Search: “benchmark”

Evaluating Agentic Configuration Repair for Computer Networks

arXiv:2606.06212v1 Announce Type: new Abstract: Misconfigurations in computer networks remain a major source of critical Internet outages. Research is turning to Large Language Models (LLMs) to automa

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Evaluation of LLMs for Mathematical Formalization in Lean

DGX agent

arXiv:2606.05632v1 Announce Type: new Abstract: Within the past few years, the ability of Large Language Models (LLMs) to generate formal mathematical proofs has improved drastically. We provide a com

model-releasesarxiv-cs-ai

6 Jun 2026

Exploring LLMs for South Asian Music Understanding and Generation

arXiv:2606.05522v1 Announce Type: cross Abstract: Recent advancements in Large Language Models (LLMs) have shown promising results in music understanding and generation tasks. However, existing works

FIDES: Faithful Inference via Deep Evidence Signals for Retrieval-Memory Conflict in RAG

arXiv:2606.05644v1 Announce Type: new Abstract: When retrieved evidence contradicts parametric memory, language models frequently ignore context and default to memorized priors -- a failure that under

safetyarxiv-cs-ai

Geographic Bias and Diversity in AI Evaluation

arXiv:2606.05187v1 Announce Type: cross Abstract: Among the many challenges hindering the responsible development and deployment of AI, arguably none has faced more intense scrutiny than bias in its v

Tutorials

GOTabPFN: From Feature Ordering to Compact Tokenization for Tabular Foundation Models on High-Dimensional Data

arXiv:2606.05441v1 Announce Type: cross Abstract: We investigate how to make small tabular foundation models effective for High-Dimensional, Low-Sample Size (HDLSS) tabular prediction without retraini

tutorialsarxiv-cs-ai

I Know What You Meme, Even If it Emerged Today: Understanding Evolving Memes through Open-World Knowledge Acquisition

arXiv:2606.05316v1 Announce Type: new Abstract: Multimodal memes are dynamic and often require up to date background knowledge for interpretation. Existing methods often overlook such knowledge or rel

Learning to replenish: A hybrid deep reinforcement learning for dynamic inventory management in the pharmaceutical supply chains

arXiv:2606.06201v1 Announce Type: new Abstract: Pharmaceutical supply chains (PSCs) struggle with inventory management (IM) due to unpredictable demand patterns and variable lead times associated with

safetyarxiv-cs-ai

Multi-ResNets for Subspace Preconditioning in Constrained Optimization

arXiv:2606.06300v1 Announce Type: new Abstract: We propose MResOpt, a staged residual neural network architecture for constrained optimization problems. Our architecture fits within predict-complete-c

researcharxiv-cs-ai

PAMF: Prior-Aware Multimodal Fusion for Incomplete Time Series Data

arXiv:2606.06328v1 Announce Type: cross Abstract: In healthcare, multimodal time series tasks often operate on incomplete observations in practice, for example when ECG segments are lost because elect

applicationsarxiv-cs-ai

ProfiliTable: Profiling-Driven Tabular Data Processing via Agentic Workflows

arXiv:2605.12376v2 Announce Type: replace Abstract: Table processing-including cleaning, transformation, augmentation, and matching-is a foundational yet error-prone stage in real-world data pipelines

Reformulating Neural Operators in d+1 Dimensions for Embedding Evolution

arXiv:2505.11766v4 Announce Type: replace-cross Abstract: Neural Operators (NOs) are powerful architectures for learning mappings between function spaces. While most advances focus on refining kernel

researcharxiv-cs-ai

Reward Learning through Ranking Mean Squared Error

arXiv:2601.09236v3 Announce Type: replace-cross Abstract: Reward design remains a significant bottleneck in applying reinforcement learning (RL) to real-world problems. A popular alternative is reward

SciVisAgentSkills: Design and Evaluation of Agent Skills for Scientific Data Analysis and Visualization

arXiv:2606.05525v1 Announce Type: new Abstract: Recent advances in agentic visualization have enabled the translation of natural language into executable scientific visualization (SciVis) workflows. W

Selective-Advantage Entropy-Adaptive Horizon GRPO: Asymmetric Token-Level Discounting for Efficient Reinforcement Learning of Language Models

arXiv:2606.05434v1 Announce Type: cross Abstract: Group Relative Policy Optimisation (GRPO) has emerged as an effective reinforcement-learning algorithm for aligning language models on reasoning tasks

Semantic Partial Grounding via LLMs

arXiv:2602.22067v2 Announce Type: replace Abstract: Grounding is a critical step in classical planning, yet it often becomes a computational bottleneck due to the exponential growth in grounded action

researcharxiv-cs-ai

TAPO: Tool-Aware Policy Optimization via Credit Transfer for Multimodal Search Agents

arXiv:2606.05784v1 Announce Type: new Abstract: We identify and formally characterize credit misassignment as a systematic failure mode of GRPO in tool-augmented multimodal search agents: its uniform

The End of Software Engineering: How AI Agents Are Fundamentally Restructuring the Software Paradigm

arXiv:2606.05608v1 Announce Type: cross Abstract: For over half a century, software engineering has operated on a foundational premise: human engineers decompose problems, encode decision logic into s

TLA-Prover: Verifiable TLA+ Specification Synthesis via Preference-Optimized Low-Rank Adaptation

arXiv:2606.06133v1 Announce Type: cross Abstract: TLA+ is a formal specification language for verifying distributed systems and safety-critical protocols. Large language models (LLMs) frequently produ

TokenMizer: Graph-Structured Session Memory for Long-Horizon LLM Context Management

arXiv:2606.06337v1 Announce Type: new Abstract: Large language model (LLM) deployments for long-horizon tasks face a fundamental constraint: context windows are finite while productive work sessions a

ToolChoiceConfusion: Causal Minimal Tool Filtering for Reliable LLM Agents

arXiv:2606.06284v1 Announce Type: new Abstract: Large language model agents increasingly rely on external tools, but larger tool menus can reduce reliability and efficiency by increasing wrong-tool ca

Towards World Models in Biomedical Research

arXiv:2606.05925v1 Announce Type: new Abstract: A central goal of biomedicine is to understand, predict and ultimately control the dynamic mechanisms by which biological systems respond to perturbatio

safetyarxiv-cs-ai

Uncertainty Aware Functional Behavior Prediction and Material Fatigue Assessment for Circular Factory

arXiv:2606.05334v1 Announce Type: new Abstract: Returned products in circular factories re-enter production with heterogeneous degradation states, usage histories, and remaining capability. Reuse cann

applicationsarxiv-cs-ai

We didn’t just start working on Recursive Self-Improvement yesterday. We have been methodically shipping the foundations for two years. To e…

We didn’t just start working on Recursive Self-Improvement yesterday. We have been methodically shipping the foundations for two years. To execute the next phase of this mission, we are seeking a sele

researchdavid-ha--x

3D Underwater Path Planning via Generative Flow Field Surrogates

model-releasesarxiv-cs-ro

arXiv:2606.06077v1 Announce Type: new Abstract: Autonomous underwater vehicle (AUV) launch and recovery (LAR) into the hull of an advancing host platform requires traversal of a complex, three-dimensi

Agents

Active Video Perception: Iterative Evidence Seeking for Agentic Long Video Understanding

arXiv:2512.05774v2 Announce Type: replace-cross Abstract: Long video understanding (LVU) is challenging because answering real-world queries often depends on sparse, temporally dispersed cues buried i

agentsarxiv-cs-cl

AdaPLD: Adaptive Retrieval and Reuse for Efficient Model-Free Speculative Decoding

arXiv:2606.05742v1 Announce Type: new Abstract: Speculative decoding accelerates generation by verifying multiple drafted tokens in a single target-model forward pass, reducing sequential decoding ite

researcharxiv-cs-cl

Adaptive Tokenisation Via Temporal Redundancy Masking And Latent Inpainting

model-releasesarxiv-cs-cv

arXiv:2606.06158v1 Announce Type: new Abstract: Adaptive video tokenisation seeks to dynamically allocate token budgets based on the underlying visual complexity of a sequence. Current continuous-regi

ATT-CR: Adaptive Triangular Transformer for Cloud Removal

arXiv:2606.05999v1 Announce Type: new Abstract: Cloud removal aims to accurately reconstruct the ground objects obscured by clouds in remote sensing images. Existing Transformer-based methods utilizin

researcharxiv-cs-cv

Better Literary Translation: A Multi-Aspect Data Generation and LLM Training Approach

arXiv:2606.05924v1 Announce Type: new Abstract: Literary translation poses unique challenges due to the scarcity of high-quality annotated data and the need to balance expression fluency with literary

Beyond Absolute Scores: Relative Edit-induced Difference for Generalizable Image Aesthetic Assessment

arXiv:2606.05778v1 Announce Type: new Abstract: Traditional Image Aesthetic Assessment (IAA) methods mainly rely on regressing absolute Mean Opinion Scores (MOS). However, such a paradigm overlooks th

researcharxiv-cs-cv

Bootstrapping Semantic Layer from Execution for Text-to-SQL

arXiv:2606.05634v1 Announce Type: new Abstract: Real-world text-to-SQL is often under-specified until user phrases are grounded in how the database stores values. Prior work attempts to address this b

applicationsarxiv-cs-cl

Agents

Building AI that Builds AI: Introducing the Sakana AI RSI Lab 🚀 https://sakana.ai/rsi-lab Today, we are announcing the Sakana AI Recursive …

Building AI that Builds AI: Introducing the Sakana AI RSI Lab 🚀 https://sakana.ai/rsi-lab Today, we are announcing the Sakana AI Recursive Self-Improvement (RSI) Lab: a dedicated research group in Tok

agentsdavid-ha--x

CaliDist: Calibrating Large Language Models via Behavioral Robustness to Distraction

arXiv:2606.05799v1 Announce Type: cross Abstract: Existing calibration methods for Large Language Models (LLMs) often overlook a critical dimension of trustworthiness: a model's {em behavioral robustn

researcharxiv-cs-cl

'Chi nas dal soch el sent de legn' -- Auditing Text Corpora for Lombard

arXiv:2606.06349v1 Announce Type: new Abstract: Several of the world's languages are still under-resourced in terms of Natural Language Processing (NLP) tools. This is mostly due to the lack of high-q

safetyarxiv-cs-cl

CLASH: Evaluating Language Models on Judging High-Stakes Dilemmas from Multiple Perspectives

arXiv:2504.10823v4 Announce Type: replace Abstract: Navigating dilemmas involving conflicting values is challenging even for humans in high-stakes domains, let alone for AI, yet prior work has been li

CLEAR: Cognition and Latent Evaluation for Adaptive Routing in End-to-End Autonomous Driving

model-releasesarxiv-cs-ro

arXiv:2606.06219v1 Announce Type: new Abstract: End-to-end autonomous driving models often struggle to balance multi-modal maneuver generation with real-time inference constraints. While diffusion mod

Code2LoRA: Hypernetwork-Generated Adapters for Code Language Models under Software Evolution

arXiv:2606.06492v1 Announce Type: cross Abstract: Code language models need repository-level context to resolve imports, APIs, and project conventions. Existing methods inject this knowledge as long i

Computation-Aware Event-to-Frame Reconstruction via Selective Attention

arXiv:2606.06142v1 Announce Type: new Abstract: Event-to-frame (E2F) reconstruction bridges asynchronous event streams with frame-based vision pipelines, but existing methods often face a trade-off be

researcharxiv-cs-cv

Contextualized Prompting For Stance Detection On Social Media

arXiv:2606.06022v1 Announce Type: new Abstract: Stance detection on social media is challenging due to short, noisy, and context-dependent language. While large language models (LLMs) show zero-shot g

Discrete-WAM: Unified Discrete Vision-Action Token Editing for World-Policy Learning

arXiv:2606.05645v1 Announce Type: new Abstract: Autonomous driving requires reasoning about how ego actions shape the evolution of the surrounding world. However, most end-to-end methods rely on direc

safetyarxiv-cs-ro

Disentangled Fine-Grained Prototype Learning for Incomplete Image-Tabular Classification

arXiv:2606.05455v1 Announce Type: new Abstract: The missing-modality problem poses a significant challenge in image-tabular multimodal learning across a wide range of multimedia applications, includin

safetyarxiv-cs-cv

Domain-Aware Mispronunciation Detection and Diagnosis Using Language-Specific Statistical Graphs

arXiv:2606.05569v1 Announce Type: new Abstract: Mispronunciation Detection and Diagnosis (MDD) has gained increasing importance in computer-assisted language learning and speech technology in recent y

EDIT: Evidence-Diagnosed Intervention Training for Rule-Faithful LLM Grading

arXiv:2606.06350v1 Announce Type: new Abstract: Reliable rubric grading requires more than accurate score prediction. Each judgement must be grounded in the mark scheme and evidence from the student a

applicationsarxiv-cs-cl

Evaluating Stochastic Collapse and Implicit Bias in Multimodal Large Language Models

arXiv:2606.05874v1 Announce Type: new Abstract: Current evaluations for Multimodal Large Language Models (MLLMs) overwhelmingly focus on utility-driven objectives, leaving model behavior under logic-n

Agents

Executable Schema Contracts: From Automatic Ingestion to Multi-Source Retrieval

arXiv:2606.05415v1 Announce Type: new Abstract: Real-world data spans tables, documents, and semi-structured files with implicit semantics. Querying this data requires integrating evidence across inco

agentsarxiv-cs-cl

FATE: Focal-modulated Attention Encoder for Multivariate Time-series Forecasting

model-releasesarxiv-cs-cv

arXiv:2408.11336v3 Announce Type: replace-cross Abstract: Climate change stands as one of the most pressing global challenges of the twenty-first century, with far-reaching consequences such as rising

FiLM-Based Speaker Conditioning of a SpeechLLM for Pathological Speech Recognition

arXiv:2606.06211v1 Announce Type: new Abstract: Automatic speech recognition (ASR) has advanced remarkably for standard speech; however, pathological speech from neurological conditions remains a sign