Search: “reasoning”

Compressed Video Aggregator: Content-driven Module for Efficient Micro-Video Recommendation

arXiv:2605.08810v1 Announce Type: cross Abstract: We propose Compressed Video Aggregator (CVA), a lightweight micro-video recommendation module that decouples video information from preference learnin

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Model Releases

ConFit v3: Improving Resume-Job Matching with LLM-based Re-Ranking

DGX agent

arXiv:2605.09760v1 Announce Type: new Abstract: A reliable resume-job matching system helps a company find suitable candidates from a pool of resumes and helps a job seeker find relevant jobs from a l

model-releasesarxiv-cs-cl

12 May 2026

ConQuR: Corner Aligned Activation Quantization via Optimized Rotations for LLMs

model-releasesarxiv-cs-lg

arXiv:2605.10793v1 Announce Type: new Abstract: Large language models (LLMs) are costly to deploy due to their large memory footprint and high inference cost. Weight-activation quantization can reduce

Context Learning for Multi-Agent Discussion

arXiv:2602.02350v2 Announce Type: replace Abstract: Multi-Agent Discussion (MAD) has garnered increasing attention very recently, where multiple LLM instances collaboratively solve problems via struct

Applications

Count Anything at Any Granularity

arXiv:2605.10887v1 Announce Type: new Abstract: Open-world object counting remains brittle: despite rapid advances in vision-language models (VLMs), reliably counting the objects a user intends is far

applicationsarxiv-cs-cv

Tutorials

Counting Still Counts: Understanding Neural Complex Query Answering Through Query Relaxation

arXiv:2511.22565v2 Announce Type: replace Abstract: Neural methods for Complex Query Answering (CQA) over knowledge graphs (KGs) are widely believed to learn patterns that generalize beyond explicit g

tutorialsarxiv-cs-ai

CrackMeBench: Binary Reverse Engineering for Agents

arXiv:2605.10597v1 Announce Type: cross Abstract: Benchmarks for coding agents increasingly measure source-level software repair, and cybersecurity benchmarks increasingly measure broad capture-the-fl

CUDAHercules: Benchmarking Hardware-Aware Expert-level CUDA Optimization for LLMs

model-releasesarxiv-cs-lg

arXiv:2605.08467v1 Announce Type: new Abstract: Large language models show promise for automated CUDA programming, however even the strongest coding models (e.g., Claude-Opus-4.6) may still fall short

DARE: Diffusion Language Model Activation Reuse for Efficient Inference

arXiv:2605.08134v1 Announce Type: cross Abstract: Diffusion Large Language Models (dLLMs) have emerged as a promising alternative to auto-regressive (AR) models, offering greater expressive capacity a

researcharxiv-cs-ai

Defense effectiveness across architectural layers: a mechanistic evaluation of persistent memory attacks on stateful LLM agents

arXiv:2605.08442v1 Announce Type: cross Abstract: Persistent memory attacks against LLM agents achieve high attack success rates against open-source models. In these attacks, malicious instructions in

researcharxiv-cs-ai

DeltaRubric: Generative Multimodal Reward Modeling via Joint Planning and Verification

arXiv:2605.09269v1 Announce Type: new Abstract: Aligning Multimodal Large Language Models (MLLMs) requires reliable reward models, yet existing single-step evaluators can suffer from lazy judging, exp

researcharxiv-cs-cl

Detect, Localize, and Explain: Interactive Hierarchical Log Anomaly Analytics with LLM Augmentation

arXiv:2605.09222v1 Announce Type: cross Abstract: Logs are ubiquitous in modern systems. Unfortunately, their unstructured nature in flat sequences limits understanding of execution behaviors, hinderi

Tutorials

Do Agents Need to Plan Step-by-Step? Rethinking Planning Horizon in Data-Centric Tool Calling

arXiv:2605.08477v1 Announce Type: new Abstract: Explicit planning is a critical capability for LLM-based agents solving complex data-centric tasks, which require precise tool calling over external dat

tutorialsarxiv-cs-cl

DUET: Optimize Token-Budget Allocation for Reinforcement Learning with Verifiable Rewards

arXiv:2605.08441v1 Announce Type: cross Abstract: Reinforcement learning with verifiable rewards (RLVR) generates hundreds of thousands of tokens per training step, with rollout generation dominating

Applications

Dynamic Cross-Modal Prompt Generation for Multimodal Continual Instruction Tuning

arXiv:2605.10765v1 Announce Type: cross Abstract: Multimodal Large Language Models (MLLMs) achieve strong performance through instruction tuning, yet real-world deployment often requires continual cap

applicationsarxiv-cs-ai

Industry

eBay rejects GameStop's $56B offer: 'Your proposal is neither credible nor attractive'

eBay rejected GameStop's unsolicited 56 billion takeover bid, with eBay's board declaring the offer 'neither credible nor attractive.' The proposal called for purchasing eBay shares at 125 apiece with

industryars-technica

Echo-LoRA: Parameter-Efficient Fine-Tuning via Cross-Layer Representation Injection

arXiv:2605.08177v1 Announce Type: cross Abstract: Parameter-efficient fine-tuning (PEFT) has become a practical route for adapting large language models to downstream tasks, with LoRA-style methods be

Efficient Hybrid CNN-GNN Architecture for Monocular Depth Estimation

arXiv:2605.10251v1 Announce Type: new Abstract: We present GraphDepth, a monocular depth estimation architecture that synergistically integrates Graph Neural Networks (GNNs) within a convolutional enc

local-aiarxiv-cs-cv

Efficient LLM Collaboration via Planning

arXiv:2506.11578v4 Announce Type: replace Abstract: Recently, large language models (LLMs) have demonstrated strong performance, ranging from simple to complex tasks. However, while large models achie

local-aiarxiv-cs-ai

Entropy-informed Decoding: Adaptive Information-Driven Branching

arXiv:2605.09745v1 Announce Type: cross Abstract: Large language models (LLMs) achieve remarkable generative performance, yet their output quality is dependent on the decoding strategy. While sampling

researcharxiv-cs-ai

ETS: Energy-Guided Test-Time Scaling for Training-Free RL Alignment

arXiv:2601.21484v2 Announce Type: replace Abstract: Reinforcement Learning (RL) post-training alignment for language models is effective, but also costly and unstable in practice, owing to its complic

safetyarxiv-cs-lg

EverydayMMQA: A Multilingual and Multimodal Framework for Culturally Grounded Spoken Visual QA

arXiv:2510.06371v2 Announce Type: replace-cross Abstract: Large-scale multimodal models achieve strong results on tasks like Visual Question Answering (VQA), but they are often limited when queries re

EvoDriveVLA: Evolving Driving VLA Models via Collaborative Perception-Planning Distillation

arXiv:2603.09465v3 Announce Type: replace-cross Abstract: Vision-Language-Action models have shown great promise for autonomous driving, yet they suffer from degraded perception after unfreezing the v

EvoMAS: Learning Execution-Time Workflows for Multi-Agent Systems

arXiv:2605.08769v1 Announce Type: new Abstract: Large language model (LLM)-based multi-agent systems have shown strong potential on complex tasks through agent specialization, tool use, and collaborat

f-GRPO and Beyond: Divergence-Based Reinforcement Learning Algorithms for General LLM Alignment

arXiv:2602.05946v3 Announce Type: replace Abstract: Recent work shows that preference alignment objectives can be interpreted as divergence estimators between aligned (preferred) & unaligned (less-pre

safetyarxiv-cs-lg

Few-Click-Driven Interactive 3D Segmentation with Semantic Embedding

arXiv:2605.08925v1 Announce Type: new Abstract: Interactive segmentation allows efficient label generation by leveraging user-provided clicks to progressively refine predictions, which is critical whe

safetyarxiv-cs-cv

From Single-Step Edit Response to Multi-Step Molecular Optimization

arXiv:2605.10035v1 Announce Type: new Abstract: Conditional molecular optimization aims to edit a molecule to realize a specified property shift. In practice, structurally similar molecule data is sca

local-aiarxiv-cs-ai

GAMBIT: A Three-Mode Benchmark for Adversarial Robustness in Multi-Agent LLM Collectives

model-releasesarxiv-cs-cl

arXiv:2605.09027v1 Announce Type: new Abstract: In multi-agent systems (MAS), a single deceptive agent can nullify all gains of an agentic AI collective and evade deployed defenses. However, existing

Geospatial-Temporal Sensemaking of Remote Sensing Activity Detections with Multimodal Large Language Model

arXiv:2605.10739v1 Announce Type: cross Abstract: We introduce SMART-HC-VQA, a Sentinel-2-based visual question answering dataset derived from the IARPA SMART Heavy Construction dataset, designed for

GLM models are now live on @tensorix_ai We’re partnering to bring cost-efficient frontier AI models to developers, startups, and enterprises…

GLM models are now live on @tensorix_ai We’re partnering to bring cost-efficient frontier AI models to developers, startups, and enterprises across Europe and beyond — and to back the Sovereign AI eco

agentszhipu-ai--x

GuardAD: Safeguarding Autonomous Driving MLLMs via Markovian Safety Logic

arXiv:2605.10386v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) are increasingly integrated into autonomous driving (AD) systems; however, they remain vulnerable to diverse sa

HAGE: Harnessing Agentic Memory via RL-Driven Weighted Graph Evolution

arXiv:2605.09942v1 Announce Type: new Abstract: Memory retrieval in agentic large language model (LLM) systems is often treated as a static lookup problem, relying on flat vector search or fixed binar

HAMLET: A Hierarchical and Adaptive Multi-Agent Framework for Live Embodied Theatrics

arXiv:2507.15518v5 Announce Type: replace Abstract: Creating an immersive and interactive theatrical experience is a long-term goal in the field of interactive narrative. The emergence of large langua

Hierarchical Causal Abduction: A Foundation Framework for Explainable Model Predictive Control

arXiv:2605.10624v1 Announce Type: new Abstract: Model Predictive Control (MPC) is widely used to operate safety-critical infrastructure by predicting future trajectories and optimizing control actions

Holmes: A Benchmark to Assess the Linguistic Competence of Language Models

model-releasesarxiv-cs-cl

arXiv:2404.18923v5 Announce Type: replace Abstract: We introduce Holmes, a new benchmark designed to assess language models (LMs) linguistic competence - their unconscious understanding of linguistic

HOME-KGQA: A Benchmark Dataset for Multimodal Knowledge Graph Question Answering on Household Daily Activities

arXiv:2605.09348v1 Announce Type: cross Abstract: Large Language Models (LLMs) provide flexible natural language processing capabilities, while knowledge graphs (KGs) offer explicit and structured kno

How LLMs Are Persuaded: A Few Attention Heads, Rerouted

arXiv:2605.09314v1 Announce Type: new Abstract: Language models can be persuaded to abandon factual knowledge. This vulnerability is central to AI safety, but its internal mechanism remains poorly und

Human-LLM Dialogue Improves Diagnostic Accuracy in Emergency Care

arXiv:2605.08533v1 Announce Type: new Abstract: Clinical decision-making in emergency medicine demands rapid, accurate diagnoses under uncertainty. Despite benchmark progress, evidence for LLMs as int

Identified-Set Geometry of Distributional Model Extraction under Top-K Censored API Access

arXiv:2605.10407v1 Announce Type: new Abstract: Modern LLM APIs often reveal only top-K logit scores and censor the remaining vocabulary. We study the per-position distribution-recovery limits of this

researcharxiv-cs-lg

Tutorials

Inductive Entity Representations from Text via Link Prediction

arXiv:2010.03496v4 Announce Type: replace-cross Abstract: Knowledge Graphs (KG) are of vital importance for multiple applications on the web, including information retrieval, recommender systems, and

tutorialsarxiv-cs-ai

IndustryBench: Probing the Industrial Knowledge Boundaries of LLMs

arXiv:2605.10267v1 Announce Type: new Abstract: In industrial procurement, an LLM answer is useful only if it survives a standards check: recommended material must match operating condition, every par

Iterative Critique-and-Routing Controller for Multi-Agent Systems with Heterogeneous LLMs

arXiv:2605.08686v1 Announce Type: new Abstract: Multi-agent large language model (LLM) systems often rely on a controller to coordinate a pool of heterogeneous models, yet existing controllers are typ

Kintsugi: Learning Policies by Repairing Executable Knowledge Bases

arXiv:2605.09487v1 Announce Type: new Abstract: Modern embodied agents achieve impressive performance, but their task knowledge is often stored in neural weights, latent state, or prompt-bound memory,

local-aiarxiv-cs-lg

Knowledge is Not Enough: Injecting RL Skills for Continual Adaptation

arXiv:2601.11258v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) face the 'knowledge cutoff' challenge, where their frozen parametric memory prevents direct internalization of ne

Lattice Deduction Transformers

arXiv:2605.08605v1 Announce Type: cross Abstract: We introduce the Lattice Deduction Transformer (LDT), a recurrent transformer that approximates logically sound deduction by projecting its latent sta

LEAF-SQL: Level-wise Exploration with Adaptive Fine-graining for Text-to-SQL Skeleton Prediction

model-releasesarxiv-cs-cl

arXiv:2605.09295v1 Announce Type: new Abstract: Text-to-SQL translates natural language questions into executable SQL queries, enabling intuitive database access for non-experts. While large language

Learning Multi-Indicator Weights for Data Selection: A Joint Task-Model Adaptation Framework with Efficient Proxies

arXiv:2605.09665v1 Announce Type: cross Abstract: Data selection is a key component of efficient instruction tuning for large language models, as recent work has shown that data quality often matters

Learning Point Cloud Geometry as a Statistical Manifold: Theory and Practice

arXiv:2605.10456v1 Announce Type: new Abstract: Point clouds are a fundamental representation for robotic perception tasks such as localization, mapping, and object pose estimation. However, LiDAR-acq

local-aiarxiv-cs-ro