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Test-Time Spatial Reasoning for Robot Manipulation Using Generative Real-to-Sim

arXiv:2609.33982v1 Announce Type: cross Abstract: Spatial reasoning is fundamental to general robot intelligence, as it enables robots to complete long-horizon tasks involving multi-object interaction

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Model Releases

Tetra: Serving Leech-Lattice Quantized LLMs at 2.7 Bits per Parameter

DGX agent

arXiv:2609.35465v1 Announce Type: new Abstract: Leech-lattice quantization gives good quality at two bits per weight, but its codebooks hold more than 10^14 points, too many for a lookup table. Our ea

model-releasesarxiv-cs-lg

29 Sept 2026

TGRL: Temperature-Grouped Reinforcement Learning for Efficient Exploration in LLMs

arXiv:2609.33589v1 Announce Type: new Abstract: Efficient exploration often remains a central bottleneck in reinforcement learning with verifiable rewards (RLVR). Although temperature control and test

The cost of useful natural gradient updates

arXiv:2609.33499v1 Announce Type: new Abstract: What information is needed to turn a natural-gradient direction into a useful finite update? Under a population Kullback-Leibler (KL) budget, we call a

The Devil in the Details: Emergent Misalignment, Format and Coherence in Open-Weights LLMs

arXiv:2511.20104v2 Announce Type: replace Abstract: Prior work has shown that fine-tuning models on a narrow domain with misaligned data can lead to broad misalignment - a phenomenon termed 'emergent

The Earth in One Gaze: Training-Free Active Focus for UHR Remote Sensing Understanding

arXiv:2609.31747v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) must balance local detail against scene context when interpreting ultra-high-resolution (UHR) remote sensing (R

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 Low-Rank Structure of VLA Reinforcement Learning

arXiv:2609.34599v1 Announce Type: new Abstract: Reinforcement learning (RL) is increasingly used to post-train vision-language-action (VLA) models, yet how RL reshapes these policies remains poorly un

The Privacy Fallacy of Crowdsourced Fine-Tuning: Extracting Proprietary Data via Topic-Based Poisoning

arXiv:2609.33985v1 Announce Type: cross Abstract: Supervised fine-tuning (SFT) is widely used to adapt large language models to downstream tasks. Crowdsourcing user conversations is an established app

The Selection Rule Decides the Winner: A Pre-Registered Audit of Open-Set Graph Anomaly Detection

arXiv:2609.33370v1 Announce Type: new Abstract: Open-set graph anomaly detection trains on a few labeled anomalies from one class and must also find anomaly classes that were never labeled. Published

The Text Beside the Image: Detection, Utility and Leakage for Trustworthy Multimodal Medical Data and Beyond

arXiv:2609.33280v1 Announce Type: cross Abstract: Medical images are released with the reports that describe them, and protecting the image does not protect the report. This paper measures the text co

THEIA: A Multimodal Dataset and Benchmark for Vision-Language Analysis of Layout

arXiv:2609.35035v1 Announce Type: new Abstract: The integration of artificial intelligence into computer-aided design frameworks has sparked a shift in the design of analog integrated circuits (ICs),

Theory Guided and Interpretable Neural Operator Design for Partial Differential Equation Learning

arXiv:2609.33715v1 Announce Type: new Abstract: Accurate numerical solutions of partial differential equations (PDEs) are crucial in numerous science and engineering applications. In this work, we int

Theory of Scene: Breaking the Symmetry Trap in Multi-Agent LLM Coordination

arXiv:2609.32939v1 Announce Type: new Abstract: Multi-agent systems built on large language models (LLMs) are largely homogeneous, as their agents behave alike even across distinct LLMs. We show that

ThinkNet: Compact Architecture Selection and Validation-Gated Ensembles for Subject-Independent MI-EEG Decoding

arXiv:2609.33967v1 Announce Type: cross Abstract: Practical assistive and rehabilitative brain--computer interfaces require subject-independent motor-imagery EEG (MI-EEG) decoders that generalize to n

Timeline-Bench: Evaluating Agents on Realistic Video-Editing Tasks, from Raw Footage to Final Cut

arXiv:2609.35143v1 Announce Type: new Abstract: AI agents increasingly carry out long-horizon professional work, but their evaluations rarely require a finished creative deliverable. To this end, we i

Token-Disentangled Latent Test-Time Scaling for Vision-Language Reasoning

arXiv:2609.35228v1 Announce Type: new Abstract: Latent test-time scaling improves reasoning by refining hidden states during inference, but existing methods typically apply a single scalar reward to a

Topology-Adaptive Hyperbolic Graph Attention Networks Guided by the Hyperbolic Sombor Index

arXiv:2609.32275v1 Announce Type: new Abstract: Hyperbolic geometry has emerged as a principled space for representing hierarchical graphs. However, existing hyperbolic graph neural networks typically

Toward AI-Assisted Poultry Coccidiosis Diagnosis: Evaluating Gemini and BiomedParse on Eimeria Microscopy Images

arXiv:2609.31679v1 Announce Type: new Abstract: Coccidiosis caused by Eimeria parasites is a major economic burden in poultry production, and effective control depends on accurate species-level diagno

Toward On-Chip Training of Spiking Neural Networks for Dense Event-Based Vision

arXiv:2609.32405v1 Announce Type: new Abstract: Event cameras provide low-latency, asynchronous visual sensing for resource-constrained robotics. Spiking neural networks (SNNs) process event streams n

Towards Eliminating Catastrophic Forgetting in the Curriculum Learning of Math Reasoning Tasks

arXiv:2609.33655v1 Announce Type: new Abstract: Curriculum learning has found broad application across numerous domains. Nevertheless, its effectiveness is intrinsically curtailed by catastrophic forg

TRACE: Expert-Aligned ECG Representation Learning with Rigorous Benchmarking and Real-World Validation in Acute Cardiac Care

arXiv:2609.34088v1 Announce Type: new Abstract: TRACE (Text-Reinforced Analysis of Cardio ECGs) is a multimodal electrocardiogram (ECG) representation model that learns clinically grounded signal embe

Traceable Human-to-Humanoid Sign Language Benchmarking

arXiv:2609.33354v1 Announce Type: new Abstract: Sign data collection is costly, and teleoperation scales poorly, motivating reuse of large video corpora. Humanoid signing requires converting video-der

Train4Merge: A Controlled Single-Teacher Study of RL vs. SFT Teachers for OPD-Based Model Merging

arXiv:2609.32303v1 Announce Type: cross Abstract: Domain experts trained from a shared checkpoint can be merged into one model through on-policy distillation (OPD), where they act as teachers supervis

Training and Inference Dynamics of PLDR-LLMs: Row-Map Collapse, Renormalization, and Predictive Reduction

arXiv:2609.34130v1 Announce Type: new Abstract: This monograph develops a unified account of training and inference in Power Law Decoder Representation language models (PLDR-LLMs). Exact finite work i

Transform-Aligned Learned Features for Lossy Point Cloud Attribute Compression

arXiv:2609.34834v1 Announce Type: new Abstract: Transform-based methods provide an effective framework for point cloud attribute compression by representing attributes as transform coefficients. Intro

Transformer MLP Gate Thresholds Are Couplings to a Carried Reference Direction

arXiv:2609.31956v1 Announce Type: cross Abstract: The corpus-mean direction of a transformer's residual stream is a component shared across all inputs, and is commonly removed by mean-centering before

TriDrive: Joint Driver, Vehicle, and Road Modeling for Forecasting and Driver Monitoring

arXiv:2609.33000v1 Announce Type: cross Abstract: Predicting how drivers, vehicles, and road scenes interact and evolve together is central to driver monitoring. Prior work models in-cabin activity or

Typed Decision Models: An Early Evidence Audit and Evaluation Checklist

arXiv:2609.32160v1 Announce Type: cross Abstract: Typed decision models (TDMs) return probability distributions over caller-defined options without generating text. TypeSafe released Jev, a commercial

UMR: Universal Manipulation Representation

arXiv:2609.34256v1 Announce Type: new Abstract: General-purpose embodied manipulation hinges on a unified action representation that generalizes across embodiments and scales readily. Yet existing pol

Uncertainty Quantification in Cardiac Model Personalisation from Ultrafast Ultrasound

arXiv:2609.35214v1 Announce Type: cross Abstract: Cardiac model personalisation requires inferring mechanical parameters that are not directly measurable in vivo. Ultrafast ultrasound shear wave elast

Understanding and Exploiting Anisotropy in Post-Training

arXiv:2609.32792v1 Announce Type: new Abstract: LLM post-training combines supervised fine-tuning (SFT), a mode-covering forward-KL objective, with reinforcement learning (RL), a mode-seeking reverse-

Understanding and Mitigating Under-Confidence in GNNs from the Final Layer

arXiv:2505.11335v4 Announce Type: replace Abstract: Graph Neural Networks (GNNs) have demonstrated remarkable effectiveness on graph-based tasks. However, their predictive confidence is often miscalib

Understanding the Subspace Stabilization of the Hessian and Gradient Covariance Matrix

arXiv:2609.31983v1 Announce Type: new Abstract: The phenomenon of the top subspace stabilization of the Hessian matrix is an surprising and critical aspect in study of the second-order information of

Understanding the Synergy between SFT, RLVR, and OPD in LLM Post-Training

arXiv:2609.31900v1 Announce Type: new Abstract: Modern LLM post-training composes supervised fine-tuning (SFT), reinforcement learning with verifiable rewards (RLVR), and on-policy distillation (OPD)

UnfoldCRF: Structured Mask Refinement with Image-Conditioned Latent Regions

arXiv:2609.33996v1 Announce Type: new Abstract: Learned mask refiners improve segmentation accuracy, but it is hard to tell how much of the improvement comes from explicit structure rather than from e

UniBYD: A Unified Framework for Learning Robotic Manipulation Across Embodiments Beyond Imitation of Human Demonstrations

arXiv:2512.11609v3 Announce Type: replace Abstract: In embodied intelligence, the embodiment gap between robotic and human hands brings significant challenges for learning from human demonstrations. A

Uniform Race: Parameter-Free Approximate Rejection Sampling

arXiv:2609.34639v1 Announce Type: new Abstract: We study approximate sampling: given N independent samples from a proposal distribution mu, the goal is to select one whose distribution is close to a t

Universal Dynamic Portfolios

arXiv:2609.34643v1 Announce Type: new Abstract: Cover's Universal Portfolio (Cover, 1991) matches the performance of the best constant rebalanced portfolio in hindsight. We generalize this framework t

UNMATCH: Selective Unbalanced Token-Patch Matching for Forensic Image-Claim Verification

arXiv:2609.31766v1 Announce Type: new Abstract: Contextual image misuse pairs an image with a misleading claim. We study image-claim correspondence in fact-checked pairs containing out-of-context reus

Unsupervised spiking feature learning for event-based pedestrian crossing detection: approaching supervised accuracy without labelled training data

arXiv:2609.31671v1 Announce Type: new Abstract: Event cameras are well suited to pedestrian crossing detection, and spiking neural networks (SNNs) can process their output natively, but current SNN de

USAI-Quant: A Quantitative Reasoning Benchmark for Vision-Language Models in Built Environments

arXiv:2609.32813v1 Announce Type: new Abstract: Large vision-language models (VLMs) have emerged as a powerful paradigm for urban and spatial AI. However, current state-of-the-art large VLMs still str

VaME: Exploring Variational Latent Reasoning for Multimodal Embeddings

arXiv:2609.33402v1 Announce Type: new Abstract: Universal multimodal retrieval requires compact embeddings that preserve task-relevant semantic information across diverse modalities. Prior works have

VastMAT: A Large-Scale Multi-Category Benchmark for Multi-Animal Tracking

arXiv:2609.34390v1 Announce Type: new Abstract: Multi-animal tracking (MAT) supports the study of animal movement, behavior, and group interactions. However, general multi-object tracking (MOT) benchm

VehDyn: A Driving World Model Benchmark for Vehicle Dynamics

arXiv:2609.33264v1 Announce Type: new Abstract: Video world models are emerging as data engines, action planners, and generative simulators for autonomous driving, but existing benchmarks primarily as

VEX-Bench: Benchmarking Verification Complexity of LLM-Generated Misinformation

arXiv:2609.35028v1 Announce Type: new Abstract: Large language models (LLMs) have made misinformation inexpensive to produce but not to verify, creating a growing asymmetry in the information ecosyste

ViBR-WM: Visual Bayesian Regression for World Modeling

arXiv:2609.33844v1 Announce Type: cross Abstract: Modeling temporal dependence and uncertainty is central to forecasting with world models. The Visual Bayesian Regression World Model combines visual f

VIDEAS: Distilling Explicit Action Semantics from Demonstration Videos for World Models via Prior-Guided Simulation

arXiv:2609.33464v1 Announce Type: new Abstract: World models learn internal representations of environment dynamics to predict future states, enabling agents to optimize action plans without physical