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arXiv:2609.31704v1 Announce Type: cross Abstract: Large language models are increasingly used as interacting agents, but it remains unclear how robust their coordination is when public communication i

agentsarxiv-cs-lg

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Before Answering: Evidence Sufficiency under Size-Matched Memory Construction

DGX agent

arXiv:2609.32269v1 Announce Type: cross Abstract: Agents that answer questions from compressed or retrieved memory must recognize when the evidence a query needs is no longer in memory. Benchmarks for

model-releasesarxiv-cs-lg

29 Sept 2026

Agents

BEHAVE: Functional Behavior Modeling Enables Self-Improving Agents for Hardware Design and Verification

arXiv:2609.34785v1 Announce Type: cross Abstract: Developing agents for hardware design and verification requires reliable correctness feedback. As a hardware specification may permit correct implemen

agentsarxiv-cs-lg

Behavioral Foundation Models for Quality Diversity

arXiv:2609.35615v1 Announce Type: new Abstract: Behavioral Foundation Models (BFMs) are an emerging paradigm in reinforcement learning, playing a role analogous to large language models in natural lan

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

Benchmarking EEG Foundation Models at Scale: Lessons from 20,000 Evaluations

arXiv:2609.32743v1 Announce Type: new Abstract: Electroencephalography (EEG) foundation models (FMs) promise transferable neural representations, yet their advantages over strong supervised baselines

Benign Overfitting for General Norms and Distributions

arXiv:2609.33675v1 Announce Type: new Abstract: Understanding why predictors can generalize despite interpolating noisy training data is a central puzzle in machine learning. Most work on such 'benign

BERT4DTI : BERT-based Model for Predicting Drug-Protein Interactions

arXiv:2609.33254v1 Announce Type: new Abstract: Understanding how drugs interact with protein targets is fundamental to drug discovery, drug repurposing and the early identification of promising thera

Beyond Calibration: Do a Typed-Decision Model's Probabilities Obey the Probability Axioms?

arXiv:2609.33209v1 Announce Type: cross Abstract: Typed-decision models such as TypeSafe's Jev answer a declared yes/no or multiple-choice question about a state with a probability instead of text, an

Beyond Conservatism: Recoverability-Conditioned Exploration for Model-Based Imitation Learning

arXiv:2609.33336v1 Announce Type: new Abstract: Model-based imitation learning (MBIL) improves real-environment interaction efficiency by optimizing policies on imagined rollouts from a learned world

Beyond Correctness: Evaluating Semantic Knowledge in Cross-Table Transfer

arXiv:2609.34098v1 Announce Type: new Abstract: Semantic knowledge is increasingly used to bridge heterogeneous schemas in tabular learning, but how much does that knowledge actually improve predictio

Beyond Energy: When Sustainability Dimensions Reshape LLM Serving Decisions

arXiv:2609.35569v1 Announce Type: cross Abstract: Large language model (LLM) serving has environmental impacts across energy consumption, carbon emission, water consumption, and biodiversity loss. Yet

Beyond Fixed Features: Architecture-Dependent Sensitivity to Node Representations under Heterophily

arXiv:2609.33764v1 Announce Type: new Abstract: Graph Neural Networks (GNNs) perform well on homophilic graphs but struggle in heterophilic settings, where connected nodes often carry dissimilar label

Beyond Gaussian Assumptions: Distribution-Aware Channel Capacity for Effective Connectivity

arXiv:2609.32774v1 Announce Type: new Abstract: Effective-connectivity estimation from brain signals often relies on Gaussian residual modeling, which enables tractable estimation but can discard info

Beyond Geometry: Benchmarking and Consistency Reasoning for 3D Logical Anomaly Detection

model-releasesarxiv-cs-cv

arXiv:2609.34143v1 Announce Type: new Abstract: Existing 3D industrial anomaly detection mainly targets local geometric deviations. In contrast, many industrial anomalies violate object-level design o

Beyond Gradient Flow: Identifiability and Recovery from Distribution Snapshots

arXiv:2609.35060v1 Announce Type: new Abstract: Inferring dynamics from snapshots of evolving distributions is fundamentally underdetermined: the Fokker-Planck equation constrains the drift F only thr

Beyond Inpainting: Unleash 3D Understanding for Stable Camera-Controlled Video Generation

arXiv:2601.10214v3 Announce Type: replace Abstract: Camera control has been extensively studied in conditioned video generation; however, performing precisely altering the camera trajectories while fa

Beyond Isolated Entities: Relation-Aware Multi-Entity Modeling for Unsupervised Video Anomaly Detection

model-releasesarxiv-cs-cv

arXiv:2609.31753v1 Announce Type: cross Abstract: Video anomaly detection for patrol robots and surveillance systems must recognize abnormal interactions among familiar entities. Existing pixel-recons

Beyond MSE: Rician Likelihood Denoising for Self-Supervised Cardiac T2 and T1rho MRI

arXiv:2609.31777v1 Announce Type: cross Abstract: Magnetic resonance imaging involves an inherent trade-off among spatial resolution, acquisition time, and noise. This trade-off contributes to long sc

Beyond One Epoch: Uncertainty-Weighted Sensitivity Regularization for Recommendation Models

arXiv:2609.34083v1 Announce Type: new Abstract: Recommendation models with sparse embeddings and a shared consumer often exhibit the one-epoch phenomenon: a second epoch lowers training loss while sha

Beyond One-Step Accuracy: State-Affine Latent Transition for Reliable Visual Planning

arXiv:2609.33595v1 Announce Type: cross Abstract: Joint-embedding world models enable visual planning by learning action-conditioned dynamics in latent space. Yet they are commonly trained for one-ste

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

safetyarxiv-cs-cv

Beyond Retrieval Relevance: Scene-Grounded Risk Entailment for Vision-Language Driving

arXiv:2609.34145v1 Announce Type: new Abstract: Retrieval-augmented generation (RAG) gives vision--language driving systems access to external safety knowledge, yet a retrieved risk rule may be releva

safetyarxiv-cs-ro

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

safetyarxiv-cs-cv

Beyond Selection: Token Parameterization for Extreme Visual Token Compression

model-releasesarxiv-cs-cv

arXiv:2609.35232v1 Announce Type: new Abstract: Visual-token compression is effective for improving the efficiency of vision-language models, but under extreme compression budgets, token pruning can b

Beyond Site Agreement: Re-estimation for Brain Network Generalization

arXiv:2609.34611v1 Announce Type: new Abstract: Cross-site out-of-distribution (OOD) generalization in resting-state functional magnetic resonance imaging (rs-fMRI) often relies on learning task-discr

Beyond Sparsity: Weight Location and Network Context in Pruned MRI Reconstruction

arXiv:2609.31812v1 Announce Type: cross Abstract: Pruning reduces the number of weights in magnetic resonance imaging (MRI) reconstruction networks. Equal sparsity, however, can retain weights with di

Local Ai

Beyond State-as-Action: Exploiting Command-State Discrepancy for Robot Imitation Learning

arXiv:2609.33145v1 Announce Type: new Abstract: Constructing action targets from measured robot motion is an established approach in imitation learning. Under interaction constraints, however, command

local-aiarxiv-cs-ro

Beyond Tasks: A Vision for Reproducing an Animal-like Behavioral Substrate Using Modern Robot Learning Techniques

arXiv:2609.33165v1 Announce Type: new Abstract: Recent advances in robot learning have produced increasingly capable embodied agents. Yet comparatively less attention has been given to a more basic fo

researcharxiv-cs-ro

Beyond Teacher Assignment: Domain-Normalized Multi-Teacher On-Policy Distillation

arXiv:2609.35347v1 Announce Type: new Abstract: Reinforcement learning can turn one language model into several specialists, each excellent at a single skill such as mathematics, coding or following i

Beyond Temporal Smoothing: Spatial Energy Budgets Stabilize One-Step Diffusion Editing

arXiv:2609.32841v1 Announce Type: new Abstract: One-step text-guided diffusion editing is efficient but prone to spatially misallocated updates that distort the edited object and alter the background.

Beyond the Graph: An Adaptive Meta-Learner Fuses Explainability, Weather, and Dynamics for Robust Bus ETA Prediction

arXiv:2609.31667v1 Announce Type: new Abstract: Accurate bus Estimated Time of Arrival (ETA) prediction is vital for urban mobility, passenger satisfaction, and transit efficiency, yet existing models

Tutorials

Beyond the Manifold Hypothesis: Hybrid Spectral Parameterizations for Flow Matching

arXiv:2609.32432v1 Announce Type: new Abstract: Flow matching and diffusion can be trained to predict different quantities, most commonly the data x_1, the source noise x_0, or the velocity~v. Althoug

tutorialsarxiv-cs-lg

Tutorials

Beyond the Training Horizon: Mechanisms and Limits of Length Generalization in Looped Transformers

arXiv:2609.33144v1 Announce Type: new Abstract: Looped Transformers can generalize to reasoning chains longer than those encountered during training, but the computations enabling this behavior and li

tutorialsarxiv-cs-lg

Beyond Timestamps: Decision-Aligned On-Policy Distillation for Long-Horizon Agents

arXiv:2609.33391v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards (RLVR) often relies on sparse outcome rewards, providing coarse supervision for long-horizon agents. On-p

Beyond Token Savings: A Systematic Study of Context Compression in LLM Agents

arXiv:2609.32961v1 Announce Type: new Abstract: As LLM agents tackle longer tasks, they increasingly compress growing histories of reasoning, actions, and tool outputs. Compression can reduce token us

Beyond Token Scale: Chunk-Level Sparse Autoencoders for Reliable Semantic Feature Discovery

arXiv:2609.35521v1 Announce Type: cross Abstract: Sparse autoencoders (SAEs) expose features that help us understand and steer language models, but faithful reconstruction does not guarantee informati

Beyond Verbalized Confidence: Calibrating Reasoners with Differentiable Readouts

arXiv:2609.34857v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards (RLVR) trains reasoning models to produce correct answers, but does not ensure that their stated confiden

Beyond Volume Overlap: Surface Matching for Topology-Aware Coronary Artery Segmentation

arXiv:2609.31740v1 Announce Type: new Abstract: Accurate coronary artery segmentation on coronary computed tomography angiography (CCTA) is essential for diagnosing coronary artery disease. Deep netwo

BiasReducer: Adaptive Bias Mitigation for Reward Models

arXiv:2609.32720v1 Announce Type: new Abstract: Reward models score responses from large language models (LLMs) and guide LLM training toward human preferences. However, reward models can favor superf

Bidirectional Information Flow (BIF) - A Sample Efficient Hierarchical Gaussian Process for Bayesian Optimization

arXiv:2505.11294v3 Announce Type: replace Abstract: Hierarchical Gaussian Process (H-GP) models divide problems into different subtasks, allowing different components to address each part, making them

Bidirectional Neural Networks for Global Nucleon-Nucleus Optical Model Calculations

arXiv:2512.22500v4 Announce Type: cross Abstract: Modern nuclear data evaluation increasingly requires not only accurate scattering calculations, but also efficient methods for uncertainty quantificat

BiMoGen: Bidirectional Motion-Text Generation via Unified Masked Discrete Diffusion

arXiv:2609.35407v1 Announce Type: new Abstract: Text-to-motion generation and motion-to-text captioning are two fundamental tasks in human motion modeling, both grounded in the same underlying motion-

Local Ai

Binaural Audio-Visual Instance Segmentation

arXiv:2609.32180v1 Announce Type: new Abstract: Audio-visual segmentation (AVS) aims to segment sounding objects at the pixel level by integrating auditory and visual cues. However, existing methods a

local-aiarxiv-cs-cv

Binding Multiple Modalities via Multimodal Wasserstein Barycenter

arXiv:2609.33800v1 Announce Type: new Abstract: Multimodal learning beyond two modalities commonly leverages a specific modality (e.g., text) to bind other modalities. However, how to establish a more

Bison: Cross-Dataset Learning for Unseen-Compound Perturbation Prediction

arXiv:2609.32467v1 Announce Type: new Abstract: Predicting transcriptional responses to unseen compounds is limited by fragmented chemical coverage and heterogeneous experimental platforms and gene pa

BITS: Rethinking Fair and Comprehensive Evaluation for Irregular Time Series Forecasting

arXiv:2609.33303v1 Announce Type: new Abstract: Despite recent progress in irregular time series forecasting, the field still lacks a unified benchmark for fair and comprehensive evaluation. Existing

Black-Box Auditing of Epistemic Reliability in Multi-Agent Debate Distillation

arXiv:2609.32361v1 Announce Type: new Abstract: Debate distillation adapts weaker verifiers using multi-agent debate transcripts to improve their judgement in subsequent debates, but gains on monitore