Search: “models”

Think Deep, Not Just Long: Measuring LLM Reasoning Effort via Deep-Thinking Tokens

arXiv:2602.13517v2 Announce Type: replace Abstract: Large language models (LLMs) have demonstrated impressive reasoning capabilities by scaling test-time compute via long Chain-of-Thought (CoT). Howev

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TRACE: Capability-Targeted Agentic Training

DGX agent

arXiv:2604.05336v2 Announce Type: replace Abstract: Models often fail to complete agentic tasks because they lack core capabilities required by the target environment. However, mainstream approaches f

model-releasesarxiv-cs-ai

7 Jul 2026

VKnowU: Evaluating Visual Knowledge Understanding in Multimodal LLMs

arXiv:2511.20272v2 Announce Type: replace Abstract: While Multimodal Large Language Models (MLLMs) have become adept at recognizing objects, they often lack the intuitive, human-like understanding of

When Agents Lie: Premeditation, Persistence, and Exploitation in Repeated Games

arXiv:2607.05132v1 Announce Type: cross Abstract: As large language models are deployed as autonomous agents that communicate intentions before acting, a critical safety question is whether agents tha

safetyarxiv-cs-cl

When Simpler Is Better: Evaluating Translation Pipelines for Medieval Latin Manuscripts

arXiv:2607.03836v1 Announce Type: cross Abstract: Despite remarkable progress in machine translation, Vision Language Models (VLMs) struggle on historical manuscripts, a domain that stresses core Natu

XPlainVerse: A Million-Scale Benchmark for Explainable Deepfake Detection

arXiv:2607.03562v1 Announce Type: new Abstract: As deepfake detection models increasingly produce natural language explanations, their reasoning often remains weakly grounded in visual artifacts, limi

Beyond Skepticism: Evaluating LLMs Pedagogical Intent Reasoning with the Adaptive Pedagogical Vigilance Framework

arXiv:2607.01581v1 Announce Type: new Abstract: The capacity of Large Language Models (LLMs) to reason about pedagogical intent within instructional communication remains underexplored, particularly i

Black-Box Inference of LLM Architectural Properties with Restrictive API Access

arXiv:2607.01313v1 Announce Type: cross Abstract: In practice, most commercial LLM providers do not publicly release details of underlying LLM architectures. However, prior work has shown that given l

BuilderBench: The Building Blocks of Intelligent Agents

arXiv:2510.06288v4 Announce Type: replace Abstract: Today's AI models learn primarily through mimicry and refining, so it is not surprising that they struggle to solve problems beyond the limits set b

DecompRL: Solving Harder Problems by Learning Modular Code Generation

arXiv:2607.02390v1 Announce Type: new Abstract: How can Large Language Models (LLMs) solve problems they currently cannot? Repeated sampling scales test-time compute but GPU cost grows linearly with a

Evidence-State Rewards for Long-Context Reasoning

arXiv:2607.02073v1 Announce Type: new Abstract: Long-context reasoning requires models to locate, revise, and synthesize evidence distributed across lengthy inputs. Existing long-context RL methods us

Fast Multi-dimensional Refusal Subspaces via RFM-AGOP

arXiv:2607.02396v1 Announce Type: new Abstract: Steering and monitoring activations in Large Language Models (LLMs) are increasingly used for both safety and interpretability. Early work assumed behav

InduceKV: Fixed-Footprint Continual Adaptation of Multimodal LLMs via Inducing KV Memories

arXiv:2607.02010v1 Announce Type: new Abstract: Multimodal large language models must adapt to evolving tasks and domains, yet continual improvement under bounded deployment footprint remains difficul

NarrativeTrack: Evaluating Entity-Centric Reasoning for Narrative Understanding

arXiv:2601.01095v3 Announce Type: replace-cross Abstract: Multimodal large language models (MLLMs) have achieved impressive progress in vision-language reasoning, yet their ability to understand tempo

PACE: A Proxy for Agentic Capability Evaluation

arXiv:2607.02032v1 Announce Type: new Abstract: Evaluating LLM agents on benchmarks like SWE-Bench and GAIA can be expensive, time-consuming, and requires complex infrastructure. A single evaluation c

Self-Supervised Test-Time Tuning for Packet Loss Concealment

arXiv:2607.01823v1 Announce Type: cross Abstract: Packet loss concealment (PLC) reconstructs audio packets that are missing at the receiver, usually with a trained model whose parameters remain fixed

researcharxiv-cs-cl

Hardware

WattGPU: Predicting Inference Power and Latency on Unseen GPUs and LLMs

arXiv:2607.02391v1 Announce Type: cross Abstract: Large Language Model (LLM) inference workloads are a rapidly growing contributor to data center energy consumption. Optimizing these deployments requi

hardwarearxiv-cs-lg

AutoMem: Automated Learning of Memory as a Cognitive Skill

arXiv:2607.01224v1 Announce Type: new Abstract: Memory expertise is a learned skill: knowing what to encode, when to retrieve, and how to organize knowledge--a capacity known in cognitive science as m

Distill to Detect: Exposing Stealth Biases in LLMs through Cartridge Distillation

arXiv:2607.01208v1 Announce Type: cross Abstract: Language models deployed in high-stakes roles can potentially favor certain entities, brands, or viewpoints, steering user decisions at scale. Such pr

safetyarxiv-cs-ai

DriveVer: Lightweight Trajectory Evaluator as Test-Time Verifier for Autonomous Driving

arXiv:2607.00399v1 Announce Type: new Abstract: End-to-end autonomous driving models often encounter performance bottlenecks, as training-time scaling leads to high computational costs and diminishing

Dual-Confidence Contrastive Decoding for Retrieval-Augmented Generation

arXiv:2607.00570v1 Announce Type: new Abstract: Retrieval-augmented generation (RAG) increasingly requires models to answer questions from multiple retrieved documents, where only some sources are rel

EgoSafetyBench: A Diagnostic Egocentric Video Benchmark for Evaluating Embodied VLMs as Runtime Safety Guards

arXiv:2607.00218v1 Announce Type: cross Abstract: Vision-language models (VLMs) are now proposed as runtime safety guards for embodied agents in homes and factories. A deployable guard must catch genu

FlowPath: Learning Data-Driven Manifolds with Invertible Flows for Robust Irregularly-sampled Time Series Classification

arXiv:2511.10841v3 Announce Type: replace-cross Abstract: Modeling continuous-time dynamics from sparse and irregularly-sampled time series remains a fundamental challenge. Neural controlled different

GEAR-Seg: A Grounded Explainable Agent for Reasoning Segmentation and Data Engine

arXiv:2607.00544v1 Announce Type: new Abstract: Reasoning segmentation requires localizing targets based on complex, implicit queries. Current end-to-end models typically entangle perception and deduc

Linguistic Relative Policy Optimization for Video Anomaly Reasoning

arXiv:2607.00654v1 Announce Type: new Abstract: Video anomaly detection (VAD) with multimodal large language models has shown strong potential, yet most existing methods still depend on large-scale an

Logit-Contribution Scoring Identifies Non-Literal Retrieval Heads

arXiv:2607.01002v1 Announce Type: cross Abstract: In long-context use, large language models frequently synthesize answers from the meaning of a relevant context span rather than literally copy-pastin

Low Perplexity is Repetition: A One-Dimensional Self-Conditioning Attractor in Continuous Diffusion LMs

arXiv:2607.00588v1 Announce Type: new Abstract: Continuous diffusion language models such as ELF report record-low generative perplexity (Gen-PPL). We find a catch: these models repeat far more than h

researcharxiv-cs-cl

LuxIT: A Luxembourgish Instruction Tuning Dataset from Monolingual Seed Data

arXiv:2510.24434v3 Announce Type: replace Abstract: The effectiveness of instruction-tuned Large Language Models (LLMs) is often limited in low-resource linguistic settings due to a lack of high-quali

Quantum vs. Classical Machine Learning: A Unified Empirical Comparison

arXiv:2607.01197v1 Announce Type: new Abstract: Quantum computing has emerged as a promising computational paradigm for machine learning (ML), with the potential to offer computational advantages over

Real-Time Hard Negative Sampling via LLM-based Clustering for Large-Scale Two-Tower Retrieval

arXiv:2607.00448v1 Announce Type: cross Abstract: The two-tower model has been widely used for large-scale recommendation systems, particularly in the retrieval stage. Industry standards for training

safetyarxiv-cs-ai

Towards High-Resolution Visual Perception via Hierarchical Entity Exploration

arXiv:2607.00816v1 Announce Type: new Abstract: High-resolution (HR) image perception remains a key challenge in multimodal large language models (MLLMs), as fine-grained details are often lost when t

VisReason: A Large-Scale Dataset for Visual Chain-of-Thought Reasoning

arXiv:2511.17731v2 Announce Type: replace Abstract: Chain-of-Thought (CoT) prompting has proven remarkably effective for eliciting complex reasoning in large language models (LLMs). Yet, its potential

WorkBench Revisited: Workplace Agents Two Years On

arXiv:2606.13715v2 Announce Type: replace Abstract: The best agent on WorkBench in March 2024, GPT-4, completed just 43% of tasks. We revisit the benchmark in June 2026 and find that the best agent to

YOMI-Bench: A Benchmark for Evaluating Kanji Reading and Phonological Understanding of LLMs for Japanese

arXiv:2607.00664v1 Announce Type: new Abstract: We propose YOMI-Bench, a benchmark for evaluating kanji reading and phonological understanding of large language models (LLMs) for Japanese. In Japanese

Addressing Over-Refusal in LLMs with Competing Rewards

arXiv:2606.31748v1 Announce Type: new Abstract: Safety training on language models often induces over-refusal: improved safety on harmful prompts at the cost of increased refusal on harmless ones. Tho

safetyarxiv-cs-lg

Arena-T2I Hard: Benchmarking and Improving Faithfulness with Dependency-Aware Checklist

arXiv:2606.31711v1 Announce Type: new Abstract: Faithfulness -- how precisely a generated image aligns with its prompt -- is increasingly central to the real-world utility of text-to-image (T2I) model

Efficient Public Verification of Private ML via Regularization

arXiv:2512.04008v2 Announce Type: replace Abstract: Training with differential privacy (DP) guarantees dataset members that they cannot be identified by users of the released model. However, those dat

Learning from Failure: Inference-Time Self-Improvement for Computer-Use Agents

arXiv:2606.31270v1 Announce Type: cross Abstract: Computer-use agents, which leverage multimodal large language models (MLLMs) to operate computers and complete tasks, have attracted significant atten

MAPE: Defending Against Transferable Adversarial Attacks Using Multi-Source Adversarial Perturbations Elimination

arXiv:2606.31378v1 Announce Type: new Abstract: Neural networks are vulnerable to meticulously crafted adversarial examples, leading to high-confidence misclassifications in image classification tasks

researcharxiv-cs-cv

Modality-Driven Search with Holistic Trace Judging for ARC-AGI-2

arXiv:2606.31543v1 Announce Type: new Abstract: Large language models can produce fluent, internally coherent reasoning traces for abstract reasoning tasks while still being confidently wrong - making

Teaching LLMs String Matching, Backtracking, and Error Recovery to Deduce Bases and Truth Tables for the Combinatorially Exploding Bit Manipulation Puzzles

arXiv:2606.23672v2 Announce Type: replace Abstract: This paper presents our algorithmic innovations for the NVIDIA Nemotron Model Reasoning Challenge, focusing on Bit Manipulation Puzzles. In this tas

Temperature Field Reconstruction of Tungsten Monoblock Divertor on EAST using Physics-aware Neural Operator Transformer

arXiv:2606.31574v1 Announce Type: cross Abstract: Accurate modeling of the divertor temperature field is essential for preventing material melting and damage and for extending the service life of fusi

WIDER-FAIR: An Annotated Version of the WIDER-FACE Dataset for Fairness Evaluation

arXiv:2606.31704v1 Announce Type: new Abstract: The deployment of face detection models in real-world applications raises important fairness concerns, as these systems may showcase performance dispari

A Machine-Verified Proof of a Quantum-Optimization Conjecture

arXiv:2606.29687v1 Announce Type: cross Abstract: We report a machine-verified resolution of a problem open for over a decade in quantum optimization: the Farhi, Goldstone and Gutmann (FGG) conjecture

Hardware

A Trainable-by-Parts Operator Learning Framework: Bridging DeepONet and Karhunen-Loeve Expansions for Large-Scale Applications

arXiv:2606.28519v1 Announce Type: new Abstract: Training operator-learning models for large-scale problems governed by partial differential equations (PDEs) is challenging due to the curse of dimensio

hardwarearxiv-cs-lg

Adaptive Financial Transformer with Regime-Gated Attention for Stock Return Prediction

arXiv:2606.29347v1 Announce Type: cross Abstract: Adaptive Financial Transformer (AFT) is proposed for stock return prediction under non-stationary financial markets. The model incorporates a Market R

Agent Safety Is Action Alignment

arXiv:2606.28739v1 Announce Type: new Abstract: Large language models increasingly act as agents: they call tools, move money, delete records, and send messages on a user's behalf. To keep them safe,

safetyarxiv-cs-ai

ArchesClimate: Probabilistic Decadal Ensemble Generation With Flow Matching

arXiv:2509.15942v3 Announce Type: replace-cross Abstract: Internal variability is a dominant contributor to the uncertainty of predictions at the interannual to decadal timescale. A typical approach t

researcharxiv-cs-ai