Search: “benchmark”

Akule: Fast and Scalable Multi-Robot Motion Planning via Sparse Interaction Diffusion

arXiv:2610.14248v1 Announce Type: new Abstract: Diffusion models provide expressive trajectory priors for multi-robot motion planning (MRMP), but scaling them to large teams requires coordinating indi

tutorialsarxiv-cs-ro

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AMemOps: Evaluating Memory Updates in Long Audio Streams

DGX agent

arXiv:2610.18652v1 Announce Type: cross Abstract: Audio memory must recognise recurring sources across acoustic changes while keeping their attributes up to date. Overall accuracy does not reveal how

model-releasesarxiv-cs-lg

14 Oct 2026

Agents

An Agentic AI System for Multi-Framework Communication Coding

arXiv:2512.08659v2 Announce Type: replace-cross Abstract: Clinical communication is central to patient outcomes, yet large-scale human annotation of patient-provider conversation remains labor-intensi

agentsarxiv-cs-lg

Anchors Enhance Controllability and Reasoning of Diffusion Language Models

arXiv:2610.15097v1 Announce Type: new Abstract: Diffusion language models (DLMs) offer new opportunities for controllable text generation. However, their controllability, defined as the ability to sat

tutorialsarxiv-cs-lg

ASM-6D: Fast Certified Pose-Shape Inference with Complete Correspondence Flow Maps

arXiv:2610.15287v1 Announce Type: new Abstract: Category-level 6D pose and shape estimation requires recovering an object's pose and geometry from partial observations without an exact instance model.

b11632: cuda, hip: GDN chunked kernel (#29353)

model-releasesllama-cpp-releases

cuda, hip: GDN chunked kernel Ported-from: tetherto#257 Source-commit: 53f0027 Co-authored-by: Aman Gupta aman.gupta@tether.io fix older builds review fixes cuda: enable GDN MMA on tested Ampere and A

BandPC: Learning Residual-Band Preconditioner Combinations for Flexible Conjugate Gradient Solvers

arXiv:2609.37140v1 Announce Type: cross Abstract: The conjugate gradient (CG) method is a classical iterative solver for sparse symmetric positive definite (SPD) linear systems, but its convergence st

Agents

Banger paper from Stanford, Together AI, and colleagues. Why is this an interesting paper? Today's LLM agents must write out every reasoning…

Banger paper from Stanford, Together AI, and colleagues. Why is this an interesting paper? Today's LLM agents must write out every reasoning step as text before they can search, run code, or take acti

agentsdair-ai--x

Barycentric Anchored Weight Search for Robust Model Merging

arXiv:2610.14017v1 Announce Type: cross Abstract: We propose Barycentric Anchored Search, a post-hoc weight-space procedure for improving fine-tuned vision models under distribution shift without addi

BAT3R: Robust Online 3D Reconstruction with Bayesian Adaptive State Updates

arXiv:2610.16330v1 Announce Type: new Abstract: Feed-forward transformer models have driven remarkable progress in 3D vision,yet their quadratic complexity renders them impractical for long video sequ

Bayesian Optimization of CVaR with Unknown Hyperparameters

arXiv:2610.18306v1 Announce Type: new Abstract: We study Bayesian optimization of the Conditional Value-at-Risk (CVaR) of an expensive black-box function that depends on a decision and an environmenta

applicationsarxiv-cs-lg

Hardware

BELT : BEhavior-Aligned Low-Rank KV Cache Compression with Learned Top-k Rank Allocation

arXiv:2610.17966v1 Announce Type: new Abstract: Long-context inference makes the key-value (KV) cache a major memory bottleneck for large language models. We propose BELT, a low-rank KV cache compress

hardwarearxiv-cs-lg

Beyond Discrimination: Evaluating Probability Calibration in Radiomics-Image Fusion for Renal Cell Carcinoma Classification

arXiv:2610.14151v1 Announce Type: cross Abstract: Reliable probability estimates are important for the clinical interpretation of machine-learning classifiers, yet radiomics--image fusion models for r

Beyond Heatmaps: Unsupervised Concept-Graph Reasoning for Interpretable Visual Explanation

arXiv:2607.01416v2 Announce Type: replace Abstract: Concept Bottleneck Models (CBMs) provide an intrinsically interpretable alternative to post-hoc explanations. However, existing CBMs often rely on p

Beyond Planning: Differentiable Control-in-the-loop End-to-End Autonomous Driving

model-releasesarxiv-cs-ro

arXiv:2610.18167v1 Announce Type: new Abstract: End-to-end autonomous driving has achieved remarkable progress by planning trajectories directly from raw sensor data. Yet the step from planning to con

Beyond Policy Rollouts: Embedding-Space Directional Exploration with Anchored Noise for LLM RLVR

arXiv:2610.16105v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards (RLVR) typically uses small rollout groups per prompt, where repeated sampling from the same policy can y

safetyarxiv-cs-lg

Beyond RMSE and Granger Gain: Multi-Horizon Forecast-Error Entropy for Nonlinear Predictive Coupling

arXiv:2610.18390v1 Announce Type: cross Abstract: We investigate whether the past of an auxiliary time series provides predictive information about a target beyond that contained in the target's own h

Bifocal: Training-Free Mixed Granularity Visual Token Pruning for Multimodal LLMs

arXiv:2610.18399v1 Announce Type: new Abstract: Visual token pruning for multimodal large language models (MLLMs) appears highly effective, with existing methods retaining over 95% of unpruned perform

BookNet: Book Image Rectification via Cross-Page Attention Network

model-releasesarxiv-cs-cv

arXiv:2601.21938v2 Announce Type: replace Abstract: Book image rectification presents unique challenges due to its complex geometric distortions from binding constraints, where left and right pages ex

Building age estimation based on widely available multi-source1 geospatial data

model-releasesarxiv-cs-cv

arXiv:2610.16320v1 Announce Type: new Abstract: Building age is fundamental for urban infrastructure safety, operation, and planning, as it directly affects structural safety, and sustainable developm

CAG-WAM: Causal Action-Gain World Action Models for Robot Manipulation

arXiv:2610.14720v1 Announce Type: new Abstract: World Action Models (WAMs) combine action generation with future prediction, allowing predicted futures to support action evaluation and robot decision

safetyarxiv-cs-ro

Can We Trust This Evaluation? Instance-Level Reliability of LLM-as-a-Judge

arXiv:2610.14029v1 Announce Type: new Abstract: LLM-as-a-judge evaluation commonly aggregates multiple judges' decisions into a majority vote, yet this panel decision process does not guarantee that t

applicationsarxiv-cs-lg

CAPTOR: a vision-language foundation model for Chest-Abdomen-Pelvis Oncologic CT

arXiv:2610.14226v1 Announce Type: new Abstract: Purpose : To develop and evaluate CAPTOR, a deep-learning vision-language foundation model for oncologic chest-abdomen-pelvis (CAP) CT interpretation. M

Carve VFM: Carving Specialists out of Generalists, Automatic Task-Specific Compression of Vision Foundation Models

model-releasesarxiv-cs-cv

arXiv:2610.16813v1 Announce Type: new Abstract: Vision foundation models (VFMs) provide transferable visual representations, but their inference cost limits deployment in many settings. Fine-tuning sp

CB-Agent: Concept-Based In-Context Learning for Sequential Action Selection

arXiv:2610.14574v1 Announce Type: cross Abstract: In-context learning (ICL) allows large language models to use demonstrations without updating their parameters. For interactive agents, demonstrations

Certified Robustness via Sparse Second-Order Cone Relaxations

arXiv:2610.16016v1 Announce Type: new Abstract: Certified robustness is critical for deploying neural networks in safety-sensitive settings, but computing exact certificates typically requires solving

CESNET-TimeSeries26: A Multi-Year Network Traffic Time Series Dataset for Annual Seasonality, Concept Drift, and Long-Term Anomaly Detection

arXiv:2610.14799v1 Announce Type: cross Abstract: Forecast-based models are fundamental to network traffic management and anomaly detection. However, it is essential to evaluate these forecasting mode

Chasing Label Shifters: A Change-Aware Framework for Dynamic Graph Node Classification

arXiv:2610.17582v1 Announce Type: new Abstract: Entities in real-world systems often evolve over time: users shift between information consumption patterns, researchers migrate between fields, and fir

applicationsarxiv-cs-lg

Cite Pretrain: Retrieval-Free Knowledge Attribution for Large Language Models

arXiv:2506.17585v4 Announce Type: replace-cross Abstract: Large language models can answer questions correctly and still hallucinate their citations. Most citation systems address this by retrieving d

Clinical value claims in multimodal AI outpace supporting evidence

arXiv:2610.14395v1 Announce Type: new Abstract: Multimodal prediction models add a data source, such as imaging or an assay, to clinical information, and health systems must decide whether that added

CLNO: Conservative Local Neural Operator for Compressible Flow Prediction

arXiv:2610.16040v1 Announce Type: cross Abstract: We propose a conservative local neural operator (CLNO) for accurate and generalizable autoregressive prediction of compressible flows. Building upon t

CoCA: Criterion-Aware Credit Assignment for Rubric-Based Reinforcement Learning

arXiv:2610.17733v1 Announce Type: new Abstract: Rubric-based reinforcement learning (RL) evaluates responses against explicit criteria to construct rewards, offering a viable way to train large langua

CodeLift: Your Test Suite Is Secretly a Dense SWE Curriculum

arXiv:2610.17224v1 Announce Type: new Abstract: The pass-all-test binary reward has become a standard recipe for training software engineering agents, but its sparsity eliminates difference of partial

Coding LLMs Can Be Harnessed to Learn Interpretable Models from Data

arXiv:2610.14006v1 Announce Type: new Abstract: Since compact, interpretable classifiers correspond to simple programs, it is natural to ask if LLM-based coding agents can be used to learn interpretab

tutorialsarxiv-cs-lg

Comparative Study of Canvas- and Coordinate-Based Geometry-Conditioned Neural Networks for Real-Time Prediction of Indoor Environments with Varying Room Dimensions

arXiv:2610.15885v1 Announce Type: cross Abstract: Computational fluid dynamics (CFD) is computationally expensive for iterative building design. Deep learning surrogates offer faster predictions, but

COMPASS: Contextual Moment Prior Alignment for Generalizable AIGI Detection

arXiv:2610.18034v1 Announce Type: new Abstract: Generalizable AI-generated image (AIGI) and deepfake detection remain challenging due to the asymmetry phenomenon, where adaptation to limited training

safetyarxiv-cs-cv

Compositional Average Velocity Modeling for Efficient Diffusion Offline Reinforcement Learning

arXiv:2610.14941v1 Announce Type: new Abstract: Diffusion-based policies have recently emerged as a powerful paradigm for offline reinforcement learning, but their reliance on multi-step denoising int

safetyarxiv-cs-lg

Compositional RL: Evaluating and Training LLMs for Collaborative Reasoning

arXiv:2610.17456v1 Announce Type: cross Abstract: Language models are often evaluated alone but increasingly deployed together, routed between, made to debate, ensembled, or collaboratively trained. F

safetyarxiv-cs-lg

Constrained Contextual Bandits with Adversarial Contexts

arXiv:2605.06190v2 Announce Type: replace Abstract: We study constrained contextual bandits with adversarial contexts, where each action yields a random reward and incurs a random cost. The conditiona

Context-Aware Precision Scheduling for Small Language Models

arXiv:2610.15640v1 Announce Type: new Abstract: Small language models (SLMs) used as on-device agents must balance accuracy and latency as their context grows. Mixed precision helps manage this tradeo

Contrastive Linearized Laplace: Loss Geometry, Valid Repairs, and Reliable Cores

arXiv:2610.14875v1 Announce Type: new Abstract: While contrastive learning is widely used to learn deterministic representations, it does not inherently provide estimates of uncertainty. Post-hoc unce

tutorialsarxiv-cs-lg

Correlation-Aware Reconstruction for Multivariate Time Series Anomaly Detection

arXiv:2610.15250v1 Announce Type: new Abstract: Reconstruction-based detectors for multivariate time series anomaly detection typically learn a single entangled representation of normal behavior and f

CowCare: A Unified Visual Framework for Viewpoint-Robust Cattle Tracking and Spatiotemporal Heat-Stress Analysis

arXiv:2610.15012v1 Announce Type: new Abstract: Reliable individual monitoring is essential for dairy cattle welfare and early heat stress detection in Precision Livestock Farming (PLF). However, tran

applicationsarxiv-cs-cv

Cross-Market Meta-Learning for Electricity Price Forecasting

arXiv:2610.17387v1 Announce Type: new Abstract: Day-ahead (DA) electricity price forecasting is usually posed per market, with a separate model fitted to each grid's own history. This caps accuracy: s

CrossMean: Model Merging by Pairing Merged and Expert Activations

arXiv:2610.14762v1 Announce Type: new Abstract: Model merging combines fine-tuned experts into one network without retraining, and closed-form methods compute each merged layer from statistics of its

Data Free Training of Diffusion Models for Lattice Field Theories

arXiv:2610.18863v1 Announce Type: cross Abstract: Sampling equilibrium field configurations remains a major computational bottleneck in lattice field theory, motivating the development of generative s

Agents

Davinci-Tool: Scaling Agentic Tool-Use Supervision with Verifiable Executable Environments

arXiv:2610.16867v1 Announce Type: new Abstract: Reliable agentic tool use requires training data that pair diverse multi-step workflows with executable evidence that the requested outcomes were achiev

agentsarxiv-cs-lg

Deep Jump Gaussian Processes for Surrogate Modeling of High-Dimensional Piecewise Continuous Functions

arXiv:2510.21974v3 Announce Type: replace Abstract: We introduce Deep Jump Gaussian Processes (DJGP), a novel method for surrogate modeling of a piecewise continuous function on a high-dimensional dom