Search: “model-releases”

Autoregressive Drillhole Modelling Under Distribution Shift

arXiv:2610.01204v1 Announce Type: new Abstract: Autoregressive modelling has achieved remarkable success in language and sequence tasks by learning to predict future states from previous observation.

Content type

All Blog X Post Paper YouTube Reddit GitHub

Clear filters

Model Releases

AVSD-Scenes: A Dataset for Audio-Visual Description of Urban Scenes

DGX agent

arXiv:2610.01861v1 Announce Type: new Abstract: Natural language descriptions can provide rich semantic representations of audio-visual urban scenes, yet datasets that jointly describe both auditory a

model-releasesarxiv-cs-ai

2 Oct 2026

Backdoor Purification for LoRA-Tuned LLMs via Null-Space Projection

arXiv:2610.00685v1 Announce Type: new Abstract: With the rapid adoption of large language models (LLMs) and parameter-efficient fine-tuning (PEFT) methods, the risk of backdoor attacks has become more

Benchmarking Generative Models for Weather Data Assimilation on Real Station Observations

arXiv:2610.00728v1 Announce Type: cross Abstract: Weather reanalysis products rely on computationally intensive numerical weather predictions followed by data assimilation that corrects the forecast t

Benchmarking Prompt Optimization of Large Language Models With Chess

arXiv:2610.00416v1 Announce Type: new Abstract: Evaluating large language models becomes increasingly challenging as their capabilities advance: benchmarks can saturate, public test sets risk contamin

Beyond Affine Transformations: A Soft Dominance Layer for Coordinate-Wise Neural Computation

arXiv:2610.00563v1 Announce Type: cross Abstract: This paper presents a preliminary study of an alternative to the affine transformation underlying conventional neural-network layers. In the proposed

Beyond Domain-Level Adaptation: Margin-Oriented Semantic-Appearance Interaction Correction for Personalized Federated Vision-Language Models

arXiv:2610.01625v1 Announce Type: new Abstract: Federated parameter-efficient fine-tuning enables distributed clients to adapt pretrained vision-language models without sharing raw data or updating th

Beyond Leaderboard Scores: A Deployment-Focused Protocol for Interpretable Tracking Evaluation in Pedestrian-Centric Environments

arXiv:2610.01682v1 Announce Type: cross Abstract: Mobile robots operating among pedestrians need trajectories that become available quickly, remain spatially credible through missed observations, pres

Beyond Linear Concepts: Discovering and Aligning Non-Linear Concept Manifolds in Large Language Models

arXiv:2610.01821v1 Announce Type: new Abstract: Understanding information processing in large language models (LLMs) requires dissecting the geometric organization of their internal token representati

Beyond Memory: Harnessing Long-Horizon Agents with Explicit Belief States

arXiv:2610.01415v1 Announce Type: new Abstract: Large language model (LLM) agents can now undertake increasingly complex tasks, but the way they organize interaction history into memory does not ensur

Beyond Pointwise Error: A Multi-Metric Evaluation of Spatial Climate Downscaling

arXiv:2610.01579v1 Announce Type: cross Abstract: Climate downscaling aims to reconstruct fine scale spatial fields from coarse resolution inputs. Evaluating the quality of these reconstructions is ch

Beyond Supra-Competitive Outcomes: Collusive Behaviour in Deep Reinforcement Learning for Optimal Execution Games

arXiv:2610.00619v1 Announce Type: cross Abstract: In this paper, we extend earlier findings of supra-competitive outcomes in optimal-execution games by identifying a learned punitive mechanism that de

Block Optimism for Nonstationary Bandits with Latent Linear Dynamics

arXiv:2610.00911v1 Announce Type: cross Abstract: We study an endogenous nonstationary stochastic bandit problem with latent linear dynamics, where actions affect both immediate rewards and the future

Bounded-Fidelity Sim-as-Demo-Stage: Mocap Handoff for Governance Benchmarks

arXiv:2610.00008v1 Announce Type: cross Abstract: Sim-to-real research pursues physics fidelity as a primary objective: simulators are judged by how closely they reproduce real-world contact dynamics.

Bridging the Sim-to-Real Gap with multipanda_ros2: A Real-Time ROS2 Framework for Multimanual Systems

arXiv:2602.02269v2 Announce Type: replace-cross Abstract: We present multipanda_ros2, a novel open-source ROS2 architecture for multi-robot control of Franka Robotics robots. Leveraging ros2 control,

BudgetSchemaBench: A Budget-Swept Diagnostic for Schema Context in Text-to-SQL

arXiv:2610.00092v1 Announce Type: cross Abstract: Data agents over structured sources must fit database schema into the model's context window. Large catalogs can span many databases and thousands of

Build2SPARQL: A Large-Scale Text-to-SPARQL Benchmark Dataset for Building Knowledge Graph Querying

arXiv:2610.00224v1 Announce Type: new Abstract: Building automation systems are increasingly represented as semantic knowledge graphs (KGs) using ontologies such as Brick and ASHRAE 223P, creating a m

CAGE-NAS: Certified Functional Descent for Efficient Model Growth

arXiv:2610.01173v1 Announce Type: new Abstract: The progressive growth of neural networks requires deciding when the current representation remains sufficient for optimization and when it should be ex

Capabilities Ain't All You Need: Measuring Propensities in AI

arXiv:2602.18182v5 Announce Type: replace-cross Abstract: AI evaluation has primarily focused on measuring capabilities, with formal approaches inspired from Item Response Theory (IRT) being increasin

Capturing In-Context Learning Dynamics with Task Operators

arXiv:2610.01054v1 Announce Type: cross Abstract: In-context learning (ICL) enables language models to perform new tasks from demonstrations without weight updates. However, every ICL inference requir

Chaining Skills to Hijack LLM Agents

arXiv:2610.01564v1 Announce Type: cross Abstract: LLM agents use skills to improve performance on specialized tasks. To complete a user request, an agent may invoke several skills in sequence, allowin

ChainLoRA: Geometry-Preserving Task Vector Merging for Continual Learning in LLMs

arXiv:2610.00431v1 Announce Type: cross Abstract: Continual parameter-efficient fine-tuning for large language models (LLMs) must balance retention of previously acquired knowledge, adaptation to new

Characterizing a Configuration Where Inference-Time PRM-Pruned Fragment Grafting Is Inert: Evidence from Three Reasoning LMs

arXiv:2610.00047v1 Announce Type: new Abstract: Diversity collapse in parallel chain-of-thought has motivated inference-time interventions built on a natural design: when a process reward model (PRM)

CHILLGuard: Towards Fine-Grained Chinese LLM Safety Guardrail with Scalable Data Construction and Model-aware Preference Alignment

arXiv:2606.15396v2 Announce Type: replace-cross Abstract: Malicious content generated from large language models (LLMs) could pose severe safety risks and ethical concerns. While existing LLM safety g

CHORDONOMICON: A Dataset of 666,000 Songs and their Chord Progressions

arXiv:2410.22046v4 Announce Type: replace-cross Abstract: Chord progressions encapsulate important information about music, pertaining to its structure and conveyed emotions. They serve as the backbon

CineMR: Tool-Integrated Vision-Language Reasoning for Quantitative Cardiac MRI Assessment

arXiv:2610.01166v1 Announce Type: cross Abstract: Cardiovascular magnetic resonance (CMR), including cine imaging, is a reference standard for the noninvasive assessment of cardiac morphology and vent

CLASP: Continual Low-rank Adapters for Spatially Placed Concepts from One Hypernetwork

arXiv:2610.01331v1 Announce Type: new Abstract: Continual personalization of text-to-image diffusion models requires sequentially acquiring new concepts while retaining previously learned ones. Howeve

Clock Diffusion: Efficient Semi-Autoregressive Continuous Diffusion Language Models

arXiv:2610.00894v1 Announce Type: new Abstract: Recent works on continuous diffusion for discrete data have demonstrated performance on par with comparable discrete diffusion models. However, these co

Closing the Loop: Practical Training Recipes for Looped Language Models

arXiv:2610.00673v1 Announce Type: cross Abstract: Looped language models increase effective depth by repeatedly applying a shared block of layers, but existing large-scale recipes require multi-stage

CommunityKV: Efficient Long-Context Decoding via Graph Partitioning

arXiv:2610.00418v1 Announce Type: new Abstract: Scaling Transformers to long contexts is constrained by the quadratic cost of self-attention and the linear growth of key-value cache memory transfer. S

CompMat-Bench: Benchmarking AI Agents for Computational Materials Science

arXiv:2610.00636v1 Announce Type: new Abstract: Evaluating AI agents on scientific research tasks is constrained by the time and resources required for the underlying experiments or calculations. In c

Concept Driven Domain Adaptation: Finding an Abstract Needle in a Haystack

arXiv:2610.00973v1 Announce Type: new Abstract: Science teachers frequently search for documentary excerpts not by describing what appears on screen, but by querying the abstract concepts they intend

Conflicting Supervision Moves Commitment, Not Capability: A 12.29{sigma} arrangement effect that is exactly zero under a convention-agnostic score

arXiv:2610.00234v1 Announce Type: new Abstract: 'Train a model on the same problems written under two incompatible conventions, both correct, and ask what the ordering of that data writes into the par

Constant-Memory Recall: Learned Associations in a Fixed Matrix State

arXiv:2610.00232v1 Announce Type: new Abstract: Fixed-size recurrent memory limits storage growth during inference, but successful recall depends on the task and training. We study a small DeltaNet va

Continual Reinforcement Learning with Neuroevolution

arXiv:2610.01583v1 Announce Type: cross Abstract: Despite many studies about causes and remedies of plasticity loss in Reinforcement Learning (RL) under continual task changes, no RL method has yet co

Cross-Benchmark Transfer from RL on Agentic Coding Tasks

arXiv:2610.00890v1 Announce Type: cross Abstract: Coding agents often fail in the last mile: they build most of a feature but drop a requirement, test only the cases their implementation already handl

CrossGMN: Graph Metanetworks for Cross-Architecture Weight-Space Transformations

arXiv:2610.01649v1 Announce Type: new Abstract: Weight-space networks operate directly on parameters of other neural networks, enabling tasks such as predicting model properties, editing trained model

Dataset Identity, Not Novelty: The Source of an Inflated OOD Detection Gain

arXiv:2610.01096v1 Announce Type: cross Abstract: A post-hoc out-of-distribution (OOD) detector reads the activations of a trained classifier and returns a score. It fits that score on in-distribution

DAYJOB: A Benchmark for Long-Horizon Professional Work

arXiv:2610.01306v1 Announce Type: new Abstract: Professional work often starts with a brief request that leaves the professional to work out what is needed, which documents matter, and whether the req

Decision Titan: Test-Time Training for Long-Term Memory in Offline Reinforcement Learning

arXiv:2610.01513v1 Announce Type: new Abstract: Long-term dependencies remain a major challenge for sequential decision-making in the field of AI: RNNs suffer from vanishing gradients and the limited

Decoding Looped Transformers Better for (Almost) Free

arXiv:2610.02185v1 Announce Type: new Abstract: Looped Transformers achieve parameter efficiency by repeatedly executing a shared block across recurrent loops. Each loop yields an intermediate represe

Decoding the Disaster: Multi-Task Geospatial Reasoning with Vision-Language Models and Crowdsourced Imagery for Disaster Mapping

arXiv:2610.00302v1 Announce Type: cross Abstract: Crowdsourced imagery provides timely, fine-grained, street-level observations for disaster mapping, complementing conventional remote sensing imagery

Deep Symmetric Autoencoders from the Eckart-Young-Schmidt Perspective

arXiv:2506.11641v2 Announce Type: replace-cross Abstract: Deep autoencoders have become a fundamental tool in various machine learning applications, ranging from dimensionality reduction and reduced o

Detect, Explain, Interpret: An End-to-End Benchmark for Time Series Anomaly Detection, Explainability and Interpretability

arXiv:2610.01168v1 Announce Type: cross Abstract: Time Series Anomaly Detection has received increasing attention, driven by the growing availability of complex time series data. This surge has led to

DiDE:Direct Injection with Color-Texture DEcoupling for 3D Stylization

arXiv:2610.02044v1 Announce Type: new Abstract: Recent advances in rectified flow-based image-to-3D generative models have enabled high-fidelity 3D asset generation. Building on this, a growing line o

Discrete Annotation, Continuous Preference: Rethinking Supervision for Accurate and Generalizable Aesthetic Image Cropping

arXiv:2610.00582v1 Announce Type: new Abstract: Aesthetic image cropping aims to identify the optimal crop of an image in terms of aesthetics and composition. While supervision based on annotated data

DiVid: Diagnosing Dimension-Specific Diversity Collapse in Video Generation Models

arXiv:2610.01661v1 Announce Type: new Abstract: Despite remarkable progress, video generation models often produce highly similar outputs when repeatedly sampled from the same prompt, limiting their u

Divide-and-Remember: Recursive Action-Relevant Memory for Long-Horizon VLA Policies

arXiv:2610.00982v1 Announce Type: cross Abstract: Vision-language-action (VLA) models struggle on history-dependent manipulation tasks, where the current observation alone does not determine the actio