Search: “benchmark”

Bellman-Taylor Score Decoding for Markov Decision Processes with State-Dependent Feasible Action Sets

arXiv:2606.10979v1 Announce Type: new Abstract: Many Markov decision processes (MDPs) in operations research have feasible actions that are state dependent and defined implicitly by various operationa

Content type

All Blog X Post Paper YouTube Reddit GitHub

Clear filters

Model Releases

Beyond APIs: Probing the Limits of MLLMs in Physical Tool Use

DGX agent

arXiv:2606.10803v1 Announce Type: cross Abstract: Multimodal Large Language Models (MLLMs) excel at utilizing digital APIs and increasingly serve as the 'brain' of embodied AI, instructing robots to i

model-releasesarxiv-cs-ai

10 Jun 2026

Beyond Memorization: Distinguishing Between Pattern-Based and Epistemic Reasoning in LLMs Using Epistemic Puzzles

arXiv:2603.21350v2 Announce Type: replace Abstract: Epistemic reasoning requires agents to infer the state of the world from partial observations and information about other agents' knowledge. Prior w

Beyond Model Size: Probing the Gaps in Visual in-Context Learning by Training a Tiny Model

arXiv:2606.10905v1 Announce Type: new Abstract: Visual in-Context Learning (VICL) aims at making progress towards adaptive vision models, that can -- based on a few examples -- adapt to a new task at

Bittensor Agent Arenas as a Trajectory Primitive: Distilling a Shopping Agent from ShoppingBench Subnet Traces

arXiv:2606.10064v1 Announce Type: cross Abstract: Small-model agentic post-training is bottlenecked less by the algorithm than by the trajectory substrate it consumes. Leading recipes (RLVR, group-rel

CGES: Confidence-Guided Early Stopping for Efficient and Accurate Self-Consistency

arXiv:2511.02603v2 Announce Type: replace Abstract: Large language models (LLMs) are often queried multiple times at test time, with predictions aggregated by majority vote. While effective, this self

researcharxiv-cs-cl

ChartAgent: A Multimodal Agent for Visually Grounded Reasoning in Complex Chart Question Answering

arXiv:2510.04514v3 Announce Type: replace Abstract: Recent multimodal LLMs have shown promise in chart-based visual question answering, but their performance declines sharply on unannotated charts-tho

Closing the Modality Gap in Zero-Shot HAR: Contrastive Training and Separability-Optimized Prototypes on IMU Data

arXiv:2606.10789v1 Announce Type: new Abstract: Zero-shot learning (ZSL) for inertial measurement unit (IMU)-based human activity recognition (HAR) faces a central challenge: bridging the gap between

safetyarxiv-cs-lg

CodeAlchemy: Synthetic Code Rewriting at Scale

arXiv:2606.10087v1 Announce Type: new Abstract: Pre-training on raw code teaches syntax but provides sparse signal for diverse real-world task formats. While synthetic data has proven transformative f

CollabSkill: Evaluating Human-Agent Collaboration On Real-World Tasks

arXiv:2606.09833v1 Announce Type: cross Abstract: AI agents are reshaping the workspace, leading to drastic change of how humans work. Despite the considerable potential of human-agent collaboration b

CommonLID: Re-evaluating State-of-the-Art Language Identification Performance on Web Data

arXiv:2601.18026v2 Announce Type: replace Abstract: Language identification (LID) is a fundamental step in curating multilingual corpora. However, LID models still perform poorly for many languages, e

Conformal Prediction for Neural Operators: Distribution-Free Uncertainty Quantification in Physics Simulation

arXiv:2606.09923v1 Announce Type: cross Abstract: Neural operators such as the Fourier Neural Operator (FNO) have emerged as powerful surrogates for solving partial differential equations (PDEs), achi

Constructing coherent spatial memory in LLM agents through graph rectification

arXiv:2510.04195v2 Announce Type: replace Abstract: Given a map description through global traversal navigation instructions, an LLM can often infer the implicit spatial layout and answer user queries

Continual LLM Upcycling: A Predictor-Gated Bank-Wise Sparsity Training Recipe for Dense-to-Sparse LLMs

arXiv:2606.10722v1 Announce Type: new Abstract: We study dense-to-sparse continual training as a way to construct channel-sparse large language models from dense checkpoints. Starting from a Qwen2.5-8

Continuous Neural Reparameterization as a Deep Geometric Prior for Robust Fixed-Chart UV Repair

arXiv:2606.10050v1 Announce Type: cross Abstract: Traditional UV unwrapping relies on direct optimization of geometric distortion energies and can fail through invalid initialization, local minima, or

Contrastive Spectral Rectification: Test-Time Defense towards Zero-shot Adversarial Robustness of CLIP

arXiv:2601.19210v2 Announce Type: replace Abstract: Vision-language models (VLMs) such as CLIP have demonstrated remarkable zero-shot generalization, yet remain highly vulnerable to adversarial exampl

safetyarxiv-cs-cv

ConvMemory v2: A Recall-Preserving Top-10 Evidence Reranker for Conversational Memory Retrieval

arXiv:2606.10842v1 Announce Type: new Abstract: We describe ConvMemory v2, an opt-in token-evidence reranker that sits after the lightweight ConvMemory v1 reranker and reorders only v1's protected top

Cross-Modal Knowledge Distillation without Paired Data: Theoretical Foundation and Algorithm

arXiv:2606.10504v1 Announce Type: new Abstract: Cross-modal knowledge distillation (CMKD) studies how a (large) teacher model trained on one type of data (e.g., images) can guide a (smaller) student m

Data-Driven Dynamic Assortment in Online Platforms: Learning about Two Sides

arXiv:2606.11118v1 Announce Type: new Abstract: We study a dynamic assortment problem on a two-sided service platform with incomplete information and heterogeneous customers in a discrete-time setting

Data-Driven Runway and Taxiway Exits Prediction of Landing Aircraft: A Case Study at Hartsfield-Jackson Atlanta International Airport

arXiv:2606.11017v1 Announce Type: new Abstract: Airport surface operations increasingly constrain performance at high-throughput hubs. This study examines arrival taxi-in decisions at Hartsfield-Jacks

Decision-Making under Combinatorial Risk

arXiv:2606.10092v1 Announce Type: new Abstract: Decision-making under risk is typically studied through single-shot lottery choices. Yet many real decisions involve combinatorial risk, where risk aris

researcharxiv-cs-lg

Density Ridge Selective Prediction for LLM and VLM Hallucination Detection under Calibration Label Scarcity

arXiv:2606.10198v1 Announce Type: cross Abstract: Hallucination detection in large language and vision-language models is increasingly framed as selective prediction, where a detector assigns a confid

researcharxiv-cs-ai

Applications

Dirichlet-Guided Group Forecasting for Alleviating Over-smoothing in Time Series Forecasting

arXiv:2606.10592v1 Announce Type: new Abstract: Time series forecasting often suffers from over-smoothing, especially when future dynamics are multi-modal. Forecasts may follow the coarse trend of the

applicationsarxiv-cs-lg

Divide and Cooperate: Role-Decomposed Multi-Agent LLM Training with Cross-Agent Learning Signals

arXiv:2606.10684v1 Announce Type: cross Abstract: Modern language agents which perform multi-step reasoning have shown strong performance in knowledge-intensive question answering. However, existing a

Dmsh: A Multi-Agent Reinforcement Learning Framework for All-Quad Mesh Generation

arXiv:2606.10601v1 Announce Type: cross Abstract: Generating high-quality meshes for arbitrary geometries remains a fundamental bottleneck in computational engineering, often demanding heuristic tunin

Does Reasoning Preserve Alignment? On the Trustworthiness of Large Reasoning Models

arXiv:2606.11046v1 Announce Type: new Abstract: Instruction-tuned LLMs are increasingly converted into reasoning models through post-training to improve multi-step task performance. This conversion is

safetyarxiv-cs-cl

Domain Adapted Large Language Models for Additive Manufacturing

arXiv:2603.22017v2 Announce Type: replace Abstract: This work presents a collection of multi-modal domain adapted large language models built upon the instruction tuned variants of open weight models

Dual-Branch Gated Fusion for Open-Set Audio Deepfake Source Tracing

arXiv:2606.10223v1 Announce Type: cross Abstract: Attributing a synthetic utterance to its originating system remains an open challenge: closed-set models fail to reject unseen synthesizers and produc

Local Ai

Dual-stream attention-guided learning for weakly supervised whole slide image classification

arXiv:2505.23341v3 Announce Type: replace Abstract: Whole slide images (WSIs) play a crucial role in cancer diagnosis due to their ultra-high resolution and rich morphological information, and multipl

local-aiarxiv-cs-cv

Effective Training Principles of Physical Reservoirs

arXiv:2606.10130v1 Announce Type: cross Abstract: Reservoir computers benefit from the inherent complexity of optical phenomena, which provide rich, often nonlinear dynamics. However, training directl

Enabling Progressive Whole-slide Image Analysis with Multi-scale Pyramidal Network

arXiv:2602.01951v2 Announce Type: replace Abstract: Multiple-instance Learning (MIL) is commonly used for computational pathology (CPath), where multi-scale features are essential for capturing both f

Encoding the Euler Characteristic Transform

arXiv:2606.10824v1 Announce Type: new Abstract: The Euler Characteristic Curve (ECC) records the Euler characteristic of a linearly embedded cell complex as a function of filtration height in a given

safetyarxiv-cs-lg

Evaluating Research-Level Math Proofs via Strict Step-Level Verification

arXiv:2606.10799v1 Announce Type: new Abstract: Large Language Models (LLMs) struggle to rigorously verify complex mathematical proofs. Standard global evaluation approaches suffer from 'context poiso

Expert-Level Crisis Detection in Mental Health Conversations

arXiv:2606.10380v1 Announce Type: cross Abstract: Real-world crisis intervention is inherently conversational, yet existing research largely focuses on static texts.Real-world crisis intervention is i

Failure Modes of Deep Multi-Agent RL in Asynchronous Pricing: Reproducible Triggers, Trace Diagnostics, and a Partial Fix

arXiv:2606.09884v1 Announce Type: cross Abstract: We study two reproducible failure modes of deep multi-agent reinforcement learning in continuous-time pricing markets: (i) tacit cartel formation betw

Few-step Generative Models as Lossy Compression

arXiv:2606.10450v1 Announce Type: new Abstract: DiffC provides a principled way to reuse pre-trained diffusion models for lossy compression, but its encoding and decoding procedures remain slow becaus

researcharxiv-cs-cv

FreshRetailNet-LT: A Stockout-Annotated Censored Demand Dataset for Latent Demand Recovery and Forecasting in Fresh Retail

arXiv:2505.16319v3 Announce Type: replace Abstract: Accurate demand estimation is critical for the retail business in guiding the inventory and pricing policies of perishable products. However, it fac

From Confident Closing to Silent Failure: Characterizing False Success in LLM Agents

arXiv:2606.09863v1 Announce Type: new Abstract: LLM agents can fail silently by asserting task completion when the environment state shows otherwise. We study this failure mode, false success, across

agentsarxiv-cs-lg

Generalized-CVO: Fast and Correspondence-Free Local Point Cloud Registration with Second Order Riemannian Optimization

arXiv:2606.10019v1 Announce Type: cross Abstract: We propose a fast and correspondence-free local point cloud registration method that leverages geometric surface structure and reproducing kernel Hilb

Geometry-Aware Reinforcement Learning for 2D Irregular Nesting

arXiv:2606.10611v1 Announce Type: cross Abstract: Traditional heuristic solvers for the 2D irregular nesting problem share a fundamental limitation: they are blind to polygon geometry, relying on guid

GitInject: Real-World Prompt Injection Attacks in AI-Powered CI/CD Pipelines

arXiv:2606.09935v1 Announce Type: cross Abstract: AI-powered agents are increasingly embedded in continuous integration and continuous delivery/deployment (CI/CD) pipelines to autonomously review pull

Gradient-Guided Reward Optimization for Inference-time Alignment

arXiv:2606.09635v1 Announce Type: cross Abstract: Ensuring the reliability of Large Language Models (LLMs) under distribution drift requires inference-time adaptation. While inference-time alignment m

safetyarxiv-cs-lg

GRID: Scaling Task-Agnostic Inference in Continual Prompt Tuning

arXiv:2507.14725v4 Announce Type: replace-cross Abstract: Prompt-based continual learning (CL) offers a parameter-efficient way to adapt large language models (LLMs) across task sequences. However, ex

Grok Build is pretty good at optimizing my code in one shot. Prompt: I want you to optimize it entirely on GPU to speed it up. Measure two m…

model-releaseselon-musk--x

Grok Build is pretty good at optimizing my code in one shot. Prompt: I want you to optimize it entirely on GPU to speed it up. Measure two metrics: the result must compare with the golden image (CPU)

HarmoView: Harmonizing Multi-View Constraints for Identity-Consistent Video Generation

arXiv:2606.10839v1 Announce Type: new Abstract: Current identity-consistent video generation methods struggle to preserve appearance fidelity under large viewpoint changes. While introducing multi-vie

HIPIF: Hierarchical Planning and Information Folding for Long-Horizon LLM Agent Learning

arXiv:2606.10507v1 Announce Type: new Abstract: While Large Language Models (LLMs) have demonstrated strong capabilities as autonomous agents across a wide range of tasks, their performance often degr

I too have felt the latest Claude's to be too smart its bored. Glad others are noticing the same

model-releasesjerry-liu--x

I too have felt the latest Claude's to be too smart its bored. Glad others are noticing the same Claude Fable 5 thinks document parsing is beneath it It is absolutely crushing on all reasoning-intensi

Impatient Users Confuse AI Agents: High-fidelity Simulations of Human Traits for Testing Agents

arXiv:2510.04491v3 Announce Type: replace Abstract: Despite rapid progress in building conversational AI agents, robustness is still largely untested. Small shifts in user behavior, such as being more