Search: “benchmark”

TGIF: Text-Guided Layer Fusion Mitigates Hallucination in Multimodal LLMs

arXiv:2601.03100v3 Announce Type: replace-cross Abstract: Multimodal large language models (MLLMs) typically rely on a single late-layer feature from a frozen vision encoder, leaving the encoder's ric

researcharxiv-cs-ai

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Model Releases

The Cell Must Go On: Agar.io for Continual Reinforcement Learning

DGX agent

arXiv:2505.18347v3 Announce Type: replace-cross Abstract: Continual reinforcement learning (RL) concerns agents that are expected to learn continually, rather than converge to a policy that is then fi

model-releasesarxiv-cs-ai

11 Aug 2026

The Cost of Adaptivity: Matching Lower Bounds Across Learning Problems

arXiv:2608.08826v1 Announce Type: new Abstract: Adaptive procedures must work without nuisance information an oracle may use, such as a gradient scale or smoothness index, and robust procedures may ha

The Neural Division of Labor: Biologically-Inspired Modular Architectures for Robust Neuromorphic Computing

arXiv:2608.08317v1 Announce Type: new Abstract: Biological neural systems achieve high efficiency and robustness through compartmentalized architectures. In contrast, modern artificial neural networks

researcharxiv-cs-lg

The Replay Gap: Static Evaluation of Model Switching in LLM Agents Scores the Wrong World

arXiv:2608.08239v1 Announce Type: cross Abstract: LLM routers promise efficiency by matching each request to the cheapest adequate model, and are increasingly applied per step inside multi-step agents

agentsarxiv-cs-cl

Think Deep, Speak Once: Relit, A Recursive Latent Implicit Transformer Framework

arXiv:2608.08113v1 Announce Type: new Abstract: Chain-of-Thought (CoT) prompting has become the dominant paradigm for eliciting reasoning in Large Language Models (LLMs), yet it creates substantial co

Thinking With Tools, Not With Pixels: Tool Calls as Text Scaffolds for Visual Reasoning

arXiv:2608.09682v1 Announce Type: new Abstract: Tool-augmented vision-language models increasingly 'think with images': they call crop, zoom, or code tools and reason over the returned pixels. However

agentsarxiv-cs-cv

Thought-Level Beam Search for Reasoning

arXiv:2608.08020v1 Announce Type: new Abstract: Test-time compute scaling is a primary driver of performance in large reasoning models (LRMs), but extreme inefficiency bounds current approaches, shift

researcharxiv-cs-ai

To Memorize or to Retrieve: Scaling the Interaction Between Pretraining and Retrieval

arXiv:2604.00715v2 Announce Type: replace-cross Abstract: Retrieval-augmented generation (RAG) improves language model (LM) performance by providing relevant context at test time for knowledge-intensi

Applications

Towards Adaptive Super-Resolution and Quality Assessment via Test-Time Adaptation

arXiv:2608.08508v1 Announce Type: new Abstract: This paper presents doctoral research on adaptive video super-resolution and perceptual quality modeling under real-world conditions. Existing video sup

applicationsarxiv-cs-cv

TRACE: TRajectory Attribution for Automated Context Engineering

arXiv:2608.09153v1 Announce Type: new Abstract: Production AI agents fail when their context sources -- system prompts, knowledge bases, tool descriptions, and procedural skills -- contain errors or g

Trajectory Divergence Horizon Decision for Reliable Dual-Arm Surgical Subtask Manipulation

model-releasesarxiv-cs-ro

arXiv:2608.09125v1 Announce Type: new Abstract: Surgical robotic systems are increasingly being adopted as clinical workload rises, motivating autonomous solutions for repetitive manipulation subtasks

TREAT: Evaluating Access to Formal Knowledge across Equivalent Mathematical Representations

arXiv:2608.07540v1 Announce Type: new Abstract: AI systems increasingly operate between flexible input representations and formal objects used by downstream tools. A key challenge is recognizing when

TrustRoboReward: Preference-Ordered Isotonic Score Editing for Multi-Paradigm Robot Reward Models

arXiv:2608.08491v1 Announce Type: new Abstract: Reward models are a bottleneck for reinforcement learning in embodied AI. Long-horizon robotic manipulation requires scalable vision feedback beyond han

Tutorials

TS-Mob: Social and Geographical-Aware Time Series Foundation-Model Framework for Human Mobility Prediction

arXiv:2507.00945v2 Announce Type: replace Abstract: Short-term forecasting of aggregated human mobility flows supports urban planning, intelligent transportation systems, and emergency response, yet e

tutorialsarxiv-cs-lg

TSDS-Toolbox: A Toolbox for Measuring Time-Series Dataset Similarity

arXiv:2608.08119v1 Announce Type: new Abstract: The rapid advancement of artificial intelligence (AI) has significantly accelerated research in time-series analysis, particularly in forecasting, class

researcharxiv-cs-lg

Safety

Uncertainty-Aware Variational Reward Factorization via Probabilistic Preference Bases for LLM Personalization

arXiv:2604.00997v2 Announce Type: replace Abstract: Reward factorization personalizes large language models (LLMs) by decomposing rewards into shared basis functions and user-specific weights. Yet, ex

safetyarxiv-cs-cl

Understanding Calibration and Truncation Error Propagation in Training-Free Low-Rank Compression for LLMs

arXiv:2608.08506v1 Announce Type: new Abstract: Training-free low-rank compression frameworks have been gaining prominence for LLM compression given their effectiveness in reducing model parameter cou

UNMASK: Discovering and Causally Verifying Spurious Shortcuts in Text Classifiers

model-releasesarxiv-cs-cl

arXiv:2608.09209v1 Announce Type: new Abstract: Neural language models trained on large crowdsourced corpora frequently exploit spurious surface patterns tied to target labels without true linguistic

UNSPECIFIC: General Constraint Synthesis for Breaking Copy-and-Paste Shortcut in LLM Instruction Following

model-releasesarxiv-cs-cl

arXiv:2608.09154v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly expected to follow long lists of constraints in complex instructions, and synthesizing instructions from a

Unsupervised Point Cloud Registration with Self-Distillation

arXiv:2409.07558v2 Announce Type: replace Abstract: Rigid point cloud registration is a fundamental problem and highly relevant in robotics and autonomous driving. Nowadays deep learning methods can b

Applications

V-Simba: Unleashing the Architectural Potential of RL in Visual Continuous Control

arXiv:2608.07870v1 Announce Type: new Abstract: Improving sample efficiency remains a core challenge in reinforcement learning (RL), especially in real-world settings like robotics, where data collect

applicationsarxiv-cs-lg

VeinCast: Physics-Guided Dynamic Field Graphs with Graph-Conditioned Fusion for Global Medium-Range Weather Forecasting

arXiv:2608.09286v1 Announce Type: cross Abstract: Global medium-range weather forecasting requires modeling structured yet state-dependent interactions among heterogeneous atmospheric fields. Existing

Verication-driven closed-loop multi-agent large language modelframework for code-compliant structural design

arXiv:2608.07978v1 Announce Type: cross Abstract: Multi-agent large language model(LLM)systems are applied to structural design,yet most use one-shot generation and cannot verify their output,leaving

Vision Meets WiFi: Physics-Grounded Estimation of Volumetric Mechanical Properties

arXiv:2608.07726v1 Announce Type: new Abstract: Estimating volumetric mechanical properties, including Young's modulus, Poisson's ratio, and density at each voxel, is intrinsically ambiguous from visi

researcharxiv-cs-cv

VLZip: Unified Visual and Textual Compression for Interleaved Long-Context Modeling

arXiv:2608.08630v1 Announce Type: new Abstract: Vision Language Models (VLMs) face significant challenges with ultra-long, interleaved image-text sequences due to the quadratic complexity of self-atte

Safety

VoxZip: Semantic-Anchored Temporal KV Cache Compression for Long-Context Audio Inference

arXiv:2608.08569v1 Announce Type: new Abstract: Recent advancements in Speech Large Language Models have demonstrated remarkable capabilities in understanding complex audio tasks. Despite this progres

safetyarxiv-cs-ai

VTO: Visual Tool Orchestration for Video Anomaly Detection

arXiv:2608.08219v1 Announce Type: cross Abstract: Video anomaly detection (VAD) is a critical yet challenging task due to the complex and diverse nature of real-world scenarios. Traditional deep learn

Local Ai

Warp-free Cross-view Geo-localization via Feature-space Consensus Mining

arXiv:2608.09321v1 Announce Type: new Abstract: Cross-view geo-localization is challenging due to drastic viewpoint changes and large appearance discrepancies between street-level and satellite imager

local-aiarxiv-cs-cv

Hardware

What Irregularity Costs: CUDA C++, Rust, and Triton on a Hash-Blocked GPU Workload

arXiv:2608.08287v1 Announce Type: new Abstract: GPU language comparisons are almost always run on tiled dense linear algebra, where every toolchain is good and the differences are small. We implement

hardwarearxiv-cs-cv

What Would Fix This RAG Failure? Auditing Counterfactual Response with Paired Evidence Interventions

arXiv:2608.08944v1 Announce Type: cross Abstract: A failed retrieval-augmented generation (RAG) answer can be consistent with several unseen responses to evidence repair. We introduce Pair-ID, an offl

When Counterbalancing Hides the Bias: Access-Conditioned Position Lock in Forced-Choice LLM Evaluation

arXiv:2607.10202v2 Announce Type: replace Abstract: Forced-choice probes with counterbalanced orientations are a standard tool for measuring language-model 'value dispositions,' and a concentration/ex

When Does An Extra View Help? Adapting Single-View 3D Reconstruction with Extra Imagery

arXiv:2608.08132v1 Announce Type: new Abstract: Reconstruction of 3D objects from a single image is a challenging research problem in computer vision. The key challenge is the lack of critical informa

When Grammar Guides the Attack: Uncovering Control-Plane Vulnerabilities in LLMs with Structured Output

arXiv:2503.24191v4 Announce Type: replace-cross Abstract: Content Warning: This paper may contain unsafe or harmful content generated by LLMs that may be offensive to readers. Large Language Models (L

When should we trust the annotation? Selective prediction for molecular structure retrieval from mass spectra

arXiv:2603.10950v2 Announce Type: replace Abstract: Machine learning methods for identifying molecular structures from tandem mass spectra (MS/MS) have advanced rapidly, yet current approaches still e

Why Does the Future Branch? Identifiable Closure Tests for Stochastic Physical World Models

arXiv:2608.00591v2 Announce Type: replace Abstract: A calibrated stochastic world model can reveal how uncertain a future is without revealing why it branches. The same conditional future law can aris

Wiener Representation Filtering for VLM Hallucination Suppression

arXiv:2608.08167v1 Announce Type: new Abstract: Vision-language models (VLMs) excel at open-ended captioning and visual QA but often describe objects, attributes, or relations absent from the image, a

WorldSimProbe: Diagnosing Simulator Faithfulness in Action-Conditioned World Models for Embodied Manipulation

arXiv:2608.09298v1 Announce Type: cross Abstract: Action-conditioned world models (ACWMs) promise to provide embodied AI with scalable predictive simulators for planning, policy evaluation, and data g

XFeat Revisited: Reproducibility and Evaluation of a Lightweight Image Matcher

arXiv:2608.09519v1 Announce Type: new Abstract: We present a reproducibility study of XFeat, a lightweight local feature extractor and matcher designed to identify corresponding points across images e

You Only Flow Once: Calibrated and Real-Time Radar Pose Estimation with Multi-Hypothesis Normalizing Flows

arXiv:2608.09579v1 Announce Type: new Abstract: Sparse and noisy millimeter-wave radar point cloud observations often correspond to multiple plausible human poses, making deterministic pose estimation

researcharxiv-cs-cv

Your VLM Already Knows When: Training-Free Temporal Grounding by Asking Yes or No

arXiv:2608.08315v1 Announce Type: new Abstract: Multimodal LLMs that recognise events reliably still fail to say when they happen. Prompted for timestamps, strong VLMs reach as little as 3.8% R@0.5 on

Zero-Shot Traffic Accident Detection via a Coarse-to-Fine VLM-Tracking Pipeline

arXiv:2608.08867v1 Announce Type: new Abstract: Traffic surveillance cameras capture accidents continuously, yet converting raw CCTV footage into structured event records that pinpoint when, where, an

ZhuLong: Execution-Grounded LLM Agent for EDA Scripting with Offline API Self-Exploration

arXiv:2608.07925v1 Announce Type: new Abstract: EDA scripting with tool-specific, often undocumented APIs remains a long-tail bottleneck that existing LLMs fail to address. This paper presents ZhuLong

ZOMP: Zeroth-Order Multi-Modal Prompt Tuning for Vision-Language Models

arXiv:2608.08060v1 Announce Type: new Abstract: Fine-tuning vision-language models such as CLIP typically requires backpropagation (BP) through the full model, which is infeasible when only forward-pa

researcharxiv-cs-cv

A Multi-Agent Framework for Automated Coarse-Grained Molecular Dynamics of Polymers

arXiv:2608.06694v1 Announce Type: new Abstract: Coarse-grained (CG) molecular dynamics extends polymer simulation beyond the scales accessible to all-atom (AA) methods, but bottom-up CG modeling is la

A Picture is Worth a Thousand Tokens: How Vision Language Models Cut AI Energy Costs While Improving Accuracy

arXiv:2608.07427v1 Announce Type: new Abstract: LLM inference accounts for over 90% of AI operational energy, scaling directly with input token count---a critical inefficiency for telecom network anal

ADIAS: Automated Design of Interactive Agentic Systems

arXiv:2608.06410v1 Announce Type: new Abstract: Automated agent design improves agent harnesses through iterative revision, evaluation, and feedback summarization. Existing methods are largely candida

agentsarxiv-cs-ai

AgentPatch: Coarse-to-Fine Weak-Task Repair for Merging Agentic Multimodal Large Language Models

arXiv:2608.06699v1 Announce Type: new Abstract: Agentic multimodal large language models (MLLMs) extend multimodal perception and reasoning with planning, tool use, and interaction in dynamic environm

agentsarxiv-cs-ai