Search: “benchmark”

AutoVerifier: Residual-Guided Non-Parametric Optimization for Reference-Based Answer Verification

arXiv:2608.25637v1 Announce Type: new Abstract: Reference-based verifiers are important for evaluating reasoning models and providing accurate outcome rewards in reinforcement learning with verifiable

researcharxiv-cs-cl

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Model Releases

Ban&Pick: Enhancing Performance and Efficiency of MoE-LLMs via Smarter Routing

DGX agent

arXiv:2509.06346v3 Announce Type: replace Abstract: Sparse Mixture-of-Experts (MoE) has become a key architecture for scaling large language models (LLMs) efficiently. Recent fine-grained MoE designs

model-releasesarxiv-cs-lg

27 Aug 2026

Beam Search, Self-Consistency, and the Limits of Inference-Time Scaling for Grammar-Constrained Text-to-SQL in Small Language Models

arXiv:2608.25761v1 Announce Type: new Abstract: One common trade-off in the use of large language models involves reducing the size of the model while increasing the amount of computation at inference

Beyond Tokens: Probing Higher-Order Epistasis in Learned Protein Representations

arXiv:2608.24953v1 Announce Type: cross Abstract: Protein fitness landscapes contain nonlinear interactions in which mutation effects depend on other residues. We introduce ORBIT, an Order-Resolved Be

BRIDLE: Generalized Self-supervised Learning with Quantization

arXiv:2502.02118v2 Announce Type: replace-cross Abstract: Self-supervised learning has been a powerful approach for learning meaningful representations from unlabeled data across various domains, redu

researcharxiv-cs-cv

Can We Read the Mind of an Audio LLM? A Verbalizable, Multilingual Middle-Layer Workspace

arXiv:2608.24958v1 Announce Type: cross Abstract: An audio language model is a black box in a specific way: we see what it says, never what it works out on the way there, and chain-of-thought monitori

Local Ai

CloSeR: Unified Relational Distillation from Closed-Set Teachers for Category Discovery

arXiv:2608.25692v1 Announce Type: new Abstract: Generalized Category Discovery (GCD) is an intriguing open-world problem that has garnered increasing attention: given partially labelled data, the goal

local-aiarxiv-cs-cv

CoRE: Weakly Supervised Coarse-to-Fine Risk Evidence Learning in Driving Videos

arXiv:2608.25344v1 Announce Type: new Abstract: Perceived risk in driving evolves over time and may be supported by specific scene entities, yet supervision is typically limited to coarse video-level

CRAMER: Control via Request-Aware Masking for Editing Recommenders

arXiv:2608.25370v1 Announce Type: cross Abstract: Sequential recommendation models, while powerful, have limited flexibility in responding to immediate user requests, making it difficult to adapt thei

D^3-MOPD: Adaptive Dynamic Domain ScheDuling for Efficient Multi-Teacher Distillation

arXiv:2608.24987v1 Announce Type: new Abstract: Multi-teacher on-policy distillation (MOPD) distills several domain-expert teachers into a single student by minimizing per-domain reverse-KL divergence

safetyarxiv-cs-lg

Hardware

DataKernelBench: Can LLMs Optimize Database Queries on GPUs?

arXiv:2608.25061v1 Announce Type: new Abstract: GPUs increasingly accelerate database systems, but query-specific peak performance still often relies on hand-written kernels. Existing LLM kernel bench

hardwarearxiv-cs-cl

DCGC: Draft-Conditioned Global Correction for Complex Reasoning with Masked Diffusion Models

arXiv:2608.25428v1 Announce Type: new Abstract: Correcting flawed reasoning traces remains a significant challenge for Large Language Models (LLMs), whose autoregressive generation can propagate early

researcharxiv-cs-cl

Local Ai

Deep Learning Segmentation of Diffusion-Weighted MRI Acute Ischaemic Stroke: A Pragmatic Evaluation Across Three Datasets

arXiv:2608.25675v1 Announce Type: new Abstract: Objective: Diffusion-weighted MRI (DWI-MRI) is the gold standard for visualizing and quantifying acute ischaemic stroke (AIS). Although deep learning me

local-aiarxiv-cs-cv

DESCENT: Directed Edge Scene Encoding for Airport Surface Movement Prediction

model-releasesarxiv-cs-ro

arXiv:2608.26002v1 Announce Type: new Abstract: Advanced automation is a key technology for enhancing the safety of ground operations amidst the increasing density of commercial air traffic. While mot

Drift Variation Autoencoder: Unifying Generation and Representation Learning through Conditional Posterior Flow Matching

arXiv:2608.25138v1 Announce Type: new Abstract: Stochastic masking, cropping, or modality removal makes deterministic reconstruction an incomplete target: one observation can admit many clean completi

E2Rank: Unifying Text Embedding and Listwise Reranking for Effective and Efficient Search

arXiv:2510.22733v3 Announce Type: replace Abstract: Text embedding models deliver competitive retrieval performance with high efficiency, but their ranking fidelity remains limited compared to LLM-bas

EduAgentQG: Multi-Agent Personalized Mathematics Question Generation with Explicit Diversity and Objective-Aware Evaluation

arXiv:2511.11635v2 Announce Type: replace-cross Abstract: In intelligent education, personalized mathematics question generation aims to produce mathematics questions that satisfy educational requirem

Emyx: Fast and efficient all-atom protein generation

arXiv:2606.19377v2 Announce Type: replace Abstract: Computational enzyme design requires generating proteins that scaffold catalytic residues and ligands, a task that demands both geometric accuracy a

EXAONE Tabular 1.0 : Technical Report

arXiv:2608.25774v1 Announce Type: new Abstract: EXAONE Tabular is a compact tabular foundation model family for classification and regression via in-context learning, producing predictions without dat

FAMPWQ: Fisher Information-based Adaptive Mixed Precision Weight Quantization for Effective LLM Inference

arXiv:2608.24945v1 Announce Type: new Abstract: Recent years have witnessed remarkable achievements of Large Language Models (LLMs) in multiple domains, while the excessive resource requirements of LL

Applications

FlashNormal: Detailed Surface Normal Estimation from Flash and No-Flash Images

arXiv:2608.25360v1 Announce Type: new Abstract: High-quality surface normal estimation is preferred for detailed surface shape recovery and image editing. Existing single image-based methods, though b

applicationsarxiv-cs-cv

Forecasting Multiple Observables with SCROLL: Score-Trained Uncertainty for Stochastic Dynamics

arXiv:2608.25898v1 Announce Type: new Abstract: Forecasting a stochastic dynamical system rarely means a single number: one wants several observables---future state, threshold event, regime label---ea

From Memorization to Absorption: Mixed-Policy RL for Continual Knowledge Injection

arXiv:2608.25243v1 Announce Type: new Abstract: Continual knowledge injection is essential for keeping large language models up-to-date in a fast-evolving world. Existing methods rely on supervised fi

safetyarxiv-cs-cl

From Passive Response to Proactive Correction: Enhancing LLM Robustness Against Input Fact Perturbations

arXiv:2608.25894v1 Announce Type: new Abstract: Large language models (LLMs) frequently produce confident yet factually incorrect responses when user inputs contain misleading premises, a phenomenon w

Gaussian Splatting Underwater: A Controlled Cross-Regime Study

arXiv:2608.25483v1 Announce Type: new Abstract: The underwater environment is challenging for 3D reconstruction, because particles suspended in the water scatter and diffuse light, turbidity varies, a

GraftSR: Grafting Authentic Textures for Real-World Image Super-Resolution via Identical-Instance Guidance

arXiv:2608.25334v1 Announce Type: new Abstract: Diffusion-based real-world image super-resolution (SR) achieves impressive perceptual quality but inherently suffers from severe texture hallucination.

GreenLeaf Law Embed Tiny: A Compact Embedding Model for Legal Domain Retrieval

arXiv:2608.24936v1 Announce Type: cross Abstract: We present GreenLeaf Law Embed Tiny, a 0.6B parameter embedding model for legal domain retrieval. GreenLeaf-Tiny achieves 75.11% on the Massive Legal

GRIP: Algorithm-Agnostic Machine Unlearning for Mixture-of-Experts via Geometric Router Constraints

arXiv:2601.16905v3 Announce Type: replace Abstract: Machine unlearning in Mixture-of-Experts (MoE) large language models presents a critical yet under-explored challenge. Current unlearning methods ap

Infer Human's Intentions Before Following Natural Language Instructions

arXiv:2409.18073v2 Announce Type: replace-cross Abstract: For AI agents to be helpful to humans, they should be able to follow natural language instructions to complete everyday cooperative tasks in h

Joint Initialization of Flux Networks and Effective Multiplication Factor for Physics-Informed Neural Networks Solving Neutron Diffusion Problems

arXiv:2608.25443v1 Announce Type: new Abstract: Efficient determination of the effective multiplication factor (keff) is an important computational task in reactor core neutronics analysis. Physics-in

Ladder Up, Memory Down: Low-Cost Fine-Tuning With Side Nets

arXiv:2512.14237v2 Announce Type: replace Abstract: Fine-tuning large language models (LLMs) is often limited by the memory available on commodity GPUs. Parameter-efficient fine-tuning (PEFT) methods

LDAC-Net: A Learnable Multi-Lag Differencing Attention-Convolution Network for Drift-Robust Recognition with Low-Cost MOX Gas Sensors

arXiv:2608.25646v1 Announce Type: new Abstract: Portable electronic-nose systems based on low-cost metal-oxide (MOX) gas sensors offer a practical solution for gas and odour recognition, but their sig

Learning to Accelerate Vision-Language-Action Models through Adaptive Visual Token Caching

arXiv:2602.00686v2 Announce Type: replace Abstract: Vision-Language-Action (VLA) models have demonstrated remarkable generalization capabilities in robotic manipulation tasks, yet their substantial co

safetyarxiv-cs-ro

Learning to Prompt: Improving Student Engagement with Adaptive LLM-based High-School Tutoring

arXiv:2606.20138v2 Announce Type: replace-cross Abstract: LLMs can personalize education, although current static-prompt tutoring systems struggle to adapt to diverse academic disciplines. We develop

Loss-Based Active Learning for Neural Abstractive Summarization

arXiv:2608.25881v1 Announce Type: new Abstract: Fine-tuning abstractive summarization models requires high-quality annotated data. However, obtaining such corpora is expensive and time-consuming, as i

LUTSeg: A Longitudinal Multi-Expert Dataset for Ulcer Tissue Segmentation

arXiv:2608.25866v1 Announce Type: new Abstract: Quantifying wound tissue composition is essential for monitoring chronic ulcer progression and guiding treatment decisions. However, pixel-level annotat

MA-VLA: Multi-Arm Vision-Language-Action Model for Collaboration and Compositional Generalization

model-releasesarxiv-cs-ro

arXiv:2608.25864v1 Announce Type: new Abstract: Multi-arm collaboration is becoming a core capability in embodied manipulation. Recent vision-language-action (VLA) models integrate perception, languag

Agents

MacroAgent: Regularity-Aware Macro Legalization with LLM-Agent-Designed Contour Algorithms

arXiv:2608.24946v1 Announce Type: new Abstract: Macros constitute a large part of the core area in modern very large-scale integration (VLSI) designs. Moreover, macro positions have a significant impa

agentsarxiv-cs-lg

MetaSieve: Faster Relational Deep Learning through SQL-Based Metapath Selection

arXiv:2608.25903v1 Announce Type: cross Abstract: Relational Deep Learning (RDL) is an effective approach to machine learning over multi-table relational databases. In RDL, a database is modeled as a

Mitigating LLM sycophancy with RL-based fine-tuning: Bayesian Truth Serum approach

arXiv:2608.25267v1 Announce Type: new Abstract: Large language models (LLMs) frequently exhibit sycophancy: they adapt their answers to a user's stated beliefs or preferences instead of reporting what

MMEmb-R1: Reasoning-Enhanced Multimodal Embedding with Pair-Aware Selection and Adaptive Control

arXiv:2604.06156v2 Announce Type: replace-cross Abstract: MLLMs have been successfully applied to multimodal embedding tasks, yet their generative reasoning capabilities remain underutilized. Directly

MObyGaze: a film dataset of multimodal objectification densely annotated by experts

arXiv:2505.22084v2 Announce Type: replace Abstract: Characterizing and quantifying gender representation disparities in audiovisual storytelling contents is necessary to grasp how stereotypes may perp

Multi-Modal Anomaly Detection: A Survey

arXiv:2608.24937v1 Announce Type: new Abstract: Multi-Modal Anomaly Detection (MMAD) detects rare abnormal events from heterogeneous data sources and is increasingly used in safety- and reliability-cr

safetyarxiv-cs-lg

Multi-output Gaussian process prediction of physical fields under linear equality constraints

arXiv:2608.25709v1 Announce Type: cross Abstract: We address the simultaneous prediction of multiple high-dimensional physical fields governed by linear equality constraints, a setting that arises in

MyoMechanix: Biomechanically-Grounded Compositional Skilled Activity Understanding and Coaching

arXiv:2608.26094v1 Announce Type: new Abstract: Existing action quality assessment (AQA) datasets and methods rely primarily on visual inputs such as RGB and pose, overlooking physiological dynamics s

Neural-Bayesian Structure Learning for Discrete Choice Modeling

arXiv:2608.25258v1 Announce Type: new Abstract: Conventional discrete choice and machine learning models are estimated primarily from observational data and typically treat explanatory covariates as p

safetyarxiv-cs-lg

NVExplain: Explaining Time Series Forecasting with Latent Trajectory Analysis and Structure-Preserving Surrogates

arXiv:2608.25080v1 Announce Type: new Abstract: Time series forecasting models are widely used in high-stakes settings, yet their predictions remain difficult to interpret because existing post-hoc me

One Policy, Many Embodiments: Unified Camera-Centric Action Geometry Pre-training for Heterogeneous Embodied Manipulation

model-releasesarxiv-cs-ro

arXiv:2608.26058v1 Announce Type: new Abstract: Scaling generalist vision-language-action (VLA) policies is severely bottlenecked by the inherent heterogeneity of embodied data, which spans diverse ro