Search: “models”

Mitigating Object Hallucination in Large Vision-Language Models via False Discovery Controlled Visual Data Splitting

arXiv:2609.38979v1 Announce Type: new Abstract: Multiple object hallucination, where large vision-language models (LVLMs) generate objects not supported by the visual input, is a persistent challenge

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Research

Raw-Routed Mixture of Adapters: A Causal Intervention for Routing Collapse in Time Series Foundation Models

DGX agent

arXiv:2609.39445v1 Announce Type: new Abstract: Time series foundation models (TSFMs) commonly adapt to new data by attaching a single trainable head to a frozen backbone, a one-size-fits-all setup th

researcharxiv-cs-lg

1 Oct 2026

ReWAM: Reciprocal World Action Models for Interactive Autonomous Driving

arXiv:2609.39245v1 Announce Type: new Abstract: In interactive scenarios, an autonomous driving system is required to generate ego actions under the influence of other agents' behaviors. Existing Worl

safetyarxiv-cs-ro

RoboCoach: World Models as Active Coaches for Compositional Robot Skills

arXiv:2609.39685v1 Announce Type: cross Abstract: Long-horizon robot manipulation reuses skills across many task compositions, but improving these compositions with additional end-to-end demonstration

safetyarxiv-cs-ai

Semantic Misalignment in Vision-Language Models under Perceptual Degradation

arXiv:2601.08355v3 Announce Type: replace Abstract: Vision-Language Models (VLMs) are increasingly deployed in autonomous driving and embodied AI systems, where reliable perception is critical for saf

safetyarxiv-cs-cv

Smaller Models, Better Rejects: Preference Distillation Scaling

arXiv:2609.38987v1 Announce Type: new Abstract: Preference distillation typically treats a teacher response as preferred and the student's own response as rejected. This assumes that self-generated fa

Steepest Guidance: A Practical and Principled Approach to Inference-Time Alignment of Flow and Diffusion-based Models

arXiv:2609.39091v1 Announce Type: cross Abstract: Inference-time alignment of flow and diffusion-based models is critical for achieving flexible generative modeling. Theoretically, Doob's h-transform

The System Prompt Illusion: How Instruction Preambles Modify Computation in Language Models

arXiv:2609.38205v1 Announce Type: new Abstract: System prompts are the primary lever practitioners use to control language model behavior, yet what they actually do to the computation inside the trans

safetyarxiv-cs-cl

Which Models Work Well Together? Measuring Heterogeneity for LLM Team Selection

arXiv:2609.38274v1 Announce Type: new Abstract: The performance ceiling of an LLM team is constrained not only by individual model capabilities, but also by inter-member error resonance and predictive

researcharxiv-cs-cl

Beyond Conditional Independence: Root Cause Analysis with Deep Causal Models

arXiv:2609.36771v1 Announce Type: cross Abstract: Root cause analysis (RCA) is a critical problem in many real-world scenarios. RCA enables the identification of faulty or failing mechanisms in a syst

applicationsarxiv-cs-ai

Beyond Rule-Based Mutation Testing: Test-Aware Mutant Generation Using Large Language Models

model-releasesarxiv-cs-ai

arXiv:2609.35841v1 Announce Type: cross Abstract: Mutation testing evaluates test-suite adequacy by injecting synthetic faults into program code. However, traditional rule-based tools often generate l

Tutorials

Bilinear World Models: Learning Representations with Structured Dynamics for Efficient Control

arXiv:2609.36305v1 Announce Type: new Abstract: World models jointly learn latent representations and dynamics that predict how high-dimensional observations evolve under actions. In this work, we pro

tutorialsarxiv-cs-ro

Agents

Distilling Agentic Systems: A Roadmap across Models, Artifacts, and Harnesses

arXiv:2609.36630v1 Announce Type: new Abstract: Modern agents increasingly rely on memories, tools, and execution logic, so their competence extends beyond model parameters. This shift exposes a limit

agentsarxiv-cs-ai

Fundamental Limits of Transferability and Equivariance in Algebraic Signal Models I: Finite Dimensions

arXiv:2609.36106v1 Announce Type: cross Abstract: We study the fundamental limits of transferability in algebraic signal processing through homomorphisms between algebraic signal models. Homomorphisms

Locating Answer-Correctness Signals in Frozen Large Language Models

arXiv:2609.37700v1 Announce Type: new Abstract: Language models expose internal signals that predict whether an answer is correct, readable from a single forward pass of a frozen model without additio

MotorMind: Scaffolding General Vision Language Models for Zero-Shot Robot Manipulation

arXiv:2609.38078v1 Announce Type: new Abstract: Vision-language-action (VLA) models have advanced robotic manipulation, but their zero-shot generalization in new tasks and environments remains limited

safetyarxiv-cs-ro

MultiTalk: Scaling Full-Duplex Speech Models to Long, Multi-Party, Bilingual Conversation

arXiv:2609.36903v1 Announce Type: cross Abstract: End-to-end full-duplex speech models have brought open-source machine conversation closer to human-like interaction, yet existing systems remain limit

applicationsarxiv-cs-ai

Mutually Adversarial Self-Training with Evolving Data for Unified Multimodal Models

arXiv:2609.36224v1 Announce Type: cross Abstract: Unified multimodal models (UMMs) combine image generation and visual understanding in a shared backbone. Since generation and understanding are invers

On Calibration of Large Language Models: From Response To Capability

arXiv:2602.13540v2 Announce Type: replace-cross Abstract: Accurate confidence estimation is critical for reliable use of large language models (LLMs). Prior work on LLM calibration largely focuses on

Local Ai

On Trajectory-Aware Training for Masked Diffusion Language Models

arXiv:2609.37974v1 Announce Type: cross Abstract: Masked diffusion models (MDMs) generate text by unmasking several tokens per step, but they are trained and sampled under different conditions. The mo

local-aiarxiv-cs-ai

PE-OPSD: Internalizing Prompt Enhancement into Flow-matching Models via On-Policy Self-Distillation

arXiv:2609.36638v1 Announce Type: cross Abstract: Text-to-image users often provide concise and underspecified prompts, whereas generative models benefit from detailed textual conditions for reliable

safetyarxiv-cs-cv

ProgressCompass: Embodied Progress Reward Models Are Lost Without the Right Context

model-releasesarxiv-cs-cl

arXiv:2609.36684v1 Announce Type: new Abstract: Embodied agents now take on ever longer tasks. For long tasks, knowing only whether a task finally succeeds or fails says little; the steps along the wa

Reprogramming Vision-Language Models via Structured Prompt Reparameterization

arXiv:2609.36680v1 Announce Type: new Abstract: Visual reprogramming adapts pretrained models to downstream tasks by modifying their input and output interfaces while keeping the backbone fixed. In vi

SRJudge: Empowering Large Language Models with Selective Reasoning for Fine-Grained Knowledge Concept Tagging

model-releasesarxiv-cs-ai

arXiv:2609.36982v1 Announce Type: cross Abstract: Knowledge concept tagging aims to assign specific concept or topic labels to educational content, which is essential for both educators and learners i

The Detectability Gap: Hidden Heterogeneity in Hallucination Detection Across Language Models

arXiv:2609.35860v1 Announce Type: cross Abstract: Sampling based consistency is widely used for hallucination detection, yet aggregate performance can conceal systematic differences in which errors ar

Train Ahead, Distill Back: Bootstrapping On-Policy Self-Distillation for Large Language Models

arXiv:2609.37132v1 Announce Type: new Abstract: On-policy self-distillation (OPSD) improves large language models by letting a self-teacher with privileged information provide dense token-level superv

safetyarxiv-cs-ai

3D Point Tracking with State Space Models

model-releasesarxiv-cs-cv

arXiv:2609.34035v1 Announce Type: new Abstract: Tracking any point of a dynamic scene in metric 3D - in absolute meters, not up to an unknown scale - underpins 3D and 4D reconstruction, robot navigati

Agents

Communication between Frozen Large Language Models via Prompt Optimization in a Referential Game

arXiv:2609.31989v1 Announce Type: cross Abstract: We study communication between two frozen large language models from different providers, with different tokenizers, accessed through their API endpoi

agentsarxiv-cs-lg

Counting on Thinking: Tracing Evidence Integration in Language Models

arXiv:2609.32932v1 Announce Type: new Abstract: Finite computational resources force a tradeoff between automatic System 1 processes and costly System 2 thinking. Large language models (LLMs) can spen

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

Integrating Language Models into Listened and Imagined Speech Decoding from MEG

arXiv:2609.31997v1 Announce Type: new Abstract: Decoding imagined speech is an important goal for brain-computer interfaces but remains challenging due to weak neural responses, low signal-to-noise ra

Learning to Generate Rigid Body Interactions with Video Diffusion Models

arXiv:2510.02284v4 Announce Type: replace Abstract: Recent video generation models have achieved remarkable progress and are now deployed in film, social media production, and advertising. Beyond thei

applicationsarxiv-cs-cv

LLM4Trust: Exploring the Capabilities of Large Language Models for Trust Evaluation

model-releasesarxiv-cs-lg

arXiv:2609.33521v1 Announce Type: new Abstract: Trust evaluation plays a critical role in cybersecurity by supporting risk mitigation and decision-making. A variety of trust evaluation methods have be

Local Ai

Output-aware Residual Stream Pruning for Large Language Models

arXiv:2609.35579v1 Announce Type: new Abstract: Residual stream pruning methods reduce inference cost by shrinking the model's hidden dimension, but existing approaches typically choose these dimensio

local-aiarxiv-cs-lg

PDMD: Projected Distribution Matching Distillation for Video Diffusion Models

arXiv:2609.35768v1 Announce Type: new Abstract: Modern video diffusion models require tens of denoising evaluations over long spatiotemporal token sequences. Distribution Matching Distillation (DMD) r

Local Ai

PhysioTRACE: Provenance-Aware Stress Tests for Physiological Foundation Models

arXiv:2609.34466v1 Announce Type: new Abstract: Physiological foundation models encode how a signal was recorded alongside the physiology it reflects. When recording conditions are associated with dia

local-aiarxiv-cs-lg

Posterior Regimes and Latent Deception: Variational Bayesian Inference in Hidden Markov Models for Sequential Fraud Detection in Financial Transactions

arXiv:2609.34031v1 Announce Type: new Abstract: We present a three-tier progression of Hidden Markov Models: maximum-likelihood (Baum-Welch), variational Bayesian (VBEM), and a neural variational exte

Quantile Head for Vision-Language-Action Models

arXiv:2609.34061v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models integrate pretrained Vision-Language Models (VLMs) with action heads for robot control. Common action heads have d

safetyarxiv-cs-cv

Hardware

Scope-WM: Scoped Computation for Efficient Visual World Models

arXiv:2609.33218v1 Announce Type: new Abstract: Visual world models enable robotic planning by predicting future observations, but dense latent-state propagation and sample-intensive trajectory optimi

hardwarearxiv-cs-cv

Agents

SegBanana: Steering Unified Multimodal Models into Medical Segmenters

arXiv:2609.34235v1 Announce Type: new Abstract: Medical image segmentation remains challenging in practical deployment, as models often struggle to generalize beyond the distributions covered by their

agentsarxiv-cs-cv

SIFT: Enhancing Time Series Foundation Models via Semantic Invariance and Structural Fidelity Fine-Tuning

arXiv:2609.32676v1 Announce Type: new Abstract: Time Series Foundation Models (TSFMs) have achieved remarkable zero-shot performance through extensive pre-training on massive time series datasets. Nev

applicationsarxiv-cs-lg

SolveEdit: Benchmarking Visual Problem Solving in Generative Models

model-releasesarxiv-cs-cv

arXiv:2609.35504v1 Announce Type: new Abstract: Machine intelligence is often evaluated through abstract reasoning problems, yet many real-world problems are visual, such as arranging objects, repairi

STeP: Signal Temporal Logic for Precise Specifications for Action Generation with Vision Language Models

model-releasesarxiv-cs-ro

arXiv:2607.18580v2 Announce Type: replace Abstract: Natural-language robot instructions often specify more than a coarse task goal: they may impose spatial, temporal, and logical requirements that mus

Still There, No Longer Seen: Exposing Compression-Induced Risk in Large Vision-Language Models

arXiv:2609.35002v1 Announce Type: new Abstract: Visual token compression reduces the inference cost of Large Vision-Language Models (LVLMs). However, aggregate robustness measures do not reveal whethe

TANGO: Watermarking Masked Diffusion Language Models in Token Pairs

arXiv:2609.35224v1 Announce Type: new Abstract: Masked-diffusion language models fill in masked positions in parallel and in no fixed order. Most practical text watermarks assume left-to-right generat

Twist, Don't Tilt: Trajectory-Exact Constrained Decoding for Masked Diffusion Models

arXiv:2609.35609v1 Announce Type: new Abstract: Constrained decoding for Masked Diffusion Language Models (MDLMs) aims to ensure that generated outputs satisfy a specified structure or syntax constrai

Diagnosing the Sources of Compositional Failure in Vision-Language Models: A Controlled Analysis

arXiv:2609.31456v1 Announce Type: new Abstract: Vision-language models (VLMs) often struggle with compositional reasoning tasks, but the reasons for this underperformance remain unclear. A common hypo

28 Sept 2026

Does Understanding Inform Generation in Unified Multimodal Models? From Analysis to Path Forward