Search: “models”

Compiling Learning Problems into Adaptation Programs for Language Models

arXiv:2609.37371v1 Announce Type: cross Abstract: Model adaptation is typically governed by a fixed recipe, even though different update programs can produce substantially different behavioral outcome

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Model Releases

Diagnosing and Improving Probabilistic Reasoning in Large Language Models

DGX agent

arXiv:2609.38005v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly proposed as decision assistants who must reason probabilistically from available evidence under explicit d

model-releasesarxiv-cs-ai

30 Sept 2026

Evaluating the Evaluators: Diagnosing Large Multimodal Models for AI-Generated Image Assessment

arXiv:2609.37576v1 Announce Type: new Abstract: With the rapid advancement of text-to-image (T2I) generation, robust evaluation becomes critical yet challenging, as traditional metrics fail to capture

Applications

Foresight at the Event Boundary: Evaluating Physical Prediction in Video World Models

arXiv:2609.36531v1 Announce Type: new Abstract: Video world models are largely regarded as predictive models of the physical world and are therefore expected to anticipate the consequences of observed

applicationsarxiv-cs-cv

IESR:Efficient MCTS-Based Modular Reasoning for Text-to-SQL with Large Language Models

model-releasesarxiv-cs-cl

arXiv:2602.05385v2 Announce Type: replace Abstract: Text-to-SQL is a key natural language processing task that maps natural language questions to SQL queries, enabling intuitive interaction with web-b

Latent Inference-Time Guidance of Time Series Foundation Models

arXiv:2609.38058v1 Announce Type: cross Abstract: Time Series Foundation Models (TSFMs) currently provide state-of-the-art results in forecasting tasks. They are available out-of-the-box and rely on i

PolyOCR-Venus: Unified OCR Foundation Models for Text-Centric Visual Intelligence

arXiv:2609.37712v1 Announce Type: cross Abstract: Optical Character Recognition (OCR) is evolving from plain-text transcription toward general visual intelligence, requiring models to recognize, local

Rethinking Soft Tokens for Parallel Decoding in Diffusion Language Models

arXiv:2609.37391v1 Announce Type: new Abstract: Diffusion language models (DLMs) enable parallel generation by predicting and committing multiple tokens at each denoising step, yet they can generate i

Local Ai

The Commit-Abstain Circuit: Why Language Models Hallucinate Instead of Abstaining

arXiv:2609.32964v2 Announce Type: replace Abstract: Language models (LMs) often hallucinate by committing to confident answers rather than abstaining, even when they do not have enough information to

local-aiarxiv-cs-ai

Agents

VACE: Validation-Gated Alternating Co-Evolution of Agent Models and Harnesses

arXiv:2609.37105v1 Announce Type: cross Abstract: Language model agents can be improved by updating their model weights or refining the harness that guides task execution. These components are coupled

agentsarxiv-cs-ai

A Survey on Efficient Vision-Language-Action Models

arXiv:2510.24795v3 Announce Type: replace Abstract: Vision-Language-Action models (VLAs) represent a significant frontier in embodied intelligence, aiming to bridge digital knowledge with physical-wor

researcharxiv-cs-cv

Adapting Vision-Language Models for Human-Readable XAI in Industrial Object Detection

arXiv:2609.31690v1 Announce Type: new Abstract: Explainable Artificial Intelligence (XAI) solutions are essential for building trust in AI technologies and their integration in real manufacturing line

Applications

AnyStep-WAM: Budget-Aligned Distillation and Adaptive Inference for World Action Models

arXiv:2609.33748v1 Announce Type: new Abstract: World-action models (WAMs) couple predictive visual modeling with action generation, typically relying on iterative denoising with a fixed denoising ste

applicationsarxiv-cs-ro

Behavioral Monitoring of JEPA World Models with Jacobian Centroids

arXiv:2609.33940v1 Announce Type: new Abstract: Detecting failures in World Model (WM)-based planning requires monitoring whether the model is behaviorally aligned with the current task, which in turn

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

Local Ai

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

local-aiarxiv-cs-lg

Enhancing Foundation Models for Imbalanced SAR Ship Classification via Targeted Oversampling

arXiv:2609.31657v1 Announce Type: new Abstract: Remote-sensing foundation models offer strong representations for SAR imagery, but their behavior under severe long-tail class imbalance is still not we

From Knowing to Abstaining: Bridging the Representation-Action Gap in Vision-Language Models

arXiv:2609.32653v1 Announce Type: cross Abstract: The ability of vision-language models (VLMs) to abstain from unanswerable questions is as important as their ability to answer answerable ones accurat

Gradient-Guided Decoupled Adaptation for Geospatial Vision-Language Models

arXiv:2609.32737v1 Announce Type: new Abstract: Existing geospatial vision-language models (Geo-VLMs) typically optimize diverse geospatial tasks through a unified multi-task adaptation paradigm witho

GUI-GenBench: Evaluating Image Generation Models as Interactive GUI Environments

arXiv:2602.09007v3 Announce Type: replace-cross Abstract: An image generation model used as a graphical user interface (GUI) environment must produce not only a plausible screen but also the correct r

Large Language Models for Automated Cross-Domain Machine Learning Task Type Identification: A Benchmark Dataset and Evaluation

arXiv:2609.35335v1 Announce Type: new Abstract: Machine learning task type identification is essential for constructing valid ML pipelines, yet in practice it is typically specified manually. We inves

Learning to Steer, Steering to See: Unveiling the Geometry of RLVR in Large Language Models via Trainable Vectors

arXiv:2609.34344v1 Announce Type: new Abstract: Reinforcement learning (RL) has become a key paradigm for enhancing the reasoning of large language models, yet the high dimensionality of parameter upd

MISHAP-Bench: A Hallucination Benchmark for Large Audio-Language Models

arXiv:2609.33893v1 Announce Type: new Abstract: Large audio-language models (LALMs) produce fluent responses about audio but often hallucinate by making plausible yet ungrounded claims. Existing audio

Safety

PruneForget: Joint Unlearning and Pruning of Vision Models

arXiv:2609.32162v1 Announce Type: new Abstract: Machine unlearning and model pruning are increasingly coupled in the real world. Models must support unlearning requests, e.g., for safety concerns, whi

safetyarxiv-cs-cv

Applications

Residual Diffusion Implicit Models

arXiv:2609.33020v1 Announce Type: new Abstract: Diffusion models achieve state-of-the-art results across multiple tasks. However, in inverse problems, standard initialization from pure Gaussian noise

applicationsarxiv-cs-cv

ReSync: Re-Aligning the Two Clocks of Asynchronous World-Action Models

model-releasesarxiv-cs-ro

arXiv:2609.33944v1 Announce Type: new Abstract: Jointly generating future video and actions has become a standard recipe for world-action models, and the strongest systems denoise the two streams on s

SafeMol: Dual-Modality Safety Alignment for Molecular Multimodal Models

arXiv:2609.33640v1 Announce Type: new Abstract: Molecular multimodal models support diverse understanding and generation tasks but may introduce safety vulnerabilities when handling hazardous molecule

SAGE: Subspace Alignment for Classifier-Free Guidance in Mixture-of-Experts Diffusion Models

arXiv:2609.34525v1 Announce Type: new Abstract: Diffusion Transformers with Mixture-of-Experts (MoE) routing are a leading recipe for scaling generative models. Classifier-Free Guidance (CFG) is essen

SAMBAR: Selective Anchoring via Method of Multipliers for Balanced Knowledge Acquisition and Retention in Vision-Language-Action Models

arXiv:2609.32108v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models leverage large-scale pretraining to ultimately achieve generalist manipulation. Deployed VLA policies must support c

Safety

Sample What You Say: Aligning Language Models to Sample the Distributions They State

arXiv:2609.34929v1 Announce Type: new Abstract: Language models are increasingly used to sample from a specified distribution, for instance, to simulate survey respondents or generate synthetic data.

safetyarxiv-cs-lg

Seeing and Solving Are Not Enough for Vision-Language Models

arXiv:2609.33694v1 Announce Type: new Abstract: Vision-language models (VLMs) answer visual questions by combining visual information extraction with downstream problem solving. We investigate a funda

researcharxiv-cs-cv

SOLO: Pretraining Billion-Parameter Language Models with Shared-Output Local Learning

arXiv:2609.35440v1 Announce Type: new Abstract: Large language models are trained with backpropagation, whose global gradient coordinates all layers but forces each to hold its activations and wait fo

TerMeZO: Ternary Sparse Zeroth-Order Optimization for Fine-tuning BitNet Models at the Edge

arXiv:2609.33548v1 Announce Type: new Abstract: Fine-tuning anguage models (LLMs) with first-order optimizers requires a memory several times larger than that required for inference. Memory-efficient

The GUI Is Not the State: Diagnosing State Aliasing in GUI World Models

arXiv:2609.32679v1 Announce Type: cross Abstract: GUI World Models (GUI-WMs) are increasingly used to predict future states for agent planning and simulation, yet most existing formulations condition

The Key Handoff: Retrieval in Hybrid Language Models

arXiv:2609.32942v1 Announce Type: cross Abstract: A two-hop question makes a language model retrieve twice: once to produce a bridge entity, and once to retrieve with it. Transformers resolve that ent

Tutorials

TRAP: Understanding and Mitigating Privacy Memorization in Language Models

arXiv:2609.32293v1 Announce Type: cross Abstract: Fine-tuning a language model on sensitive records can leave it able to reproduce them. We ask when this memorization arises and how to prevent it with

tutorialsarxiv-cs-lg

Tutorials

Unifying Deep Predicate Invention with Pre-trained Foundation Models

arXiv:2512.17992v2 Announce Type: replace Abstract: Long-horizon robotic tasks are hard due to continuous state-action spaces and sparse feedback. Symbolic world models help by decomposing tasks into

tutorialsarxiv-cs-ro

ZeroGAR: Benchmarking the Adversarial Robustness of Zero-Shot Graph Models

arXiv:2609.33314v1 Announce Type: new Abstract: Zero-shot graph models (ZGMs), which learn transferable knowledge from source graphs and directly apply to unseen target graphs without any adaptation,

Acoustic-to-Text KV Compression for Full-Duplex Speech Models

arXiv:2609.31224v1 Announce Type: cross Abstract: Full-duplex speech language models continuously accumulate acoustic key-value (KV) states, making long-running interactions memory-intensive. During l

researcharxiv-cs-ai

DepthEvidence: Unifying Metric Depth Prediction and Geometric Reasoning in Multimodal Language Models

arXiv:2609.31103v1 Announce Type: cross Abstract: Spatial reasoning with metric constraints requires linking objects to geometric measurements and preserving their numerical content during language re

FTB Graph: Determining and Validating First-token Broadcasters and Language-Identity Head Circuits in Multilingual Language Models

arXiv:2609.30954v1 Announce Type: new Abstract: Large language models operating in multilingual contexts must resolve target response languages early in generation, yet the causal circuitry governing

researcharxiv-cs-ai

Low-Bit Recurrent States in Hybrid Language Models

arXiv:2609.30950v1 Announce Type: new Abstract: Hybrid language models maintain fixed-size recurrent states, but existing quantizers typically use eight bits or more. Quantization errors persist accor

MVVBench: Benchmarking 4D Reasoning in Vision-Language Models

arXiv:2609.30952v1 Announce Type: cross Abstract: Multi-view video understanding requires integrating spatial and temporal evidence across multiple, often non-overlapping camera streams: tracking enti

Self-Supervised Representation Learning: From Spectral Foundation Models to Auroral Emission Spectra

arXiv:2609.31206v1 Announce Type: new Abstract: Auroral spectrographs such as the Auroral Spectrograph In Skibotn (ASIS) record hundreds of thousands of emission spectra, but only a few hundred can be

SLMFix: Leveraging Small Language Models for Domain Specific Language Error Fixing with Reinforcement Learning

arXiv:2511.19422v2 Announce Type: replace-cross Abstract: Large language models (LLMs) have shown impressive capabilities in code generation across many programming languages but even state-of-the-art

researcharxiv-cs-ai

The Linear Representation Hypothesis for Vision-Language-Action Models

arXiv:2609.30996v1 Announce Type: cross Abstract: The linear representation hypothesis (LRH) has become a standard lens for measuring and intervening on semantic information through the internal repre

Safety

Why Jailbreaks Succeed in Diffusion Language Models: An Energy Landscape Analysis

arXiv:2609.30841v1 Announce Type: new Abstract: Existing attacks and defenses for diffusion-based large language models (dLLMs) target specific vulnerabilities but lack a shared framework explaining w

safetyarxiv-cs-ai

Local Ai

A Rapid Pipeline for Training and Deploying ML Models on WeBe Band