language models (LLMs) with audio-understanding capabilities without fine-tuning their weights

researcharxiv-cs-cl

Applications

Teacher-Anchored Selection of Post-Training Quantized Models under Domain Shift

arXiv:2609.31155v1 Announce Type: cross Abstract: Compressing a trained model yields a family of deployment candidates, and under domain shift the most compressed one need not be the one to deploy. We

applicationsarxiv-cs-ai

Testing the Utility of Using Large Language Models to Create Personalized Networks From Therapy Session Transcripts: A Proof of Concept Study

arXiv:2512.05836v2 Announce Type: replace Abstract: Recent advances in psychotherapy have focused on treatment personalization, such as by selecting treatment modules based on individual networks. How

researcharxiv-cs-ai

an implicit concession that they cannot control their models.

an implicit concession that they cannot control their models. Notable new disclosures from OpenAI that shouldn't get lost in all the other news. Of particular interest (and seems sensible!): '~all inf

safetygary-marcus--x

26 Sept 2026

Controlling Backchannels in Streamable Full-duplex Models

arXiv:2609.29418v1 Announce Type: new Abstract: Backchannels, brief acknowledgements like 'uh-huh' produced while the other party may still be talking, are central to natural conversation, but full-du

researcharxiv-cs-cl

FB-GDM: Fully-Bayesian Guided Diffusion Models for High-Dimensional Linear Inverse Problems via Unsupervised Variational Inference

arXiv:2609.29216v1 Announce Type: cross Abstract: Diffusion models are powerful priors for linear inverse problems, but the reference guidance methods, Diffusion Posterior Sampling (DPS) and Pseudoinv

researcharxiv-cs-ai

Applications

Learned Cross-Task Relationships in Multi-Task Models

arXiv:2609.28776v1 Announce Type: new Abstract: We propose a framework that learns cross-task relationships in multi-task models by approximating the joint distribution of task labels through targeted

applicationsarxiv-cs-ai

SGA: Uncertainty Quantification for Multi-Step Forecasting in Time Series Foundation Models

arXiv:2609.28582v1 Announce Type: cross Abstract: The recent emergence of Time Series Foundation Models (TSFMs) has significantly advanced multi-step forecasting performance, enabling accurate predict

safetyarxiv-cs-ai

Unraveling the cognitive patterns of Large Language Models through module communities

arXiv:2508.18192v2 Announce Type: replace Abstract: Large Language Models (LLMs) have reshaped our world with significant advancements in science, engineering, and society through applications ranging

researcharxiv-cs-ai

A Decade of Climate Polarization on Brazilian YouTube using Language Models

model-releasesarxiv-cs-cl

arXiv:2609.27811v1 Announce Type: cross Abstract: Online platforms have become arenas for the public contestation of climate change, shaping how scientific knowledge, denial, and uncertainty are expre

24 Sept 2026

Tutorials

A Native-Reference Coordinate Geometry for L2 Pronunciation Deviation Using Self-Supervised Speech Models

arXiv:2609.28060v1 Announce Type: new Abstract: Self-supervised speech models encode rich phonetic information, but it remains unclear how to transform this information into interpretable metrics for

tutorialsarxiv-cs-cl

24 Sept 2026

Attention Routing Stabilizes Early: Working-Set Inference for Recurrent Language Models