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Blaming Across the Aisle: Political Contrasting and Blame Attribution in the Danish Parliament

arXiv:2609.26346v1 Announce Type: new Abstract: Political discourse is widely perceived to be growing more hostile, yet robust evidence remains scarce. This study examines blame attribution in the Dan

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Model Releases

Block-Level Weight-Space Structure Persists Under Post-Training: An Empirical Study Across LLM Families

DGX agent

arXiv:2609.26147v1 Announce Type: new Abstract: Modern LLMs are deployed as families of post-trained variants (base, instruct, chat, code) derived from a shared set of pre-trained weights. We present

model-releasesarxiv-cs-lg

23 Sept 2026

BOBA: Dynamic Bayesian Optimization through Bayesian Active Inference

arXiv:2609.26021v1 Announce Type: new Abstract: Dynamic black-box optimization presents significant challenges for Bayesian Optimization (BO), as the objective function evolves over time, causing opti

Brace Yourself: Task-Conditioned Environmental Bracing for Forceful Humanoid Manipulation

arXiv:2609.25486v1 Announce Type: new Abstract: Forceful manipulation is challenging for humanoid robots because interaction forces can disturb whole-body balance. We introduce the Supporting Hand Str

researcharxiv-cs-ro

Tutorials

Brain-Inspired Hierarchical Modularity for General Continual Learning

arXiv:2609.25146v1 Announce Type: new Abstract: Continual learning, the ability to learn from sequential experience while retaining and adapting prior knowledge, is central to intelligent systems oper

tutorialsarxiv-cs-lg

Brain-Token Learning: Microstate-Based Tokenization and Multi-Scale Interaction for Long-Horizon EEG Sequence Modeling

arXiv:2609.24324v1 Announce Type: new Abstract: Electroencephalography (EEG) provides a non-invasive window into dynamic brain activity, yet modeling long-horizon EEG sequences remains challenging due

researcharxiv-cs-ai

Tutorials

Bridge of Psi's: Quantum Circuit Optimization with Schrodinger Bridges

arXiv:2609.25947v1 Announce Type: cross Abstract: Quantum circuit optimization replaces a circuit with an equivalent one of fewer gates and lower depth, reducing execution cost and error rate. We ask

tutorialsarxiv-cs-lg

Applications

BridgeShield: Risk-Aware Graph Modeling for Cross-Chain Bridge Attack Detection

arXiv:2508.20517v2 Announce Type: replace-cross Abstract: Cross-chain bridges enable asset and state transfers across heterogeneous blockchains, but their complex cross-domain interactions introduce n

applicationsarxiv-cs-ai

Local Ai

Bridging the Data Gap: Digital Twin as a New Paradigm for AI-based Radio Sensing

arXiv:2609.26214v1 Announce Type: new Abstract: We present a methodology that places a 3D digital twin (DT) of the environment as the main enabler behind the development of radio sensing at scale. The

local-aiarxiv-cs-lg

Building Trustworthy Mental Health Benchmarks on Bluesky: A Validation-Aware Weak-Supervision Framework

arXiv:2609.22696v1 Announce Type: new Abstract: Decentralized social media platforms create new opportunities and challenges for computational mental health research because data access, moderation, l

C2FXNet: Coarse-to-Fine Scene Expert for Unified Object Detection across Adverse Weather

model-releasesarxiv-cs-cv

arXiv:2609.25693v1 Announce Type: new Abstract: Object detection in adverse weather remains challenging because severe degradations weaken visual quality and disrupt semantic feature representations a

CableVLA: Simulation-Privileged Global-Local Representation Learning for Cable Routing

model-releasesarxiv-cs-ro

arXiv:2609.25606v1 Announce Type: new Abstract: Cable routing requires coordinated control of global cable topology and changing local contacts. We present CableVLA, an end-to-end multimodal vision-la

CacheDyG: Decoupling Temporal Propagation for Efficient Dynamic Graph Learning

arXiv:2609.25814v1 Announce Type: new Abstract: Dynamic graphs are widely used to model time-evolving relational systems in real-world applications. Dynamic graph neural networks provide an effective

Calibrated Confidence Expression for Radiology Report Generation

arXiv:2603.29492v2 Announce Type: replace Abstract: Safe deployment of Large Vision-Language Models (LVLMs) in radiology report generation requires not only accurate predictions but also clinically in

Calibrating Retrieval Geometry: Reliability-Guided Training-Free Aggregation for Visual Place Recognition

arXiv:2609.25937v1 Announce Type: new Abstract: Frozen visual foundation models provide transferable features for visual place recognition, but fixed aggregation can suppress useful distinctions in ne

researcharxiv-cs-cv

Safety

Calibration as a First-Class Criterion in LLM Evaluation

arXiv:2609.26489v1 Announce Type: new Abstract: Calibration of language models -- the alignment between expressed or implicit confidence and empirical correctness -- is a well-studied subfield within

safetyarxiv-cs-cl

Calibration Count Reuse: Validity Does Not Determine Efficiency

arXiv:2609.25138v1 Announce Type: cross Abstract: Calibration count reuse raises separate validity and efficiency questions. We give a validity criterion for general count-dependent nonconformity scor

Local Ai

Can We Predict Anomaly Detection Performance from Embedding-Space Geometry?

arXiv:2609.26460v1 Announce Type: new Abstract: Anomaly detection systems are often trained using normal data alone, while model selection and evaluation typically require labeled anomalies. We study

local-aiarxiv-cs-lg

Can You Delete a Year of Market Data? Machine Unlearning Against Exact Retraining Oracles

arXiv:2609.26242v1 Announce Type: new Abstract: When a data license expires, deleting stored records does not remove influence encoded in a trained forecaster. Machine unlearning seeks to remove this

Safety

Capability-Aware Arbitration for Semantic Intent-Based Shared Control

arXiv:2609.25369v1 Announce Type: new Abstract: Shared control often allocates robot authority based on confidence in inferred human intent, assuming reliable autonomous execution. When this assumptio

safetyarxiv-cs-ro

Capable yet Parsimonious: Extracting and Characterizing Hidden Chain-of-Thought in Frontier Models

arXiv:2609.26637v1 Announce Type: new Abstract: The rapid capability gains of frontier language models are widely attributed to improved reasoning abilities, yet this cannot be verified as raw CoT tra

Agents

CDKF-Track: Cluster-aware Data-Driven Kalman Filtering for Cooperative 3D Multi-Object Tracking

arXiv:2609.25668v1 Announce Type: new Abstract: Multi-Object Tracking (MOT) is essential for EdgeAI perception systems, where accurate object localization and reliable identification enable safe decis

agentsarxiv-cs-ro

Cellular-Communication-Level Interpretability for Pathology Foundation Models via Graph Distillation on Microenvironment

arXiv:2609.26073v1 Announce Type: new Abstract: Pathology foundation models (PFMs) provide strong tile-level representations but remain difficult to interpret at the cellular and microenvironmental sc

researcharxiv-cs-cv

Certified Against Which Oracle? Execution Labels Set the Reported Risk of Conformal Abstention for Text-to-SQL

arXiv:2609.25938v1 Announce Type: cross Abstract: A conformal abstention certificate for text-to-SQL is only as truthful as the correctness labels it is calibrated on. The uncertainty pipelines that r

Certified Mechanistic Interpretability: Lifting Single-Input Findings to Bounded Neighbourhoods

arXiv:2609.26112v1 Announce Type: new Abstract: Mechanistic interpretability reverse-engineers transformer circuits one input at a time, leaving observed mechanisms without guarantees over bounded inp

ChainDoRA: Tensor-Train Factorized Weight-Decomposed Low-Rank Adaptation for Parameter-Efficient LLM Fine-Tuning

arXiv:2609.25058v1 Announce Type: new Abstract: Parameter-efficient fine-tuning (PEFT) adapts large language models (LLMs) to downstream tasks while updating only a small fraction of their pretrained

Challenges of Multi-Speaker Extraction for Real Conversational Speech Enhancement

arXiv:2609.25948v1 Announce Type: cross Abstract: Target-speaker and multi-speaker extraction are techniques for extracting speech from a desired speaker or desired speakers in the presence of other s

ChemVTS-Bench: Evaluating Visual-Textual-Symbolic Reasoning of Multimodal Large Language Models in Chemistry

arXiv:2511.17909v2 Announce Type: replace Abstract: Chemical reasoning inherently integrates visual, textual, and symbolic modalities, yet existing benchmarks rarely capture this complexity, often rel

CHiME-9 ECHI: A Machine Learning Challenge for Enhancing Conversations to Address Hearing Impairment

arXiv:2609.26306v1 Announce Type: new Abstract: This work presents the task and results of the CHiME-9 challenge for Enhancing Conversations to address Hearing Impairment. The challenge considers the

Local Ai

CID: Measuring Feature Importance Through Counterfactual Distributions

arXiv:2511.15371v3 Announce Type: replace Abstract: Assessing the importance of individual features in Machine Learning is critical to understand the model's decision-making process. While numerous me

local-aiarxiv-cs-lg

CivBench: Progress-Based Evaluation for LLMs' Strategic Decision-Making in Civilization V

arXiv:2604.07733v2 Announce Type: replace Abstract: Evaluating strategic decision-making in LLM-based agents requires generative, competitive, and longitudinal environments, yet few benchmarks provide

CliffCompaction: Cost-Efficient Compaction for Long-Horizon Coding Agents

arXiv:2609.26779v1 Announce Type: cross Abstract: Agents often work on complex problems that require millions of tokens of context, which necessitates compacting across sessions due to limited context

Tutorials

ClusterFewshot: Improving Few-shot Optimization for LLMs workflow

arXiv:2609.25939v1 Announce Type: new Abstract: The performance of large language model (LLM) workflows often depends on selecting a small set of in-context demonstrations to guide model behavior on n

tutorialsarxiv-cs-cl

CODA: Depth-Aligned Scene Completion and Object Decomposition from a Single RGB-D Image

arXiv:2609.25654v1 Announce Type: cross Abstract: Robots operating safely in cluttered everyday environments often need to infer scene geometry from partial observations. Methods that detect objects i

Agents

Code Plans, Diffusion Renders: Open-Ended Generative World Modeling

arXiv:2609.26458v1 Announce Type: new Abstract: We introduce extbf{CoDeR}, a new paradigm for world modeling. Unlike existing video world models that implicitly represent world dynamics through visual

agentsarxiv-cs-cv

Safety

CoEvo: Oracle-Grounded Self-Evolution of a Single Model for Multi-Step Causal Reasoning

arXiv:2609.26094v1 Announce Type: new Abstract: Multi-step causal reasoning requires chaining inferences where each step constrains the next. An early error propagates silently, and a correct answer r

safetyarxiv-cs-lg

Combinatorial Network-Based Manifold Topological Deep Learning for Image Analysis

model-releasesarxiv-cs-cv

arXiv:2609.25453v1 Announce Type: new Abstract: Medical image analysis remains fundamentally challenging because of the intricate geometric and topological structures present in medical data. Conventi

Combining Hierarchical Cognitive Process with Process Supervision for Interpretable Scene Safety Understanding

arXiv:2609.26399v1 Announce Type: new Abstract: Scene safety understanding plays a life-or-death role in situational awareness in various critical domains. Traditional methods that rely on learning di

Compared to What? A Human-Anchored Security Benchmark for LLM-Generated Infrastructure-as-Code

arXiv:2608.28021v2 Announce Type: replace-cross Abstract: Large language models increasingly author Infrastructure-as-Code (IaC), where one insecure default is provisioned straight into production. Pr

CompKV: Compensation-Aware KV Selection for Long-Context LLM Inference

arXiv:2609.26300v1 Announce Type: new Abstract: Despite their strong performance, large language models (LLMs) are bottlenecked by KV cache memory traffic during long-context inference. Sparse attenti

Complementary Roles of Radiomics and Foundation Representations in Renal Cell Carcinoma Classification: A Comparative Study of 2D and 3D CT Encodings

arXiv:2609.26463v1 Announce Type: new Abstract: Accurate preoperative subtype classification of renal cell carcinoma (RCC) from contrast-enhanced computed tomography remains clinically challenging. Ra

researcharxiv-cs-cv

Component Type, Not Reconstruction Error, Predicts Attention Quantization Sensitivity

arXiv:2609.26173v1 Announce Type: new Abstract: Many post-training quantization (PTQ) methods use layer-wise reconstruction, second-order proxy objectives, or activation-aware transformations to reduc

Compressing Long Context into Answer-Aligned Memory Embeddings for LLM Inference

arXiv:2609.25537v1 Announce Type: new Abstract: Large language model (LLM) inference is constrained by the quadratic scaling of self-attention and the linear scaling of the KV cache, increasing latenc

Applications

Concept Drift from a Causal Perspective

arXiv:2609.25340v1 Announce Type: new Abstract: Concept drift is a common phenomenon in real-world data streams, in which changes in the data-generating distribution can degrade predictive model perfo

applicationsarxiv-cs-lg

Conditional Distributional Treatment Effects: Doubly Robust Estimation and Testing

arXiv:2603.16829v2 Announce Type: replace-cross Abstract: Beyond conditional average treatment effects, treatments may impact the entire outcome distribution in covariate-dependent ways, for example,

Conditional Tensor Diffusion: Distributional Counterfactual Learning and Inference

arXiv:2609.25924v1 Announce Type: cross Abstract: Causal inference guides operational and managerial decisions but remains challenging in high-dimensional panel or tensor settings, where decisions may

Conduct Under Pressure: What Sixty Language Models Do When a User Pushes

arXiv:2609.25447v1 Announce Type: new Abstract: We study what LLMs do when a user applies pressure in an uncomfortable situation: a user insists, begs, flatters or grieves, and the model gives up a co

Hardware

Conduit: An Experience Data Plane for Distributed Reinforcement Learning

arXiv:2609.24456v1 Announce Type: cross Abstract: Distributed reinforcement learning (RL) scales training by parallelizing actors and learners around an Experience Buffer. As RL workloads grow, howeve

hardwarearxiv-cs-ai