Search: “benchmark”

Advancing Multi-Agent RAG Systems with Minimalist Reinforcement Learning

arXiv:2505.17086v4 Announce Type: replace Abstract: Large Language Models (LLMs) equipped with modern Retrieval-Augmented Generation (RAG) systems often employ multi-turn interaction pipelines to inte

safetyarxiv-cs-cl

Agents

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ALL-FEM: Agentic Large Language models Fine-tuned for Finite Element Methods

DGX agent

arXiv:2603.21011v2 Announce Type: replace-cross Abstract: Finite element (FE) analysis guides the design and verification of nearly all manufactured objects. It is at the core of computational enginee

agentsarxiv-cs-ai

15 Apr 2026

ASTRA: Let Arbitrary Subjects Transform in Video Editing

arXiv:2510.01186v2 Announce Type: replace Abstract: While existing video editing methods excel with single subjects, they struggle in dense, multi-subject scenes, frequently suffering from attention d

Applications

BarbieGait: An Identity-Consistent Synthetic Human Dataset with Versatile Cloth-Changing for Gait Recognition

arXiv:2604.12221v1 Announce Type: new Abstract: Gait recognition, as a reliable biometric technology, has seen rapid development in recent years while it faces significant challenges caused by diverse

applicationsarxiv-cs-cv

Beyond Factual Grounding: The Case for Opinion-Aware Retrieval-Augmented Generation

arXiv:2604.12138v1 Announce Type: new Abstract: RAG systems have transformed how LLMs access external knowledge, but we find that current implementations exhibit a bias toward factual, objective conte

Agents

Beyond Majority Voting: Efficient Best-Of-N with Radial Consensus Score

arXiv:2604.12196v1 Announce Type: new Abstract: Large language models (LLMs) frequently generate multiple candidate responses for a given prompt, yet selecting the most reliable one remains challengin

agentsarxiv-cs-cl

Beyond Perception Errors: Semantic Fixation in Large Vision-Language Models

arXiv:2604.12119v1 Announce Type: new Abstract: Large vision-language models (VLMs) often rely on familiar semantic priors, but existing evaluations do not cleanly separate perception failures from ru

Black-Box Optimization From Small Offline Datasets via Meta Learning with Synthetic Tasks

arXiv:2604.12325v1 Announce Type: cross Abstract: We consider the problem of offline black-box optimization, where the goal is to discover optimal designs (e.g., molecules or materials) from past expe

Boosting Visual Instruction Tuning with Self-Supervised Guidance

arXiv:2604.12966v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) perform well on many vision-language tasks but often struggle with vision-centric problems that require fine-gr

BRAIN: Bias-Mitigation Continual Learning Approach to Vision-Brain Understanding

arXiv:2508.18187v2 Announce Type: replace-cross Abstract: Memory decay makes it harder for the human brain to recognize visual objects and retain details. Consequently, recorded brain signals become w

Bridging the Micro--Macro Gap: Frequency-Aware Semantic Alignment for Image Manipulation Localization

arXiv:2604.12341v1 Announce Type: new Abstract: As generative image editing advances, image manipulation localization (IML) must handle both traditional manipulations with conspicuous forensic artifac

safetyarxiv-cs-cv

Calibration-Aware Policy Optimization for Reasoning LLMs

arXiv:2604.12632v1 Announce Type: cross Abstract: Group Relative Policy Optimization (GRPO) enhances LLM reasoning but often induces overconfidence, where incorrect responses yield lower perplexity th

CamReasoner: Reinforcing Camera Movement Understanding via Structured Spatial Reasoning

arXiv:2602.00181v3 Announce Type: replace-cross Abstract: Understanding camera dynamics is a fundamental pillar of video spatial intelligence. However, existing multimodal models predominantly treat t

Agents

CascadeDebate: Multi-Agent Deliberation for Cost-Aware LLM Cascades

arXiv:2604.12262v1 Announce Type: cross Abstract: Cascaded LLM systems coordinate models of varying sizes with human experts to balance accuracy, cost, and abstention under uncertainty. However, singl

agentsarxiv-cs-ai

Applications

ChemDFM-R: A Chemical Reasoning LLM Enhanced with Atomized Chemical Knowledge

arXiv:2507.21990v4 Announce Type: replace-cross Abstract: Atomized chemical knowledge, such as functional group information of molecules and reactions, plays a pivotal intermediate role in the reasoni

applicationsarxiv-cs-ai

CLASP: Class-Adaptive Layer Fusion and Dual-Stage Pruning for Multimodal Large Language Models

arXiv:2604.12767v1 Announce Type: cross Abstract: Multimodal Large Language Models (MLLMs) suffer from substantial computational overhead due to the high redundancy in visual token sequences. Existing

Clustering with Uniformity- and Neighbor-Based Random Geometric Graphs

arXiv:2501.06268v3 Announce Type: replace Abstract: We propose a graph-based clustering method based on Cluster Catch Digraphs (CCDs) that extends their applicability to moderate-dimensional data sett

CoDe-R: Refining Decompiler Output with LLMs via Rationale Guidance and Adaptive Inference

arXiv:2604.12913v1 Announce Type: cross Abstract: Binary decompilation is a critical reverse engineering task aimed at reconstructing high-level source code from stripped executables. Although Large L

CoG: Controllable Graph Reasoning via Relational Blueprints and Failure-Aware Refinement over Knowledge Graphs

arXiv:2601.11047v2 Announce Type: replace Abstract: Large Language Models (LLMs) have demonstrated remarkable reasoning capabilities but often grapple with reliability challenges like hallucinations.

researcharxiv-cs-cl

Cognition-Inspired Dual-Stream Semantic Enhancement for Vision-Based Dynamic Emotion Modeling

arXiv:2604.12777v1 Announce Type: cross Abstract: The human brain constructs emotional percepts not by processing facial expressions in isolation, but through a dynamic, hierarchical integration of se

Compiling Activation Steering into Weights via Null-Space Constraints for Stealthy Backdoors

arXiv:2604.12359v1 Announce Type: cross Abstract: Safety-aligned large language models (LLMs) are increasingly deployed in real-world pipelines, yet this deployment also enlarges the supply-chain atta

safetyarxiv-cs-cl

Complementarity by Construction: A Lie-Group Approach to Solving Quadratic Programs with Linear Complementarity Constraints

model-releasesarxiv-cs-ro

arXiv:2604.11991v1 Announce Type: new Abstract: Many problems in robotics require reasoning over a mix of continuous dynamics and discrete events, such as making and breaking contact in manipulation a

ContextLens: Modeling Imperfect Privacy and Safety Context for Legal Compliance

arXiv:2604.12308v1 Announce Type: new Abstract: Individuals' concerns about data privacy and AI safety are highly contextualized and extend beyond sensitive patterns. Addressing these issues requires

safetyarxiv-cs-cl

Cooperative Memory Paging with Keyword Bookmarks for Long-Horizon LLM Conversations

arXiv:2604.12376v1 Announce Type: cross Abstract: When LLM conversations grow beyond the context window, old content must be evicted -- but how does the model recover it when needed? We propose cooper

Local Ai

CREG: Compass Relational Evidence Graph for Characterizing Directional Structure in VLM Spatial-Reasoning Attribution

arXiv:2603.20475v3 Announce Type: replace Abstract: Standard attribution heatmaps show where a vision-language model (VLM) focuses, but they do not reveal whether the recovered evidence is organized b

local-aiarxiv-cs-cv

CropVLM: Learning to Zoom for Fine-Grained Vision-Language Perception

arXiv:2511.19820v2 Announce Type: replace-cross Abstract: Vision-Language Models (VLMs) often struggle with tasks that require fine-grained image understanding, such as scene-text recognition or docum

Cross-Attentive Multiview Fusion of Vision-Language Embeddings

arXiv:2604.12551v1 Announce Type: new Abstract: Vision-language models have been key to the development of open-vocabulary 2D semantic segmentation. Lifting these models from 2D images to 3D scenes, h

Cycle-Consistent Search: Question Reconstructability as a Proxy Reward for Search Agent Training

arXiv:2604.12967v1 Announce Type: new Abstract: Reinforcement Learning (RL) has shown strong potential for optimizing search agents in complex information retrieval tasks. However, existing approaches

DC-TTA: Divide-and-Conquer Framework for Test-Time Adaptation of Interactive Segmentation

arXiv:2506.23104v2 Announce Type: replace Abstract: Interactive segmentation (IS) allows users to iteratively refine object boundaries with minimal cues, such as positive and negative clicks. While th

Decoding by Perturbation: Mitigating MLLM Hallucinations via Dynamic Textual Perturbation

arXiv:2604.12424v1 Announce Type: cross Abstract: Multimodal Large Language Models frequently suffer from inference hallucinations, partially stemming from language priors dominating visual evidence.

Distorted or Fabricated? A Survey on Hallucination in Video LLMs

arXiv:2604.12944v1 Announce Type: cross Abstract: Despite significant progress in video-language modeling, hallucinations remain a persistent challenge in Video Large Language Models (Vid-LLMs), refer

Does RLVR Extend Reasoning Boundaries? Investigating Capability Expansion in Vision-Language Models

arXiv:2511.00710v4 Announce Type: replace Abstract: Recent studies posit that Reinforcement Learning with Verifiable Rewards (RLVR) primarily amplifies behaviors inherent to the pre-training distribut

DPC-VQA: Decoupling Quality Perception and Residual Calibration for Video Quality Assessment

arXiv:2604.12813v1 Announce Type: new Abstract: Recent multimodal large language models (MLLMs) have shown promising performance on video quality assessment (VQA) tasks. However, adapting them to new

Drawing on Memory: Dual-Trace Encoding Improves Cross-Session Recall in LLM Agents

arXiv:2604.12948v1 Announce Type: new Abstract: LLM agents with persistent memory store information as flat factual records, providing little context for temporal reasoning, change tracking, or cross-

Dreamer-CDP: Improving Reconstruction-free World Models Via Continuous Deterministic Representation Prediction

arXiv:2603.07083v2 Announce Type: replace Abstract: Model-based reinforcement learning (MBRL) agents operating in high-dimensional observation spaces, such as Dreamer, rely on learning abstract repres

Dual-Modality Anchor-Guided Filtering for Test-time Prompt Tuning

arXiv:2604.12403v1 Announce Type: new Abstract: Test-Time Prompt Tuning (TPT) adapts vision-language models using augmented views, but its effectiveness is hindered by the challenge of determining whi

DyBBT: Dynamic Balance via Bandit-inspired Targeting for Dialog Policy with Cognitive Dual-Systems

arXiv:2509.19695v3 Announce Type: replace-cross Abstract: Task oriented dialog systems often rely on static exploration strategies that do not adapt to dynamic dialog contexts, leading to inefficient

EEG-Based Multimodal Learning via Hyperbolic Mixture-of-Curvature Experts

arXiv:2604.12579v1 Announce Type: new Abstract: Electroencephalography (EEG)-based multimodal learning integrates brain signals with complementary modalities to improve mental state assessment, provid

ELoG-GS: Dual-Branch Gaussian Splatting with Luminance-Guided Enhancement for Extreme Low-light 3D Reconstruction

arXiv:2604.12592v1 Announce Type: new Abstract: This paper presents our approach to the NTIRE 2026 3D Restoration and Reconstruction Challenge (Track 1), which focuses on reconstructing high-quality 3

Evaluating Differential Privacy Against Membership Inference in Federated Learning: Insights from the NIST Genomics Red Team Challenge

arXiv:2604.12737v1 Announce Type: cross Abstract: While Federated Learning (FL) mitigates direct data exposure, the resulting trained models remain susceptible to membership inference attacks (MIAs).

Evaluating LLM-Generated ACSL Annotations for Formal Verification

arXiv:2602.13851v3 Announce Type: replace-cross Abstract: Formal specifications are crucial for building verifiable and dependable software systems, yet generating accurate and verifiable specificatio

Applications

Evaluating Robustness of Large Language Models Against Multilingual Typographical Errors

arXiv:2510.09536v2 Announce Type: replace Abstract: Large language models (LLMs) are increasingly deployed in multilingual, real-world applications with user inputs -- naturally introducing typographi

applicationsarxiv-cs-cl

FABLE: Fine-grained Fact Anchoring for Unstructured Model Editing

model-releasesarxiv-cs-cl

arXiv:2604.12559v1 Announce Type: new Abstract: Unstructured model editing aims to update models with real-world text, yet existing methods often memorize text holistically without reliable fine-grain

Fast and principled equation discovery from chaos to climate

arXiv:2604.11929v1 Announce Type: new Abstract: Our ability to predict, control, and ultimately understand complex systems rests on discovering the equations that govern their dynamics. Identifying th

FeaXDrive: Feasibility-aware Trajectory-Centric Diffusion Planning for End-to-End Autonomous Driving

arXiv:2604.12656v1 Announce Type: cross Abstract: End-to-end diffusion planning has shown strong potential for autonomous driving, but the physical feasibility of generated trajectories remains insuff

Free Random Projection for In-Context Reinforcement Learning

arXiv:2504.06983v3 Announce Type: replace Abstract: Hierarchical inductive biases are hypothesized to promote generalizable policies in reinforcement learning, as demonstrated by explicit hyperbolic l

researcharxiv-cs-lg

From Attenuation to Attention: Variational Information Flow Manipulation for Fine-Grained Visual Perception

arXiv:2604.12508v1 Announce Type: new Abstract: While Multimodal Large Language Models (MLLMs) have demonstrated impressive capabilities in general visual understanding, they frequently falter in fine

From Plan to Action: How Well Do Agents Follow the Plan?

arXiv:2604.12147v1 Announce Type: cross Abstract: Agents aspire to eliminate the need for task-specific prompt crafting through autonomous reason-act-observe loops. Still, they are commonly instructed