Search: “benchmark”

StreamTTT: Reconciling Real-Time Perception and Long-Term Memory in Streaming VLMs

arXiv:2608.13416v1 Announce Type: new Abstract: Humans effortlessly perceive the present while remembering the past, yet streaming VLMs often trade off real-time perception against long-term memory. P

14 Aug 2026

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Training AI Scientists to Replicate Research

DGX agent

arXiv:2608.13331v1 Announce Type: cross Abstract: The replicability of papers is a cornerstone of scientific knowledge, ensuring the reliability of existing results and providing a base for further ex

model-releasesarxiv-cs-ai

14 Aug 2026

TsuGO: Probing Search Efficiency in LLM Reasoning via Go Life-and-Death Problems

arXiv:2608.13221v1 Announce Type: new Abstract: The evaluation of LLM reasoning is moving from final-answer accuracy to process-level assessment, yet existing methods still fail to capture how models

14 Aug 2026

Deployment Decision Reliability: A Generalizability-Theory Framework for Sizing Long-Horizon Agent Evaluations

arXiv:2608.11323v1 Announce Type: new Abstract: Enterprise practitioners read agent leaderboards as if they ranked agent capability. We show, across three open agent-trace benchmarks (TheAgentCompany,

From Synthesis to Removal: Physics-Grounded Reflection Simulation and Diffusion-Based Video Dereflection

arXiv:2608.11562v1 Announce Type: cross Abstract: Videos captured through glass often contain reflections that degrade visual quality and interfere with downstream vision tasks. Although single-image

Agents

Harness-IF: Evaluating Instruction Following Across Instruction Surfaces in Coding Agents

arXiv:2608.11727v1 Announce Type: new Abstract: When a coding agent obeys a rule, it may simply have been going to do that anyway. Existing instruction-following benchmarks cannot tell the difference:

agentsarxiv-cs-ai

Localizing Safety Alignment: MLP Layers and Mid-Network Blocks Encode Refusal Behavior in Large Language Models

arXiv:2608.11583v1 Announce Type: new Abstract: Safety alignment in large language models is often treated as a distributed property of the entire network, yet its practical brittleness suggests that

VAKRA: Evaluating Multi-Hop Reasoning Across APIs and Retrieval Under Tool-Use Policies

arXiv:2608.12282v1 Announce Type: new Abstract: Agents deployed in enterprise settings must reason across structured APIs and document collections, yet existing benchmarks evaluate these capabilities

Closed-Loop LLM Co-Pilots for Digital Agriculture

arXiv:2608.09949v1 Announce Type: new Abstract: This study evaluates the application of Large Language Models (LLMs) in complex biological systems, evolving from data analysis to autonomous, AI-guided

Four small architecture decisions can cost up to 47% of a model's long-context performance. New research from Ai2, Carnegie Mellon, and the …

Four small architecture decisions can cost up to 47% of a model's long-context performance. New research from Ai2, Carnegie Mellon, and the University of Washington isolates them. Normalization, GQA,

model-releasesdair-ai--x

From Reasoning Depth to Reasoning Breadth: Evaluating Multi-Point Associative Reasoning in Large Language Models

arXiv:2608.10444v1 Announce Type: cross Abstract: Large language models (LLMs) have made substantial progress on reasoning tasks that require increasingly long and complex inferential chains. This pro

Grok 4.6 reaches 1753 ELO

model-releaseselon-musk--x

Grok 4.6 topped the GDPVal-AA benchmark with an Elo score of 1,753. It surpassed competitors Fable 5 Max (1,741 Elo), GPT‑5.6 Sol Max (1,728 Elo) and Grok 4.5 High (1,526 Elo). Elon Musk publicly ackn

Agents

If you’re interested in checking out LlamaParse for document extraction, sign up here: https://login.llamaindex.ai/sign-up

A 36‑page ArXiv whitepaper titled **ExtractBench** was released by Jerry Liu (jerryjliu0), describing a large‑scale, schema‑guided benchmark for real‑world document extraction from complex enterprise

agentsjerry-liu--x

Is There Really a Camouflaged Object? Towards Realistic Camouflaged Object Detection

arXiv:2608.11135v1 Announce Type: new Abstract: Camouflaged object detection (COD) aims to segment objects that are visually concealed in their surroundings and has attracted increasing attention in r

Rethinking LLM Verification: Evidence Structure, Uncertainty, and Selective Refinement

arXiv:2608.10725v1 Announce Type: new Abstract: Large language models (LLMs) often rely on shortcuts rather than systematic reasoning, raising safety concerns in medical applications. Allowing models

RLMOpt: Adaptive Prompt Optimization via Recursive Language Models

arXiv:2608.10471v1 Announce Type: new Abstract: Prompt optimizers automate the search for prompts that improve language-model performance, but existing methods rely on a predefined optimization proced

Stream Forcing: Constructing Unified Training Trajectory for Robust Streaming Video Generation

arXiv:2608.10439v1 Announce Type: new Abstract: Streaming video generation holds strong potential for world modeling, where future frames must be inferred online sequentially to form a continuous vide

Surfacing the Unsaid: CUE-Bench for Affective Stance in Chinese Discourse

arXiv:2608.10810v1 Announce Type: cross Abstract: Emotion understanding in discourse requires reasoning beyond surface sentiment because speakers often convey affect through indirect, implicit, polite

VisEditBench: Can Vision-Language Models Edit Visualization Code from Multimodal Feedback?

arXiv:2608.10408v1 Announce Type: new Abstract: Vision-language models (VLMs) have shown strong capabilities in generating visualization code from textual or visual specifications. However, real-world

VoxSumm: A Multilingual Corpus of Long-Form Spoken News for Joint Summarization and Translation

arXiv:2608.10359v1 Announce Type: cross Abstract: As information increasingly traverses linguistic boundaries, users require concise cross-lingual representations of long-form content. Nevertheless, l

When Chain-of-Thought Helps and When It Hurts: An Empirical Investigation of the Serial-Depth Bottleneck in LLM Reasoning

arXiv:2608.09942v1 Announce Type: cross Abstract: It is widely assumed that chain-of-thought (CoT) prompting universally improves LLM reasoning. We investigate this through the conceptual framework of

ActiveFly-Bench: Aligning Embodied Question Answering with Vision-Language-Action for Aerial Embodied Perception

arXiv:2607.10180v2 Announce Type: replace-cross Abstract: We introduce ActiveFly-Bench, the first benchmark to bridge cyberspace reasoning and physical-world interaction for UAV embodied perception. T

DevIntent: How Much Does LLM-Generated Code Violate Developer Intent?

arXiv:2608.07614v1 Announce Type: cross Abstract: Code generated by LLMs can violate a developer's implicit intentions when given an ambiguous prompt, yet standard benchmarks measure only whether code

Eikonal Regularisation in Physics-Informed Neural Networks for Three-Dimensional Level-Set Advection: Transferability of Two-Dimensional Design Principles

model-releasesarxiv-cs-lg

arXiv:2608.08322v1 Announce Type: cross Abstract: Physics-informed neural networks applied to the level-set formulation of interface advection commonly augment the residual and initial-condition losse

FoMoH: A clinically meaningful foundation model evaluation for structured electronic health records

arXiv:2505.16941v4 Announce Type: replace-cross Abstract: Foundation models (FMs) promise to address core limitations of traditional supervised machine learning: (i) reliance on large amounts of label

Research

LookAgain: Closed-Loop GUI Grounding with Visually Grounded Reflection

arXiv:2608.09723v1 Announce Type: new Abstract: Recent graphical user interface (GUI) grounders have significantly advanced single-shot accuracy on standard benchmarks, yet their performance degrades

researcharxiv-cs-cv

OmnilingualGAIA2: Evaluating the Multilingual Gap in Frontier AI Agents

arXiv:2608.08775v1 Announce Type: new Abstract: Agentic benchmarks aim to measure how well AI agents plan, search, execute, and recover within realistic multi-tool environments, but they are almost ex

P^{3}: Joint Program-and-Proof Planning for Verified Code Generation

arXiv:2608.09277v1 Announce Type: new Abstract: Verified code generation asks a large language model (LLM) to generate both an executable program and a machine-checkable proof that the program meets a

PragMatch: Separating Pragmatic Incongruity from Cross-Modal Mismatch in Large Vision-Language Models

arXiv:2608.09772v1 Announce Type: new Abstract: Large Vision-Language Models (LVLMs) have demonstrated strong performance on multimodal benchmarks, yet it remains unclear whether they genuinely reason

Sci-VBench: Evaluating Knowledge- and Reasoning-Intensive Video Generation in Science Domains

arXiv:2608.09873v1 Announce Type: cross Abstract: We introduce Sci-VBench, a comprehensive benchmark for evaluating knowledge- and reasoning-intensive video generation across scientific domains. It co

Safety

Software Engineering for and with GUI Agent

arXiv:2608.09278v1 Announce Type: cross Abstract: GUI agents have advanced rapidly, producing a growing body of frameworks, benchmarks, and applications. However, this growth has outpaced the maturity

safetyarxiv-cs-ai

Unified Hallucination Fuzzing for Multimodal Large Language Models

arXiv:2608.07525v1 Announce Type: cross Abstract: Hallucination remains a persistent challenge for Multimodal Large Language Models (MLLMs), severely limiting their reliability in high-stakes applicat

VIGIL: Tackling Hallucination Detection in Image Recontextualization

arXiv:2602.14633v2 Announce Type: replace Abstract: We introduce VIGIL (Visual Inconsistency & Generative In-context Lucidity), a benchmark dataset and framework that provides a fine-grained categoriz

Ask-E: An Environment for Calibrated Question Generation

arXiv:2608.06933v1 Announce Type: cross Abstract: Today, we improve models by training and evaluating them on problems at the frontier of their abilities. Creating such problems is itself a demanding

AVCap: Reinforcing Audio-Video Joint Caption with Detail-Aware Reward

arXiv:2608.06930v1 Announce Type: new Abstract: Detailed audio-video joint captioning is essential for multimodal video understanding and generation. However, prior works are constrained by three main

Do Audio Language Models Use Paralinguistic Evidence? Counterfactual Audits for Response Evaluation

arXiv:2608.06718v1 Announce Type: new Abstract: Audio-language models (ALMs) are increasingly used as judges for speech-to-speech systems, but a judge that receives audio may not actually use paraling

Tutorials

Don't `Well, Actually' Me Unless You Know What You're Talking About: Weak Presupposition Verification Degrades General QA Performance

arXiv:2608.06539v1 Announce Type: new Abstract: False-presupposition QA (FPQA) tests LLMs on their ability to identify false presuppositions in questions and abstain or correct them rather than reinfo

tutorialsarxiv-cs-cl

ECAD: Expanding Class-Agnostic Detection Beyond Thing-Centric Objectness

arXiv:2608.06841v1 Announce Type: new Abstract: Object detection is a fundamental task in visual perception, providing structured region representations for recognition, grounding, reasoning, and inte

Geo-Spatial Concept Probing of Large Language Models: Abstraction, Compositionality, and Grounding

arXiv:2608.07353v1 Announce Type: cross Abstract: Understanding concepts is fundamental to generalization. Despite their impressive performance on a wide range of tasks, Large Language Models (LLMs) s

I Seek You in Videos: Identity-Conditioned Queries for Person-Centric Video Reasoning

arXiv:2608.07417v1 Announce Type: cross Abstract: Real-world video reasoning often involves multimodal, multi-source inputs, whereas existing video reasoning tasks typically assume a simplified video-

InsertFuse: A Unified Framework for Multi-Category Reference-Guided Image Insertion

arXiv:2608.06490v1 Announce Type: new Abstract: We present InsertFuse, a unified framework for multi-category reference-guided image insertion. Its key idea is to decouple category-specific expertise

Newton-Schulz Retraction-Based Inference Enables Hidden Quantum Markov Models to Outperform Classical HMMs

model-releasesarxiv-cs-lg

arXiv:2608.06554v1 Announce Type: new Abstract: Hidden Markov models (HMMs) are widely used probabilistic models for discrete sequential data but can be limited when hidden dynamics are complex. Hidde

Not All Problems Are Best Modeled as MILP: A DSL-Centric Framework for Flexible and Accurate Optimization Modeling

arXiv:2608.07040v1 Announce Type: new Abstract: Solving combinatorial optimization problems (COPs) requires not only efficient algorithms but also carefully crafted formulations. While recent works ha

Social World Models

arXiv:2509.00559v3 Announce Type: replace Abstract: Humans intuitively navigate social interactions by simulating unspoken dynamics and reasoning about others' perspectives, even with limited informat

A Unified Risk View of Uncertainty: Posterior Risk for Disentanglement and Evaluation Beyond Proxies

model-releasesarxiv-cs-lg

arXiv:2608.05995v1 Announce Type: new Abstract: Reliable uncertainty estimates are critical in safety-sensitive applications, where understanding the sources of predictive uncertainty is essential. Th

Robust Native Language Identification through Agentic Decomposition

arXiv:2509.16666v2 Announce Type: replace Abstract: Large language models (LLMs) often achieve high performance in native language identification (NLI) benchmarks by leveraging superficial contextual

Seeing Is Not Deciding: Can Multimodal LLMs Act as Effective CEOs?

arXiv:2608.05864v1 Announce Type: new Abstract: Large language models are increasingly applied as autonomous decision-making agents. However, in executive business decisions, existing benchmarks are l

The Low Frequency Trap: Video Language Models Fail at Simple Event Bookkeeping

arXiv:2608.06361v1 Announce Type: new Abstract: Real-world video benchmarks provide broad coverage, but their fixed clips entangle event count, rate, duration, and visual complexity, making failure mo